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Peter Griffin AI

PeterAi

This class provides methods to explain Linux commands using the Google Generative AI model.

Source code in peter_explains/peter_ai.py 
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class PeterAi:
    """
    This class provides methods to explain Linux commands using the Google Generative AI model.
    """

    def __init__(self, model_name: str = "gemini-pro"):
        """
        Initialize the PeterAi class with the specified model.

        Args:
        - model_name (str): The name of the model to use for explaining Linux commands.

        Returns:
        - None
        """
        api_key = GoogleApiKey().get()
        self.safety_settings = [
            {
                "category": "HARM_CATEGORY_DANGEROUS",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_HARASSMENT",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_HATE_SPEECH",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                "threshold": "BLOCK_NONE",
            },
            {
                "category": "HARM_CATEGORY_SEXUAL",
                "threshold": "BLOCK_NONE",
            },
            # TODO: Uncomment the following lines to enable additional safety settings
            # {
            #     "category": "HARM_CATEGORY_DEROGATORY",
            #     "threshold": "BLOCK_NONE",
            # },
            # {
            #     "category": "HARM_CATEGORY_MEDICAL",
            #     "threshold": "BLOCK_NONE",
            # },
            # {
            #     "category": "HARM_CATEGORY_VIOLENCE",
            #     "threshold": "BLOCK_NONE",
            # },
            # {
            #     "category": "HARM_CATEGORY_TOXICITY",
            #     "threshold": "BLOCK_NONE",
            # },
        ]
        genai.configure(transport="grpc_asyncio", api_key=api_key)
        self.model = genai.GenerativeModel(
            model_name, safety_settings=self.safety_settings
        )

    async def explain_command(
        self, command: str
    ) -> CommandExplanation | CommandExplanationWithArguments:
        """
        Explain a Linux command in a JSON format suitable for use in a command-line tool.

        This function uses the Google Generative AI model to explain a Linux command in a JSON format suitable for use in a command-line tool. It takes a Linux command as input and returns a JSON object with the following keys:
        > command_name: The name of the Linux command
        > purpose: A short, funny explanation of its purpose in Peter Griffin's voice.
        > syntax: Basic command structure with optional placeholders for arguments (e.g., "command_name [options] <file_or_directory>")
        > options: A few common options. Provide brief, humorous explanations for each.
        > examples: 2-3 examples demonstrating the command's usage. Keep it simple and funny!

        Args:
            command (str): The Linux command to explain.

        Returns:
            CommandExplanation | CommandExplanationWithArguments: The explanation of the Linux command.
        """

        result = None
        if " " in command:  # Check if command includes arguments
            prompt = PromptType.WITH_ARGUMENTS.format(command=command)
            response = await self.model.generate_content_async(
                prompt, safety_settings=self.safety_settings
            )
            result = CommandExplanationWithArguments.from_response(response.text)
        else:
            prompt = PromptType.WITHOUT_ARGUMENTS.format(command=command)
            response = await self.model.generate_content_async(
                prompt, safety_settings=self.safety_settings
            )
            result = CommandExplanation.from_response(response.text)
        return result

__init__(model_name='gemini-pro')

Initialize the PeterAi class with the specified model.

Args: - model_name (str): The name of the model to use for explaining Linux commands.

Returns: - None

Source code in peter_explains/peter_ai.py 
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def __init__(self, model_name: str = "gemini-pro"):
    """
    Initialize the PeterAi class with the specified model.

    Args:
    - model_name (str): The name of the model to use for explaining Linux commands.

    Returns:
    - None
    """
    api_key = GoogleApiKey().get()
    self.safety_settings = [
        {
            "category": "HARM_CATEGORY_DANGEROUS",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_HARASSMENT",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_HATE_SPEECH",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
            "threshold": "BLOCK_NONE",
        },
        {
            "category": "HARM_CATEGORY_SEXUAL",
            "threshold": "BLOCK_NONE",
        },
        # TODO: Uncomment the following lines to enable additional safety settings
        # {
        #     "category": "HARM_CATEGORY_DEROGATORY",
        #     "threshold": "BLOCK_NONE",
        # },
        # {
        #     "category": "HARM_CATEGORY_MEDICAL",
        #     "threshold": "BLOCK_NONE",
        # },
        # {
        #     "category": "HARM_CATEGORY_VIOLENCE",
        #     "threshold": "BLOCK_NONE",
        # },
        # {
        #     "category": "HARM_CATEGORY_TOXICITY",
        #     "threshold": "BLOCK_NONE",
        # },
    ]
    genai.configure(transport="grpc_asyncio", api_key=api_key)
    self.model = genai.GenerativeModel(
        model_name, safety_settings=self.safety_settings
    )

explain_command(command) async

Explain a Linux command in a JSON format suitable for use in a command-line tool.

This function uses the Google Generative AI model to explain a Linux command in a JSON format suitable for use in a command-line tool. It takes a Linux command as input and returns a JSON object with the following keys:

command_name: The name of the Linux command purpose: A short, funny explanation of its purpose in Peter Griffin's voice. syntax: Basic command structure with optional placeholders for arguments (e.g., "command_name [options] ") options: A few common options. Provide brief, humorous explanations for each. examples: 2-3 examples demonstrating the command's usage. Keep it simple and funny!

Parameters:

Name Type Description Default
command str

The Linux command to explain.

 required

Returns:

Type Description
CommandExplanation | CommandExplanationWithArguments

CommandExplanation | CommandExplanationWithArguments: The explanation of the Linux command.
Source code in peter_explains/peter_ai.py 
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async def explain_command(
    self, command: str
) -> CommandExplanation | CommandExplanationWithArguments:
    """
    Explain a Linux command in a JSON format suitable for use in a command-line tool.

    This function uses the Google Generative AI model to explain a Linux command in a JSON format suitable for use in a command-line tool. It takes a Linux command as input and returns a JSON object with the following keys:
    > command_name: The name of the Linux command
    > purpose: A short, funny explanation of its purpose in Peter Griffin's voice.
    > syntax: Basic command structure with optional placeholders for arguments (e.g., "command_name [options] <file_or_directory>")
    > options: A few common options. Provide brief, humorous explanations for each.
    > examples: 2-3 examples demonstrating the command's usage. Keep it simple and funny!

    Args:
        command (str): The Linux command to explain.

    Returns:
        CommandExplanation | CommandExplanationWithArguments: The explanation of the Linux command.
    """

    result = None
    if " " in command:  # Check if command includes arguments
        prompt = PromptType.WITH_ARGUMENTS.format(command=command)
        response = await self.model.generate_content_async(
            prompt, safety_settings=self.safety_settings
        )
        result = CommandExplanationWithArguments.from_response(response.text)
    else:
        prompt = PromptType.WITHOUT_ARGUMENTS.format(command=command)
        response = await self.model.generate_content_async(
            prompt, safety_settings=self.safety_settings
        )
        result = CommandExplanation.from_response(response.text)
    return result