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import evoagentx.workflow.operators as operator
import examples.aflow.mbppplus_full.optimized.round_3.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.sc_ensemble = operator.ScEnsemble(self.llm)  # Added ScEnsemble operator
        self.test = operator.Test(self.llm)  # Added Test operator

    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)
        
        # Use ScEnsemble to refine the generated solution
        ensemble_result = await self.sc_ensemble(solutions=[solution['response']], problem=problem)
        
        # Use Test operator to validate the refined solution
        test_result = await self.test(problem=problem, solution=ensemble_result['response'], entry_point=entry_point, benchmark=self.benchmark)
        
        if test_result['result']:  # If the solution passes the tests
            return ensemble_result['response']  # Return the valid solution
        else:
            return test_result['solution']  # Return the modified solution if it fails the tests

	import evoagentx.workflow.operators as operator
	import examples.aflow.mbppplus_full.optimized.round_3.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.sc_ensemble = operator.ScEnsemble(self.llm) # Added ScEnsemble operator
	self.test = operator.Test(self.llm) # Added Test operator

	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)

	# Use ScEnsemble to refine the generated solution
	ensemble_result = await self.sc_ensemble(solutions=[solution['response']], problem=problem)

	# Use Test operator to validate the refined solution
	test_result = await self.test(problem=problem, solution=ensemble_result['response'], entry_point=entry_point, benchmark=self.benchmark)

	if test_result['result']: # If the solution passes the tests
	return ensemble_result['response'] # Return the valid solution
	else:
	return test_result['solution'] # Return the modified solution if it fails the tests