Generate Image with Leonardo AI API on New Message In Channels (Instant) from Slack API

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New Message In Channels (Instant) from the Slack API

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Generate Image with the Leonardo AI API

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Getting Started#

This integration creates a workflow with a Slack trigger and Leonardo 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 Message In Channels (Instant) trigger
1. Connect your Slack account
2. Optional- Select one or more Channels
3. Configure slackApphook
4. Optional- Configure Resolve Names
5. Optional- Configure Ignore Bots
6. Optional- Configure Ignore replies in threads
Configure the Generate Image action
1. Connect your Leonardo AI account
2. Configure Prompt
3. Optional- Select a Model
4. Configure Width
5. Configure Height
6. Configure Number of Images
7. Optional- Configure Guidance Scale
8. Optional- Configure Inference Steps
9. Optional- Configure Seed
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 Message In Channels (Instant) on Slack

Description:Emit new event when a new message is posted to one or more channels

Version:1.0.25

Key:slack-new-message-in-channels

View on GitHub

Slack Overview#

The Pipedream app for Slack enables you to build event-driven workflows that interact with the Slack API. Once you authorize the app's access to your workspace, you can use Pipedream workflows to perform common Slack actions or write your own code against the Slack API.

The Pipedream app for Slack is not a typical app. You don't interact with it directly as a bot, and it doesn't add custom functionality to your workspace out of the box. It makes it easier to automate anything you'd typically use the Slack API for, using Pipedream workflows.

Automate posting updates to your team channels
Create a bot to answer common questions
Integrate with your existing tools and services
And much more!

Trigger Code#

import common from "../common/base.mjs";
import constants from "../common/constants.mjs";
import sampleEmit from "./test-event.mjs";

export default {
  ...common,
  key: "slack-new-message-in-channels",
  name: "New Message In Channels (Instant)",
  version: "1.0.25",
  description: "Emit new event when a new message is posted to one or more channels",
  type: "source",
  dedupe: "unique",
  props: {
    ...common.props,
    conversations: {
      propDefinition: [
        common.props.slack,
        "conversation",
      ],
      type: "string[]",
      label: "Channels",
      description: "Select one or more channels to monitor for new messages.",
      optional: true,
    },
    // eslint-disable-next-line pipedream/props-description,pipedream/props-label
    slackApphook: {
      type: "$.interface.apphook",
      appProp: "slack",
      async eventNames() {
        return this.conversations || [
          "message",
        ];
      },
    },
    resolveNames: {
      propDefinition: [
        common.props.slack,
        "resolveNames",
      ],
    },
    ignoreBot: {
      propDefinition: [
        common.props.slack,
        "ignoreBot",
      ],
    },
    ignoreThreads: {
      type: "boolean",
      label: "Ignore replies in threads",
      description: "Ignore replies to messages in threads",
      optional: true,
    },
  },
  methods: {
    ...common.methods,
    getSummary() {
      return "New message in channel";
    },
    async processEvent(event) {
      if (event.type !== "message") {
        console.log(`Ignoring event with unexpected type "${event.type}"`);
        return;
      }
      if (event.subtype && !constants.ALLOWED_MESSAGE_IN_CHANNEL_SUBTYPES.includes(event.subtype)) {
        // This source is designed to just emit an event for each new message received.
        // Due to inconsistencies with the shape of message_changed and message_deleted
        // events, we are ignoring them for now. If you want to handle these types of
        // events, feel free to change this code!!
        console.log("Ignoring message with subtype.");
        return;
      }
      if ((this.ignoreBot) && (event.subtype == "bot_message" || event.bot_id)) {
        return;
      }
      // There is no thread message type only the thread_ts field
      // indicates if the message is part of a thread in the event.
      if (this.ignoreThreads && event.thread_ts) {
        console.log("Ignoring reply in thread");
        return;
      }
      if (this.resolveNames) {
        if (event.user) {
          event.user_id = event.user;
          event.user = await this.getUserName(event.user);
        } else if (event.bot_id) {
          event.bot = await this.getBotName(event.bot_id);
        }
        event.channel_id = event.channel;
        event.channel = await this.getConversationName(event.channel);
        if (event.team) {
          event.team_id = event.team;
          event.team = await this.getTeamName(event.team);
        }
      }
      return event;
    },
  },
  sampleEmit,
};

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
Slack	`slack`	`app`	This component uses the Slack app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Channels	`conversations`	`string[]`	Select a value from the drop down menu.
	`slackApphook`	`$.interface.apphook`
Resolve Names	`resolveNames`	`boolean`	Instead of returning `channel`, `team`, and `user` as IDs, return their human-readable names.
Ignore Bots	`ignoreBot`	`boolean`	Ignore messages from bots
Ignore replies in threads	`ignoreThreads`	`boolean`	Ignore replies to messages in threads

Trigger Authentication#

Slack uses OAuth authentication. When you connect your Slack account, Pipedream will open a popup window where you can sign into Slack and grant Pipedream permission to connect to your account. Pipedream securely stores and automatically refreshes the OAuth tokens so you can easily authenticate any Slack API.

