Enhance Code Review Process with Custom Prompts, Language Selection, and Automated Testing

Overview:

This PR introduces significant enhancements to the code review automation process, including support for custom prompts, language selection, and integration of automated testing using pytest and pylint.

Changes:

Custom Prompts:

• Added support for user-defined custom prompts.
• Example usage: custom_prompt: "Give a rating from 1 to 10 for this code:".

Language Selection:

• ntroduced the ability to specify the language for code review responses.
• Default language is set to English (en).

Automated Testing:

• Implemented unit tests using pytest for core functionalities.

Refactoring:

• Modularized the code for better maintainability.
• Improved error handling and logging.
• Ensured compliance with PEP8 standards.

Code Review with ChatGPT

Now we do a code review.

v1.3

A new way to capture PR changes and send them to OpenAI's AI was created.

The patch file that is read, and like this file, is parsed per file and sent to the AI to be interpreted.

-[x] Added new 'mode' parameter to have the option to generate the PR description
-[x] Separate modes into functions
-[x] Update readme

v1.2

A minor fix to improve the comments with the filename of explanation.

V1.1

Now you can use the new OpenAI parameters:

• openai_engine, GPT-3 models can understand and generate natural language. We offer four main models with different levels of power suitable for different tasks. Davinci is the most capable model, and Ada is the fastest.
• openai_temperature, Sampling temperature to use. Higher values means the model will take more risks. Recommended: 0.5
• openai_max_tokens, The maximum number of tokens to generate in the completion.

And a lot of better docs.

v1

Initial release