import evoagentx.workflow.operators as operator
import examples.aflow.scicode_full.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 = operator.Test(self.llm)  # Initialized the test operator
        self.sc_ensemble_operator = operator.ScEnsemble(self.llm)  # Initialized ScEnsemble operator

    async def __call__(self, problem: str, entry_point: str):
        """
        Implementation of the workflow
        Custom operator to generate multiple solutions for the problem. To get standard code, use custom_code_generate operator.
        """
        solutions = [await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) for _ in range(3)] 
        solution_responses = [sol['response'] for sol in solutions]
        ensemble_result = await self.sc_ensemble_operator.sc_ensemble(solution_responses, problem)  # Get the best solution via ensemble
        test_result = await self.test_operator.test(problem=problem, solution=ensemble_result['response'], entry_point=entry_point, benchmark=self.benchmark)  # Testing the selected solution
        if not test_result['result']:  # If the test fails, log the current solution and return failure indication
            return {"success": False, "current_solution": test_result['solution']}
        return {"success": True, "final_solution": ensemble_result['response']}  # Return the final verified solution