This was built with PyTorch/FastAI for the Machine Learning part and Flask as a Webserver. For containerization and easy deployment I use Docker. The dataset in use is the Food-101 Dataset but I chose to only use two of the foods contained there.

You can easily use this as a template for your own models.

Go ahead and open the Notebook in Colab with a simple press of a button! Most steps will be described in detail there. Just follow along!

You can decide which two foods you want to classify by changing:

#Deciding which two foods we want to classify
labelA = 'samosa'
labelB = 'churros'

Of course it is also possible to train all the foods contained in the Food-101 Dataset but in order to achieve this you have to modify the code a bit.

At the end of the notebook it will download an export.pkl file, which is your model.

Deploying your on model is a easy as replacing the model (server/export.pkl) with your own model. Of course, it makes sense to also modify the HTML/CSS a bit to your liking.

Command to launch the container:

docker build -t churros_samosa_classifier . && docker run --rm -it -p 5000:5000 churros_samosa_classifier

Then you can deploy the Container to any Cloud Provider of your choosing.

I recommend Render, which is what I used for my deployment but everything else should work fine, too.

Steps for deploying on Render

• Fork this repository

• Replace the model (export.pkl)

• Change the HTML and CSS to your liking

• In the Render Dashboard create a new Web Service

• Link your repository

• Make sure Docker is selected

• Done!