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

import evoagentx.workflow.operators as operator
import examples.aflow.scicode_full.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)  # Added test operator for solution verification

    async def __call__(self, problem: str, entry_point: str):
        """
        Implementation of the workflow
        Custom operator to generate anything you want.
        But when you want to get standard code, you should use custom_code_generate operator.
        """
        solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) 
        test_result = await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark)  # Testing the generated solution
        if not test_result['result']:  # If the solution does not pass the tests, we can attempt to regenerate it
            # Incorporate the feedback from the test result to improve the regeneration of the code.
            feedback = test_result['solution']
            solution = await self.custom(input=problem + feedback, instruction=prompt_custom.REGENERATE_CODE_WITH_FEEDBACK_PROMPT)  # Regenerate the code with feedback
        return solution['response']