Cup O' Byte

Inspiration

The COVID-19 pandemic has brought many uncertainties in life. People are panic buying, causing food supply chain disruptions. Dine-in options are not available in many states in the USA and even in international countries. People are resorting to buying ingredients and cooking at home. However, due to supply chain disruptions, not all ingredients would be available in the market, and one may have to cook with whatever she has in her home.

What it does

Combined Machine Learning with API calls, created web application in jQuery and Python and used state of the art libraries like Wordnet to prepare our app - Cup O' Bytes.

How I built it

Machine Learning: Predicting what ingredients are readily available in the nearby market like Walmart, Safeway, etc. Technologies: Python Machine Learning, Logistic Regression Machine Learning to train and give predictions

API Model: Presenting a list of dishes that can be prepared with store-bought or available-at-home ingredients. Technologies used: Spoonacular API calls to retrieve the recipes

Challenges I ran into

Mapping the needs of people in this COVID situation with the application, API learning, collaborating remotely across three time zones.

Accomplishments that I'm proud of

Building the end to end solution for those who have been affected by COVID-19.

What I learned

Understanding how Machine learning and API can be used to build an application that benefits people who want to experiment in the kitchen.

What's next for Cup O' Byte

Pushing code to production.

Built With

• api
• bootstrap
• css3
• html5
• javascript
• jquery
• logistic
• machine-learning
• python
• regression
• wordnet