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import evoagentx.workflow.operators as operator
import examples.aflow.mbpp_new.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)
async def __call__(self, problem: str, entry_point: str):
solution_list = []
error_logs = [] # List to keep track of error messages
for attempt in range(3): # Retry generating multiple solutions on failure
solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
if 'error' in solution: # Log any errors encountered
error_logs.append(solution['error'])
continue
solution_list.append(solution['response'])
if not solution_list:
return "All attempts failed. Errors: " + ', '.join(error_logs)
final_solution = await self.sc_ensemble(solutions=solution_list, problem=problem)
test_result = await self.test(problem=problem, solution=final_solution['response'], entry_point=entry_point, benchmark=self.benchmark)
if not test_result['result']:
modifications = await self.custom(input=final_solution['response'], instruction=prompt_custom.MODIFY_CODE_PROMPT)
return modifications['response']
return final_solution['response']
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