"""
Emma Adapter to provide a canonical, consistent interface to the Emma agent core.
"""
# This will be copied to /app/emma_identity.py by the Dockerfile
from emma_identity import get_runtime_system_prompt

class CanonicalEmma:
    """
    A facade for the Emma agent that enforces a canonical identity and contract,
    while allowing the underlying chat function to be injected as a dependency.
    """
    def __init__(self, chat_function, model, system_prompt=None):
        """
        Initializes the CanonicalEmma adapter.

        Args:
            chat_function: The async function to call for the LLM interaction.
            model: The name of the model to use.
            system_prompt: An optional system prompt to override the default.
        """
        self._chat_function = chat_function
        self._model = model
        self._system_prompt = system_prompt or get_runtime_system_prompt()

    async def run(self, prompt, history=None, system_context: str | None = None):
        """
        Runs the Emma agent with the given prompt.

        In Phase 1, this is a simple pass-through to the injected chat_function,
        ensuring the canonical system prompt is used. History is ignored for now.

        Args:
            prompt: The user\'s prompt.
            history: The conversation history (currently ignored).
            system_context: Optional context to append to the system prompt.

        Returns:
            The raw dictionary response from the chat_function.
        """
        # Phase 1 does not use history or memory.

        composed_system_prompt = self._system_prompt
        if system_context:
            composed_system_prompt = f"{self._system_prompt}\n\n{system_context.strip()}"

        return await self._chat_function(
            self._model,
            prompt,
            composed_system_prompt,
            history=history or [],
        )