--- title: "TFClassificationForest" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6c13e37f-45eb-4de3-bff3-6c5f2a15a077" updated: "2026-11-13" --- # TFClassificationForest > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. TFClassificationForest is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by keras-io ## 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. ```sh 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```sh 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [keras-io/TF-GB-Forest on Hugging Face](https://huggingface.co/spaces/keras-io/TF-GB-Forest) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | TFClassificationForest (this page) | 1 | gradio | apache-2.0 | 2022-02-22 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-02-22 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open keras-io/TF-GB-Forest on Hugging Face](https://huggingface.co/spaces/keras-io/TF-GB-Forest) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6c13e37f-45eb-4de3-bff3-6c5f2a15a077) - [Its page on Mindket](https://mindket.com/p/6c13e37f-45eb-4de3-bff3-6c5f2a15a077)