Code Review Automation Tool

This program is designed to assist developers by automating the process of code review. By leveraging a pre-trained standalone machine learning model (e.g., GPT-4), it reads source code files and provides suggestions for improvements.

Features

• File Scanning: Ability to scan a single file or all files in the current directory.
• Multiple Output Formats: Results can be exported as plain text, JSON, or XML files.
• Customizable Model: Utilizes a customizable language model, allowing users to select the appropriate model for their needs.

Installation

Make sure you have Python 3.x installed.

pip install gpt4all-code-review

For updates, use:

pip install gpt4all-code-review --upgrade

Usage

You can run the program from the command line with various options:

gpt4all_code_review --file <file_path>

Options

• h, --help: Show this help message and exit
• --model: Specifies the model name. Default is "orca-mini-3b.ggmlv3.q4_0.bin".
• --file: Specifies the file path to analyze. If not provided, all files in the current directory will be analyzed.
• --all: Includes all files and folders in the current directory for scanning.
• --output: Output type (default: plain). Options: plain, txt (prettytable), json, xml.
• --export: Export to file (default: False).
• --export-folder: Export to folder (default: ./code_review_results).

Example

To analyze a single file:

gpt4all_code_review --file=./path/to/yourfile.py

To analyze all files in the current directory:

gpt4all_code_review --all

Output Formats

• PLAIN: A human-readable plain txt file.
• TXT: A human-readable table (prettytable) with file paths and suggestions.
• JSON: A machine-readable format that can be parsed programmatically.
• XML: An alternative machine-readable format.

Dependencies

• os: Standard Python library for interacting with the operating system.
• json: Standard Python library for working with JSON data.
• argparse: Standard Python library for parsing command-line arguments.
• gpt4all: A Python library for interfacing with GPT-4 models. Used to apply the AI models to the code.
• prettytable: A Python library to print tabular data in a visually appealing ASCII table format.
• datetime: Standard Python library for working with dates and times.
• console_progressbar: A Python library for displaying progress bars in the console.

License

MIT License