import requests
import pandas as pd
from pathlib import Path
from bs4 import BeautifulSoup

API_URL = "https://energy-iot.chinatowercom.cn/api/device/device/historyPerformance"

headers = {
    "Accept-Encoding": "gzip, deflate, br, zstd", 
    "Connection": "keep-alive", 
    "Content-Length": "211", 
    "Cookie": "HWWAFSESTIME=1732173820506; HWWAFSESID=1739685743c73769ff; dc04ed2361044be8a9355f6efb378cf2=WyIzNTI0NjE3OTgzIl0", 
    "Host": "energy-iot.chinatowercom.cn", 
    "Origin": "https://energy-iot.chinatowercom.cn", 
    "Sec-Fetch-Dest": "empty", 
    "Sec-Fetch-Mode": "cors", 
    "Sec-Fetch-Site": "same-origin", 
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36 Edg/131.0.0.0", 
    "accept": "application/json, text/plain, */*", 
    "accept-language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6", 
    "authorization": "Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOlsiIl0sInVzZXJfbmFtZSI6IndlYl9tYW5hZ2V8d2FuZ2xlaTQiLCJzY29wZSI6WyJhbGwiXSwiZXhwIjoxNzMyMjc4NDA5LCJ1c2VySWQiOjI0Mjg1LCJqdGkiOiJkODE0YTZhYy05YmJmLTQ0ZjQtYWRhYi0wMzAzNjUzNmNhNWIiLCJjbGllbnRfaWQiOiJ3ZWJfbWFuYWdlIn0.VhJaDKwzjekwOCsw_jOF_jvg7sX45okFcxkLyWtbfFVGWVWANhKNhVqj5Dn0Qb3wUXH3e-w74sDN1RI9QADngMOGP_H7aTwI_nukj6VmjpFA7kEtOBwa6ouvPZQMa1qa3UWl21Ac6GoLu14T4TIf4kQAMTdaYAMFrwDAXAkqvIDmKKjZbnDFUjUIcj-J_Y-LfHCEBjtcz7Rp_wMO-PMA5wII6kbcNoSFiYb0djcFQyeBcIUSUTRPixPcTYBkS-IhNrsOePIWlpNYMHbPxZdrZkV4M65BmBn4A9MUjWYHm7iIut8WVMdCXR4Sxp9m0mJHXR_IPWES4O7aBcuMkOmjyw", 
    "content-type": "application/json;charset=UTF-8", 
    "sec-ch-ua": "\"Microsoft Edge\";v=\"131\", \"Chromium\";v=\"131\", \"Not_A Brand\";v=\"24\"", 
    "sec-ch-ua-mobile": "?0", 
    "sec-ch-ua-platform": "Windows", 
}

body = {
  "startTimestamp": 1732032000000,
  "endTimestamp": 1732291199000,
  "deviceCode": "TTE0102DX2406240497",
  "mid": "0305120001",
  "businessType": "7",
  "pageNum": 2,
  "pageSize": 5,
  "total": 0
}

# 1. 读取本地HTML文件
file_path = Path(r'D:\WorkingProject\LightStackAdapter\Log\设备测试数据记录-铁塔主站\南和县牧村\Untitled-1.html')
html_content = file_path.read_text()


# 2. 解析HTML文件
soup = BeautifulSoup(html_content, 'html.parser')

# 3. 找到表格元素
table = soup.find_all('table')  # 假设页面中只有一个表格，如果有多个表格，可能需要进一步筛选

# 4. 提取表格数据
data = []
headers = []

# 提取表头
header_row = table.find('thead').find('tr')
for header in header_row.find_all('th'):
    headers.append(header.text.strip())

# 提取数据行
for row in table.find('tbody').find_all('tr'):
    row_data = []
    for cell in row.find_all(['td', 'th']):
        row_data.append(cell.text.strip())
    data.append(row_data)

# 5. 将数据保存为DataFrame
df = pd.DataFrame(data, columns=headers)

# 6. 将DataFrame保存为CSV文件
output_file = 'extracted_table.csv'
df.to_csv(output_file, index=False, encoding='utf-8')

print(f'表格数据已成功提取并保存到 {output_file}')