# Salary Assessment App

Demonstration of an interactive web-application built in python.
This application takes in an employee roster (built interactively, best for small teams), with names, positions, and salaries for each employee.
It then allows someone (e.g. a manager) to simulate promotions and/or salary raises and see the overall impact on the final payroll budget (visualized as a pair of overlaying distributions which show the before/after scenario under the provided ranges).
Finally, the app "solves the problem" of assigning a new salary to each employee while staying below some pre-defined budget for payroll, using a novel Monte-Carlo method implemented directly within the app itself (rather than relying on importing another library).

> This example shows "a small amount of effort" exerted to ensure reproducibility and readability but ultimately lacks in overall user-friendliness.

Takeaways: *Good reproducibility, passable style/formatting.*

- Of particular note here is the executable `run` shell script which provides support for running the application using a docker image as well as simultaneously supporting native shell execution if `docker` is not in the system `$PATH`
- Note the style in which functions are written in `app.py`
    - There is a mixture of functions and procedural code, wide abuse of global variables, and a lot of messy plotting code. Is it readable overall?
    - The app's "state" is held in `data.json`, is saved in a human-readable format (as opposed to binary), and is small enough to be negligible
    - The functions that are defined at least have readable names
    - If the app grew any larger than this, one might be wise to migrate functions into a separate module and import it in `app.py`
- Note the lack of a proper `README` / we just "presume" the user knows what to do with the presence of a `Makefile`. Is this acceptable in your opinion?
    - Very minimal `Makefile` is "sort of self-documenting" but many people don't know to look there. It could use documentation so that `make help` at least produces some sort of a helpful guide


## Usage

(if using local `python` instead of `docker`):
```
make install
```

then

```bash
make run
```

and visit `localhost:8501` (or `<tld>/<user>/proxy/8501/` in Jupyter if you have `jupyter-server-proxy` installed to access the app via proxy)