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app.py

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+import gradio as gr
+from scholarly import scholarly
+import openai
+
+def process(key, choice, artitle, trans):
+    openai.api_key = str(key)
+    
+    results = []
+    if choice=='单个生成':
+        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}
+            ]
+            )
+            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:
+                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 = [
+        gradio.inputs.Textbox(label="输入OpenAI的API-key",
+                          default="",
+                          type='password'),
+        gradio.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)"),
+        gradio.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"),
+        gradio.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()

requirements.txt

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+gradio==3.19.0
+scholarly
+openai