Levity

Train your own AI on documents, images, or text data to perform daily, repetitive tasks so your team can reach the next level of productivity

Integrate the Levity API with the Python API

Setup the Levity API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Levity and Python remarkably fast. Free for developers.

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Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

 
Try it

Overview of Levity

The Levity API provides a platform for creating AI-powered workflows that can classify images, text, and data with ease. Using this API in Pipedream, you can automate decision-making processes, enhance data categorization, and streamline content moderation by leveraging machine learning models. It integrates smoothly within Pipedream's serverless environment, allowing you to build complex workflows without spinning up a single server.

Connect Levity

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    levity: {
      type: "app",
      app: "levity",
    }
  },
  async run({steps, $}) {
    const data = {
      "url": `{replace_with_image_url}`,
    }
    return await axios($, {
      method: "post",
      url: `https://api.levity.ai/v1/classifiers/{your_classifier_id}/classify/`,
      headers: {
        "Authorization": `Token ${this.levity.$auth.api_key}`,
        "Content-Type": `application/json`,
      },
      data,
    })
  },
})

Overview of Python

Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.

This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.

Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:

Connect Python

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def handler(pd: "pipedream"):
  # Reference data from previous steps
  print(pd.steps["trigger"]["context"]["id"])
  # Return data for use in future steps
  return {"foo": {"test":True}}

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

Adyen logo
Appcues logo
Bandwidth logo
Checkr logo
ChartMogul logo
Dataminr logo
Gopuff logo
Gorgias logo
LinkedIn logo
Logitech logo
Replicated logo
Rudderstack logo
SAS logo
Scale AI logo
Webflow logo
Warner Bros. logo
Adyen logo
Appcues logo
Bandwidth logo
Checkr logo
ChartMogul logo
Dataminr logo
Gopuff logo
Gorgias logo
LinkedIn logo
Logitech logo
Replicated logo
Rudderstack logo
SAS logo
Scale AI logo
Webflow logo
Warner Bros. logo