examples/aflow/humaneval/optimized/round_6/graph.py

import evoagentx.workflow.operators as operator
import examples.aflow.humaneval.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.Test(self.llm)  
        self.ensemble = operator.ScEnsemble(self.llm)  # New operator added for selecting the best solution

    async def __call__(self, problem: str, entry_point: str):
        solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
        
        # Testing the solution before returning
        test_result = await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark)
        
        if test_result['result']:
            return test_result['solution']  
        else:
            # Implementing ensemble for generating alternative solutions and improving selection
            alternative_solutions = await self.custom(problem=problem, instruction=prompt_custom.GENERATE_ALTERNATIVE_SOLUTIONS_PROMPT)
            final_solution = await self.ensemble(solutions=[test_result['solution']] + alternative_solutions['response'], problem=problem)
            return final_solution['response']  # Return the best aggregated solution based on ensemble