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| import gradio as gr | |
| from scholarly import scholarly, ProxyGenerator | |
| import openai | |
| def process(key, choice, artitle, trans): | |
| openai.api_key = str(key) | |
| results = [] | |
| if choice=='单个生成': | |
| pg = ProxyGenerator() | |
| pg.FreeProxies() | |
| scholarly.use_proxy(pg) | |
| search_query = scholarly.search_pubs(str(artitle)) | |
| pub = next(search_query) | |
| bib = scholarly.bibtex(pub) | |
| if trans=='bib': | |
| results.append(bib) | |
| else: | |
| prompt = "请把以下bib格式转为"+str(trans)+"格式："+str(bib) | |
| completion = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| ) | |
| print(completion.choices[0].message['content']) | |
| print(completion.choices[0].text) | |
| results.append(completion.choices[0].message) | |
| if choice=='批量生成': | |
| m_artitle = artitle.split('\n') | |
| for i in range(len(m_artitle)): | |
| if trans=='bib': | |
| results.append(bib) | |
| else: | |
| pg = ProxyGenerator() | |
| pg.FreeProxies() | |
| scholarly.use_proxy(pg) | |
| search_query = scholarly.search_pubs(str(m_artitle[i])) | |
| pub = next(search_query) | |
| bib = scholarly.bibtex(pub) | |
| prompt = "请把以下bib格式转为"+str(trans)+"格式："+str(bib) | |
| completion = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| ) | |
| results.append(completion.choices[0].message+'\n') | |
| return results | |
| # 标题 | |
| title = "ChatCitation" | |
| # 描述 | |
| description = '''<div align='left'> | |
| 论文引用 | |
| </div> | |
| ''' | |
| input_c = [ | |
| gr.inputs.Textbox(label="输入OpenAI的API-key", | |
| default="", | |
| type='password'), | |
| gr.inputs.Radio(choices=["单个生成", "批量生成"], | |
| default="单个生成", | |
| label="题目生成(默认单个生成)"), | |
| gr.inputs.Textbox( | |
| label="输入论文标题(如果为批量则每行一个标题)", | |
| default="Transfer learning based plant diseases detection using ResNet50"), | |
| gr.inputs.Dropdown( | |
| choices=["AMA", "MLA", "APA", "GB/T 7714", "bib"], | |
| label="转化的格式(默认bib)", | |
| default="APA"), | |
| ] | |
| demo = gr.Interface(fn=process, | |
| inputs=input_c, | |
| outputs="text", | |
| title=title, | |
| description=description) | |
| demo.launch(share=False) |