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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
@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'<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')
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()
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