Inspiration
Everyday thousands of people get to decide what they wear for the day. The first thing they do is check the weather. Once they check the weather they make outfits based on that. But what if they make the wrong choice? It never hurts to have a second opinion. Skyline is here to save the day! With an API to quickly grab your location and generate the forecast right then and there and used our very own dataset that we’ve obtained through our own diligent research we can generate outfits that suit the weather that very day.
What it does
Skyline is a solution to everyone’s problems. It uses Geolocation and an API to grab your location and quickly generate a forecast that is highly accurate and based on the weather we generate various outfits that suit the weather for that very day. We have 3 options which is outfit generation for Men, Women, and Other. Generation for men is outfits like tshirts, shorts, or sweatpants sweatshirts and hoodies, and of course if its raining and snowing the outfit would be a jacket. It would similarly work the same with the Women section but we do also have a taste in fashion so we decided to give women and men a really wide variety of outfits that everyone will like.
How we built it
We used HTML and CSS for our frontend, Javascript for utilizing our dataset, Microsoft Excel for creating our very own dataset, and Python is used for our backend mostly with page routing and using the Geolocation and weather forecast API. National Weather Service API: https://api.weather.gov Coordinates to City API: https://api.bigdatacloud.net
Challenges we ran into
We couldn’t find the right clothing API, so we had to make our own data set with JSON in order to correctly display the outfits on the website
Accomplishments that we're proud of
We created our own dataset, which is something we hadn’t done before We learned new libraries, and got more experience in Javascript and Python We continued to acquire more skills about website development through new libraries and CSS animations
What we learned
We learned how to be innovative and make datasets through our own research Backend for Flask and using an API to fetch data, manipulate it, and produce results We used Extensive javascript to use the dataset that we’ve made from our own research and connected with the frontend to get a seamless experience of using an API and using data science. We learned a few new CSS tricks that we never knew before.
What's next for Skyline
Implement AI and ML into predicting preferences for coding so that our generation can be only based on preferences by the user. Partnering with multiple companies to convert Skyline into a full-fledged ecommerce website so that customers can directly buy from our store. Upgrade the front-end of the website to be more UI/UX friendly and satisfy customers that visit our website.
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