Our inspiration stemmed from the desire to implement a machine learning / A.I. API.

What it does

Taper analyzes images using IBM's Watson API and our custom classifiers. This data is used to query the USDA food database and return nutritional facts about the product.

How we built it

Using android Studio and associated libraries we created the UI in the form of an Android App. To improve Watson's image recognition we created our custom classifier and to recognize specific product brands.

Challenges we ran into

For most of us this was our first time using both Android Studios and Watson so there was a steep initial learning curve. Additionally we attempted to use Microsoft Azure along side Watson but were unsuccessful.

Accomplishments that we're proud of

-Successful integrating Watson API into a Android App. -Training our own visual recognition classifier using python and bash scripts. -Retrieving a products nutritional information based on data from visual recognition.

What we learned

We experience and learned the difficulty of product integration. As well, we learned how to better consume API's

What's next for taper

-Creating a cleaner UI -Text analysis of nutritional data -day to day nutrition tracking

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