This is basically complete pipeline explaining how deployment works and how a post API works when we give parameters to it.
Here you can see that there are different files and folders which include libs , data , config , requirenment.txt , app.py Every file has its unique role and every file is equally important , data contains all the dataset required for the implementation like pickle files and etc , libs contain all the modelling related files , requirement.txt contains all the required libraries that needs to be important , app.py contains the pipeline for models deployment.
In general if we see this is an Integrated DonorsChoose.org Application Screening project in which I have used two models Naive Bayes and Logistic Regression and tried to find the accuracy and Predicted score on a unseen data and compare both the models on the basis of accuracy and predicted score.
Working of the model is such that When a user enters the Model name and path of the dataset , I should get the Accuracy percentage and the predicted classes of every value in the test data. (for example : Users Input is Model = Naive Bayes , Path of Text data = Path so the output should be Accuracy of the model in % and the Predicted classes of every value in the test data .
You can find all the files required for the modelling and deployment are very systemettically organised in the files .