## Inspiration

For our first ever attempt at mobile application development, we wanted to create something simple yet fun at the same time.

## What it does

It doesn't do anything, really. Just tap on Tappy to accumulate points and get that false sense of achievement.

## How we built it

We used Android Studio and Java.

## Challenges we ran into

None of us were familiar with mobile development so we had to play around a lot with the functionalities.

## Accomplishments that we're proud of

We made our first mobile application.

## What we learned

We learned the basics of mobile application development for Android using Java.

## What's next for Tappy Bird

Nothing; it's completely useless.
## Inspiration

After looking at the Hack the 6ix prizes, we were all drawn to the BLAHAJ. On a more serious note, we realized that one thing we all have in common is accidentally killing our house plants. This inspired a sense of environmental awareness and we wanted to create a project that would encourage others to take better care of their plants.

## What it does

Poképlants employs a combination of cameras, moisture sensors, and a photoresistor to provide real-time insight into the health of our household plants. Using this information, the web app creates an interactive gaming experience where users can gain insight into their plants while levelling up and battling other players’ plants. Stronger plants have stronger abilities, so our game is meant to encourage environmental awareness while creating an incentive for players to take better care of their plants.

## How we built it + Back-end:

The back end was a LOT of python, we took a new challenge on us and decided to try out using socketIO, for a websocket so that we can have multiplayers, this messed us up for hours and hours, until we finally got it working. Aside from this, we have an arduino to read for the moistness of the soil, the brightness of the surroundings, as well as the picture of it, where we leveraged computer vision to realize what the plant is. Finally, using langchain, we developed an agent to handle all of the arduino info to the front end and managing the states, and for storage, we used mongodb to hold all of the data needed.
[backend explanation here]

### Front-end:

The front-end was developed with **React.js**, which we used to create a web-based game. We were inspired by the design of old pokémon games, which we thought might evoke nostalgia for many players.

## Challenges we ran into

We had a lot of difficulty setting up socketio and connecting the api with it to the front end or the database

## Accomplishments that we're proud of

We are incredibly proud of integrating our web sockets between frontend and backend and using arduino data from the sensors.

## What's next for Poképlants

* Since the game was designed with a multiplayer experience in mind, we want to have more social capabilities by creating a friends list and leaderboard
* Another area to explore would be a connection to the community; for plants that are seriously injured, we could suggest and contact local botanists for help
* Some users might prefer the feeling of a mobile app, so one next step would be to create a mobile solution for our project
## Inspiration

The world today has changed significantly because of COVID 19, with the increased prominence of food delivery services. We decided to get people back into the kitchen to cook for themselves again. Additionally, everyone has a lot of groceries that they never get around to eating because they don't know what to make. We wanted to make a service that would make it easier than ever to cook based on what you already have.

## What it does

Recognizes food ingredients through pictures taken on a smartphone, to build a catalog of ingredients lying around the house. These ingredients are then processed into delicious recipes that you can make at home. Common ingredients and the location of users are also stored to help reduce waste from local grocery stores through better demographic data.

## How we built it

We used to express and Node for the backend and react native for the front end. To process the images we used the Google Vision API to detect the ingredients. The final list of ingredients was then sent to the Spoonacular API to find recipes that best match the ingredients at hand. Finally, we used CockroachDB to store the locational and ingredient data of users, so they can be used for data analysis in the future.

## Challenges we ran into

* Working with Android is much more challenging than expected.
* Filtering food words for the image recognition suggestion.
* Team members having multiple time zones.
* Understanding and formatting inputs and outputs of APIs used

## Accomplishments that we're proud of

* We have an awesome-looking UI prototype to demonstrate our vision with our app.
* We were able to build our app with tools that we are unfamiliar with prior to the hackathon.
* We have a functional app apk that's ready to demonstrate to everyone at the hackathon.
* We were able to create something collaboratively in a team of people each with a drastically different skill set.

## What we learned

* Spoonacular API
* React Native
* Google Vision API
* CockroachDB

## What's next for Foodeck

* Implement personalized recipe suggestions using machine learning techniques. ( Including health and personal preferences )
* Learn user behavior of a certain region and make more localized recipe recommendations for each region.
* Implement an optional login system for increased personalization that can be transferred through d
* Extend to multi-platform, allowing users to sync the profile throughout different devices.
Integrate with grocery delivery services such as instacart, uber eats.