import evoagentx.workflow.operators as operator
import examples.aflow.pertqa.optimized_adamson_update.round_20.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.qas_ensemble = operator.QAScEnsemble(self.llm)
    
    async def __call__(self, problem: str):
        """
        Implementation of the workflow
        """
        solution = await self.answer_generate(input=problem)
        solutions_list = [solution['answer']]
        review_response = await self.custom(input=problem + " Review the answer: " + solution['answer'], instruction=prompt_custom.Review_PROMPT)  # Added review step
        solutions_list.append(review_response['response'])  # Collecting reviewed solution
        ensemble_response = await self.qas_ensemble(solutions=solutions_list)
        consistency_check = await self.custom(input=problem + " Check the consistency of the ensemble answer: " + ensemble_response['response'], instruction=prompt_custom.Review_PROMPT)  # Added consistency check
        final_review_response = await self.custom(input=problem + " Final review of the ensemble answer: " + consistency_check['response'], instruction=prompt_custom.Review_PROMPT)  # Added final review step
        return final_review_response['response']  # Return the final reviewed response