Apontador

Apontador

Runs a saved logistic regression model in a Gradio page, with the model file and a training script in the repository. The listing does not say what it predicts, so it suits students and developers who want to read it.

Made by Reyne 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 app from Reyne loads a saved logistic regression model, stored in a file named regressao_logistica.joblib. A script called log_regress.py sits beside it. The source does not say what the model predicts. You use it in a browser, or you run a copy on your own computer.

Who it suits: It suits students and developers who want to see a small classical machine learning model behind a web page, and read the code that builds it.

Industry | Who uses it | How they use it | Example
Education | Data science students | See a logistic regression model in a Gradio page | Open the Space and try its inputs
Software | Python developers | Read how a saved model file is loaded and served | Download the Space and open app.py
Research | Data analysts | Check whether the setup fits a similar task | Read log_regress.py next to the model file

Skip it if you need documentation or a clear purpose, because the source does not say what the model is for.

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 model | Students | A prediction on the page for the inputs they enter
Read the code | Developers | The app.py and log_regress.py files behind the demo
Reuse the setup | Analysts | A small pattern for serving a saved model

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 Reyne/apontador --repo-type space --local-dir apontador
cd apontador
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 and open the page it serves. You fill in the inputs the page asks for and read the prediction the saved model returns.

For newcomers and experts: If this is new to you, start online. Go to the Space and look at the inputs before you install anything. Logistic regression is a simple model that sorts inputs into classes.

If you know the field, note that the model is a joblib file, which is a pickle-style format. The source gives no features, labels or metrics, so read log_regress.py to see how it was trained.

Open the Space or run app.py
The Gradio page loads
You enter the inputs
The saved model predicts
The result appears on the page

Good to know: The repository holds a joblib file that the app loads, so run a copy only from a source you trust. The source gives no accuracy figures.

• License: gpl-3
• Cost to run: none named, no API key, account or paid service
• Data it reaches: the inputs you enter and the saved model file in the repository, with nothing else listed

On Hugging Face, 13 November 2026

• Likes: 0
• Built with: gradio
• License: gpl-3.0

Links

• Space: https://huggingface.co/spaces/Reyne/apontador
• Files: https://huggingface.co/spaces/Reyne/apontador/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.