## Inspiration

We got our inspiration from the countless calorie tracking apps. First of all, there isn't a single website we could find that tracked calories. There are a ton of apps, but not one website. Secondly, None of them offered recipes built in. In our website, the user can search for food items, and directly look at their recipes. Lastly, our nutrition analysis app analyses any food item you've ever heard of.

## What it does

Add food you eat in a day, track your calories, track fat%, and other nutrients, search recipes, and get DETAILED info about any food item/recipe.

## How we built it

Html, min.css, min.js, js, were planning on using deso/auth0 for login but couldnt due to time constraints.

## Challenges we ran into

We initially used react, but couldn't make the full app using react since we used static html to interact with the food apis. We also had another sole recipe finder app which we removed due to it being react only. Integrating the botdoc api was a MAJOR challenge, since we had no prior experience, and had no idea what we were doing basically. A suggestion to the BotDoc team would be to add demo apps to their documentation/tutorials, since currently theres literally nothing available except the documentation. The api is quite unheard of too as of now.

## Accomplishments that we're proud of

Making the website working, and getting it up and running using a github pages deployment

## What we learned

A LOT about botdoc, and refreshed our knowledge of html, css, js.

## What's next for Foodify

Improving the css firstly lol, right now its REALLY REALLY BAD.
## Inspiration

Partially inspired by the Smart Cities track, we wanted our app to have the direct utility of ordering food, while still being fun to interact with. We aimed to combine convenience with entertainment, making the experience more enjoyable than your typical drive-through order.

## What it does

You interact using only your voice. The app automatically detects when you start and stop talking, uses AI to transcribe what you say, figures out the food items (with modifications) you want to order, and adds them to your current order. It even handles details like size and flavor preferences. The AI then generates text-to-speech audio, which is played back to confirm your order in a humorous, engaging way. There is absolutely zero set-up or management necessary, as the program will completely ignore all background noises and conversation. Even then, it will still take your order with staggering precision.

## How we built it

The frontend of the app is built with React and TypeScript, while the backend uses Flask and Python. We containerized the app using Docker and deployed it using Defang. The design of the menu is also done in Canva with a dash of Harvard colors.

## Challenges we ran into

One major challenge was getting the different parts of the app—frontend, backend, and AI—to communicate effectively. From media file conversions to AI prompt engineering, we worked through each of the problems together. We struggled particularly with maintaining smooth communication once the app was deployed. Additionally, fine-tuning the AI to accurately extract order information from voice inputs while keeping the interaction natural was a big hurdle.

## Accomplishments that we're proud of

We're proud of building a fully functioning product that successfully integrates all the features we envisioned. We also managed to deploy the app, which was a huge achievement given the complexity of the project. Completing our initial feature set within the hackathon timeframe was a key success for us. Trying to work with Python data type was difficult to manage, and we were proud to navigate around that. We are also extremely proud to meet a bunch of new people and tackle new challenges that we were not previously comfortable with.

## What we learned

We honed our skills in React, TypeScript, Flask, and Python, especially in how to make these technologies work together. We also learned how to containerize and deploy applications using Docker and Docker Compose, as well as how to use Defang for cloud deployment.

## What's next for Harvard Burger

Moving forward, we want to add a business-facing interface, where restaurant staff would be able to view and fulfill customer orders. There will also be individual kiosk devices to handle order inputs. These features would allow *Harvard Burger* to move from a demo to a fully functional app that restaurants could actually use. Lastly, we can sell the product by designing marketing strategies for fast food chains.
(Local Program for data in + hardware firmware)<https://github.com/assasin2gamer/CalHacks>
(Website with Reflex) <https://github.com/mishcoder/calhacks>
(Intel integration) <https://github.com/AKUMAR0019/calhacks>

## Inspiration

Our project draws inspiration from the challenge of classifying emotions, a complex task. We aim to provide a reliable and cost-effective solution for training EEGs (Electroencephalograms), which can contribute to better understanding and analysis of emotions. We used a research grade EEG headset to get reliable data and do accurate sentiment analysis.

## What it does

Our project leverages machine learning to train on vast datasets of EEG data and Hume's audio analysis. We use this training to develop a sentiment analysis model specifically tailored to EEGs. EEGs capture brainwaves resulting from the neuro-physiological interactions in the brain. By employing techniques like Fast Fourier Transform (FFT) and random tree (RT) modeling of time series data, we can identify EEG characteristics associated with different emotions. Our model allows us to generate visually appealing images using TogetherAI's image generation service based on the emotion classification and even further!

## How we built it

We built our product using three main components:
1) Hume for sentiment data labelling
2) CockroachDB serves as our database for storing EEG data.
3) Intel Cloud Compute to compute our model
4) TogetherAI to generate images based on the emotion classification.
5) Reflex to host our website which combines the multiple data streams into one websocket.

## Challenges we ran into

We ran into a few challenges:
1) We could not figure out why, but when we pinged spesifically the CockroachDB from the Intel Compute instance, the instance would freeze.
2) Our concept of using the model to be input sources for a VR game fell through when we realized the computer we brought could not handle the processing required.

## Accomplishments that we're proud of

We take pride in successfully integrating multiple services, including Intel's cloud computing resources, CockroachDB for data storage, and TogetherAI for image generation, to create a cohesive solution, leverage platform advantages to create a complete and scalable tech stack.

## What's next for MindScape

In the future, we plan to refine our model further and explore additional applications, such as incorporating it into a VR game or expanding our analysis capabilities for emotion classification.

## We can read your mind

The details: Using reverse referencing through bloom filters, we figured out a way to introduce entropy with specific feature matches and using advanced software and hardware noise reduction we can do something most teams cannot. Through EEG data timeseries we can estimate brain activity and through that, classify emotions among other brain activities.