import evoagentx.workflow.operators as operator
import examples.aflow.humaneval.optimized.round_13.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.ensemble = operator.ScEnsemble(self.llm)  
        self.review = operator.Custom(self.llm)  # Added Custom operator for review process

    async def __call__(self, problem: str, entry_point: str):
        """
        Implementation of the workflow
        Custom operator to generate code, review it, and validate it with tests.
        """
        solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
        reviewed_solution = await self.review(input=solution['response'], instruction=prompt_custom.REVIEW_CODE_PROMPT)  # Reviewing generated code
        validation = await self.test(problem=problem, solution=reviewed_solution['response'], entry_point=entry_point, benchmark=self.benchmark)  
        
        if validation['result']:
            return reviewed_solution['response']
        else:
            modified_solution = await self.custom(input=problem + f" with problems: {validation['solution']}", instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
            solutions_list = [reviewed_solution['response'], modified_solution['response']]
            ensemble_result = await self.ensemble(solutions=solutions_list, problem=problem)
            return ensemble_result['response']