## Inspiration

Pranit and Sashank are a part of Boy Scouts, and we both spend a lot of time helping our community and giving back. We have had personal accounts with the organizations we aim to help. Krish is also a local advocate for sustainability, he regularly looks for opportunities to help.

We saw a need and we thought of a cool way to process and collect data and wanted to build it out. Who wouldn’t want to be earning Money and feeling like you're playing a game?

## What it does

Datability is an app and web platform that gamifies and incentivizes crowdsourcing data. The problem we are trying to solve is simple: sustainable organizations struggle to receive the data they need because it is either scattered, expensive, or extremely difficult to access. Without this data, organizations can’t take action where their efforts make the most difference.

Datability works by allowing organizations to request data and we crowdsource data from users by gamifying and incentivizing the process. Organizations give data requests and give us information on what data they want to capture and from where. Next, users in the geofence of the challenge are eligible to participate and can upload data they collect. In turn they get points and real money. At the end of it, organizations get actionable data and users get to compete with each other and make real money all while helping the environment.

Our business model is simple too, the top 3 contributing users of a challenge get 35% of the pot and the rest is proportionality distributed to all contributors using this formula. We take a small cut from the payouts offered by Organizers and we want to give 50% of our profits back to sustainable efforts

## How we built it

We built 2 platforms. One geared toward organizations and the other toward the public. And an IOS App for the users, and the web app for non-profits/business.

Our tech stack included Swift, Swift UI, Firebase, Google Cloud APIs, Plant Identification, HuggingFace ML Models, NLP, Javascript, CSS, HTML, Bootstrap, Apexcharst, Google Maps API, Apple Maps

On the organization side, we streamlined the onboarding process and added a smooth Stripe Setup. Using multithreading we can get immediate API responses and dynamic updating on the screen. We allow organizations to enable geofencing and location boundaries. On top of that we provide advanced analytics with Google Maps APIs as well as data aggregations and collections. We also have the cool feature of exporting all the data into a JSON format.

On the consumer side, we were able to create an easy-to-use application that allowed easy interaction and understanding of all of its features. We were able to create NLP based descriptions for each of the plants to make sure everyone learns something from sustainability. All of the user data/images/coordinates were uploaded to our database to make sure that the web app could easily interact with that data too.

## Challenges we ran into

This was our 4th Hackathon, but first time competing on a college campus. We went through multiple phases of ideation and several technical challenges. For example, when we were first discussing how to implement a psp (payment service provider) into our platform we exhaustively went through all the potential options before landing back on stripe as the optimal solution for us. Though these discussions could be seemingly tedious at times, it taught us a great deal on why planning is incredibly important, especially in computer science.

The lack of sleep is also always a challenge :)

## Accomplishments that we're proud of

We have a working product that with a few tweaks would be beta launch ready. That means we wrote a whole lot of code this weekend and are happy to see the final product.

We’re proud of making a full stack iOS & Web application that works with various parts of Machine Learning and other APIs to deliver an impactful yet clean user interface. In addition, we are proud that the iOS and the web app seamlessly work together with no difficulties to make the overall platform better.

## What we learned

We learned a whole lot. From several technical challenges and countless debugging hours, we learned the ins and outs of Swift/Swift UI, integrating stripe into web payments and mobile payouts, Google Maps API…

## What's next for Datability

Over the weekend we loved the process of building and ideating over Datability. We want to continue so we are going to start a Beta Launch in just 2 weeks. Yes that’s right we are doing a beta launch in two weeks in the Bay Area community.

We want to continue iterating on our tech and get it ready for scale. We look forward to what’s next.
## Inspiration

Since this was the first hackathon for most of our group, we wanted to work on a project where we could learn something new while sticking to familiar territory. Thus we settled on programming a discord bot, something all of us have extensive experience using, that works with UiPath, a tool equally as intriguing as it is foreign to us. We wanted to create an application that will allow us to track the prices and other related information of tech products in order to streamline the buying process and enable the user to get the best deals. We decided to program a bot that utilizes user input, web automation, and web-scraping to generate information on various items, focusing on computer components.

## What it does

Once online, our PriceTracker bot runs under two main commands: !add and !price. Using these two commands, a few external CSV files, and UiPath, this stores items input by the user and returns related information found via UiPath's web-scraping features. A concise display of the product’s price, stock, and sale discount is displayed to the user through the Discord bot.

