Final project for McGill AI Society Intro to ML Bootcamp by Karl, Tofic, and Tristan.
Training data retieved from Kaggle.
This is a web app that classifies images into 12 different categories of materials commonly found in the garbage. The classification is done with RESNET in Tensorflow and the backend is built with Flask while the frontend is done with Vue and Tailwind.
Currently the backend server URLs in the frontend are hardcoded to localhost, port 5000. Flask defaults to port 5000.
To setup the backend, initiate a python virtual environment if desired. Run pip install -r requirements.txt inside backend to install all required packages.
Then, start the server with flask run.
To setup the frontend, navigate to the frontend folder and run yarn install to install all packages. Then run yarn serve to start at the webserver. All commands can also be done with npm.