from typing import List, Optional

from llmops.base import AbstractOperation
from llmops.business.business_expert import BusinessExpert
from llmops.config import prompt_path
from llmops.utils.llm_client import LlmClient, LlmConfig


class Preprocessor:
    """user requirements in natural language to abstract operations"""

    def __init__(self, business_expert: BusinessExpert, model: str):
        self.client = LlmClient(prompt_path / "preprocessor.md", llm_config=LlmConfig(model=model))
        self.business_expert = business_expert
        self.all_operations = self.business_expert.operation_ids
        if business_expert.biz_system_prompt_preprocessor != "":
            self.client.update_system_prompt(
                self.client.system_prompt
                + "\nBusiness Logic:\n"
                + business_expert.biz_system_prompt_preprocessor
            )

    def process(
        self, scenario_steps: Optional[List[str]] = None
    ) -> List[AbstractOperation]:
        """process Swagger specification to generate abstract state sequence

        Args:
            scenario_steps: Optional scenario steps list, if provided, generate state sequence based on steps

        Returns:
            Abstract state sequence
        """

        # generate state sequence based on steps
        states = []
        for step in scenario_steps:
            # state = self.extract_state(step)
            # states.append(state)
            states.append(AbstractOperation(original_description=step))
        return states

    # def extract_state(self, step_description: str) -> AbstractOperation:
    #     """extract abstract operation from step description"""
    #     user_prompt = {
    #         "step_description": step_description,
    #         "swagger_spec": self.business_expert.extract_ids_and_descriptions,
    #     }
    #     AllOperationID = Enum("AllOperationID", {op: op for op in self.all_operations})

    #     class AbstractOperation4GPT(BaseModel):
    #         operation_id: AllOperationID
    #         attributes: str

    #     llm_response = self.client.generate(
    #         user_prompt, AbstractOperation4GPT
    #     )  # mainly to get operation_id

    #     operation_id = llm_response.operation_id.value
    #     spec = self.business_expert.extract_operation(operation_id)
    #     user_prompt["swagger_spec"] = spec

    #     llm_response = self.client.generate(
    #         user_prompt, AbstractOperation4GPT
    #     )  # mainly to get attributes
    #     llm_response.attributes = format_json_string_by_ai(llm_response.attributes)
    #     return AbstractOperation(
    #         operation_id=llm_response.operation_id,
    #         attributes=llm_response.attributes,
    #         original_description=step_description,
    #     )