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mealmaster

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f7daacd40104dcbc93e0068cd7b6ff04413a11e2

chore: update lockfile

YYash-Singh1 committed 2 years ago
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8f0c45d28eaf349e54ce7cbe28960e9c1b919e1b

fix: next ts plugin lint

YYash-Singh1 committed 2 years ago
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9fcbba6f51119e3e1293f6a1045c24e99fc8db70

fix: lockfile

YYash-Singh1 committed 2 years ago
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b6b41ee25dab3021b96ced758623d90a57df484f

fix: lint

YYash-Singh1 committed 2 years ago
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8617c1e42427f78f99b9dece0fa2b542476b932f

feat: init

jjuliusmarminge committed 2 years ago

README

The README file for this repository.

mealmaster

A nutrition app that helps you find recipes and recommendations based on your diet. Worked in collaboration with @shreyvish5678 for Cross-Club Hacks.

Inspiration

Our main source of inspiration came from our nutrition lesson in Biology Class. As me and my friend were talking about nutrition, and how it’s often overlooked. Then we found out about this hackathon. Knowing that we wanted to help people with nutrition, and now had the chance to do so, we began!

What it does

Our app, called Meal-Master, gives users what they can eat, based on their diets. First, you can enter some information, such as weight, height, and more, to get your Daily Recommended Intake (DRI). Next the app compares this with their actual intake, and does this by inputting their meals throughout the day, and then calculating it. Then by comparing the two, the app figures out the user's needs. By telling the prompt to ChatGPT’s AI, it can give ingredients and recipes that the user can eat. Then they can look up those recipes on the app, and look at all the information, and a link to the website.

How we Built it

Our application consists of Expo and React Native for the frontend mobile application. This application connects to a tRPC server that runs on a Vercel Lambda instance built using Next.js. For receiving the nutrition and recommendation data, we made use of the Edaman and OpenAI API respectively. While building our backend we prototyped our API routes in Python and then translated it to TypeScript for our tRPC server.

Challenges we had

Installing the ChatGPT API was a hassle, since the documentation for it was a bit confusing but we figured it out. Trying to set up a development environment to work with our app.

Accomplishments we’re proud of

Using the ChatGPT API, as we could basically use ChatGPT in our own code Working with an enterprise-grade modern mobile application development workflow

What we learned

Learned more about API Installation, and how to use the documentation properly. Learned how to work with OpenAI systems. Working with tRPC in the backend

What’s next for Meal-Master

Due to the shortage of time that we had, we weren’t obviously able to build a perfect nutrition app with all the features we wanted, and be able to refine it well. So we will implement new ideas into the app, and maybe make our own API, since the ones we were dealing with, aren’t the best.

What we used:

  • Python
  • OpenAI
  • Edamam
  • Expo
  • React Native
  • TypeScript
  • tRPC
  • Prisma