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selfevolveagent
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# import os 
# from dotenv import load_dotenv
# from evoagentx.optimizers import AFlowOptimizer
# from evoagentx.models import LiteLLMConfig, LiteLLM, OpenAILLMConfig, OpenAILLM 
# from evoagentx.benchmark import AFlowHumanEval

# load_dotenv()
# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
# ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")

# EXPERIMENTAL_CONFIG = {
#     "humaneval": {
#         "question_type": "code", 
#         "operators": ["Custom", "CustomCodeGenerate", "Test", "ScEnsemble"] 
#     }, 
#     "mbpp": {
#         "question_type": "code", 
#         "operators": ["Custom", "CustomCodeGenerate", "Test", "ScEnsemble"] 
#     },
#     "hotpotqa": {
#         "question_type": "qa", 
#         "operators": ["Custom", "AnswerGenerate", "QAScEnsemble"]
#     },
#     "gsm8k": {
#         "question_type": "math", 
#         "operators": ["Custom", "ScEnsemble", "Programmer"]
#     },
#     "math": {
#         "question_type": "math", 
#         "operators": ["Custom", "ScEnsemble", "Programmer"]
#     }
# }

# def main():

#     claude_config = LiteLLMConfig(model="anthropic/claude-3-5-sonnet-20240620", anthropic_key=ANTHROPIC_API_KEY)
#     optimizer_llm = LiteLLM(config=claude_config)
#     openai_config = OpenAILLMConfig(model="gpt-4o-mini", openai_key=OPENAI_API_KEY)
#     executor_llm = OpenAILLM(config=openai_config)

#     # load benchmark
#     humaneval = AFlowHumanEval()

#     # create optimizer
#     optimizer = AFlowOptimizer(
#         graph_path = "examples/aflow/code_generation",
#         optimized_path = "examples/aflow/humaneval/optimized",
#         optimizer_llm=optimizer_llm,
#         executor_llm=executor_llm,
#         validation_rounds=5,
#         eval_rounds=3,
#         max_rounds=20,
#         **EXPERIMENTAL_CONFIG["humaneval"]
#     )

#     # run optimization
#     optimizer.optimize(humaneval)

#     # run test 
#     optimizer.test(humaneval) # use `test_rounds: List[int]` to specify the rounds to test 


# if __name__ == "__main__":
#     main() 

import os 
from dotenv import load_dotenv
from evoagentx.optimizers import AFlowOptimizer
from evoagentx.models import LiteLLMConfig, LiteLLM, OpenAILLMConfig, OpenAILLM 
from evoagentx.benchmark import AFlowHumanEval

load_dotenv()

api_key =  "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"
# Function to encode the image
import os
os.environ["OPENAI_API_KEY"] = api_key
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")


EXPERIMENTAL_CONFIG = {
    "humaneval": {
        "question_type": "code", 
        "operators": ["Custom", "CustomCodeGenerate", "Test", "ScEnsemble"] 
    }, 
    "mbpp": {
        "question_type": "code", 
        "operators": ["Custom", "CustomCodeGenerate", "Test", "ScEnsemble"] 
    },
    "hotpotqa": {
        "question_type": "qa", 
        "operators": ["Custom", "AnswerGenerate", "QAScEnsemble"]
    },
    "gsm8k": {
        "question_type": "math", 
        "operators": ["Custom", "ScEnsemble", "Programmer"]
    },
    "math": {
        "question_type": "math", 
        "operators": ["Custom", "ScEnsemble", "Programmer"]
    }
}

def main():

    openai_config = OpenAILLMConfig(
        model="gpt-4o-mini", 
        openai_key=OPENAI_API_KEY
    )

    claude_config = LiteLLMConfig(
        model="gpt-4o-mini", 
        openai_key=OPENAI_API_KEY
    )
    executor_llm = OpenAILLM(config=openai_config)
    optimizer_llm = LiteLLM(config=claude_config)

    # load benchmark
    humaneval = AFlowHumanEval()

    # create optimizer
    optimizer = AFlowOptimizer(
        graph_path = "examples/aflow/code_generation",
        optimized_path = "examples/aflow/humaneval/optimized",
        optimizer_llm=optimizer_llm,
        executor_llm=executor_llm,
        validation_rounds=5,
        eval_rounds=3,
        max_rounds=20,
        **EXPERIMENTAL_CONFIG["humaneval"]
    )

    # run optimization
    optimizer.optimize(humaneval)

    # run test 
    optimizer.test(humaneval) # use `test_rounds: List[int]` to specify the rounds to test 


if __name__ == "__main__":
    main()