As students, we are not excellent at cooking. Thus, we decided to make a program that will help us become the next Gordon Ramsay.
What it does
This WebApp generates recipes with optimized ingredients and instructions, based on user data. It has a base recipe, that is then given slight variations when presented to the user. These variations are randomized, and users provide a rating on how good the recipe was. The program then uses the data from the rating, and the variations from the original recipe to generate an optimized recipe.
How we built it
Challenges we ran into
We ran into challenges with using C# (Asp.net core) for the backend with MongoDB. There were issues with making the model for the database and the crud operations. Thus, it was decided to use Node.JS with MongoDB.
Data generation for testing. We do not have a user base, so we were able to make datasets, however, they were all randomized, meaning we don't get an idea of how well our model actually performs.
Accomplishments that we're proud of
Clean user interface
What we learned
The two group members who wrote the python code had little python experience One group member set up and managed a database for the first time We also learned how to set up a server on the google cloud platform
What's next for Autochef
The main component that can be improved for Autochef is the algorithm for determining the optimized recipe. For the time being it is determined using weighted averages depending on the score, and the deviation from the original recipe. The next step is to incorporate machine learning, as well as statistical tools such as regression analysis to determine better optimization for the recipes.