examples/aflow/humaneval/optimized/round_7/graph.py

import evoagentx.workflow.operators as operator
import examples.aflow.humaneval.optimized.round_7.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)
        self.validate_solution = operator.Custom(self.llm)  # New operator to validate the solution

    async def __call__(self, problem: str, entry_point: str):
        """
        Workflow implementation to generate, validate, and test solutions.
        """
        solutions = []
        for _ in range(3):  
            solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
            solutions.append(solution['response'])

        best_solution = await self.sc_ensemble(solutions=solutions, problem=problem)

        # Validate the best solution before testing
        validation_result = await self.validate_solution(input=best_solution['response'], instruction="Validate this Python code.")
        if validation_result['response'] == "Valid":
            test_result = await self.test(problem=problem, solution=best_solution['response'], entry_point=entry_point, benchmark=self.benchmark)
            return test_result['solution']
        else:
            return "Best solution failed validation."  # Handle case where validation fails