import evoagentx.workflow.operators as operator
import examples.aflow.pertqa.optimized.round_7.prompt as prompt_custom
from evoagentx.models.model_configs import LLMConfig
from evoagentx.benchmark.benchmark import Benchmark
from evoagentx.models.model_utils import create_llm_instance

class Workflow:
    
    def __init__(
        self,
        name: str,
        llm_config: LLMConfig,
        benchmark: Benchmark
    ):
        self.name = name
        self.llm = create_llm_instance(llm_config)
        self.benchmark = benchmark
        self.custom = operator.Custom(self.llm)
        self.answer_generate = operator.AnswerGenerate(self.llm)
        self.sc_ensemble = operator.QAScEnsemble(self.llm)
        self.review = operator.Custom(self.llm)  # Added review operator for enhanced reasoning
    
    async def __call__(self, problem: str):
        """
        Implementation of the workflow
        """
        solution = await self.answer_generate(input=problem)
        # Generate a review of the solution for better reasoning
        review_response = await self.review(input=solution['answer'], instruction="Review the following solution for accuracy and completeness.")
        # Generate multiple answers for self-consistency
        ensemble_response = await self.sc_ensemble(solutions=[solution['answer'], review_response['response']])
        # Check for self-consistency before returning the final answer
        final_response = await self.sc_ensemble(solutions=[ensemble_response['response'], solution['answer']])
        return final_response['response']  # Return the best solution from ensemble