when i started Where the Meteor at, the idea was pretty straightforward : ask for someone’s location , account for the condition in their sky ,and tell them how many meteors they could realistically see instead of just throwing the theoretical ZHR at them.

The first couple of sessions were mostly about figuring out the logic and getting the Flask backend to work . I was learning as i went , breaking things , debugging them , and occasionally wondering why did i even pick this project because of the back and forth

but i finally gave the UI the project Deserved.

The redesign

I took a lot of inspiration from Teenage Engineering’s Industrial design language . I wanted the interface to feel like a scientific instrument rather than another generic AI dashboard as it looked before .

SO i went with a warm off-white background ,near-black text and a sharp orange accent .Syne handles the big heading while jetbrains mono gives the measurement and data a technical , instrument-panel feel.

I also re-worked the layout around how someone would actually use the tool . Location search and sky condition sit in their sidebar , while the meteor shower gets the main stage . The estimated meteor count is the first thing you notice and the more detailed information stays tucked inside expandable rows until you need it .

those rows contain things like radiant altitude , viewing direction air quality and the best viewing window .I also kept the hourly chart so you can get a better idea of how the condition change instead of relying on one number .

The goal was to make the interface feel engineered not just decorated. No unnecessary elements ; just a strong grid , deliberate typography , and i especially love the orange accents

Conclusion
This project has been a bit out of my comfort zone i started out learning and experimenting with Flask , and ended up with a project that combines astronomy environmental data , calculation and interface design.

The direction the UI took was amazing .It finally feels like a coherent product instead of a collection of features i managed to get working.

The next step is to finish the final check , update the documentation , and ship it

If you spot a bug or have an idea for improving the prediction ; feel free to open an issue or submit a PR. I’d genuinely love to see what people do with it