with Nano Nets and fal.ai?
Adds a request to the queue for asynchronous processing, including specifying a webhook URL for receiving updates. See the documentation
Cancels a request in the queue. This allows you to stop a long-running task if it's no longer needed. See the documentation
Gets the response of a completed request in the queue. This retrieves the results of your asynchronous task. See the documentation
Gets the status of a request in the queue. This allows you to monitor the progress of your asynchronous tasks. See the documentation
The Nano Nets API offers machine learning capabilities to classify images, extract data, and automate processes with custom models. Through Pipedream's serverless platform, you can trigger workflows from various events, manipulate and route data from the Nano Nets API, and connect it to 3,000+ other apps to automate complex tasks. Pipedream's built-in code steps also allow you to transform data, make HTTP requests, and handle logic right inside your workflows.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
nano_nets: {
type: "app",
app: "nano_nets",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://app.nanonets.com/api/v2/OCR/Model/{your_model_id_here}`,
auth: {
username: `${this.nano_nets.$auth.api_key}`,
password: ``,
},
})
},
})
import { fal } from "@fal-ai/client"
export default defineComponent({
props: {
fal_ai: {
type: "app",
app: "fal_ai",
}
},
async run({ steps, $ }) {
fal.config({
credentials: `${this.fal_ai.$auth.api_key}`,
});
const result = await fal.subscribe("fal-ai/lora", {
input: {
model_name: "stabilityai/stable-diffusion-xl-base-1.0",
prompt:
"Photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang",
},
logs: true,
});
return result;
},
})