Pytorch Image Classifier

Pytorch Image Classifier

A Gradio image classifier demo with four sample photos, a banana, a car, a cheetah and a lion. It suits people who want to try image classification in a browser.

Made by abidlabs on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: Pytorch Image Classifier is a Gradio app on Hugging Face Spaces that works as an image classifier demo. The repository includes four sample photos of a banana, a car, a cheetah and a lion. The README holds only a settings pointer, so the details are on the page itself.

Who it suits: It suits people who want to try image classification in a browser, and learners who want to see how a classifier is put behind a Gradio page.

Industry | Who uses it | How they use it | Example
Machine learning | Students | Read app.py to see how a classifier is wired to a page | Download the Space and open app.py
Education | Instructors | Show image classification with ready sample photos | Click a sample photo in class
Software | Python developers | Reuse the layout for their own image demo | Swap in other sample files

Anyone who needs documented accuracy or a list of supported classes can skip it, since the README gives none.

Where to use it:

• A web browser, straight from the Space page on Hugging Face
• Windows, macOS or Linux with Python, as a local copy

Use case | Who | What they get back
Classify a photo online | Anyone curious | A prediction for the picture, shown in the browser
Study an image demo | Learners | An app file, a requirements file and four sample photos

How to set it up: Using it online needs nothing. A local copy needs these first.

• Python and pip
• The huggingface_hub package, to fetch the files
• The packages in requirements.txt, plus Gradio

1. Install the Hugging Face Hub package.
2. Download the Space into a folder called keras-image-classifier.
3. Move into that folder.
4. Install Gradio, then the packages from requirements.txt.
5. Start the app.

pip install -U huggingface_hub
hf download abidlabs/keras-image-classifier --repo-type space --local-dir keras-image-classifier
cd keras-image-classifier
pip install gradio
pip install -r requirements.txt
python app.py

How to use it: Open the Space page on Hugging Face, then give it a picture or pick a sample photo. For a local copy, run python app.py in the folder and open the page it starts.

Example: You pick the lion photo on the Space page. The app runs the classifier on it and shows what it thinks the picture contains.

For newcomers and experts: If you are new, click one of the sample photos first. That shows the whole idea in one step, with nothing to install.

If you know machine learning, the source names no outside model, so the classifier logic sits in app.py and its requirements. The settings do not report accuracy or the set of classes.

You open the page and choose a photo
app.py runs the classifier on it
The prediction shows in your browser

Good to know:

• The README gives no description, so the exact inputs and outputs are clear only on the page
• It needs Gradio and the packages in requirements.txt
• License: mit
• Cost to run: none named
• Data it reaches: the pictures you give it and its own sample photos

On Hugging Face, 13 November 2026

• Likes: 5
• Built with: gradio
• License: mit

Links

• Space: https://huggingface.co/spaces/abidlabs/keras-image-classifier
• Files: https://huggingface.co/spaces/abidlabs/keras-image-classifier/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.