Inspiration

We wanted to make the best use of Wegman's nutrition/Recipe API to its well-deserved place. Wegman's sell quality products and at this time and age when all kinds of chemicals are used in food, Wegman's really stands-out for its quality and confidence to western New Yorkers. We think we should share this amazing experience not only in western new york but also in all cities in the United States. On a smaller note, we also want to support local suppliers.

What it does

It helps us with those quick impulse decisions which sometimes bother us and leave us frustrated. Like what do I eat now? Little moments like these if we make a bad decision we blame ourselves saying we should have done the other. But our app will take the blame for you because it will give you the quick decision on which option to choose and food advise for the corresponding mealtime so that you, the consumer, do not waste extra time deciding what to cook and with the help of Wegman's you can get quality ingredients. This means you and your precious family are even healthier!

How we built it

We used Wegman's nutrition/recipe API for fetching the recipe data. Since it is still under development, the app so far posts a randomized meal-recipe of the meal period on tweeter through a bot which was programmed in python. This basically saves the consumer time to think about what to cook for a meal and reduce stress.

Challenges we ran into

The Wegman's API fetching was confusing at first but we rammed through it. Other than that understanding all the new tools such as google cloud platform to deploy, new to python, lack of tutorials made it really difficult for us to connect the dots.

Accomplishments that we're proud of

We are proud to see that our bot is currently posting on tweeter successfully.

What we learned?

We learned many new frameworks which we never worked with previously as well as how to better work as a team.

What's next for Confused?

We will add many other questions other than what food to eat and will integrate ML to predict what the customer is trying to decide between two things the consumer is trying to decide between and use that to suggest products correlating to the given decision.

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