examples/aflow/humaneval/optimized/round_11/graph.py

import evoagentx.workflow.operators as operator
import examples.aflow.humaneval.optimized.round_11.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)  # Added ScEnsemble operator

    async def __call__(self, problem: str, entry_point: str):
        solutions = []  # Initialize a list to collect multiple solutions
        for _ in range(3):  # Generate three variations of the solution
            solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, 
                                                        instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
            solutions.append(solution['response'])  # Collect each solution

        selected_solution = await self.sc_ensemble(solutions=solutions, problem=problem)  # Select best solution
        validation = await self.test(problem=problem, solution=selected_solution['response'], 
                                      entry_point=entry_point, benchmark=self.benchmark)

        if validation['result']:
            return selected_solution['response']
        else:
            modified_solution = await self.custom(input=problem + f" with issues: {validation['solution']}", 
                                                   instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
            return modified_solution['response']  # Return the modified solution if tests fail