The most valuable feedback often comes not from what users say, but from what they do! When was the last time you clicked a “Give Feedback” button in an app? Exactly. Yet product teams keep adding these explicit feedback mechanisms, hoping for insights that rarely materialize into actionable data. In ...

At Appsilon, we’ve been integrating Large Language Models into Shiny for Python applications for a while now. One thing became clear: the challenge isn’t in the initial integration. Shiny for Python’s ui.Chat component makes that straightforward. The real complexity lies in building applications that can evolve ...

In the past, many organizations relied on static file reports to make decisions. Typically, several Excel and PowerPoint reports are generated each month. Management would gather, discuss results, decide, and move to email other systems or meet with their teams to pass these decisions forward. There are multiple problems with ...

We’re thrilled to announce the release of Tapyr 0.2, a major update that incorporates valuable feedback from the Shiny for Python developer community. Learn how Tapyr enhances Shiny for Python in our Open Source Spotlight. See how it simplifies deployment for Shiny apps. This version focuses on improving the developer ...

Web developers love JavaScript. And who can blame them, it’s a fantastic do-it-all language for making stunning websites. But what about Shiny and Shiny for Python? As it turns out, you can add JavaScript scripts to your Shiny for Python dashboards without too much trouble. Today you’ll learn ...

Think of Shiny for Python custom modules as reusable components. You write the logic once, make it as tweakable as possible, and then reuse it throughout your application. As it turns out, creating custom modules in Shiny for Python is simple enough, but there are some new concepts you need ...

PyShiny (Shiny for Python) represents a significant advancement in the field of data dashboarding, setting new standards for design, maintainability, and scalability. This framework is not just an extension of R Shiny to Python but a refined approach that encapsulates the best practices learned from years of dashboard development: Modular ...

Efficiently handling complex workflows is crucial in computational biology, going beyond necessity to become a competitive differentiator. As professionals in this dynamic field, you’re likely familiar with the challenges: integrating diverse data sources, ensuring reproducibility, and scaling operations efficiently. Enter Nextflow, a robust solution designed to simplify and streamline ...

In today’s data-driven landscape, the way we store and manage data can significantly impact both efficiency and decision-making processes. While CSV files have long been the go-to format for quick data dumps and simple storage, they come with inherent drawbacks that can hinder performance and data integrity. Enter Parquet—...

In an era where data drives critical decisions, interactive dashboards and data applications have ascended as indispensable tools across sectors like business, scientific research, and more. This is especially true in fields like life sciences, where the need for accessible data analytics and visualization is critical for research and healthcare ...

Streamlit Tutorial on RStudio Connect At Appsilon we specialize in R and Shiny, but as data scientists and ML engineers we also know our way around Python. In our recent Streamlit tutorial series, we created a Streamlit application from scratch. If you completed the tutorial you might’ve noticed it ...

Machine learning projects are a beautiful medley of the code used to build models and the data used to train them. ML models are complex beasts that change through a myriad of methods from the datasets used and the way they’re transformed to the changing of the code itself. ...