Streamlit Best Practices
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Streamlit is an excellent tool for rapidly prototyping healthcare and data-driven applications. It automatically generates a user-friendly interface from your Python code, allowing you to focus on functionality and logic without needing to manually design front-end elements. This makes Streamlit a great starting point when developing apps for Health Universe.
Many developers choose to start with Streamlit when building a new app because:
It allows for fast iteration: You can immediately visualize how inputs, outputs, and layout affect the user experience.
You can debug and refine your logic more easily in a visual context.
It supports integration with healthcare data formats like FHIR, CCDA, and CSV using native Python libraries.
Once the app’s behavior and interface are solid, developers then are able to easily migrate the codebase to FastAPI for production use with Navigator, where performance, modularity, and robust API control are priorities.
Here are some best practices to keep in mind when using Streamlit for your initial app development:
1. Organize Your Code Clearly
Use functions or modular components to keep the code clean and readable.
Separate function logic from UI logic where possible.
2. Minimize Reruns
Use Streamlit’s @st.cache_data, @st.cache_resource, or st.session_state to store data or avoid expensive re-computations on every input change.
Learn more:
3. Use Layout Features Thoughtfully
Leverage columns (st.columns()), expanders (st.expander()), and sidebar components (st.sidebar) for better organization.
Use st.markdown with custom HTML/CSS for layout tweaks when needed.
4. Make It User-Friendly
Add clear labels, tooltips (help=...), and default values.
Handle errors gracefully with st.error() or try/except blocks around sensitive logic.
5. Test with Real Data
Test with a variety of realistic files (e.g., FHIR JSON, CCDAs) early on to uncover edge cases and formatting issues.
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