Flutter AI Weather — Forecasts, Radar, Alerts, Chat

I built an AI‑powered weather app that delivers accurate forecasts, live radar maps, and real‑time alerts. It uses AI to synthesize multiple data sources into detailed predictions (temperature, precipitation, wind, air quality, and storm risk). A built‑in AI Chat assistant answers natural‑language questions like “Will it rain after 5 PM?” or “What’s the weekend outlook?” The app supports location‑based tracking, severe weather push notifications, and a clean, responsive design for iOS and Android.

Forecasts: Hourly, daily, and 10‑day outlooks with feels‑like temp, UV index, humidity, and wind.

Live Maps: Radar, satellite, and precipitation layers with time‑lapse and pinch‑zoom.

AI Chat Assistant: Natural‑language Q&A, trip planning tips, umbrella/jacket suggestions, and alerts explanations.

Alerts & Notifications: Severe weather push alerts, rain‑starting‑soon notifications, and morning summaries.

Locations: Follow current location with GPS, add/save multiple cities, and quick switching.

Insights: Rain probability graphs, storm tracks, air quality and pollen indexes where available.

Offline Readability: Cached last forecast and saved locations for quick access without network.

Accessibility: High contrast, large text, voice‑over labels, and color‑blind friendly palettes.

Flutter (Dart) for iOS/Android UI

State: Riverpod or GetX

Weather API: OpenWeather or Tomorrow.io

Maps: Google Maps or Mapbox

AI: OpenAI API for chat/insights

Backend: Firebase (Firestore, FCM, Functions)

Storage/Caching: Local cache (Hive/SQflite)

Analytics/Quality: Crashlytics, Performance, Analytics

CI/CD: GitHub Actions or Codemagic

Data Ingestion: Cloud Functions fetch forecasts/alerts from the configured weather provider at intervals.

Normalization: Functions unify units and fields (temp, precip intensity, probability, wind, AQI).

AI Reasoning: The app calls the OpenAI endpoint with structured weather context to produce concise answers.

Delivery: App consumes Firestore docs and provider APIs for live data; FCM triggers push alerts.Clear Visuals: Color‑safe radar gradients, trend charts, and readable typography.

Provider Variability → Created a provider abstraction layer with unit normalization and fallback logic.

Noisy Alerts → Added severity thresholds, user preferences, and quiet hours to reduce alert fatigue.

AI Hallucinations → Grounded the assistant with tools/function calls and explicit weather context.

Offline/Low Signal → Local cache and graceful degradation for forecasts and saved locations.

Accurate, timely forecasts with clear explanations via the AI assistant.

Higher engagement from live radar, proactive alerts, and multi‑location tracking.

Portable, scalable architecture with swappable data providers and secure key handling.