## Inspiration

With many people's stressful, fast-paced lives, there is not always time for reflection and understanding of our feelings. Journaling is a powerful way to help reflect but it can often be not only overwhelming to start on a blank page, but hard to figure out even what to write. We were inspired to provide the user with prompts and questions to give them a starting point for their reflection. In addition, we created features that summarized the user's thoughts into a short blurb to help them contextualize and reflect on their day and emotions.

## What it does

Whispers is an app that aims to help the user talk about and decipher their emotions by talking with Whisper, the app mascot, as the adorable cat prompts you with questions about your day in an effort to help you reflect and sum up your emotions. Through the use of randomly generated prompts, Whispers collects your responses to create a summary of your day and tries to help you figure out what emotions you are feeling.

## How we built it

The back-end was developed with Node.js libraries and the Co:here API to collect the user-inputted data and summarize it. Furthermore, the classify functionality from Co:here was used to conduct sentiment analysis on the user's inputs to determine the general emotions of their answers

The front-end was developed with React-native and UX designs from Figma. Express.js was used as middleware to connect the front-end to back-end.

## Challenges we ran into

Overall, we did not run into any new challenges when it comes to development. We had issues with debugging and connecting the front-end to the back-end. There was also trouble with resolving dependencies both within back--end and conducting requests from front-end for analysis. We resolved this using Express.js as a simple, easy-to-use middleware.
In addition, we had trouble translating some of our UI elements to the front-end. To resolve this, we used both online resources and re-adjusted our scope.

## Accomplishments that we're proud of

We are proud of the model that was made for our backend which was trained on a massive dataset to summarize the user's thoughts and emotions. Additionally, we are proud of the chat feature that prompts the user with questions as they go along and our overall UI design. Our team worked hard on this challenging project.

## What we learned

We learned a lot about front-end development and adjusting our scope as we go along. This helped us learn to resolve problems efficiently and quickly. We also learned a lot while working with our backend of how to train Co:here models and connect both the front-end and back-end and perform requests to the server.

## What's next for Whispers

We would like to continue to develop Whispers with several more features. The first would be a user authentication and login system so that it could be used across several platforms and users. Additionally, we would like to add voice-to-text functionality to our chat journaling feature to improve on the accessibility of our app. Lastly, we would like to expand on the archive functions so that users can go back further than just the current week and see their journals and emotions from previous weeks or months
## Inspiration

* As college students, one of the most difficult things to do is to split the bill for grocery payments. Everyone in the house wants a different combination of items, and paying back the purchaser has become a bi-weekly ordeal. We were inspired by the need that we saw for an application such as the one we created, and the potential that this work could have for data collection and health.

## What it does

* This app uses a single photo and a tap-to-select iOS UI to split a bill between people, record a category, and prepare receipt data to send to the cloud.
* Data can be used for tracking nutrition based on different food bought

## How we built it

* We learned how to use Xcode and Swift to program this app, using google Firebase's text reading AI kit.

## Challenges we ran into

* The ML Kit wasn't as accurate as we expected, and parsed data differently than expected.
* We had to narrow down the text to parse through, as the kit read the entire receipt
* We were new to Xcode, Swift, Google Firebase, and cloud computing

## Accomplishments that we're proud of

* We learned how to code with Xcode
* We learned how to set up cloud computing for our project
* We implemented regex that would correctly filter the receipt to read necessary items
* We were able to make a data table based on the ML read of the receipt and create a UI based on the data

## What we learned

* Swift and Xcode, IOS development.
* Cloud computing

## What's next for reSEEt: see your receipts in a new light

* We want to be able to handle more data than just the item name and cost. We want to read the date, location, shop type, payment method, etc.
* Better UI with more settings for easier use of the app.
* ML training for auto-categorization of groceries
## Inspiration

Whether you’re thriving in life or really going through it, research shows that writing down your thoughts throughout the day has many benefits. We wanted to add a social element to this valuable habit and build a sense of community through sharing and acknowledging each other’s feelings. However, even on the internet, we've noticed that it is difficult for people to be vulnerable for fear of judgement, criticism, or rejection.

Thus, we centred our problem around this challenge and asked the question: How might we create a sense of community and connection among journalers without compromising their sense of safety and authenticity when sharing their thoughts?

## What it does

With Yapyap, you can write daily journal entries and share them anonymously with the public. Before posting, our AI model analyzes your written entry and provides you with an emotion, helping to label and acknowledge your feelings.

Once your thoughts are out in the world, you can see how other people's days are going too and offer mutual support and encouragement through post reactions.

Then, the next day comes, and the cycle repeats.

## How we built it

After careful consideration, we recognized that most users of our app would favour a mobile version as it is more versatile and accessible throughout the day. We used Figma to create an interesting and interactive design before implementing it in React Native. On the backend, we created an API using AWS Lambda and API Gateway to read and modify our MongoDB database. As a bonus, we prepared a sentimental analyzer using Tensorflow that could predict the overall mood of the written entry.

## Challenges we ran into

Learning new technologies and figuring out how to deploy our app so that they could all communicate were huge challenges for us.

## Accomplishments that we're proud of

Being able to apply what we learned about the new technologies in an efficient and collaborative way. We're also proud of getting a Bidirectional RNN for sentiment analysis ready in a few hours!

## What we learned

How to easily deal with merge conflicts, what it's like developing software as a group, and overall just knowing how to have fun even when you're pulling an all-nighter!

## What's next for yapyap

More personable AI Chatbots, and more emotions available for analysis!