TFClassificationForest

TFClassificationForest

Shows a forest-based classifier built in TensorFlow inside a Gradio page, from an example by Khalid Salama and adapted by Tannia Dubon. For students and developers who want to try it.

Made by keras-io 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 Gradio demo from keras-io shows a forest model for classification. The name suggests TensorFlow, with TF probably standing for it. The notes credit Khalid Salama as the author and Tannia Dubon for the adapted implementation. You use it in a browser, or run a copy on your own computer.

Who it suits: It suits students and developers who want to see a forest classifier running before they read the code. Teachers can show it as a live demo.

Industry | Who uses it | How they use it | Example
Education | Machine learning students | Try a classification forest without writing code | Open the Space and use the interface it shows
Research | Data scientists | See how a forest model is wired into a demo | Read app.py next to config.json
Software | Python developers | Run a copy locally and adapt it | Download the Space and start app.py

Skip it if you need a training script or a general tool for tabular data, because the source describes only a small demo.

Where to use it:

• A web browser: go to the Space on Hugging Face and try it with no install
• Windows, macOS or Linux: run a copy with Python

Use case | Who | What they get back
Try the classifier | Students | A prediction shown on the page for the input they give
Read the code | Developers | The app.py and config.json files behind the demo
Teach with it | Instructors | A demo they can run on their own computer

How to set it up: You need a few things in place first.

• Python with pip, on Windows, macOS or Linux
• An internet connection to fetch the files from Hugging Face

1. Install the Hugging Face hub package.
2. Download the Space into a local folder.
3. Move into that folder.
4. Install Gradio and the packages listed in requirements.txt.
5. Start the app.

pip install -U huggingface_hub
hf download keras-io/TF-GB-Forest --repo-type space --local-dir TF-GB-Forest
cd TF-GB-Forest
pip install gradio
pip install -r requirements.txt
python app.py

How to use it: Online, use the Gradio interface the Space page shows. On your own computer, run the main file from the project folder and open the page it serves in your browser.

python app.py

Example: You run python app.py inside the project folder. The Gradio page starts, you give it an input, and the page shows the class the forest model returns for it.

For newcomers and experts: If this is new to you, start online. Go to the Space and try the interface before you install anything. It is the quickest way to see a classifier working.

If you know the field, note that this Space names no Hugging Face model. It keeps a config.json beside app.py, so the model setup likely comes from there. The source gives no accuracy figures and no training details.

Open the Space or run app.py
The Gradio page loads
You give it an input
The forest model classifies it
The result appears on the page

Good to know: The source says little about what the demo takes in or how well it performs, so test it on your own examples before you rely on it.

• License: apache-2
• Cost to run: none named, no API key, account or paid service
• Data it reaches: the files in the repository, with no outside account or web service listed

On Hugging Face, 13 November 2026

• Likes: 1
• Built with: gradio
• License: apache-2.0

Links

• Space: https://huggingface.co/spaces/keras-io/TF-GB-Forest
• Files: https://huggingface.co/spaces/keras-io/TF-GB-Forest/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.