## Inspiration

Research shows that maximum people face mental or physical health problems due to their unhealthy daily diet or ignored symptoms at the early stages. This app will help you track your diet and your symptoms daily and provide recommendations to provide you with an overall healthy diet. We were inspired by MyFitnessPal's ability to access the nutrition information from foods at home, restaurants, and the grocery store. Diet is extremely important to the body's wellness, but something that is hard for any one person to narrow down is: What foods should I eat to feel better? It is a simple question, but actually very hard to answer. We eat so many different things in a day, how do you know what is making positive impacts on your health, and what is not?

## What it does

Right now, the app is in a pre-alpha phase. It takes some things as input, carbs, fats, protein, vitamins, and electrolyte intake in a day. It sends this data to a Mage API, and Mage predicts how well they will feel in that day. The Mage AI is based off of sample data that is not real-world data, but as the app gets users it will get more accurate. Based off of our data set that we gather and the model type, the AI maintains 96.4% accuracy at predicting the wellness of a user on a given day. This is based off of 10000 users over 1 day, or 1 user over 10000 days, or somewhere in between. The idea is that the AI will be constantly learning as the app gains users and individual users enter more data.

## How we built it

We built it in Swift using the Mage.ai for data processing and API

## Challenges we ran into

Outputting the result on the App after the API returns the final prediction. We have the prediction score displayed in the terminal, but we could not display it on the app initially. We were able to do that after a lot of struggle. All of us made an app and implemented an API for the very first time.

## Accomplishments that we're proud of

-- Successfully implementing the API with our app
-- Building an App for the very first time
-- Creating a model for AI data processing with a 96% accuracy

## What we learned

-- How to implement an API and it's working
-- How to build an IOS app
-- Using AI in our application without actually knowing AI in depth

## What's next for NutriCorr

--Adding different categories of symptoms
-- giving the user recommendations on how to change their diet
-- Add food object to the app so that the user can enter specific food instead of the nutrient details
-- Connect our results to mental health wellness and recommendations. Research shows that people who generally have more sugar intake in their diet generally stay more depressed.
# We'd love if you read through this in its entirety, but we suggest reading "What it does" if you're limited on time

## The Boring Stuff (Intro)

* Christina Zhao - 1st-time hacker - aka "Is cucumber a fruit"
* Peng Lu - 2nd-time hacker - aka "Why is this not working!!" x 30
* Matthew Yang - ML specialist - aka "What is an API"

## What it does

It's a cross-platform app that can promote mental health and healthier eating habits!

* Log when you eat healthy food.
* Feed your "munch buddies" and level them up!
* Learn about the different types of nutrients, what they do, and which foods contain them.

Since we are not very experienced at full-stack development, we just wanted to have fun and learn some new things. However, we feel that our project idea really ended up being a perfect fit for a few challenges, including the Otsuka Valuenex challenge!

Specifically,

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> Many of us underestimate how important eating and mental health are to our overall wellness.
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That's why we we made this app! After doing some research on the compounding relationship between eating, mental health, and wellness, we were quite shocked by the overwhelming amount of evidence and studies detailing the negative consequences..

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> We will be judging for the best **mental wellness solution** that incorporates **food in a digital manner.** Projects will be judged on their ability to make **proactive stress management solutions to users.**
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Our app has a two-pronged approach—it addresses mental wellness through both healthy eating, and through having fun and stress relief! Additionally, not only is eating healthy a great method of proactive stress management, but another key aspect of being proactive is making your de-stressing activites part of your daily routine. I think this app would really do a great job of that!

Additionally, we also focused really hard on accessibility and ease-of-use. Whether you're on android, iphone, or a computer, it only takes a few seconds to track your healthy eating and play with some cute animals ;)

## How we built it

The front-end is react-native, and the back-end is FastAPI (Python). Aside from our individual talents, I think we did a really great job of working together. We employed pair-programming strategies to great success, since each of us has our own individual strengths and weaknesses.

## Challenges we ran into

Most of us have minimal experience with full-stack development. If you look at my LinkedIn (this is Matt), all of my CS knowledge is concentrated in machine learning!

There were so many random errors with just setting up the back-end server and learning how to make API endpoints, as well as writing boilerplate JS from scratch.

