Our inspiration was to create a cleaner and more sustainable future by promoting recycling. The largest problem with recycling is people not knowing what can and can't be recycled.

What it does

Revitacycle uses Machine Learning to identify objects in images and from there it is decided whether the object is recyclable.

How I built it

The final project was built using Python Flask and deployed using Heroku.

Challenges I ran into

The largest problem we ran into was with an asynchronous function. We were originally using Node.js all the way through last night. However, after a whole day of work trying to deal with the asynchronous function, it was impossible to extract the data from the function which meant our website couldn't work. Overnight the website was transformed into Python, where the API's function wasn't asynchronous.

Accomplishments that I'm proud of

I am proud of the possibilities and potential of this project, especially if we could grow this into an app. I am also proud of the work that went into it.

What I learned

I learned a lot about recycling but also a lot about dealing with Asynchronous functions in Javascript.

What's next for Revitacycle

Developing this into an app where it will be more accessible and easier to use. The large cost of an Apple Developer account is what holds us back from pursuing this.

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