"""Prompt templates for command generation."""

from typing import Optional


class PromptTemplates:
    """Manages prompt templates for different shells and scenarios."""

    BASE_PROMPT = """You are a shell command expert. Convert the following natural language description into a {shell} command.

Return ONLY the command in a code block, followed by a brief explanation.

Description: {description}
{directory}

{shell} command:"""

    BASH_SPECIFIC = """Generate a bash command. You can use:
- Bash-specific syntax like [[ ]], parameter expansion ${{...}}
- Process substitution <(), >()
- Arrays and associative arrays
- Extended globbing patterns
- Here-strings <<<"""

    ZSH_SPECIFIC = """Generate a zsh command. You can use:
- Zsh-specific features like glob qualifiers
- Extended globbing (**,omg, etc.)
- Zsh arithmetic $((...))
- Named directories and magic assignments
- Zsh-specific builtins"""

    GIT_CONTEXT = """Context: Git repository detected. Use git commands when appropriate."""

    DOCKER_CONTEXT = """Context: Docker environment detected. Use docker/docker-compose when appropriate."""

    def generate_prompt(
        self,
        description: str,
        shell: str,
        context: Optional[str] = None,
    ) -> str:
        """Generate a complete prompt for command generation.

        Args:
            description: Natural language description of the task.
            shell: Target shell (bash or zsh).
            context: Optional context information.

        Returns:
            Complete prompt string.
        """
        shell_specific = self.ZSH_SPECIFIC if shell == "zsh" else self.BASH_SPECIFIC

        directory_info = ""
        if context:
            directory_info = f"Current directory context: {context}"

        prompt = self.BASE_PROMPT.format(
            shell=shell.upper(),
            description=description,
            directory=directory_info,
        )

        prompt += "\n\n" + shell_specific

        return prompt

    def generate_feedback_prompt(
        self,
        original_description: str,
        generated_command: str,
        user_correction: str,
    ) -> str:
        """Generate a prompt to learn from user feedback.

        Args:
            original_description: The original natural language description.
            generated_command: The command that was originally generated.
            user_correction: The user's corrected command.

        Returns:
            Prompt for learning from feedback.
        """
        return f"""The user wanted to: {original_description}

I generated: {generated_command}

The user corrected it to: {user_correction}

What was wrong with my original command, and what should I learn for similar requests?"""