import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks


# 替换换行字符
def replace_newlines(text, new_line=''):
    # 替换所有类型的换行符
    return text.replace('\r\n', new_line).replace('\r', new_line).replace('\n', new_line)


# 使用bert计算文本相识度
class Bert_nlp(object):

    def __init__(self, nlp_type):
        self.nlp_type = nlp_type
        if nlp_type == "structbert":
            model_id = "damo/nlp_structbert_sentence-similarity_chinese-large"
            self.semantic_cls = pipeline(Tasks.sentence_similarity, model_id)
        elif nlp_type == "corom":
            # , sequence_length=1024   /Users/kebobo/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_chinese-tiny
            model_id = "damo/nlp_corom_sentence-embedding_chinese-tiny"
            self.semantic_cls = pipeline(Tasks.sentence_embedding, model=model_id)

    def main(self, content1, contents):
        # if content1 is None or content1 == "None":
        #     return 0, "", "", -1
        score = 0.0
        if len(contents) == 0:
            return score, content1, "", -1
        if self.nlp_type == "structbert":
            result = self.semantic_cls(input=(content1, contents[0]))
            print(result)
            labels = result["labels"]
            acq = labels.index("1")
            score = result["scores"][acq]
        elif self.nlp_type == "corom":
            inputs = {
                    "source_sentence": [
                        replace_newlines(content1)
                    ],
                    "sentences_to_compare": contents
                }
            result = self.semantic_cls(input=inputs)
            print(result)

            arr = result["scores"]
            score = max(arr)
            idx = arr.index(score)

        return score, content1, contents[idx], idx




if __name__ == "__main__":
    content1 = """主要功能为快速进行学生课堂评价及小组评价"""
    content2 = ["""用户通过建设单位账户进入建设单位门户，建设单位门户主要展示本单位项目信息、通知公告与政策文件栏、待办事项栏、本单位进行中项目栏模块。""",
                """主要功能为快速进行学生课堂评价及小组评价"""
    ]
    nlp = Bert_nlp("corom")
    print(nlp.main(content1, content2))