from utils.comm import *
from snownlp import SnowNLP
import jieba
import jieba.posseg as pseg
import jieba.analyse


class News:
	def __init__(self, trade_date=datetime.now()):
		self.trade_date = trade_date.strftime('%Y%m%d')

	@staticmethod
	def preprocess(text):
		seg_list = jieba.lcut(text)  # 文本预处理及分词
		return seg_list

	@staticmethod
	def sentiment_analysis(text):  # 情感分析
		s = SnowNLP(text)
		sentiment = s.sentiments  # 情感得分，范围0-1，越接近1表示正面情感，越接近0表示负面情感
		return sentiment

	@staticmethod
	def extract_keywords(text, top_k=5):  # 关键词提取
		keywords = jieba.analyse.extract_tags(text, topK=top_k, withWeight=False, allowPOS=())  # 提取关键词，默认返回前5个
		return keywords

	@staticmethod
	def named_entity_recognition(text):  # 命名实体识别
		words = pseg.cut(text)
		entities = []
		for word, flag in words:
			# 识别人名、地名、机构名、其它专名
			if flag in ['nr', 'ns', 'nt', 'nz']:
				entities.append((word, flag))
		return entities

	def result(self, news):
		print("原文：", news)
		preprocessed_news = self.preprocess(news)
		sentiment_score = self.sentiment_analysis(news)
		keywords = self.extract_keywords(news, top_k=3)
		entities = self.named_entity_recognition(news)

		print("分词结果：", preprocessed_news)
		print("情感分析得分：", round(sentiment_score, 2))
		print("关键词提取：", keywords)
		print("命名实体识别：", entities)

	def news_cctv(self):
		for _ in range(5):
			try:
				# 中央新闻
				df = ak.news_cctv(date=self.trade_date)
				# 情感分析
				# df['title'].apply(lambda x: self.result(x))
				# print(df['情感'])
				return df
			except Exception as e:
				print(f"{self.trade_date} 日的新闻咨询拉取发生错误！{e.__traceback__}")
				time.sleep(1)
				continue
		else:
			print("中央新闻 5次出现错误，请关注！！！")
			return None

	def news_cls(self):
		for _ in range(5):
			try:
				# 财联社-电报
				df = ak.stock_telegraph_cls()
				# 情感分析
				# df['title'].apply(lambda x: self.result(x))
				# print(df['情感'])
				return df
			except Exception as e:
				print(f"{self.trade_date} 财联社-电报 咨询拉取发生错误！{e.__traceback__}")
				continue
		else:
			print("财联社-电报 5次出现错误，请关注！！！")
			return None

	@staticmethod
	def news_stock_by_code(symbol, date_time=None):
		# 个股新闻
		stock_news_em_df = ak.stock_news_em(symbol=symbol)
		# 将发布时间列转换为日期时间类型
		stock_news_em_df['发布时间'] = pd.to_datetime(stock_news_em_df['发布时间'])
		# print(stock_news_em_df)
		if date_time is not None:
			if len(date_time.split("-")) == 1:
				str_date = datetime.strptime(str(date_time), '%Y%m%d').strftime('%Y-%m-%d')
			else:
				str_date = datetime.strptime(str(date_time), '%Y-%m-%d').strftime('%Y-%m-%d')
		else:
			# 获取今天的日期
			str_date = datetime.now().date()
		# 使用日期过滤器筛选出今天的新闻
		today_news = stock_news_em_df[stock_news_em_df['发布时间'].dt.date == str_date]
		# 打印今天的新闻
		print(today_news['新闻标题'])
		print(today_news["新闻内容"])
		# today_news['新闻标题'].apply(lambda x: result(x))
		return today_news

	def html_page_data(self):
		cctv_df = self.news_cctv()
		cls_df = self.news_cls()
		cctv_content, cls_content = '', ''
		for index, row in cctv_df.iterrows():
			cctv_content += f'<tr><th scope="row">{row["title"]}</th>' \
							f'<td>开发中...</td>'
			cctv_content += '</tr>'
		for index, row in cls_df.iterrows():
			if len(row["标题"]) > 1:
				cls_content += f'<tr><th scope="row">{row["标题"]}</th>' \
							   f'<td>开发中...</td>'
				cls_content += '</tr>'
		return cctv_content, cls_content


if __name__ == '__main__':
	date_obj = datetime.strptime('20231019', "%Y%m%d")
	print(News().news_cctv())