Dubbed “The Shazam For Sneakers” helps you to identify 1M sneakers & Fashion Products with 95% accuracy!
Identifies sneakers from a size tag photo and returns sneaker name and details. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Grades and authenticates sneakers using provided images. See the documentation
Identifies sneakers from an uploaded image and returns details such as name, links, images, prices, and confidence scores. See the documentation.
import { axios } from "@pipedream/platform"
import request from "request";
export default defineComponent({
props: {
what_are_those: {
type: "app",
app: "what_are_those",
}
},
async run({steps, $}) {
const data = request("https://res.cloudinary.com/daiebfyiw/image/upload/v1735863877/what_those_are_sku_image_sample_nyhw4i.jpg");
let config = {
method: 'POST',
maxBodyLength: Infinity,
url: 'https://0blrzg7ahc.execute-api.us-east-1.amazonaws.com/Prod/skus',
headers: {
'Content-Type': 'image/jpeg',
'x-api-key': this.what_are_those.$auth.api_key
},
data
};
return await axios($,config);
},
})
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}}