This redesign keeps the dropdown workflow you liked, but turns the experience into a cleaner course-briefing interface. Select the weather, run the Naive Bayes model in the browser, and get a stronger visual recommendation with confidence, signal breakdown, and a live summary of the selected playing conditions.
Weather Inputs
Pick the four conditions used in your dataset. The predictor updates as soon as the scenario is complete.
Decision Engine
The result area is designed to feel like a course-read panel, with one primary call and the reasons behind it.
Complete the four inputs to generate the model confidence.
Fill in the course conditions to see whether the model recommends playing golf or holding off.
No complete scenario selected yet.
Once a scenario is complete, this section will explain how confident the model is in the recommendation.
The form remains simple, but the hierarchy is stronger: one clear hero, one clear action area, and one clear prediction panel.
Instead of a basic card stack, the page now uses a scorecard-style left rail and a richer right-side recommendation layout with a confidence dial.
If you want the next version, I can connect this same UI to Flask or FastAPI so the browser calls your real Python model directly.
Benchmark shown from your notebook export: Naive Bayes `0.67` accuracy versus logistic regression `0.33` on the saved split.