A pretrained programming languages by image classifier that sorts an image into one of 2 categories — which programming language the code snippet is written in. Use the programming languages by image 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 30 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": "Python Logo",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 programming languages by image 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 multilabel image classification function can be used in online learning platforms to automatically identify the programming language being used in coding screenshots. This helps in categorizing and organizing content, ensuring it's easy for learners to identify and access resources for specific programming languages.
Companies can use this functionality to sift through applicant portfolios. By identifying images representing different programming languages, companies can quickly gauge an applicant's expertise and versatility in different coding languages.
Software companies can use this function to analyze competitors' software products/screenshots to determine which programming languages they're using. This information can be useful in developing competitive strategies.
In platforms where users share their code snippets or screen captures, this tool can be used to automatically tag or sort posts based on the detected programming language, streamlining search and navigation for other users.
For entities involved in cybersecurity and forensic analysis, this function can help identify the programming languages of malicious software or suspicious files, assisting in comprehending how a piece of software works and potentially identifying its creators.
In tech companies where multiple projects are in progress, this feature can be incorporated into work management tools to identify and categorize tasks according to the programming language, aiding project managers in assigning work to developers with corresponding expertise.
For companies that maintain large technical document libraries, this tool can help classify the content based on the programming languages identified in attached screenshots or diagrams, making it easier for employees to locate relevant documents.
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 programming languages by image 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.