## How we built it

We programmed the Discord bot using the comprehensive discord.py API. Using its thorough documentation and a handful of tutorials online, we quickly learned how to initialize a bot using Discord's personal Development Portal and create commands that would work with specified text channels. To scrape web pages, in our case, the Canada Computers website, we used a UiPath sequence along with the aforementioned CSV file, which contained input retrieved from the bot's "!add" command. In the UiPath process, each product is searched on the Canada Computers website and then through data scraping, the most relevant results from the search and all related information are processed into a csv file. This csv file is then parsed through to create a concise description which is returned in Discord whenever the bot's "!prices" command was called.

## Challenges we ran into

The most challenging aspect of our project was figuring out how to use UiPath. Since Python was such a large part of programming the discord bot, our experience with the language helped exponentially. The same could be said about working with text and CSV files. However, because automation was a topic none of us hardly had any knowledge of; naturally, our first encounter with it was rough. Another big problem with UiPath was learning how to use variables as we wanted to generalize the process so that it would work for any product inputted.
Eventually, with enough perseverance, we were able to incorporate UiPath into our project exactly the way we wanted to.

## Accomplishments that we're proud of

Learning the ins and outs of automation alone was a strenuous task. Being able to incorporate it into a functional program is even more difficult, but incredibly satisfying as well. Albeit small in scale, this introduction to automation serves as a good stepping stone for further research on the topic of automation and its capabilities.

## What we learned

Although we stuck close to our roots by relying on Python for programming the discord bot, we learned a ton of new things about how these bots are initialized, the various attributes and roles they can have, and how we can use IDEs like Pycharm in combination with larger platforms like Discord. Additionally, we learned a great deal about automation and how it functions through UiPath which absolutely fascinated us the first time we saw it in action. As this was the first Hackathon for most of us, we also got a glimpse into what we have been missing out on and how beneficial these competitions can be. Getting the extra push to start working on side-projects and indulging in solo research was greatly appreciated.

## What's next for Tech4U

We went into this project with a plethora of different ideas, and although we were not able to incorporate all of them, we did finish with something we were proud of. Some other ideas we wanted to integrate include: scraping multiple different websites, formatting output differently on Discord, automating the act of purchasing an item, taking input and giving output under the same command, and more.
## Inspiration

As young college students who want to make sustainable life choices but are too bogged down by schoolwork to find time to do so, we were looking for a simple service that could "green-ify" our eating choices. Because nearly 1/3 of greenhouse gas emissions come from food production, eating sustainably has never been more important. So, we built SustainaBite!

## What it does

SustainaBite gives your typical calorie-counting meal tracker a unique twist: it breaks down each of your meal into environmental impact and gives your meal an overall "sustainability score"! The higher your scores, the more points you accrue, letting you earn badges and even redeem free food at sustainable restaurants. Best of all, the social media aspect of our app lets you and your friends cheer each other on in your sustainability goals, and even meet new people in your area with similar food preferences using AI models. For example, if you love Mexican food, we'll match you up with someone with similar tastes--first date's on SustainaBite! This is the perfect way to reach your sustainability goals while connecting with other users on a shared interest everyone enjoys: food!

## How we built it

We built the front-end with React, Next.js. We built the back-end with Convex and Flask. We also worked with OpenAI's API to compare users' food preferences.

Specifically for Convex, we were impressed by its broad, yet powerful utility. We needed a framework that could update databases in real-time while immediately updating our website's front-end. Especially since our website acts as a social media channel, the ability to balance multiple users' requests with Convex was extremely useful. We definitely plan on using Convex in our future full-stack projects.

## Challenges we ran into

Technical challenges: connecting our back-end with OpenAI's API, designing the UI of multiple tabs while coding "sustainability score" algorithms
Ethical challenges: how do we objectively "score" a meal, how do we prevent users from gaming the score system
Task allocation: Learning each others' strengths to effectively allocate tasks while also finding time for sleep and TreeHacks events.

## Accomplishments that we're proud of

We're proud of how we focused on impact and design first, then building the technical components from that. We designed our app together as a team, bouncing ideas off each other, then played to our strengths in the building process. We also didn't cut any corners when designing our scoring algorithms: we researched different methods of sustainability measures, how various factors could affect the environmental impact of certain foods, etc.

## What we learned

Full-stack programming is hard. Since each of us had different technical strengths, we split up our work into various tasks. However, connecting them all together took a lot of debugging and installations, but it was worth the effort in the end, and we all came away with a better understanding of full-stack frameworks.

