The rise of the food delivery industry and people's busy lifestyles, more and more people order take outs with too much salt, sugar-sweetened beverages and processed meats to save cooking time. On the other hand, some people enjoy cooking and preparing a healthy and tasty meal.

What is MyChef?

MyChef is a web platform for students to order healthy meals from local cooks who prepare fresh food. The online platform provides various features to incentivizes students to eat healthy and provide them with various dietary options.


  1. The home page is quick to navigate to access information that matters for students.
  2. Easy to use dashboard that has personalized recommendations for students to eat healthy with filters on rating, price and dietary restrictions.
  3. Surveys for students to gather information about their food habits, goals to improve and preferences to lead a healthy lifestyle.

How we built it

MyChef is a web application built to provide a streamlined user experience for students seeking healthy, on-the-go home-cooked meals. The UI/UX is designed using Figma to model and provide a simple visualization of the interfaces. The front-end is built with React, which offers a simple and modern UI as well as powerful client-side functionality for our personalized meal recommendation system. The backend of the website is developed using Firebase, and a recommendation system is built using a filtering approach for recommending customized meal recommendations. The initial recipe recommendations are based on each user's survey during registration. Therefore after the user selects their dietary restrictions and favourite meals, recommendations will be made by computing similarities between different user’s preference data and each chef’s meal.

Challenges we ran into

Working in different time zones and coordinating work flows. Unbalanced skill types. We are a group of all backend developers with knowledge of different frameworks, in order to collaborate many of us have to give up the setups we are used to and pick up a new skill. Like learning React.js, Firebase and using Figma and using it for the first time. We faced some hiccups while pushing the code to GitHub. Website not loading up in the localhost. Minor bugs and errors in the HTML page. Facing several constraints coding for our front-end. Issues of connecting the front-end with the back-end was a challenge. However, with tutorials and quick learning we were able to successfully overcome them.

Accomplishments that we're proud of

We are proud of the platform we created. The topic of mental and physical health in the student community is a daily problem for us and we are happy that we could contribute our product to have a better student lifestyle.

  1. We brainstormed for solutions about an issue that was affecting our daily life as students and came up with MyChef.
  2. We created a website from scratch through rapid prototyping to help students like us to lead a healthy life.
  3. We learned Firebase and React in 24 hours! Learning Firebase, React and JavaScript through self motivation.
  4. We created a simple UI that is easy to navigate and is not cluttered with too many irrelevant/unhealthy options.
  5. A great learning experience on collaborative skills while working in a good team!

What we learned

How to build websites using React, HTML, CSS, Figma, Firebase and JavaScript. How to create a web application in 24 hours through rapid-prototyping. Handling frustrating debugging moments through incremental and bottom-up program development. We learned valuable information about how a website operates from the back-end and front-end.

What's next for MyChef

We want to reach a border target audience by making MyChef available on all platforms including Android and Apple, expanding our database for all local cooks and chefs. We also want to raise awareness of the importance of nutrients by adding a quiz and news section to increasing users' engagement and motivations for the business owners to increase their performance. We also want to improve our current recommendation system by building it using a collaborative filtering approach ML algorithm for recommending customized meal recommendations. The similarity for each chef's meal is computed using Tanimoto Coefficient similarity and LogLikelihood similarity, and the user's likelihood to enjoy a meal is computed using Euclidean Distance and similarity and Pearson Correlation. Due to time limitation we didn't have time to finish the implementation for Calgary Hack.

Thanks for reading our Devpost. We hope you love it!

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