Create a NEW repository from scratch and transfer any relevant work as you complete the assignment, you need to show a clear history of work through your commits, or your project could be given as low as a

$0$

$$for not showing your work.

Submit through a GitHub repository link containing the necessary documentation, configuration examples, and a coherent commit history.

You are required to write a short description and link to your implememtation of the design patterns you use.

You need to provide a description of how you used environment variables and link to your code to illustrate.

You need to explain and link to how you are using logging

$.$

You need to link to and explain how you are using try

$/$

catch

$/$

$$exceptions to illustrate "Look Before You Leap"

$($

LBYL

$)$

$$and "Easier to Ask for Forgiveness than Permission"

$($

EAFP

$)$

$/$

REQUIRED

$-$

$$YOU MUST USE GITHUB ACTIONS AND YOUR CODE MUST PASS ALL THE TESTS ON GITHUB

Core Functionalities

Command

$-$

Line Interface

$($

REPL

$)$

Implement a Read

$-$

Eval

$-$

Print Loop

$($

REPL

$)$

$$to facilitate direct interaction with the calculator. This interface should support:

Execution of arithmetic operations

$($

Add

$,$

$$Subtract$,$ Multiply, and Divide

$)$

Management of calculation history.

Access to extended functionalities through dynamically loaded plugins.

Plugin System

Create a flexible plugin system to allow seamless integration of new commands or features. This system should:

Dynamically load and integrate plugins without modifying the core application code.

Include a REPL "Menu" command to list all available plugin commands, ensuring user discoverability and interaction.

Calculation History Management with Pandas

Utilize Pandas to manage a robust calculation history, enabling users to:

Load, save, clear, and delete history records through the REPL interface.

Professional Logging Practices

Establish a comprehensive logging system to record:

Detailed application operations, data manipulations, errors, and informational messages.

Differentiate log messages by severity

$($

INFO

$,$

$$WARNING$,$ ERROR

$)$

$$for effective monitoring.

Dynamic logging configuration through environment variables for levels and output destinations.

Advanced Data Handling with Pandas

Employ Pandas for:

Efficient data reading and writing to CSV files.

Managing calculation history.

Design Patterns for Scalable Architecture

Incorporate key design patterns to address software design challenges, including:

Facade Pattern: Offer a simplified interface for complex Pandas data manipulations.

Command Pattern: Structure commands within the REPL for effective calculation and history management.

Factory Method, Singleton, and Strategy Patterns: Further enhance the application's code structure, flexibility, and scalability.

Development, Testing, and Documentation Requirements

Testing and Code Quality

Achieve a minimum of

$9$

$0$

$\%$

$$test coverage with Pytest.

Ensure code quality and adherence to PEP

$8$

$$standards$,$ verified by Pylint.

Version Control Best Practices

Utilize logical commits that clearly group feature development and corresponding tests, evidencing clear development progression.

Comprehensive Documentation

Compile detailed documentation in README.md

$,$

$$covering setup instructions, usage examples, and an in

$-$

depth analysis of architectural decisions, particularly emphasizing the implementation and impact of chosen design patterns and the logging strategy.