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 @cached(cache=TTLCache(maxsize=500, ttl=CACHE_TIME)) 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 @cached(cache=TTLCache(maxsize=500, ttl=CACHE_TIME)) 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'
在这里获取您的Open AI API密钥 here
') 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("