## Inspiration

The project was born out of a passion for both music and technology. I wanted to create an app that would allow users to explore their musical preferences in a fun and interactive way. After being so excited to see my Spotify wrapped at the end of the year and being a bit disappointed that a lot of the data insights I wanted to see just weren't included, I wanted to make my own version. While it takes inspiration from "Spotify Wrapped," this project has many of its own twists and turns :) which I hope you enjoy. I wanted to use Cohere's LLM in this hackathon as it's an incredibly exciting technology, and its applications are endless; getting more experience with Cohere was my favourite part of the experience.

## What it does

With Statify, you can discover new trends, relive memories, and even brag about your top artists on social media. You can even generate user personas for yourself (this is the most awesome part)! One feature that I really love is the ability to view your listening history, as I'm always forgetting to write down the name of some amazing songs I'm jamming out or studying, and this addresses that!

## How I built it

I built the web app using JS and React; I used a W3 CSS styles template to import the styles designs for this app. The best parts of this app come from its API integrations; all data and authorizations are retrieved using Spotify's Developer API. The Statify AI user personas are powered by prompt engineering through Cohere's Command-Light large language model. I absolutely LOVED using Cohere. Cohere makes it incredibly easy to learn and use their models. From their various tutorials, articles, and their sandbox, it was a blast working with Cohere's tech, and I'm excited about their future.

## Challenges I ran into

A challenge I ran into was ensuring the prompt used for generating user personas was outputted in a reliable and consistent format but also gave creative and fun results. This took some playing around in Cohere's sandbox and fine-tuning the prompt as well as the temperature of the prompts. To ensure these prompts could be returned efficiently, the light-command model was picked.

I also wanted to integrate Dream Studio to create avatars and artwork throughout the web app to represent each user individually. My goal was to use Cohere to create personalized prompts which could be fed into the Dream Studio API, which would create a custom avatar. I ran into some issues fine-tuning this feature and couldn't ship it yet, but I look forward to completing it soon!

## Accomplishments that I'm proud of

I'm proud I could finish almost every feature in the app I set out to! It was such a fun experience and really pushed me to the limit, but as a result, I grew immensely. I'm proud of making something that I personally love to use and believe that other people would be too!

## What I learned

I learned a lot about making web pages reactive, the importance of architecture and planning during the design phase, and the fact that I can do almost anything if I set my mind to it.

## What's next for Statify

In the short term, I'd like to finish the genres page as I believe it brings lots of value to the product. In the long term, I plan on using Dream Studio to create a beautiful personalized backdrop for the Statify AI feature. The goal is to use prompt engineering with Cohere to create a prompt from the user's Spotify stats that will be passed into an AI art generator. The art generated from this tool would be used throughout the app in places like the Statify AI personas, genres, and top artists.
## Inspiration

This year, all the members in our team entered the first year of post-secondary and attended our various orientation event. We all realized that during orientation, we talked to many people but really only carried on a couple of friendships or acquaintances. As such, we decided to make an app so that people could quickly find others in their vicinity who share their common interests rather than talking to every single person in the room until they find someone who's a good match.

## What it does

Linkr is an app which shows the people in your area and sorts them by how much they have in common with you. When first installing the app, Linkr asks a series of questions to determine your personality and interests. Based on this, it will find matches for you, no matter where you go. The app uses geolocation of the users to determine how close other users are to you. Based on this and using AI to determine how much they have in common with you, you will get a sorted list of people with the person who has the most in common with you being at the top.

## How we built it

We planned to use Cohere to analyze the similarities between the preferences of different people, using Django for the backend and React Native for the front end. Additionally, we believe that music is a great indicator of personality. As such, we looked into using Spotipy to collect data on people's Spotify history and profiles.

With Cohere, we made use of the get\_embedded function, allowing us to relate preferences and interests to a numerical value, which we then could mathematically compare to others using the numpy module. Additionally, we used Cohere to generate a brief summary of users' Spotify data, as otherwise, there is quite a bit of information, some which may not be as relevant.

In the end, we ended up using React Native with Expo Go to develop the mobile app. We also used Appwrite as a database and authentication service.