But that's what made this project so fun. We all tried to learn something we're not that great at, and luckily we were able to get past the initial bumps.

## Accomplishments that we're proud of

As I'm typing this in the final hour, in retrospect, it really is an awesome experience getting to pull an all-nighter hacking. It makes us wish that we attended more hackathons during college.

Above all, it was awesome that we got to create something meaningful (at least, to us).

## What we learned

We all learned a lot about full-stack development (React Native + FastAPI). Getting to finish the project for once has also taught us that we shouldn't give up so easily at hackathons :)

I also learned that the power of midnight doordash credits is akin to magic.

## What's next for Munch Buddies!

We have so many cool ideas that we just didn't have the technical chops to implement in time

* customizing your munch buddies!
* advanced data analysis on your food history (data science is my specialty)
* exporting your munch buddies and stats!

However, I'd also like to emphasize that any further work on the app should be done WITHOUT losing sight of the original goal. Munch buddies is supposed to be a fun way to promote healthy eating and wellbeing. Some other apps have gone down the path of too much gamification / social features, which can lead to negativity and toxic competitiveness.

## Final Remark

One of our favorite parts about making this project, is that we all feel that it is something that we would (and will) actually use in our day-to-day!
View the SlideDeck for this project at: [slides](https://docs.google.com/presentation/d/1G1M9v0Vk2-tAhulnirHIsoivKq3WK7E2tx3RZW12Zas/edit?usp=sharing)

## Inspiration / Why

It is no surprise that mental health has been a prevailing issue in modern society. 16.2 million adults in the US and 300 million people in the world have depression according to the World Health Organization. Nearly 50 percent of all people diagnosed with depression are also diagnosed with anxiety. Furthermore, anxiety and depression rates are a rising issue among the teenage and adolescent population. About 20 percent of all teens experience depression before they reach adulthood, and only 30 percent of depressed teens are being treated for it.

To help battle for mental well-being within this space, we created DearAI. Since many teenagers do not actively seek out support for potential mental health issues (either due to financial or personal reasons), we want to find a way to inform teens about their emotions using machine learning and NLP and recommend to them activities designed to improve their well-being.

## Our Product:

To help us achieve this goal, we wanted to create an app that integrated journaling, a great way for users to input and track their emotions over time. Journaling has been shown to reduce stress, improve immune function, boost mood, and strengthen emotional functions. Journaling apps already exist, however, our app performs sentiment analysis on the user entries to help users be aware of and keep track of their emotions over time.

Furthermore, every time a user inputs an entry, we want to recommend the user something that will lighten up their day if they are having a bad day, or something that will keep their day strong if they are having a good day. As a result, if the natural language processing results return a negative sentiment like fear or sadness, we will recommend a variety of prescriptions from meditation, which has shown to decrease anxiety and depression, to cat videos on Youtube. We currently also recommend dining options and can expand these recommendations to other activities such as outdoors activities (i.e. hiking, climbing) or movies.

**We want to improve the mental well-being and lifestyle of our users through machine learning and journaling.This is why we created DearAI.**

## Implementation / How

Research has found that ML/AI can detect the emotions of a user better than the user themself can. As a result, we leveraged the power of IBM Watson’s NLP algorithms to extract the sentiments within a user’s textual journal entries. With the user’s emotions now quantified, DearAI then makes recommendations to either improve or strengthen the user’s current state of mind. The program makes a series of requests to various API endpoints, and we explored many APIs including Yelp, Spotify, OMDb, and Youtube. Their databases have been integrated and this has allowed us to curate the content of the recommendation based on the user’s specific emotion, because not all forms of entertainment are relevant to all emotions.

For example, the detection of sadness could result in recommendations ranging from guided meditation to comedy. Each journal entry is also saved so that users can monitor the development of their emotions over time.

## Future

There are a considerable amount of features that we did not have the opportunity to implement that we believe would have improved the app experience. In the future, we would like to include video and audio recording so that the user can feel more natural speaking their thoughts and also so that we can use computer vision analysis on the video to help us more accurately determine users’ emotions. Also, we would like to integrate a recommendation system via reinforcement learning by having the user input whether our recommendations improved their mood or not, so that we can more accurately prescribe recommendations as well. Lastly, we can also expand the APIs we use to allow for more recommendations.