## What's next for SustainaBite

Building a user base. We'll start with our friends and ask them to sign up. We'll ask our college's local Mexican joint to do a test partnership, and try various incentive programs to see which would maximize our users' increases in sustainability. We hope to expand so that many sustainable restaurants partner with us, and we can begin a two-way exchange in which we help sustainable restaurants attract customers and we help people who want to eat sustainably to find sustainable restaurants.

## Prizes we are entering:

**Sustainability Grand Prize:** Our project is focused on sustainable eating, an aspect of sustainability that can be overlooked but has a heavy impact on the environment. With our website, every single one of us can become more aware of the impact of each of our meals and make easy, small changes to increase sustainable eating habits.

**Best Use of Convex:** Our project used Convex for the back-end, allowing us to update our database of user posts in real-time while updates our website's front-end. Our use of Convex allowed the social media aspect of our website to function much more smoothly and balance multiple users' requests simultaneously.

**Best Natural Language Hack by Mem:** We used OpenAI to take in user food preferences and give users recommendations for related sustainable restaurants, as well as match users with other users with similar food preferences.

**Best Hack to Connect With Others Through Food any Otsuka VALUENEX:** Our project not only encourages sustainable eating, but also brings users together around sustainable eating. Our AI model matches users based on food preferences and allows them to enjoy a sustainable meal together on us, helping people connect through their love of food.

**Best Startup by YCombinator:** Our project has real value in the market, as we provide a unique, simple way to combine food tracking and sustainability, combined with a social media aspect to help build a community of users, and an opportunity for sustainable restaurants to sponsor our website to grow their own business and message of sustainability.

**Best Use of OpenAI Models:** We used OpenAI to take in user food preferences and give users recommendations and match users with similar food preferences. This use of OpenAI adds an aspect of community and connection to our project, allowing users to come together around food, especially sustainable food.

**Most Ethically Engaged Hack by Stanford Center for Ethics & Society (Our Ethical Considerations):**

Our team of hackers took the time to consider the ethics of our app, SustainaBite. The app's focus on sustainability and its potential impact on users' consumption habits could have significant implications, so it's important to approach this with care.

One ethical consideration that our team grappled with was how to accurately report the sustainability of consuming a certain number of portions of food without deterring users from eating more food. Our team had to recognize that a negative or overly restrictive message about the greater carbon emissions associated with a larger quantity of food could lead to unhealthy eating habits for a user.

Another ethical consideration that our team addressed was making high-quality information available to users without providing so much data that it was difficult for a user to interpret. Sustainability-related data can be complex and difficult to understand, which can lead to confusion or misinformation. By using data about the sustainability of various foods from a study published in the Proceedings of the National Academy of Sciences and simplifying the information into categories, our team ensured that users could easily interpret the data and make informed decisions.

In order to avoid ethical issues with our technology in the future, our team has action plans for the following areas:

Data privacy: As SustainaBite requires users to input personal information such as dietary preferences, there is a risk that this data could be compromised or misused. To address this issue, SustainaBite should ensure that users are informed about how their data is being collected, stored, and used.

Discrimination: There is a risk that SustainaBite's AI models could perpetuate bias or discrimination based on factors such as race, gender, or socio-economic status. To mitigate this risk, SustainaBite should take steps to ensure that its algorithms are transparent and auditable, and that it has a diverse and inclusive team working on its development.

Health and safety: While SustainaBite is designed to encourage healthy eating habits, there is a risk that users could become too focused on their sustainability score and ignore other important factors such as nutritional value and food safety. To address this, SustainaBite could in the future also provide users with clear guidelines on healthy eating habits and safe food handling practices.

Environmental impact: SustainaBite's sustainability score is based on a range of environmental factors, such as carbon footprint and water usage using the best available data for users. However, there is a risk that this could oversimplify complex issues and fail to capture the full environmental impact of different foods. To address this, SustainaBite should work with experts in the sustainability to develop a robust and evidence-based sustainability scoring system.

Disparities: There is a risk that some users may have less access to sustainable food options or be unable to afford meals at sustainable restaurants. To address this, SustainaBite will work to promote equity and inclusivity in its platform, and consider offering alternative rewards for users who may not have the means to redeem free food at sustainable restaurants. We want all users to have access to sustainable foods.

Overall, it's essential for SustainaBite to prioritize ethical considerations throughout the development and deployment of its technology. By taking a thoughtful and inclusive approach, SustainaBite can maximize its potential to promote sustainable eating habits and foster positive social connections among users.