Integrate the MoDeck API with the Python API

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

Popular Ways to Connect MoDeck with Python#

Run Python Code with Python API on New Render Completed from MoDeck API

MoDeck + Python

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Popular MoDeck and Python Triggers#

New Render Completed from the MoDeck API

Emit new event when a render is completed.

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Popular MoDeck and Python Actions#

Create Render with the MoDeck API

Create a new edit with the data supplied. See the documentation

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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.

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Overview of MoDeck#

The MoDeck API offers an interface for managing playlists and videos within their platform, providing endpoints for various operations like retrieving video details, updating playlists, or managing users. Integrating MoDeck with Pipedream allows you to automate interactions with your MoDeck data, such as syncing playlists, updating video statuses, or triggering actions based on video analytics. With Pipedream's serverless platform, you can build powerful workflows that react to events in real-time, schedule tasks, and connect MoDeck with hundreds of other services.

Connect MoDeck#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    modeck: {
      type: "app",
      app: "modeck",
    }
  },
  async run({steps, $}) {
    const data = {
      "apiKey": `${this.modeck.$auth.api_key}`,
    }
    return await axios($, {
      method: "POST",
      url: `https://api.modeck.io/listdecks`,
      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#

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}}