Inspiration
This project grew from a simple question: why does getting dressed in the morning feel harder than it should? I noticed how often people wake up groggy, check five different apps, and still walk outside unprepared. I wanted a tool that felt calm and automatic, something that would quietly hand you the weather and what to wear before your brain even boots up. That idea turned into this app: a small service that texts you a morning weather update tailored to your location and wake-up time.
What it does
Building this taught me how front ends and back ends talk to each other, how APIs hand off data like relay runners, and how SMS automation works behind the scenes. I also learned how to handle errors, manage environment variables safely, and keep different parts of the system organized.
How we built it
The front end collects user information through a simple HTML form. A Flask backend receives those details, stores them, and calls the WeatherAPI to fetch real-time conditions. After processing the data, the app uses Twilio to send a personalized SMS. The whole flow becomes a smooth pipeline: user → server → weather → message.
Challenges we ran into
I ran into issues with broken file paths, environment variables not loading, and handling bad API responses. Each challenge pushed me to debug more carefully and understand how each layer connects.
Accomplishments that we're proud of
We’re proud that we built a fully connected loop that goes from a browser form all the way to a real phone with real-time weather results. Setting up Twilio and WeatherAPI correctly was a milestone, and seeing the first successful SMS felt like watching the whole project breathe for the first time.
What we learned
We learned how to connect front-end and back-end logic, work with REST APIs, manage secrets, debug chained systems, and design a smooth user flow. We also learned how valuable small automations can be when they remove mental overhead from daily life.
What's next for Weathersms
Next steps include adding customized clothing suggestions based on user preferences, supporting multiple wake-up reminders, improving the front-end design, storing users in a database instead of memory, and expanding into weekly summaries. We also want to add simple machine learning rules to tailor messages based on past behavior.
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