import argparse
from scoring.assessment_engine import AssessmentEngine


def main(args):
    assessment_engine = AssessmentEngine("logs/lxh", args.api_key, args.llm_engine)
    assessment_engine.eval_result_diff("rslora.csv")
    # assessment_engine.eval_result("output-npt-sft-1-0.95-0.5-1.2.json", "json")

    # synthesis_rouge_score = 0.30358589506467687
    # print("微调模型ROUGE分数：", synthesis_rouge_score)
    # original_rouge_score = 0.26004000118452175
    # print("原模型ROUGE分数：", original_rouge_score)
    # synthesis_acc_score = 2.768
    # print("微调模型准确性分数：", synthesis_acc_score)
    # original_acc_score = 2.724
    # print("原模型准确性分数：", original_acc_score)
    # synthesis_fluency_score = 2.098
    # print("微调模型流畅度分数：", synthesis_fluency_score)
    # original_fluency_score = 2.236
    # print("原模型流畅度分数：", original_fluency_score)
    # synthesis_diff_score = 2.278
    # print("微调模型优于原模型分数：", synthesis_diff_score)
    #
    # synthesis_score = (synthesis_rouge_score * 100 + synthesis_acc_score * 100/4 + synthesis_fluency_score * 100/3
    #                    + synthesis_diff_score * 100/3 ) / 4
    # original_synthesis_score = (original_rouge_score * 100 + original_acc_score * 100/4 +
    #                             original_fluency_score * 100/3 + 66 ) / 4
    #
    # print("综合评分：", synthesis_score)
    # print("原模型综合评分：", original_synthesis_score)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--api_key", type=str, default="xxx")
    parser.add_argument("--llm_engine", type=str, default="gpt")
    user_args = parser.parse_args()
    main(user_args)