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#from paddlenlp import Taskflow | |
#用Ie抽取信息 | |
#def emo_analy(): | |
# ie = Taskflow("information_extraction") | |
#定义schema | |
# schema ='[情感分析 正向,负向]' | |
# ie.set_schema(schema) | |
# while True: | |
# text=input() | |
# if text == "exit": | |
# break | |
# result = ie(text) | |
# print(result) | |
from paddlenlp import Taskflow | |
#用Ie抽取信息 | |
ie = Taskflow("information_extraction") | |
#定义schema | |
schema ='[情感分析 正向,负向]' | |
ie.set_schema(schema) | |
def emo_analy(text): | |
result = ie(text) | |
#print(text) | |
# print(result) #后端是否显示结果 代码,如果服务器需要结果 请把此行代码解开 | |
#return result | |
if result and isinstance(result, list) and len(result) > 0: | |
# 获取第一个键值 | |
first_item = result[0] | |
if '[情感分析 正向,负向]' in first_item: | |
# 获取第一个对应值 | |
sentiment_info = first_item['[情感分析 正向,负向]'] | |
if sentiment_info and isinstance(sentiment_info, list) and len(sentiment_info) > 0: | |
sentiment_result = sentiment_info[0] | |
# print(sentiment_result) #最终结果,也就是值. | |
#此时结果有些差错,因为底层原理的原因,输出的结果文本就是一个text如: | |
#{'text': '正向', 'probability': 0.9980062049349883} | |
#目前要修改text为所输入的文本如:我很开心,那么就要重新创建一个字典了: | |
new_sentiment_result = {text:sentiment_result['text'],'probability':sentiment_result['probability']} | |
print(new_sentiment_result) | |
return new_sentiment_result | |
return "无法分析结果" | |