import json
import datetime
import pandas as pd
from difflib import SequenceMatcher

def string_similarity(str1, str2):
    # 规则1: 一个为空另一个不为空，相似度为0
    if (str1 is None or str1 == "") and (str2 is not None and str2 != ""):
        return 0.0
    if (str2 is None or str2 == "") and (str1 is not None and str1 != ""):
        return 0.0

    # 规则2: 二者完全相同（包括全为空），相似度为1
    if (str1 or "") == (str2 or ""):
        return 1.0


    # 规则3: 忽略大小写进行比较
    s1_lower = str1.lower()
    s2_lower = str2.lower()

    # 如果忽略大小写后相同，直接返回1
    if s1_lower == s2_lower:
        return 1.0

    # 使用SequenceMatcher计算相似度
    matcher = SequenceMatcher(None, s1_lower, s2_lower)
    similarity = matcher.ratio()

    return similarity

def main(file_llm = '', file_bench = '', key_list = []):
    all_data = {}
    with open(file_llm, 'r', encoding='utf-8') as f:
        for line in f:
            j = json.loads(line).get('llm_response_dict')
            # print(j)
            all_data[j['sha256']] = {}
            all_data[j['sha256']]['llm_response_dict'] = j

    with open(file_bench, 'r', encoding='utf-8') as f:
        for line in f:
            j = json.loads(line)
            if j['sha256'] not in all_data:
                all_data[j['sha256']] = {}
            all_data[j['sha256']]['benchmark_dict'] = j

    sha256_to_remove = []
    for sha256, value in all_data.items():
        # 检查是否同时包含这两个键
        if 'llm_response_dict' not in value or 'benchmark_dict' not in value:
            sha256_to_remove.append(sha256)
    for sha256 in sha256_to_remove:
        all_data.pop(sha256)

    for sha256, value in all_data.items():
        all_data[sha256]['similarity'] = {}
        for key in key_list:
            # print(key)
            all_data[sha256]['similarity'][key] = string_similarity(all_data[sha256]['llm_response_dict'].get(key), all_data[sha256]['benchmark_dict'][key])

    # print(all_data)
    key_accuracy_tmp = {key: 0 for key in key_list}
    for sha256, value in all_data.items():
        for key in key_list:
            key_accuracy_tmp[key] += value['similarity'][key]

    # print(key_accuracy_tmp)
    key_accuracy = {k: v / len(all_data) for k,v in key_accuracy_tmp.items()}
    # print(key_accuracy)
    accuracy = sum(list(key_accuracy.values())) / len(list(key_accuracy.values()))
    return accuracy, key_accuracy, all_data

def write_similarity_data_to_excel(key_list, data_dict, output_file="similarity_analysis.xlsx"):
    """
    将相似度分析数据写入Excel文件

    Args:
        data_dict: 包含相似度分析数据的字典
        output_file: 输出Excel文件名
    """

    # 准备数据列表
    rows = []

    for sha256, data in data_dict.items():
        row = {
            'sha256': sha256
        }

        for field in key_list:
            # llm_response_dict 字段
            row[f'llm_{field}'] = data['llm_response_dict'].get(field)
            # benchmark_dict 字段
            row[f'benchmark_{field}'] = data['benchmark_dict'].get(field)
            # similarity 字段
            row[f'similarity_{field}'] = data['similarity'].get(field)
        rows.append(row)

    # 创建DataFrame
    df = pd.DataFrame(rows)

    # 定义列的顺序（可选，让Excel更易读）
    column_order = ['sha256']
    for field in key_list:
        column_order.extend([f'llm_{field}', f'benchmark_{field}', f'similarity_{field}'])

    # 重新排列列顺序
    df = df[column_order]

    # 写入Excel文件
    with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
        df.to_excel(writer, sheet_name='相似度分析', index=False)

        # # 获取工作表并调整列宽
        # worksheet = writer.sheets['相似度分析']
        # worksheet.column_dimensions['A'].width = 70  # sha256列


    print(f"数据已成功写入 {output_file}")
    print(f"总共处理了 {len(rows)} 条记录")

    return df

if __name__ == '__main__':
    file_llm = 'data/llm-label_textbook.jsonl'
    file_bench = 'data/benchmark_textbook.jsonl'
    # key_list = ['doi', 'title', 'author', 'keyword', 'abstract', 'pub_time']
    key_list = ['isbn', 'title', 'author', 'abstract', 'category', 'pub_time', 'publisher']

    accuracy, key_accuracy, detail_data = main(file_llm, file_bench, key_list)
    # print(detail_data)
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    output_filename = f"similarity_analysis_{timestamp}.xlsx"
    write_similarity_data_to_excel(key_list, detail_data, output_filename)

    print(key_accuracy)
    print(accuracy)