Inspiration
I don’t personally suffer from allergies, but many people around me do—friends, family, and classmates. I noticed how often they struggled to find quick, reliable pollen information to help them manage their day. That inspired me to build a tool that could provide real-time, location-based pollen data in a simple and accessible way.
What I Learned
This project taught me a lot about: • Working with REST APIs (Google’s Pollen API in particular) • Backend development using Flask in Python • Handling rate limits and data caching • Structuring clean, readable, and maintainable code • Thinking from the user’s perspective to deliver meaningful information, not just raw data
How I Built It • Language: Python • Backend Framework: Flask • API: Google Pollen API (for real-time and forecasted pollen levels) • Data Handling: Parsed and formatted API responses into readable summaries • User Interface: Basic frontend (or CLI, if applicable) to display pollen levels clearly • Output: Shows tree, grass, and weed pollen levels, along with the overall pollen index
Challenges I Faced • API Limitations: The Google Pollen API had daily request limits, so I had to optimize calls and implement basic caching. • Formatting Raw Data: Turning numerical pollen scores into useful health indicators required interpretation and custom logic. • Backend Deployment: Making sure the Flask app ran reliably in different environments.
Future Plans • Add personalized health tips based on pollen exposure and symptom logs • Integrate with other environmental data sources (e.g., air quality, humidity) • Build a simple mobile-friendly frontend using Flask templates or connect with a Flutter client • Use machine learning to track and predict symptom triggers
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