import evoagentx.workflow.operators as operator
import examples.aflow.pertqa.optimized_reploge.round_3.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.ensemble = operator.QAScEnsemble(self.llm)  # Added ensemble operator for better solution selection
        self.review = operator.Custom(self.llm)  # Added review operator to refine the generated answer
    
    async def __call__(self, problem: str):
        """
        Implementation of the workflow
        """
        solution = await self.answer_generate(input=problem)
        solutions = [solution['answer']]  # Collecting solutions for ensemble
        review_response = await self.review(input=solution['answer'], instruction=prompt_custom.REVIEW_PROMPT)  # Refining the answer
        solutions.append(review_response['response'])  # Adding reviewed response to solutions
        ensemble_result = await self.ensemble(solutions=solutions)  # Using ensemble to select the best solution
        return ensemble_result['response']  # Returning the selected response from ensemble