About

for i in range(len(title)):
    db = pymysql.connect(host='localhost', port=3306, user='root', password='', database='pachong', charset='utf8')
    cur = db.cursor()
    sql = 'INSERT INTO 要闻(title, href, date,classname,score) VALUES (%s,%s,%s,%s,%s)'
    cur.execute(sql, (title[i], href[i], date[i],industry_pro[i],score_pro[i]))
    db.commit()
    cur.close()
    db.close()

import requests
import re
import pymysql
import time
import pandas as pd
from selenium import webdriver

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36'}

# Chrome headless mode
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument('--headless') 
browser = webdriver.Chrome(options=chrome_options)

def getData(url):
    # Declare global variables
    global title
    global href
    global date
    
    # Open the webpage and get the source code, Selenium seems to provide more accurate and easier to handle source code
    browser.get(url)
    data = browser.page_source
    
    # Find title, URL, and date
    ptitle = re.findall('<h4>.*?<a href=.*?>(.*?)</a>', data, re.S)
    phref = re.findall('<h4>.*?<a href="(.*?)">', data, re.S)
    pdate = re.findall('<span class="date">(.*?)</span>', data, re.S)
    
    # Clean up the dates
    for i in range(len(pdate)):
        pdate[i] = pdate[i].strip()
    
    # Store the titles, URLs, and dates as global variables
    title = title + ptitle
    href = href + phref
    date = date + pdate
    return

# Clear the data of the first page
title = []
href = []
date = []

# Collect data from the first five pages
for i in range(5):
    url = 'http://sousuo.gov.cn/column/31421/' + str(i) + '.htm'
    getData(url)

# Create a DataFrame
pd2 = pd.DataFrame()
pd2['Title'] = title
pd2['URL'] = href
pd2['Date'] = date

score = []
Energy_key= ['能源','冶炼','石油','天然气','开采','风能','煤炭','减排','低碳','化石能源','减排','电价','核燃料','燃料','装机','碳达峰','碳中和','有色','绿色'，'降碳'，'二氧化碳'，'太阳能发电'，'节能'，'储能'，'再生资源'，'氢能'，'碳排放']
Macroecon_key=['财政收入','经济增长','宏观调控','物价','汇率','总需求','通货膨胀','总供给','货币政策','税收','央行','供应链','产业链','宏观','RCEP','贸易'，'生产经营'，'金融政策'，'经济圈'，'商业特许经营'，'企业法人'，'市场监督'，'市场主体'，'个体工商户'，'市场监管总局'，'财政部'，'经济体系'，'城乡融合发展'，'国民收入'，'经济周期'，'经济政策'，'通货紧缩']
MFG_key=['制造业','食品加工','酒','饮料','茶','烟草','纺织','服装','服饰','皮革','毛皮','鞋','木材','家具','造纸','汽车','建材'，'钢铁'，'石化化工'、'煤炭开采'、'冶金'、'化纤'，'建设工程'，'农产品'，'养殖'，'电商'，'通用设备'，'专用设备'，'交通运输设备']
Edu_key=['教育','招生','教育机构','培训','进修','研修','辅导','减负','网课','职业院校'，'职业教育'，'办学'，'招生'，'课程'，'学分'，'产教融合'，'教学'，'教师队伍'，'育人机制'，'留学'，'奖学金'，'学费'，'职称评审'，'技能人才'，'学术发展'，'作业']
Medical_key=['医疗保险','救助制度','疫情','防疫','疫苗','医药','门诊','医保','健康'，'医疗'，'疾病'，'大病保险'，'药品'，'医疗救助'，'就医负担'，'看病'，'创新药'，'病种'，'零售药店'，'医用耗材'，'康复'，'护理'，'电子处方'，'银保监'，'药监']
all_keys= dict(能源=Energy_key,宏观经济=Macroecon_key,制造业=MFG_key,教育=Edu_key,医疗=Medical_key)

industry_name = ['Energy', 'Macroeconomics', 'Manufacturing', 'Education', 'Healthcare']
industry_pro = []
score_pro = []

for item in range(len(title)):   
    score = []
    industry = []   
    try:     # Fetch the specific content of each news article
        article = requests.get(href[item], headers=headers, timeout=10).text
    except:
        article = 'Failed to fetch'
        print('Failed to fetch')
    try:
        article = article.encode('ISO-8859-1').decode('utf-8')
    except:
        try:
            article = article.encode('ISO-8859-1').decode('gbk')
        except:
            article = article
    p_article = '<p>(.*?)</p>'
    article_main = re.findall(p_article, article)  # Get the main text information inside <p> tags
    article = ''.join(article_main)  # Convert the list to a string
    
    for i in all_keys:   
        num = 0
        for e in range(len(c[i])):   
            a = c[i][e]
            if (a in article) or (a in title[item]):  # If there's a match, add points
                num += 5
        score.append(num)   # Sum up each score as the total score for that industry
    score_pro.append(max(score))
    
    if max(score) == 0:      # If the news doesn't have any keywords from any industry, categorize as 'Others'
        industry_pro.append('Others')
    else:
        for o in range(len(score)):    
            if score[o] == max(score):
                industry.append(d[o])
        z = '、'.join(industry)   # If multiple industries have the highest score, categorize as 'Energy, Manufacturing', etc.
        industry_pro.append(z)

# 1. Import necessary libraries and functions
from pptx import Presentation
import os
import docx
from docx.shared import RGBColor
from docx.shared import Inches
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.oxml.ns import qn
import time

# 2. Create a blank Word document and set the font
wordfile = docx.Document()
# Can be replaced with any font in Word
wordfile.styles['Normal'].font.name = u'Dengxian' 
wordfile.styles['Normal']._element.rPr.rFonts.set(qn('w:eastAsia'), u'Dengxian')

# 3. Specify the path of the ppt file
filepath = r'C:\Users\lenovo\Desktop\1.pptx'
pptx = Presentation(filepath)

# 4. Iterate through all slides in the ppt file
for slide in pptx.slides:
    # Iterate through all shapes in the slide
    for obj in slide.shapes:
        # Check if the shape contains a text box, if so, execute the code in order
        if obj.has_text_frame:
            # Get the text box
            text_frame = obj.text_frame
            # Iterate through all paragraphs in the text box
            for paragraph in text_frame.paragraphs:
                # Write the paragraph to the Word document
                wordfile.add_paragraph(paragraph.text)
                # Save the Word document
                wordfile.save(r'C:\Users\lenovo\Desktop\2.docx')
        else:
            # Use try/except because non-image elements do not have an Image method and will throw an exception
            try:
                # Get binary character stream
                imdata = obj.image.blob

                # Determine the file suffix type, the content-type of the image should be image/jpg
                imagetype = obj.image.content_type

                # In string, find() returns the position (index) of the letter in the string, 
                # if not found, it returns a special marker npos
                typekey = imagetype.find('/') + 1
                imtype = imagetype[typekey:]

                # Create an image folder to save the extracted images
                path = "./image/"
                if not os.path.exists(path):
                    os.makedirs(path)

                # Image generation
                image_file = path + obj.name + "." + imtype

                # Insert the generated image into the Word document
                wordfile.add_picture(image_file, width=Inches(4), height=Inches(3))
                wordfile.save(r'C:\Users\lenovo\Desktop\2.docx')
            except:
                pass