Pipedream requests the following authorization scopes when you connect your account:

channels:historychannels:readchannels:writeemoji:readgroups:historygroups:readgroups:writeim:historyim:readim:writelinks:readlinks:writempim:historympim:readmpim:writereactions:readreactions:writereminders:readreminders:writestars:readteam:readusergroups:readusergroups:writeusers:readusers:read.emailusers:writechat:write:botchat:write:userfiles:write:userfiles:readcommandsusers.profile:writeusers.profile:readsearch:read

About Slack#

Slack is a channel-based messaging platform. With Slack, people can work together more effectively, connect all their software tools and services, and find the information they need to do their best work — all within a secure, enterprise-grade environment.

Action#

Generate Image on Leonardo AI

Description:Generates new images using Leonardo AI's image generation API. [See the documentation](https://docs.leonardo.ai/reference/creategeneration)

Version:0.0.1

Key:leonardo_ai-generate-image

View on GitHub

Action Code#

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

export default {
  key: "leonardo_ai-generate-image",
  name: "Generate Image",
  description: "Generates new images using Leonardo AI's image generation API.  [See the documentation](https://docs.leonardo.ai/reference/creategeneration)",
  version: "0.0.1",
  type: "action",
  props: {
    app,
    prompt: {
      type: "string",
      label: "Prompt",
      description: "The text prompt describing the image you want to generate.",
    },
    modelId: {
      type: "string",
      label: "Model",
      description: "The model to use for generation. Leave empty to use the default model.",
      async options() {
        const models = await this.app.getPlatformModels();
        return models.map((model) => ({
          label: model.name || model.id,
          value: model.id,
        }));
      },
      optional: true,
    },
    width: {
      type: "integer",
      label: "Width",
      description: "Width of the generated image in pixels.",
      default: 1024,
      min: 32,
      max: 1536,
    },
    height: {
      type: "integer",
      label: "Height",
      description: "Height of the generated image in pixels.",
      default: 768,
      min: 32,
      max: 1536,
    },
    numImages: {
      type: "integer",
      label: "Number of Images",
      description: "Number of images to generate (1-8). If either width or height is over 768, must be between 1 and 4.",
      default: 1,
      min: 1,
      max: 8,
    },
    guidanceScale: {
      type: "integer",
      label: "Guidance Scale",
      description: "How closely the model should follow the prompt. Must be between 1 and 20. Higher values = more adherence to prompt.",
      default: 7,
      min: 1,
      max: 20,
      optional: true,
    },
    numInferenceSteps: {
      type: "integer",
      label: "Inference Steps",
      description: "Number of denoising steps. More steps = higher quality but slower generation.",
      default: 15,
      min: 10,
      max: 60,
      optional: true,
    },
    seed: {
      type: "integer",
      label: "Seed",
      description: "Random seed for reproducible generation. Leave empty for random generation.",
      optional: true,
    },
  },
  async run({ $ }) {
    const {
      prompt,
      modelId,
      width,
      height,
      numImages,
      guidanceScale,
      numInferenceSteps,
      seed,
    } = this;

    const data = {
      prompt,
      width,
      height,
      num_images: numImages,
      modelId,
      guidance_scale: guidanceScale,
      num_inference_steps: numInferenceSteps,
      seed,
    };

    const response = await this.app._makeRequest({
      $,
      method: "POST",
      path: "/generations",
      data,
    });

    $.export("$summary", `Successfully generated ${numImages} image(s)`);
    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
Leonardo AI	`app`	`app`	This component uses the Leonardo AI app.
Prompt	`prompt`	`string`	The text prompt describing the image you want to generate.
Model	`modelId`	`string`	Select a value from the drop down menu.
Width	`width`	`integer`	Width of the generated image in pixels.
Height	`height`	`integer`	Height of the generated image in pixels.
Number of Images	`numImages`	`integer`	Number of images to generate (1-8). If either width or height is over 768, must be between 1 and 4.
Guidance Scale	`guidanceScale`	`integer`	How closely the model should follow the prompt. Must be between 1 and 20. Higher values = more adherence to prompt.
Inference Steps	`numInferenceSteps`	`integer`	Number of denoising steps. More steps = higher quality but slower generation.
Seed	`seed`	`integer`	Random seed for reproducible generation. Leave empty for random generation.

Generate Image with Leonardo AI API on New Message In Channels (Instant) from Slack API

Pipedream makes it easy to connect APIs for Leonardo AI, Slack and 2,800+ other apps remarkably fast.

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

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