Days like today reassure me that I was not cut out to exclusively be a front end developer. I spent a good 4 hours or so rewriting the template from HTML to Dash's HTML Component Library, fiddling with React's CSS and JS extention modules, and figuring out a way to make the whole thing (relatively) object oriented. After understanding that tag classes were the same as the className function modifier and that you can't modify the inner HTML of a tag safely, I had what was in all rights a presentable beta version of a web app. Now all I have to do is write my own REST API and I can call myself a Full-Stack Developer! I was more than thrilled to have written a completely serverless project that managed to be fast as well.
I proudly showed my achievements to a group of friends grinning ear to ear without giving them much information as to how to use the website. Fiddling with it for the last week or so made me believe the application was intuitive without instructions. This proved to be the easy part as I explained that it grabbed publicly available information about used cars, and the price it was sold at around a given time. Then the unexpected came. "What's an R129 SL? I know my dad had an old Mercedes, but it was a 560 SEL and I don't see that on here." Not everyone has the chassis names of cars memorized. First major user problem.
Thankfully I tried to not get too disheartened and that lead to innovation. What I needed to do was create a common name for each car model that would appear next to a chosen model. So, for example, if you were to select the BMW brand, and scroll down to the E30 model, it would say 1982-94 3 Series in parentheses next to it. This would make it easier for those who are not car people to have an easier time recognizing the chassis models. Originally I had thought about creating a script to go through each link, grabbing the year, and parsing out a common name with BeautifulSoup, but a quick SELECT DISTINCT car_model
query and I realized I only had something like 26 different models to find names for. That's something that could be done with 30 minutes of Wikipedia searching by hand. And it'd be way more fun.