import evoagentx.workflow.operators as operator
import examples.aflow.pubmedqa.optimized.round_9.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)
        self.review = operator.Custom(self.llm)  # Added review operator for additional context
    
    async def __call__(self, problem: str):
        """
        Implementation of the workflow
        """
        # Self-ask to refine the problem statement
        self_ask_response = await self.custom(input=problem, instruction="Clarify and refine the problem statement.")
        refined_problem = self_ask_response['response']
        
        # Generate initial answer
        solution = await self.answer_generate(input=refined_problem)
        solutions = [solution['answer']]
        
        # Generate additional answers
        for _ in range(2):
            additional_solution = await self.answer_generate(input=refined_problem)
            solutions.append(additional_solution['answer'])
        
        # Review the generated answers for better context
        review_response = await self.review(input=refined_problem + " " + " ".join(solutions), instruction="Review these answers.")
        
        # Use QAScEnsemble to select the best solution
        ensemble_result = await self.qas_ensemble(solutions=solutions + [review_response['response']])
        return ensemble_result['response']  # Return the best selected answer