Separate Configurations into a TOML File

[Zingzy]

This issue addresses Issue #9

Summary by Sourcery

Load bot configuration from a TOML file instead of environment variables and constants.

Build:

• Load bot configuration from a TOML file.

Chores:

• Replace requests library with aiohttp.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request refactors the bot to load configurations from a TOML file instead of using environment variables and hardcoded values. This change improves maintainability and allows for easier configuration of the bot's behavior.

Sequence diagram for configuration loading process

sequenceDiagram
    participant App
    participant ConfigLoader
    participant TOML
    participant Pydantic
    participant API

    App->>ConfigLoader: Import config
    ConfigLoader->>TOML: Read config.toml
    TOML-->>ConfigLoader: Return config data
    ConfigLoader->>Pydantic: Validate config data
    Pydantic-->>ConfigLoader: Return validated Config instance
    ConfigLoader->>API: Initialize models list
    API-->>ConfigLoader: Return available models
    ConfigLoader-->>App: Return complete config

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Class diagram for the new configuration system

classDiagram
    class Config {
        +bot: BotConfig
        +api: APIConfig
        +image_generation: ImageGenerationConfig
        +commands: Dict[str, CommandConfig]
        +ui: UIConfig
        +resources: ResourcesConfig
        +MODELS: List[str]
        +validate_structure()
    }

    class BotConfig {
        +command_prefix: str
        +bot_id: str
        +avatar_url: str
        +commands: Dict[str, str]
        +emojis: Dict[str, str]
    }

    class APIConfig {
        +models_list_endpoint: str
        +image_gen_endpoint: str
        +models_refresh_interval_minutes: int
        +max_timeout_seconds: int
    }

    class ImageGenerationConfig {
        +referer: str
        +fallback_model: str
        +defaults: ImageGenerationDefaults
        +validation: ImageGenerationValidation
    }

    Config --> BotConfig
    Config --> APIConfig
    Config --> ImageGenerationConfig

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File-Level Changes

Change	Details	Files
Configuration is loaded from a TOML file.	Added a new config.py file to handle loading and validation of the TOML configuration. Added a config.template.toml file to serve as a template for the configuration. Added tomli to the project dependencies in pyproject.toml. The config.py file uses pydantic to define the structure of the configuration and validate the data. The config.py file includes a function to pre-initialize the models list by fetching from the API.	config.py config.template.toml pyproject.toml
Bot commands and their descriptions are now loaded from the configuration file.	The bot's command prefix is now loaded from the configuration. The bot's command descriptions are now loaded from the configuration. The bot's command IDs are now loaded from the configuration.	main.py
API endpoints and refresh intervals are now loaded from the configuration file.	The API endpoint for fetching models is now loaded from the configuration. The interval for refreshing models is now loaded from the configuration.	main.py
Image generation parameters are now loaded from the configuration file.	Default image dimensions are now loaded from the configuration. Default image generation flags (safe, cached, nologo, enhance, private) are now loaded from the configuration. Image validation parameters (min width, min height, max prompt length) are now loaded from the configuration.	cogs/imagine_cog.py cogs/multi_pollinate_cog.py cogs/random_cog.py utils/image_gen_utils.py
UI elements and error messages are now loaded from the configuration file.	The bot's invite URL is now loaded from the configuration. The bot's support server URL is now loaded from the configuration. The bot's GitHub repository URL is now loaded from the configuration. The bot's API provider URL is now loaded from the configuration. The bot's creator avatar is now loaded from the configuration. The bot's UI colors are now loaded from the configuration. Error messages are now loaded from the configuration.	main.py cogs/imagine_cog.py cogs/multi_pollinate_cog.py cogs/random_cog.py utils/error_handler.py utils/embed_utils.py
Bot resources are now loaded from the configuration file.	The bot's waiting GIFs are now loaded from the configuration.	utils/embed_utils.py
Command cooldowns are now loaded from the configuration file.	Command cooldown rates and seconds are now loaded from the configuration.	cogs/imagine_cog.py cogs/multi_pollinate_cog.py cogs/random_cog.py
Removed the constants.py file.	Removed the constants.py file. Removed all references to the constants.py file.	main.py cogs/imagine_cog.py cogs/multi_pollinate_cog.py cogs/random_cog.py utils/pollinate_utils.py
Updated dependencies in requirements.txt.	Added tomli and pydantic to the project dependencies. Removed requests from the project dependencies.	requirements.txt
Added aiohttp for asynchronous HTTP requests.	Replaced requests with aiohttp for asynchronous HTTP requests.	main.py

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[sourcery-ai]

Hey @Zingzy - I've reviewed your changes and they look great!

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟡 Complexity: 1 issue found
• 🟢 Documentation: all looks good

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[sourcery-ai]

issue (complexity): Consider using a command registry pattern to centralize command definitions and reduce string interpolation noise.

The command configuration can be simplified while maintaining flexibility. Consider using a command registry pattern:

# commands.py
class CommandRegistry:
    def __init__(self, config):
        self.commands = {
            "pollinate": {
                "id": config.bot.commands['pollinate_id'],
                "emoji": "🎨",
                "description": """Generates AI Images based on your prompts..."""
            },
            # ... other commands
        }

    def get_formatted_command(self, name):
        cmd = self.commands[name]
        return f"</{name}:{cmd['id']}> {cmd['emoji']}"

    def get_description(self, name):
        return self.commands[name]["description"]

# Usage in main code
cmd_registry = CommandRegistry(config)
commands_ = {
    cmd_registry.get_formatted_command("pollinate"): cmd_registry.get_description("pollinate"),
    # ... other commands
}

This approach:

1. Reduces string interpolation noise
2. Keeps configuration flexibility
3. Makes command management more maintainable
4. Centralizes command definitions
5. Makes it easier to add new commands