Hardcoded paths and values reduce flexibility and maintainability of the code.

[tawada]

One potential issue with the existing code is the use of hardcoded configuration paths and values. In various files (e.g., utils/config_loader.py, utils/github_utils.py, services/github/__init__.py, etc.), paths such as config.json, and downloads are hardcoded directly into the functions. This can make the code less flexible and harder to maintain, especially if the configuration files or repository paths change in the future.

Example of hardcoded values:

def load_config(file_path='config.json'):
    # ...
    return get_default_config()

def get_default_config():
    return {
        "repository_path": "downloads",
        "exclude_dirs": ["__pycache__", ".git", "downloads"],
        "openai_model_name": "gpt-4o",
    }

Potential Solution:

Refactor the code to make these paths configurable via environment variables or command-line arguments. This would increase the flexibility and maintainability of the code.

Example Refactor:

import os

def load_config(file_path=None):
    if file_path is None:
        file_path = os.getenv('CONFIG_PATH', 'config.json')
    try:
        with open(file_path) as config_file:
            return json.load(config_file)
    except FileNotFoundError:
        log(
            f"Configuration file {file_path} not found. Loading default configuration.",
            level="warning",
        )
        return get_default_config()

def get_default_config():
    return {
        "repository_path": os.getenv('REPOSITORY_PATH', 'downloads'),
        "exclude_dirs": ["__pycache__", ".git", os.getenv('REPOSITORY_PATH', 'downloads')],
        "openai_model_name": os.getenv('OPENAI_MODEL_NAME', 'gpt-4o'),
    }

By making these paths configurable, you can easily adapt the application to different environments and use cases without changing the code.