import evoagentx.workflow.operators as operator
import examples.aflow.mbpp_new_full.optimized.round_5.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.custom_code_generate = operator.CustomCodeGenerate(self.llm)
        self.test = operator.Test(self.llm)  # Added test operator to enhance solution checks
        self.sc_ensemble = operator.ScEnsemble(self.llm)  # Added ensemble operator for better selection

    async def __call__(self, problem: str, entry_point: str):
        """
        Implementation of the workflow
        Custom operator to generate anything you want.
        But when you want to get standard code, you should use custom_code_generate operator.
        """
        solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
        test_result = await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark)  # Implementing test functionality
        attempts = 0  # Added an attempt counter to manage retries
        while not test_result['result'] and attempts < 2:  # Allow up to 2 attempts
            attempts += 1
            # Gather multiple attempts for the same problem to apply the ensemble method
            alternate_solutions = await self.custom(input=problem, instruction=prompt_custom.ALTERNATE_SOLUTIONS_PROMPT)
            selected_solution = await self.sc_ensemble(solutions=alternate_solutions['response'], problem=problem)  # Enhancing solution selection
            revision_response = await self.custom(input=problem+f" Current Solution: {selected_solution['response']}", instruction=prompt_custom.REVISE_PROMPT)
            test_result = await self.test(problem=problem, solution=revision_response['response'], entry_point=entry_point, benchmark=self.benchmark)  # Retest with revised solution
        return revision_response['response'] if not test_result['result'] else solution['response']