CommonCents is a web application that is intended to be used by individuals who are visually impaired. After researching, my team members and I realized that there isn't many resources for people with visual disabilities, especially when it relates to their finances. We decided to create this application to help these individuals be able to differentiate between the USD currency.

For right now, the application is only able to differentiate between a $50 dollar bill and a $100 dollar bill. We are hoping to incorporate other USD, such as $1, $5, $10 and $20 dollar bills.

1. React (https://reactjs.org/)
2. Express (https://expressjs.com/)
3. Python (https://www.python.org/)
4. Pipenv (https://pipenv.pypa.io/en/latest/)
5. Teachable machine (https://teachablemachine.withgoogle.com/)

Note: This web application uses machine learning algorithms to be able to differentiate between the different dollar bills. For this we relied heavily on teachable machine to do the heavylifting for us.

1. Clone the project.
2. cd into the frontend folder and run npm install.
3. cd into the backend folder and run npm install.
4. Make sure you have python and pipenv installed on your machine, this is mandatory to be able to get this project up and running.
5. cd into the model folder and run pipenv install.
6. Run pipenv shell to activate the python environment.

Please note: You will need three separate terminals: one for the frontend folder, one for the backend folder and one for the model folder.

The web application uses a react frontend, to allow the user to upload a file. Which then passes along the file that was uploaded to the express backend. The backend will pass that file to the trained machine learning model, that is in the model folder. The prediction is then returned to express, which is then returned to react and displayed to the user.

To run the react app:

1. cd into the frontend folder.
2. Run npm start, this should open up a browser with a url of 'http://localhost:3000'.

To run the express api:

1. cd into backend folder.
2. Run npm start, this should get the server up and running.

To run the flask server:

1. cd into model folder.
2. Run flask run, this is to get the flask server up and running as well.

Note: Express is used to proxy the request to the flask server, which is where the trained model lives (the app.py file). Please read the comments in the files for a more detailed explanation.

Once the application is up and running, you can upload a file/picture of a $50 or $100 dollar bill and the output will be displayed onto the screen.

Please be aware that the trained model is not 100% accurate. There might be an instance where the file you upload will have the wrong output.

There are a few features that we want to add to our project:

1. Display the image that the user uploaded onto the screen.
2. Feedback section, where the user can tell us if the output that was provided was correct or not.
3. Accessibility options: have the output be read out loud to the user.

1. Caesar Cuzco, student at John Jay College: github: caesarc6
2. Shaniya Snagg, student at John Jay College: github: shaniyas13
3. Melissa Persaud, student at Queens College: github: melissapersaud17