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How to Publish a Scientific Model using Streamlit

Written by
Daniel Caron
Published on

Step 1: Determine the goal and target audience of the project

Before beginning the conversion process, it is important to first determine the goal and target audience of the project. In this case, the goal is to allow physicians to utilize a predictive model in a clinical setting, and the target audience is physicians.

Step 2: Gather the necessary materials and resources

Next, gather all of the necessary materials and resources that will be needed for the project. This may include the published scientific study, any relevant code or data, and any additional resources that will be needed to build the interactive project.

Step 3: Familiarize yourself with streamlit

Streamlit is an open-source Python library that allows you to build interactive web-based applications, such as predictive models, with minimal effort. If you are not already familiar with streamlit, take some time to familiarize yourself with the basics of how it works. There are many tutorials and resources available online that can help with this.

Step 4: Clean and organize the code and data

Before building the interactive project, it is important to clean and organize the code and data from the published scientific study. This may involve refactoring the code to make it more readable and easy to understand, as well as organizing the data in a way that is easy to work with.

Step 5: Build the interactive project with streamlit

Now it is time to build the interactive project with streamlit. Start by creating a new streamlit project and importing the necessary libraries and modules. Then, use streamlit's built-in functions and widgets to build the user interface of the project. This may include input fields for data entry, buttons for triggering actions, and visualizations for displaying results.

Step 6: Test and debug the project

After the interactive project is built, it is important to thoroughly test and debug it to ensure that it is working correctly. This may involve testing different input scenarios and checking for any errors or bugs that may have been introduced during the conversion process.

Step 7: Share the project with others

Once the interactive project is complete and fully tested, it is time to share it with others. This may involve sharing the code and data on a platform such as GitHub, or hosting the project on a web server for others to access.


Converting a published scientific study into an interactive streamlit project can be a valuable way to make our work more accessible and useful to others. By following the steps outlined in this blog post, researchers can easily convert their studies into interactive projects that can be used by physicians in a clinical setting

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