import pandas as pd
import json
import re
import random
import os

def replace_numbers(text):
    pattern = r'(当前(?:的)?值(?:是|为)\s*)(\d+(\.\d+)?)'  # 匹配"当前的值"后面的数字

    def repl(match):
        num_str = match.group(2)
        num = float(num_str)
        
        if '.' in num_str:
            random_offset = round(random.uniform(-0.001, 0.05), 2)
        elif len(num_str) == 1:
            random_offset = round(random.uniform(-0.05, 0.05), 2)
        elif len(num_str) == 2:
            random_offset = round(random.uniform(-1.00, 1.00), 2)
        else:
            random_offset = round(random.uniform(-2, 2), 2)
        
        new_num = num + random_offset
        formatted_num = '{:.2f}'.format(new_num)
        
        # 将随机化后的数字应用到匹配结果中
        return match.group(1) + formatted_num

    new_text = re.sub(pattern, repl, text)
    return new_text

def excel_to_json(excel_file_path):
    excel_data = pd.ExcelFile(excel_file_path)
    sheet_names = excel_data.sheet_names
    
    json_data = {}
    all_data = []
    
    for sheet_name in sheet_names:
        sheet_data = []
        df_sheet = pd.read_excel(excel_data, sheet_name=sheet_name)
        
        
        for _ in range(12):
            for _, row in df_sheet.iterrows():
                conversations = []
                system = ''
                check_value = ''
                for col_name, cell_value in row.items():
                    if pd.isna(cell_value):
                        continue
                    
                    if col_name == 'check':
                        check_value = eval(cell_value)
                    cell_value = re.sub(r'{{(?:export)}}', "", str(cell_value).strip()).strip()
                    if col_name == 'system':
                        if pd.notna(cell_value):
                            system = replace_numbers(cell_value).format_map(check_value)
                        else:
                            system = ''
                        
                    elif col_name.startswith('问') and pd.notna(cell_value):
                        conversations.append({"from": "human", "value": cell_value})
                    elif col_name.startswith('答') and pd.notna(cell_value):
                        conversations.append({"from": "gpt", "value": cell_value})
                
                if conversations:
                    sheet_data.append({"system": system,"conversations": conversations})
                    all_data.append({"system": system,"conversations": conversations})
        
        json_data[sheet_name] = sheet_data
        print(len(all_data))
    return json_data ,all_data

def save_json_per_sheet(json_data, output_folder):
    for sheet_name, sheet_data in json_data.items():
        output_file = os.path.join(output_folder, f'{sheet_name}.json')
        with open(output_file, 'w', encoding='utf-8') as f_out:
            json.dump(sheet_data, f_out, ensure_ascii=False, indent=4)


def save_json_all(all_data, output_file):
    with open(output_file, 'w', encoding='utf-8') as f_out:
        json.dump(all_data, f_out, ensure_ascii=False, indent=4)
    
# Excel 文件路径
excel_file_path = './excel/决策标注模版4.0_demo.xlsx'
# 输出 JSON 文件夹路径
output_folder = './excel/demo'


# 确保输出文件夹存在，如果不存在则创建
os.makedirs(output_folder, exist_ok=True)

# 将 Excel 转换为 JSON 数据
json_data,all_data = excel_to_json(excel_file_path)

# 将每个 sheet 的 JSON 数据保存为单独的 JSON 文件
save_json_per_sheet(json_data, output_folder)


# 输出 JSON 文件
output_file = './public/jueceDemo.json'
save_json_all(all_data, output_file)