examples/aflow/humaneval/optimized/round_2/graph.py

import evoagentx.workflow.operators as operator
import examples.aflow.humaneval.optimized.round_2.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)  # New operator to test solutions
        self.sc_ensemble = operator.ScEnsemble(self.llm)  # New operator for ensemble selection

    async def __call__(self, problem: str, entry_point: str):
        """
        Implementation of the workflow
        Generate multiple solutions first and then test the best option.
        """
        solutions = []
        for _ in range(3):  # Collecting three solutions
            solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
            solutions.append(solution['response'])

        # Use self-consistency to select the best solution
        best_solution = await self.sc_ensemble(solutions=solutions, problem=problem)
        
        # Now test the selected best solution
        test_result = await self.test(problem=problem, solution=best_solution['response'], entry_point=entry_point, benchmark=self.benchmark)
        
        return test_result['solution']  # Return the tested solution