import evoagentx.workflow.operators as operator
import examples.aflow.mbpp_new.optimized.round_6.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)  
        self.sc_ensemble = operator.ScEnsemble(self.llm)  

    async def __call__(self, problem: str, entry_point: str):
        solution_list = []
        for attempt in range(3):  # Retry generating multiple solutions on failure
            solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
            solution_list.append(solution['response'])
            if solution['response'] and await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark):
                break  # Exit loop if a valid solution is found

        final_solution = await self.sc_ensemble(solutions=solution_list, problem=problem)
        
        test_result = await self.test(problem=problem, solution=final_solution['response'], entry_point=entry_point, benchmark=self.benchmark)
        if not test_result['result']:
            modifications = await self.custom(input=final_solution['response'], instruction=prompt_custom.MODIFY_CODE_PROMPT)
            return modifications['response']
        return final_solution['response']