A pretrained if plumbing has issues classifier that sorts an image into one of 2 categories. Use the if plumbing has issues API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 2 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Good Condition",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if plumbing has issues categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
This function can be integrated into home inspection software to automatically identify plumbing issues during property evaluations. By providing real-time alerts, inspectors can prioritize repairs and give clients a comprehensive overview of potential problems.
Facility management systems can utilize this function to monitor plumbing systems in large buildings. By identifying issues early, maintenance teams can schedule repairs proactively, reducing downtime and costs related to plumbing failures.
Insurance companies can leverage this identifier to analyze claims related to plumbing damages. By automating the detection of plumbing issues, insurers can expedite claims processing and improve customer satisfaction through quicker resolutions.
Smart home devices can use this classification function to monitor plumbing systems in real-time. If a potential issue is detected, the system can notify homeowners and recommend service providers, enhancing the value of smart home features.
Plumbing service companies can implement this function in their diagnostic tools. By quickly pinpointing issues during service calls, technicians can strategize repairs more effectively, leading to improved service efficiency and reduced labor costs.
Real estate platforms could incorporate this identifier to flag plumbing concerns in properties listed for sale. This functionality allows agents and potential buyers to make informed decisions and negotiate repairs before finalizing deals.
Educational institutions offering plumbing training can utilize this function for instructional purposes. By simulating various plumbing issues, trainees can learn to diagnose problems effectively, enhancing their skills and preparing them for real-world scenarios.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This if plumbing has issues classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.