Add AI features to your app in minutes. Generate images, fine tune models, and more with our easy-to-use API.
Emit new event when a new image sample is created for a model. See the documentation
Emit new event when a new model is created. See the documentation
Emit new event when a new model version is created/queued for training. See the documentation
Queues an image generation job and returns the job details. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Creates a new custom model entity, which serves as a container that can be trained on custom images. See the documentation
Uploads image samples to a custom model. See the documentation
The Leap API enables automated interactions with the Leap.ai platform, which focuses on matching users with optimal job opportunities based on skills and preferences. In Pipedream, you can harness this API to create workflows that streamline the job search process, manage and analyze job matching data, or even integrate with other platforms to enhance the job seeking experience. With Pipedream's serverless execution environment, you can trigger these workflows on a schedule, via webhooks, or in response to events from other apps.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
leap: {
type: "app",
app: "leap",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.tryleap.ai/api/v1/images/models`,
headers: {
Authorization: `Bearer ${this.leap.$auth.api_key}`,
"accept": `application/json`,
},
})
},
})
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:
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}}