## Inspiration

eCommerce is a field that has seen astronomical growth in recent years, and shows no signs of slowing down. With a forecasted growth rate of 10.4% this year, up to $6.3 trillion in global revenues, we decided to tackle Noibu’s challenge to develop an extension to aide ecommerce developers with the impossible task of staying ahead amongst the fierce competitions in this space, all whilst providing a tremendous unique value to shoppers and eCommerce brands alike.

## What it does

Our extension. ShopSmart, aim to provide developers and brands with an accurate idea of how their website is being used, naturally. Unlike A/B testing, which forces a participant to use a given platform and provide feedback, ShopSmart analyzes user activity on any given website and produces a heatmap showing their exact usage patterns, all without collecting user identifying data. In tandem with the heatmap, ShopSmart provides insights as to the sequences of actions taken on their website, correlated with their heatmap usage, allowing an ever deeper understanding of what average usage truly looks like. To incentivize consumers, brands may elect to provide exclusive discount codes only available through ShopSmart, giving the shoppers a kickback for their invaluable input to the brand partners.

## How we built it

ShopSmart was built using the classic web languages HTML, CSS, and Javascript, keeping it simple, lightweight, and speedy.

## Challenges we ran into

We ran into several challenges throughout our development process, largely due to the complexity of the extension in theory being limited in execution to HTML, CSS, and Javascript (as those are the only allowed languages for use in developing extensions). One issue we had was finding a way to overlay the heatmap over the website so as to visually show the paths the user took. Whilst we were able to solve that challenge, we were sadly unable to finish fully integrating our database within the given timeframe into the extension due to the frequency of data collection/communication, and the complexity of the data itself.

## Accomplishments that we're proud of

Our team is very proud in being able to put out a working extension capable of tracking usage and overlaying the resulting heatmap data over the used website, especially as neither of us had any experience with developing extensions. Despite not being able to showcase our extensive database connections in the end as they are not finalized, we are proud of achieving reliable and consistent data flow to our cloud-based database within our testing environment. We are also proud of coming together and solving a problem none of us had considered before, and of course, of the sheer amount we learned over this short time span.

## What we learned

Our hackathon experience was truly transformative, as we not only gained invaluable technical knowledge in Javascript, but also cultivated essential soft skills that will serve us well in any future endeavors. By working together as a team, we were able to pool our unique strengths and collaborate effectively to solve complex problems and bring our ideas to life.

## What's next for ShopSmart

The next steps for ShopSmart are to focus on expanding its capabilities and increasing its reach. One area of focus could be on integrating the extension with more e-commerce platforms to make it more widely accessible to developers and brands. Another area for improvement could be on enhancing the heatmap visualization and adding more advanced analytics features to provide even deeper insights into user behavior. With the help of Machine Learning, developers and brands can utilize the data provided by ShopSmart to better recognize patterns within their customer's usage of their site to make better adjustments and improvements. Additionally, exploring partnerships with e-commerce brands to promote the extension and offer more exclusive discount codes to incentivize consumers could help increase its adoption. Overall, the goal is to continuously improve the extension and make it an indispensable tool for e-commerce businesses looking to stay ahead of the competition.
## Inspiration

It all started a couple days ago when my brother told me he'd need over an hour to pick up a few items from a grocery store because of the weekend checkout line. This led to us reaching out to other friends of ours and asking them about the biggest pitfalls of existing shopping systems. We got a whole variety of answers, but the overwhelming response was the time it takes to shop and more particularly checkout. This inspired us to ideate and come up with an innovative solution.

## What it does

Our app uses computer vision to add items to a customer's bill as they place items in the cart. Similarly, removing an item from the cart automatically subtracts them from the bill. After a customer has completed shopping, they can checkout on the app with the tap of a button, and walk out the store. It's that simple!

## How we built it

We used react with ionic for the frontend, and node.js for the backend. Our main priority was the completion of the computer vision model that detects items being added and removed from the cart. The model we used is a custom YOLO-v3Tiny model implemented in Tensorflow. We chose Tensorflow so that we could run the model using TensorflowJS on mobile.

## Challenges we ran into

The development phase had it's fair share of challenges. Some of these were:

* Deep learning models can never have too much data! Scraping enough images to get accurate predictions was a challenge.
* Adding our custom classes to the pre-trained YOLO-v3Tiny model.
* Coming up with solutions to security concerns.
* Last but not least, simulating shopping while quarantining at home.

## Accomplishments that we're proud of

We're extremely proud of completing a model that can detect objects in real time, as well as our rapid pace of frontend and backend development.

## What we learned

We learned and got hands on experience of Transfer Learning. This was always a concept that we knew in theory but had never implemented before. We also learned how to host tensorflow deep learning models on cloud, as well as make requests to them. Using google maps API with ionic react was a fun learning experience too!

## What's next for MoboShop

* Integrate with customer shopping lists.
* Display ingredients for recipes added by customer.
* Integration with existing security systems.
* Provide analytics and shopping trends to retailers, including insights based on previous orders, customer shopping trends among other statistics.
## Inspiration

As a team, we had a collective interest in sustainability and knew that if we could focus on online consumerism, we would have much greater potential to influence sustainable purchases. We also were inspired by Honey -- we wanted to create something that is easily accessible across many websites, with readily available information for people to compare.

Lots of people we know don’t take the time to look for sustainable items. People typically say if they had a choice between sustainable and non-sustainable products around the same price point, they would choose the sustainable option. But, consumers often don't make the deliberate effort themselves. We’re making it easier for people to buy sustainably -- placing the products right in front of consumers.

## What it does

greenbeans is a Chrome extension that pops up when users are shopping for items online, offering similar alternative products that are more eco-friendly. The extension also displays a message if the product meets our sustainability criteria.

## How we built it

Designs in Figma, Bubble for backend, React for frontend.

## Challenges we ran into

Three beginner hackers! First time at a hackathon for three of us, for two of those three it was our first time formally coding in a product experience. Ideation was also challenging to decide which broad issue to focus on (nutrition, mental health, environment, education, etc.) and in determining specifics of project (how to implement, what audience/products we wanted to focus on, etc.)

## Accomplishments that we're proud of

Navigating Bubble for the first time, multiple members coding in a product setting for the first time... Pretty much creating a solid MVP with a team of beginners!

## What we learned

In order to ensure that a project is feasible, at times it’s necessary to scale back features and implementation to consider constraints. Especially when working on a team with 3 first time hackathon-goers, we had to ensure we were working in spaces where we could balance learning with making progress on the project.

## What's next for greenbeans

Lots to add on in the future:
Systems to reward sustainable product purchases. Storing data over time and tracking sustainable purchases. Incorporating a community aspect, where small businesses can link their products or websites to certain searches.
Including information on best prices for the various sustainable alternatives, or indicators that a product is being sold by a small business. More tailored or specific product recommendations that recognize style, scent, or other niche qualities.