import streamlit as st import requests import jieba # 设置API Key API_KEY = 'YOUR_API_KEY' # 定义抽取函数 def extract_relation(sentence): url = 'https://aip.baidubce.com/rpc/2.0/kg/v1/cognitive/entity_annotation' header = { 'content-type': 'application/json', 'charset': 'UTF-8' } # 构建请求体 data = { 'data': sentence, 'mode': 1 } # 发送请求 response = requests.post(url, headers=header, json=data, auth=('apikey', API_KEY)) if response.status_code == 200: result = response.json() if 'items' in result: relations = [] for item in result['items']: if 'kg' in item: relation = item['kg']['relation'] relations.append(relation) return relations return [] # Streamlit界面 def main(): st.title('关系抽取') sentence = st.text_input('请输入要抽取关系的句子：') if sentence: # 分词 words = jieba.cut(sentence) words = [word for word in words if len(word) > 1] # 把关系抽取结果展示在界面上 relations = extract_relation(''.join(words)) if len(relations) > 0: res_str = ', '.join(relations) st.success(f'关系抽取结果：{res_str}') else: st.warning('未能抽取到关系') if __name__ == '__main__': main()请将两个函数合并成一个函数

好的，这里是将两个函数合并的代码：

import streamlit as st
import requests
import jieba

# 设置API Key
API_KEY = 'YOUR_API_KEY'

# Streamlit界面和抽取函数合并
def main():
    st.title('关系抽取')
    sentence = st.text_input('请输入要抽取关系的句子：')
    if sentence:
        # 构建请求体
        url = 'https://aip.baidubce.com/rpc/2.0/kg/v1/cognitive/entity_annotation'
        header = {
            'content-type': 'application/json',
            'charset': 'UTF-8'
        }
        data = {
            'data': sentence,
            'mode': 1
        }
        # 发送请求
        response = requests.post(url, headers=header, json=data, auth=('apikey', API_KEY))
        if response.status_code == 200:
            result = response.json()
            if 'items' in result:
                relations = []
                # 分词
                words = jieba.cut(sentence)
                words = [word for word in words if len(word) > 1]
                # 抽取关系
                for item in result['items']:
                    if 'kg' in item:
                        relation = item['kg']['relation']
                        relations.append(relation)
                # 把关系抽取结果展示在界面上
                if len(relations) > 0:
                    res_str = ', '.join(relations)
                    st.success(f'关系抽取结果：{res_str}')
                else:
                    st.warning('未能抽取到关系')
            
if __name__ == '__main__':
    main()

在这个代码中，`main()` 函数里面包含了原来的 Streamlit 界面和抽取函数的代码。首先用户输入要抽取关系的句子，然后构建请求体，并使用 requests 发送请求。如果请求成功，就从返回结果中抽取关系，并将结果展示在 Streamlit 界面上。