## Inspiration

How do you define a sandwich? According to your definition is hotdog a sandwich? Well, according to Merriam Webster it is and so are sliders and many other food items that you might or might not consider sandwiches yourself (check out the link for more details- <https://www.merriam-webster.com/words-at-play/to-chew-on-10-kinds-of-sandwiches/sloppy-joe>)!

Expanding on this concept, this project aims to explore the classification of sandwiches and the boundaries to the definition.

## What it does

The Webapp determines whether an image classifies as a sandwich.

## How we built it

We downloaded 10,000 images from Google Images and 20,000 images from the Food101 dataset to train a binary classification algorithm on sandwiches using SqueezeNet Network in DeepLearning4J.

## Challenges we ran into

We had to switch from using Tensorflow and Python to using DeepLearning4J and Java because we wanted to do everything in memory of the server of the Webapp but the backend of the Webapp is in Java.

## What's next for Sandwichinator
## Inspiration

Sign Language is what majority of the people who are part of the deaf and mute community use as a part of their daily conversing. **Not everyone knows sign language** and hence this necessitates the need for a tool to help others understand Sign. This basically emphasizes the possibility for the deaf and mute to be **solely independent** and not in need of translators when they have to address or even have a normal conversation with those who do not understand sign language.

## What it does

The platform basically takes in input via a camera of a hand gesture and tells you which alphabet does the letter stand for. It is based on the American Sign Language conventions and can recognize all the alphabets given the conditions are met which are derived from it's training data.

## How we built it

The backend was done using Python-Flask, Tailwind CSS was used for frontend development along with HTML and JS. For the AI part , Microsoft Azure Custom Vision service was used. The custom vision service can be used to train and deploy models with high availability and efficiency.

I used the ASL image dataset from Kaggle where 190 random images from the entire ASL was taken and used to train the model for each alphabet. Hence a total of 190\*26 images were used to train the Azure Custom Vision Model.

The application has been separately deployed on Azure WebApp service with GitHub Actions auto redeploying on new commit using a simple CI workflow.

## Challenges we ran into

1. Securing the critical keys in the code before pushing to github
2. Bottleneck on model efficiency when it comes to the use of real time data.
3. Azure Limitations on 5000 images per Custom Vision Project.

## Accomplishments that we're proud of

1. Making a model that successfully classified the ASL test data from Kaggle.
2. More deep understanding of Azure technologies and cloud.

## What we learned

1. Frontend Development with Tailwind CSS
2. Integrating Azure Services into Python Flask
3. Deployment on Azure

## What's next for Sign-To-Text

1. More efficient model.
2. Real-Time sign to text conversion followed by a text-to-voice converter.
3. Sign-To-Voice Converter
## **Inspiration**

Ever had to wipe your hands constantly to search for recipes and ingredients while cooking?
Ever wondered about the difference between your daily nutrition needs and the nutrition of your diets?
Vocal Recipe is an integrated platform where users can easily find everything they need to know about home-cooked meals! Information includes recipes with nutrition information, measurement conversions, daily nutrition needs, cooking tools, and more! The coolest feature of Vocal Recipe is that users can access the platform through voice control, which means they do not need to constantly wipe their hands to search for information while cooking. Our platform aims to support healthy lifestyles and make cooking easier for everyone.

## **How we built Vocal Recipe**

Recipes and nutrition information is implemented by retrieving data from Spoonacular - an integrated food and recipe API.
The voice control system is implemented using Dasha AI - an AI voice recognition system that supports conversation between our platform and the end user.
The measurement conversion tool is implemented using a simple calculator.

## **Challenges and Learning Outcomes**

One of the main challenges we faced was the limited trials that Spoonacular offers for new users. To combat this difficulty, we had to switch between team members' accounts to retrieve data from the API.
Time constraint is another challenge that we faced. We do not have enough time to formulate and develop the whole platform in just 36 hours, thus we broke down the project into stages and completed the first three stages.
It is also our first time using Dasha AI - a relatively new platform which little open source code could be found. We got the opportunity to explore and experiment with this tool. It was a memorable experience.