Inspiration

We have all heard about the nutrition and health issues from those who surround us. Yes, adult obesity has plateaued since 2003, but it's at an extremely high rate. Two out of three U.S. adults are overweight or obese. If we look at diabetes, it's prevalent in 25.9% americans over 65. That's 11.8 million people! Those are the most common instances, let's not forget about people affected by high blood pressure, allergies, digestive or eating disorders—the list goes on. We've created a user friendly platform that utilizes Alexa to help users create healthy recipes tailored to their dietary restrictions. The voice interaction allows for a broad range of ages to learn how to use our platform. On top of that, we provide a hands free environment to ease multitasking and users are more inclined to follow the diet since it’s simple and quick to use.

How we built it

The backend is built with Flask on Python, with the server containerized and deployed on AWS served over nginx and wsgi. We also built this with scale in mind as this should be able to scale to many millions of users, and by containerizing the server with docker and hosting it on AWS, scaling it horizontally is as easy as scaling it vertically, with a few clicks on the AWS dev console. The front end is powered by bootstrap and Jinja (JavaScript Framework) that interfaces with a mySQL database on AWS through Flask’s object relational mapping. All in all, Ramsay is a product built on sweat, pressure, lack of sleep and <3

Challenges we ran into

The deployment pipeline for alexa is extremely cumbersome due to the fact that alexa has a separate dev console and debugging has to be done on the page. The way lambda handles code change is also extremely inefficient. It has taken a big toll on the development cycle and caused a lot of frustrating debugging times..

It was also very time consuming for us to manually scrape all the recipe and ingredients data from the web, because there no open source recipe API that satisfies our needs. Many of them are either costly or had rate limit restrictions on the endpoints for the free tier, which we are not content with because we wanted to provide a wide range of recipe selection for the user.

Scraping different sites gave us a lot of dirty data that required a lot of work to make it usable. We ended up using NLTK to employ noun and entity extraction to get meaningful data from a sea of garbage.

Accomplishments that we're proud of

We managed to build out a Alexa/Lambda deployment pipeline that utilizes AWS S3 buckets and sshfs. The local source files are mounted on a remote S3 bucket that syncs with the Lambda server, enabling the developer to skip over the hassle of manually uploading the files to the lambda console everytime there is a change in the codebase.

We also built up a very comprehensive recipe database with over 10000 recipes and 3000 ingredients that allows the user to have tons of selection.

This is also the first Alexa app that we made that has a well thought out user experience and it works surprisingly well. For once Alexa is not super confused every time a user ask a question.

What we learned:

We learnt how to web scrape implementing NLTK and BeautifulSoup python libraries. This was essential to create of database containing information about ingredients and recipe steps as well. We also became more proficient in using git and SQL. We are now git sergeants and SQL soldiers.

What's next for Ramsay:

Make up for the sleep that we missed out on over the weekend :')

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