Smart Pollen Allergy Tracker

"Breathe Easy: Get real-time pollen levels, allergy risk alerts, and health tips based on your location. Stay safe with instant notifications via email and SMS—anytime, anywhere."

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🌿 Breathe Easy – Your Personal Pollen & Allergy Tracker

Tagline: Real-time pollen alerts powered by AI and geolocation to keep allergy sufferers safe.

🧠 Inspiration

Millions of people suffer from pollen allergies that disrupt their daily lives. Whether it's itchy eyes or breathing difficulties, stepping outside can be risky—especially without knowing the pollen levels in the area. We wanted to create a smart, automated solution that keeps users informed before symptoms start—right from their pockets.

⚙️ What It Does

  • 🌍 Tracks your live location (with consent).
  • 🌿 Fetches real-time pollen data (tree, weed, and grass) using the Ambee API.
  • ☁️ Gets weather data (humidity, temperature, PM2.5/PM10) from AirVisual API.
  • 🧠 Uses a trained machine learning model to assess allergy risk based on environment.
  • 📧 Sends email and SMS alerts if you're allergic and in a high-risk area.
  • ✅ Progressive Web App (PWA) ready — works like a native app and can be installed.

🛠️ How We Built It

  • Frontend: HTML/CSS/JS with location detection and installable PWA support.
  • Backend: Flask (Python), SQLite for storing user data securely.
  • ML Model: Trained with scikit-learn using environmental & pollen features to predict allergy risk.
  • APIs Used:
    • Ambee API for pollen data
    • AirVisual API for weather and air quality
    • Twilio for SMS
    • Flask-Mail for email alerts

🤯 Challenges We Faced

  • Getting accurate real-time pollen data for specific locations.
  • Managing session storage of user preferences and location securely.
  • Balancing alert sensitivity to avoid false positives.
  • Handling API errors gracefully (e.g., timeouts, missing data).
  • Implementing the OTP verification system for secure user registration.

💡 What We Learned

  • Deep integration of multiple APIs in a Flask app.
  • How to build and deploy a machine learning model in a live web service.
  • Improving user privacy and security in location-based apps.
  • Using PWA features for a smoother mobile experience.

📈 What's Next

  • Add push notifications via service workers.
  • Integrate wearable support (e.g., smartwatches).
  • Collect anonymized data to improve ML predictions.
  • User-specific allergy profiles for more accurate alerts.

Link Of The Website : https://pollen-allergy-tracker.onrender.com/

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