## Inspiration

We asked ourselves what tools we could use to help eliminate the problem of food waste plaguing North American retailers, and considered weather patterns to be an exciting potential solution.

## What it does

Compares historical sales figures of products to historical weather data in the region, and derives formulas to make sales projections for the near future using relationships with factors like temperature and precipitation.

## How we built it

We built a simple GUI in Java that allows the user to input sales data as a file. This data is compared to historical weather data from Environment Canada, and linear regression is applied to determine correlation. Using the Weather Network API we determine the forecast for the next week and determine projected sales, then display the information to the user.

## Challenges we ran into

Neither of us had experience using APIs, so it was definitely a learning process on how to incorporate the Weather Network.

## Accomplishments that we're proud of

It's our first Hackathon and we managed to finish a product!

## What we learned

## What's next for Sales Forecasting Using Weather Patterns

Developing more complicated algorithms to find relationships with more obscure weather types.
## Inspiration

I was cooking at home one day and I kept noticing we had half a carrot, half an onion, and like a quarter of a pound of ground pork lying around all the time. More often than not it was from me cooking a fun dish that my mother have to somehow clean up over the week. So I wanted to create an app that would help me use those ingredients that I have neglected so that even if both my mother and I forget about it we would not contribute to food waste.

## What it does

Our app uses a database to store our user's fridge and keeps track of the food in their fridge. When the user wants a food recipe recommendation our app will help our user finish off their food waste. Using the power of chatGPT our app is super flexable and all the unknown food and food that you are too lazy to measure the weight of can be quickly put into a flexible and delicious recipe.

## How we built it

Using figma for design, react.JS bootstrap for frontend, flask backend, a mongoDB database, and openAI APIs we were able to create this stunning looking demo.

## Challenges we ran into

We messed up our database schema and poor design choices in our APIs resulting in a complete refactor. Our group also ran into problems with react being that we were relearning it. OpenAI API gave us inconsistency problems too. We pushed past these challenges together by dropping our immediate work and thinking of a solution together.

## Accomplishments that we're proud of

We finished our demo and it looks good. Our dev-ops practices were professional and efficient, our kan-ban board saved us a lot of time when planning and implementing tasks. We also wrote plenty of documentations where after our first bout of failure we planned out everything with our group.

## What we learned

We learned the importance of good API design and planning to save headaches when implementing out our API endpoints. We also learned much about the nuance and intricacies when using CORS technology. Another interesting thing we learned is how to write detailed prompts to retrieve formatted data from LLMs.

## What's next for Food ResQ : AI Recommended Recipes To Reduce Food Waste

We are planning to add a receipt scanning feature so that our users would not have to manually add in each ingredients into their fridge. We are also working on a feature where we would prioritize ingredients that are closer to expiry. Another feature we are looking at is notifications to remind our users that their ingredients should be used soon to drive up our engagement more. We are looking for payment processing vendors to allow our users to operate the most advanced LLMs at a slight premium for less than a coffee a month.

## Challenges, themes, prizes we are submitting for

Sponsor Challenges: None
Themes: Artificial Intelligence & Sustainability
Prizes: Best AI Hack, Best Sustainability Hack, Best Use of MongoDB Atlas, Most Creative Use of Github, Top 3 Prize
## Inspiration

Food is a basic human need. As someone who often finds themselves wandering the aisles of Target, I know firsthand how easy it is to get lost among the countless products and displays. The experience can quickly become overwhelming, leading to forgotten items and a less-than-efficient shopping trip. This project was born from the desire to transform that chaos into a seamless shopping experience. We aim to create a tool that not only helps users stay organized with their grocery lists but also guides them through the store in a way that makes shopping enjoyable and stress-free.

## What it does

**TAShopping** is a smart grocery list app that records your grocery list in an intuitive user interface and generates a personalized route in **(almost)** any Target location across the United States. Users can easily add items to their lists, and the app will optimize their shopping journey by mapping out the most efficient path through the store.

## How we built it

* **Data Aggregation:** We utilized `Selenium` for web scraping, gathering product information and store layouts from Target's website.
* **Object Storage:** `Amazon S3` was used for storing images and other static files related to the products.
* **User Data Storage:** User preferences and grocery lists are securely stored using `Google Firebase`.
* **Backend Compute:** The backend is powered by `AWS Lambda`, allowing for serverless computing that scales with demand.
* **Data Categorization:** User items are classified with `Google Gemini`
* **API:** `AWS API Endpoint` provides a reliable way to interact with the backend services and handle requests from the front end.
* **Webapp:** The web application is developed using `Reflex`, providing a responsive and modern interface for users.
* **iPhone App:** The iPhone application is built with `Swift`, ensuring a seamless experience for iOS users.

## Challenges we ran into

* **Data Aggregation:** Encountered challenges with the rigidity of `Selenium` for scraping dynamic content and navigating web page structures.
* **Object Storage:** N/A (No significant issues reported)
* **User Data Storage:** N/A (No significant issues reported)
* **Backend Compute:** Faced long compute times; resolved this by breaking the Lambda function into smaller, more manageable pieces for quicker processing.
* **Backend Compute:** Dockerized various builds to ensure compatibility with the AWS Linux environment and streamline deployment.
* **API:** Managed the complexities of dealing with and securing credentials to ensure safe API access.
* **Webapp:** Struggled with a lack of documentation for `Reflex`, along with complicated Python dependencies that slowed development.
* **iPhone App:** N/A (No significant issues reported)

## Accomplishments that we're proud of

* Successfully delivered a finished product with a relatively good user experience that has received positive feedback.
* Achieved support for hundreds of Target stores across the United States, enabling a wide range of users to benefit from the app.

## What we learned

>
> We learned a lot about:
>
>
> * **Gemini:** Gained insights into effective data aggregation and user interface design.
> * **AWS:** Improved our understanding of cloud computing and serverless architecture with AWS Lambda.
> * **Docker:** Mastered the process of containerization for development and deployment, ensuring consistency across environments.
> * **Reflex:** Overcame challenges related to the framework, gaining hands-on experience with Python web development.
> * **Firebase:** Understood user authentication and real-time database capabilities through Google Firebase.
> * **User Experience (UX) Design:** Emphasized the importance of intuitive navigation and clear presentation of information in app design.
> * **Version Control:** Enhanced our collaboration skills and code management practices using Git.
>
>
>

## What's next for TAShopping

>
> There are many exciting features on the horizon, including:
>
>
> * **Google SSO for web app user data:** Implementing Single Sign-On functionality to simplify user authentication.
> * **Better UX for grocery list manipulation:** Improving the user interface for adding, removing, and organizing items on grocery lists.
> * **More stores:** Expanding support to additional retailers, including Walmart and Home Depot, to broaden our user base and shopping capabilities.
>
>
>