# guardianhub_sdk/services/llm/client.py
from typing import List, Dict, Any, Optional, Type
from ...services.base import BaseServiceClient
from ...models.registry.client import client as registry_client
from pydantic import BaseModel as PydanticBaseModel


class LLMClient(BaseServiceClient):
    def __init__(self, base_url: str = "https://llm.internal.local", token_provider=None):
        super().__init__(base_url, token_provider=token_provider, logger_name=__name__)

    async def generate_structured(self,
                                  messages: List[Dict[str, Any]],
                                  output_model: Optional[Type[PydanticBaseModel]] = None,
                                  **kwargs) -> Any:
        """Call local aura-llm or upstream provider and return validated output.


        This method should wrap the provider logic (like the earlier generate_structured). For the skeleton
        we assume aura-llm endpoint is "POST /generate" that accepts messages and optionally model reference.
        """

        # If the output_model is a string reference, try to fetch it from registry
        if isinstance(output_model, str):
            Model = await registry_client.fetch_model(output_model, version=None)
        output_model = Model

        # For skeleton, just echo back a simulated response
        self.logger.debug("Simulating structured generation for model=%s", getattr(output_model, "__name__", None))
        if output_model is not None and issubclass(output_model, PydanticBaseModel):
            # sample instantiation - in reality you'd parse provider output
            return output_model(**{k.name: None for k in getattr(output_model, "__fields__", [])})
        return {"mock": True}


