Demo Integrated Gradients Alibi Gradio

Demo Integrated Gradients Alibi Gradio

A Gradio demo that, going by its title, shows the integrated gradients explanation method from the Alibi library. Suits students and developers who want to see how a model's prediction can be explained.

Made by mawady 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: This Space is a Gradio demo by mawady. Its title names integrated gradients and Alibi, so it appears to show that explanation method on a model's prediction. Integrated gradients is usually used to show which parts of an input pushed a model toward its answer. The page has no description, so the model and inputs are not confirmed.

Who it suits: Students and developers who want to see model explanation in practice, and anyone who wants a short Gradio example of an explainability tool.

Industry | Who uses it | How they use it | Example
Education | Machine learning students | Run the demo to see what an attribution method shows | Open the Space and look at the highlighted result
Software | Developers | Use the project as a pattern for an explainability demo in Gradio | Swap in their own model
Research | Interpretability readers | Look at one attribution method on a sample | Compare the explanation with their expectation

Skip it if you need a full explainability toolkit, because this is a three file demo.

Where to use it:

• A web browser, on the Space's page on Hugging Face
• Python on Windows, macOS or Linux, using a downloaded copy

Use case | Who | What they get back
Try an explanation method online | Anyone curious | The explanation the demo produces for its sample
Study a short explainability demo | Developers | A small Python project built around Alibi

How to set it up:

• Nothing to install for the online version
• Python on your computer for a local copy
• The packages listed in requirements.txt

1. Install the Hugging Face hub package.
2. Download the Space's files to a folder.
3. Move into that folder.
4. Install gradio, then the packages from requirements.txt.
5. Start the app with app.py.

pip install -U huggingface_hub
hf download mawady/Demo-integrated-gradients-alibi-gradio --repo-type space --local-dir Demo-integrated-gradients-alibi-gradio
cd Demo-integrated-gradients-alibi-gradio
pip install gradio
pip install -r requirements.txt
python app.py

How to use it: Open the Space page in a browser and use the controls it shows. For a local copy, run the app and open the page Gradio opens for you.

python app.py

Tool | What it does
python app.py | Starts the Gradio app on your computer
hf download | Copies the Space's files into a local folder

Example: Open the Space and run its default input. The page should show the explanation the method produces for that input. The page does not describe the controls or the output, so follow what you see.

For newcomers and experts: If you are new to this, open the Space and run it once with the default input. Then read the result as a hint about what the model paid attention to, not as proof.

If you know the field, the title points to Alibi's integrated gradients. The page does not name the model, the baseline or the number of steps, so read app.py before you draw conclusions from it.

An input goes in
A model makes a prediction
Integrated gradients scores the input
Gradio shows the explanation

Good to know:

• The page gives no description of the model, input or output.
• The online version needs nothing installed, and a local copy needs Python.
• License: mit.
• Cost to run: none named, only the packages in requirements.txt.
• Data it reaches: only the input you give it. The source names no account or other service.
• Not for: the source names none.

On Hugging Face, 13 November 2026

• Likes: 2
• Built with: gradio
• License: mit

Links

• Space: https://huggingface.co/spaces/mawady/Demo-integrated-gradients-alibi-gradio
• Files: https://huggingface.co/spaces/mawady/Demo-integrated-gradients-alibi-gradio/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.