Creating an Interactive Dashboard: A Guide with Python and Gradio in 9 Easy Steps
In the realm of data visualization and interactive dashboard creation, two Python frameworks have emerged as popular choices: Gradio and Streamlit. Both are minimal-code tools designed to simplify the process of creating Web applications, but they cater to different needs and offer distinct strengths.
To illustrate, let's explore a project where we build an interactive Gradio dashboard to visualize user engagement data. The first step is to prepare the underlying data, using a synthetic CSV file containing 100,000 records simulating website user engagement. This file is then loaded into the dashboard, which is created using Plotly for more detailed and engaging visualizations.
Gradio, primarily focused on fast prototyping of machine learning models and creating simple interactive UIs, requires Python 3.8 or a newer version. The Gradio interface is built using utility functions, and the main execution function, `build_engagement_dashboard`, is run to prepare the interface for the launch of the web application.
Data filtering functions are implemented to filter data based on user input or actions on the dashboard, while key metrics functions are used to calculate metrics like total sessions, unique users, and top page by number of visitors. Table display and data update functions are employed to prepare data for tabular display and update dashboard values after user input or function execution.
Caching functions are also utilised to create a fast data loading system, reducing calculation time. One of the advantages of Gradio is that it does not require extensive background in web technologies like HTML, JavaScript, and CSS, as it handles complexities internally, allowing focus on Python code.
Comparatively, Streamlit is better suited for building fully-featured data applications and dashboards with more control over UI layout and interactivity. It offers flexible layout control, theming, and integration with other Python tools, facilitating richer dashboards. Streamlit is popular in the data science community for applications beyond ML demos, with a larger adoption and strong community.
When choosing between Gradio and Streamlit, it's essential to consider the specific needs of your project. If you're looking for a quick and easy way to build and share an interactive interface for an ML model or AI demo, Gradio is a great choice. However, if you need a more robust interactive dashboard, including detailed data visualizations, multiple widgets, and greater layout flexibility, Streamlit is the better option.
In conclusion, both Gradio and Streamlit are valuable tools for creating dynamic and user-friendly interactive data dashboards. Gradio shines when you want to quickly build and share a minimal interactive interface for an ML model or AI demo, while Streamlit is better suited for more comprehensive and customizable interactive data dashboards.
References: [1] LangFlow: [2] Databricks:
- In the realms of data-and-cloud-computing and technology, Artificial Intelligence (AI) and machine learning (ML) could potentially be integrated with home-and-garden lifestyle apps, enabling smart gardens that adapt to user preferences based on data visualization and analysis.
- Gradio, with its focus on rapid prototyping and simple UI construction, could be employed to develop interactive dashboards that visualize how solar panels or energy-efficient appliances are contributing to energy savings within a household.
- Streamlit, known for its versatility and comprehensive feature set, could empower users to design intricate and highly interactive data dashboards visualizing various factors impacting their lifestyle, such as exercise patterns, sleep quality, or even mental well-being metrics.