## Challenges we ran into

Indecisiveness: We spent a lot of time choosing between and experimenting on two different projects. We were also indecisive about the stack we wanted to use (we started with wanting to use C++ but it took us time to realize that this would not actually be feasible) .

Combining different components: While we were successful in developing the different parts of the project individually, we had a difficult time combining the different technologies together.

## Accomplishments that we're proud of

We are proud of our perseverance. Working on this project was not easy, and we were met with many challenges. Especially considering how our indecisiveness cost us a large part of our time, we believe that we did well and completed a fairly large portion of the final product.

## What we learned

Incorporating different API's together.
Working with a completely new technology (all of us were new to mobile app dev).
Cohere's excellent functionalities.

## What's next for Linkr

Allowing more user customizability and ability to communicate with other matches.
Precise finding of other matches
Memento is an all in one IoT application that provides therapy and care for dementia patients. It runs on two separate platforms that communicate their data with each other. The first part of our solution is a mobile Android Application. In this application, the user can enter Admin mode and create their settings for voice control and right and wrong answers. Then the user or their family members can create stimuli that will then be used in their game. The game then will show the user pictures of people, places, or memories, and ask them what the picture is of. The user can then use voice commands to tell the application what is in that picture. If the user gets the answer right, he scores a point and continue playing the game. This data is then sent to the second part of this solution. But, there is an additional part of the mobile application.

## Location Services for Tracking the Patient

One important concern for dementia patients is that they begin to wander and don't remember where they were going or how they got there, and most importantly, they don't remember how to get home. So in our mobile application, we have incorporated location services that keep track of the user in real time. The user can go and check how far he has strayed from home by simply pressing a single button, and this will send data to our web application as well. In addition, with the integration of the Uber API, the user can simply press a single button to call for an uber to bring them home using the location they are currently at.

## Web Application

The second portion of this hack, is the web application hosted by Microsoft Azure. The web application allows the user to log into their dashboard and access information about themselves in the case that they begin to forget things from their history. We focused on the personal aspect of this mental illness, such that the patient could trigger their memories or answer the questions the ask over and over again by viewing this web page. So, once they log in, they will have access to the most basic of information which includes their name, birthday, phone number, address, and a link to 911 in case of an emergency. They will see links to "Family", "Friends", "Memories", or "Home" with corresponding pictures. If the user clicks on the **Family** or **Friends** tab, they will be brought to their contacts page, where they have access to a database of family and friends' contact information along with corresponding pictures of the contact. This page shows the contact's picture, their relation to the user, their phone number, address, and email. This way, the user can contact them however they'd like.

If the user were to click on the **Home** button, they would be brought to a map page. Using the Google Maps API, we integrated a map that provides them with interactive access to past events in their life as well as their current location. They can scan over the map to see where they were born and on what date. Other examples of events that could potentially go on the map include the date of marriage or when their first child was born.

If the user were to click on the **Charts** tab, they would be brought to a page full of data visualizations for various things regarding Dementia in general as well as their own personal data. The first graph they would see is how well they are performing in the mobile application on a day to day basis. Then, they'd have access to a pie chart providing awareness on the most common forms of Dementia, and lastly, they could see how much time they are spending within their home by gathering data from the location tracking aspect of the mobile application. This data can be used to see how long they have wandered for to see how far away they might have gone, or track daily habits on how much time they typically spend in their homes on a day to day basis. These data visualizations were made using Morris and Flot.

Finally, if the user were to click on the **Memories** tab, they would be brought to an interactive time line that is easily customizable. They can add new events or delete old ones. This time line will show them the date, time, place, information, and a picture of the event taking place at the current time. And the user can swipe through events to recall what he or she has done throughout their life as well as view pictures of the event. This is really important to the user because this will provide them answers to a lot of questions, such as who was at which event and what exactly took place or when the event took place and what the importance was. It provides the user not only with information about the date, but with information on when it happened and pictures for further memory recollection.

## Purpose

As some of our team member have witness how hard it is for Dementia patients first hand, we thought this series of applications would come in great use not only for the patient themselves, but for their families and care takers. We put everything a patient would need in one easy to access place. They can view their memories, call for an Uber if they get lost, play memory games and track their progress, they have access to a database of contacts, and they have location services so their caretaker can see how far from home they are.