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| import urllib.request | |
| import fitz | |
| import re | |
| import numpy as np | |
| import tensorflow_hub as hub | |
| import openai | |
| import gradio as gr | |
| import os | |
| from sklearn.neighbors import NearestNeighbors | |
| import requests | |
| from cachetools import cached, TTLCache | |
| CACHE_TIME = 60 * 60 * 6 # 6小时 | |
| # 全局的推荐器对象 | |
| recommender = None | |
| # 第二个功能的全局变量 | |
| def get_recommendations_from_semantic_scholar(semantic_scholar_id: str): | |
| try: | |
| r = requests.post( | |
| "https://api.semanticscholar.org/recommendations/v1/papers/", | |
| json={ | |
| "positivePaperIds": [semantic_scholar_id], | |
| }, | |
| params={"fields": "externalIds,title,year", "limit": 10}, | |
| ) | |
| return r.json()["recommendedPapers"] | |
| except KeyError as e: | |
| raise gr.Error( | |
| "获取推荐时出错，如果这是一篇新论文或尚未被Semantic Scholar索引，则可能尚未有推荐。" | |
| ) from e | |
| def filter_recommendations(recommendations, max_paper_count=5): | |
| arxiv_paper = [ | |
| r for r in recommendations if r["externalIds"].get("ArXiv", None) is not None | |
| ] | |
| if len(arxiv_paper) > max_paper_count: | |
| arxiv_paper = arxiv_paper[:max_paper_count] | |
| return arxiv_paper | |
| def get_paper_title_from_arxiv_id(arxiv_id): | |
| try: | |
| return requests.get(f"https://huggingface.co/api/papers/{arxiv_id}").json()[ | |
| "title" | |
| ] | |
| except Exception as e: | |
| print(f"获取论文标题时出错 {arxiv_id}: {e}") | |
| raise gr.Error(f"获取论文标题时出错 {arxiv_id}: {e}") from e | |
| def format_recommendation_into_markdown(arxiv_id, recommendations): | |
| comment = "以下论文由Semantic Scholar API推荐\n\n" | |
| for r in recommendations: | |
| hub_paper_url = f"https://huggingface.co/papers/{r['externalIds']['ArXiv']}" | |
| comment += f"* [{r['title']}]({hub_paper_url}) ({r['year']})\n" | |
| return comment | |
| def return_recommendations(url): | |
| arxiv_id = parse_arxiv_id_from_paper_url(url) | |
| recommendations = get_recommendations_from_semantic_scholar(f"ArXiv:{arxiv_id}") | |
| filtered_recommendations = filter_recommendations(recommendations) | |
| return format_recommendation_into_markdown(arxiv_id, filtered_recommendations) | |
| # Gradio界面 | |
| title = 'PDF GPT Turbo' | |
| description = """ PDF GPT Turbo允许您与PDF文件交流。它使用Google的Universal Sentence Encoder与Deep averaging network（DAN）来提供无幻觉的响应，通过提高OpenAI的嵌入质量。它在方括号（[Page No.]）中引用页码，显示信息的位置，增强了响应的可信度。""" | |
| # 预定义的问题 | |
| questions = [ | |
| "研究调查了什么？", | |
| "能否提供本文的摘要？", | |
| "这项研究使用了什么方法？", | |
| # 需要时添加更多的问题 | |
| ] | |
| with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as demo: | |
| gr.Markdown(f'<center><h3>{title}</h3></center>') | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| with gr.Group(): | |
| gr.Markdown(f'<p style="text-align:center">在这里获取您的Open AI API密钥 <a href="https://platform.openai.com/account/api-keys">here</a></p>') | |
| with gr.Accordion("API Key"): | |
| openAI_key = gr.Textbox(label='在此输入您的OpenAI API密钥', password=True) | |
| url = gr.Textbox(label='在此输入PDF的URL (示例: https://arxiv.org/pdf/1706.03762.pdf )') | |
| gr.Markdown("<center><h4>或<h4></center>") | |
| file = gr.File(label='在此上传您的PDF/研究论文/书籍', file_types=['.pdf']) | |
| question = gr.Textbox(label='在此输入您的问题') | |
| gr.Examples( | |
| [[q] for q in questions], | |
| inputs=[question], | |
| label="预定义问题：点击问题以自动填充输入框，然后按Enter键！", | |
| ) | |
| model = gr.Radio([ | |
| 'gpt-3.5-turbo', | |
| 'gpt-3.5-turbo-16k', | |
| 'gpt-3.5-turbo-0613', | |
| 'gpt-3.5-turbo-16k-0613', | |
| 'text-davinci-003', | |
| 'gpt-4', | |
| 'gpt-4-32k' | |
| ], label='选择模型', default='gpt-3.5-turbo') | |
| btn = gr.Button(value='提交') | |
| btn.style(full_width=True) | |
| with gr.Group(): | |
| chatbot = gr.Chatbot(placeholder="聊天历史", label="聊天历史", lines=50, elem_id="chatbot") | |
| # 将按钮的点击事件绑定到question_answer函数 | |
| btn.click( | |
| question_answer, | |
| inputs=[chatbot, url, file, question, openAI_key, model], | |
| outputs=[chatbot], | |
| ) | |
| # 第二个标签 | |
| gr.Tab("论文推荐", [ | |
| gr.Textbox(label="输入Hugging Face Papers的URL", lines=1), | |
| gr.Button("获取推荐", return_recommendations), | |
| gr.Markdown(), | |
| ]) | |
| demo.launch() | |