NannyML provides Python tools for post-deployment data science and monitoring model performance under data drift.
Gradio
Gradio lets users build and share machine learning applications in Python.

About Gradio
Gradio is an open-source Python package for building demos and web applications for machine learning models, APIs, and other Python functions. It is maintained in a public repository and is documented with guides and examples for getting started.
You install it with pip and write a few lines of Python to define inputs, outputs, and a function. The gr.Interface class connects a function to input and output components, and demo.launch() opens the app in a browser or embeds it in a notebook. Built-in sharing features can create a link to share the demo. It runs in code editors, Jupyter notebooks, Google Colab, and other Python environments.
It suits Python developers, data scientists, and others working with models or data analysis who need a working web interface without JavaScript, CSS, or hosting setup. Python 3.10 or higher is required.
Key features
- Build web apps for models, APIs, and Python functions
- Create demos with Interface class for inputs and outputs
- Share demos with a link using built-in sharing
- Run in editors, Jupyter notebooks, and Google Colab
- Install with pip on Python 3.10 or higher
- Launch apps locally in a browser or notebook
- Open-source Python package
- No JavaScript, CSS, or hosting experience needed
- Works in notebooks, Colab, and code editors
- Includes built-in sharing for demos
- Requires Python 3.10 or higher
Gradio at a glance
- Website
- github.com/gradio-app/gradio
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
Gradio alternatives
Tools and sites you could use instead, ranked by how closely they match.
igel lets users train, test, and use machine learning models without writing code.
Aim tracks machine learning experiments and describes itself as an open-source experiment tracker.
Pachyderm provides data-focused pipelines and version control for datasets and related workflows.
TPOT uses genetic programming to optimize machine learning pipelines in Python.
FLAML provides a fast Python library for automated machine learning and tuning.
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Gradio FAQ
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