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Conditional logic

As explained in Build Python Web Apps with Streamlit

Branching logic is control flow that selects among alternative execution paths based on conditions or decisions.

Conditional logic selects which code runs by testing a condition or decision. In interactive apps, it connects user actions and application state to visible interface changes. section 4.3.3 section 9.4

Apply conditional logic to:

  • Run an action only when a button expression evaluates to True, such as adding a task after a user clicks Add task. section 4.3.3
  • Branch the interface from a stored status: show a spinner while obtaining or evaluating an answer, an input while asking, and feedback after evaluation. section 9.4
  • Prevent repeated work by allowing each sequential action and language-model request only in its appropriate state. section 9.4

A state-driven question game

Store a status such as GET_QUESTION, ASK_QUESTION, EVALUATE_ANSWER, or STATE_RESULT. Test that status with if and elif branches, then call the matching game method or render the matching Streamlit element. After each transition, rerun the app so the new branch becomes visible. section 9.4

Figure 9.7 Conditional branching in the frontend based on the game’s state

Trace how a central game status branches the frontend into different display elements. section 9.4

How Build Python Web Apps with Streamlit covers it

How deep it goes
Explains condition tests through button and state examples, but stops short of formal language semantics or advanced branching patterns.
Examples
Demonstrates task addition, a four-state question game, spinners, answer inputs, and correctness feedback.
What it assumes
Assumes basic Python and Streamlit interaction knowledge while demonstrating conditions through concrete application behavior.
What you can do after
Apply it via Python and Streamlit to connect user actions or stored state with targeted code and interface outcomes.
How it connects
Connects conditional logic to application state, Streamlit reruns, user interaction, LangGraph, and multi-step workflows.

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This explainer was generated by Manning's knowledge graph from the text of Build Python Web Apps with Streamlit. The section links point to the parts of the book it draws on.