Bank Note Authenticator with Flask and Streamlit

In this repo, I have created a Bank Note Authentication Model. After creating the model, I have also deployed it onto Hroku platform using Flask and Streamlit.

The Link for Deployed Bank Note Authenticator app

Tools Used:

1. Jupyter Notebook
2. GitHub
3. Docker
4. Flask
5. Streamlit
6. Heroku

Steps

To Create a Web App and host it on Heroku Platform:

1. Create a Model
2. Pickle the Model
3. Create a Flask App
4. Use Streamlit for frontend
5. Use Docker for Containerization
6. Create a Heroku Account.
7. Create the Procfile and Setup.sh files
8. Push all the code to a Github Repo
9. Link the Heroku accont with Github
10. Deploy the model on Heroku.
11. Test using the app link

For a more detailed description, follow my Blog.

Bank Note Authentication

Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extract features from images.

Dataset Link: https://www.kaggle.com/ritesaluja/bank-note-authentication-uci-data