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import gradio as gr
import xml.etree.ElementTree as ET
import re
import urllib
import torch
from transformers import pipeline
classifier = pipeline(model="Yozhikoff/arxiv-topics-distilbert-base-cased")
def get_arxiv_title_and_abstract(link):
# Regular expression pattern for arXiv link validation
try:
pattern = r'^https?://arxiv\.org/(?:abs|pdf)/(\d{4}\.\d{4,5})(?:\.pdf)?/?$'
match = re.match(pattern, link)
if not match:
raise ValueError("Invalid arXiv link")
# Construct the arXiv API URL for the paper
arxiv_id = match.group(1)
api_url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}"
# Retrieve the paper metadata using the arXiv API
with urllib.request.urlopen(api_url) as response:
xml_data = response.read().decode()
# Extract the title and abstract from the XML data
title = re.search(r'<title>(.*?)</title>', xml_data).group(1)
abstract = re.search(r'<summary>(.*?)</summary>', xml_data, re.DOTALL).group(1)
# Clean up the title and abstract
title = re.sub(r'\s+', ' ', title).strip()
abstract = re.sub(r'\s+', ' ', abstract).strip()
return title, abstract
except:
raise gr.Error('Invalid arXiv URL!')
def classify_paper(title, abstract):
text = f"TITLE\n{title}\n\nABSTRACT\n{abstract}"
item = classifier.tokenizer(text)
input_tensor = torch.tensor(item['input_ids'])[None]
logits = classifier.model(input_tensor).logits[0]
preds = torch.sigmoid(logits).detach().cpu().numpy()
result = {classifier.model.config.id2label[num]: float(prob) for num, prob in enumerate(preds) if prob > 0.1}
return result
with gr.Blocks(title='Paper classifier') as demo:
gr.Markdown('Please enter an arXiv link **OR** fill title and abstract manually')
with gr.Row():
with gr.Column():
arxiv_link = gr.Textbox(label="Arxiv link")
b1 = gr.Button("Parse Link")
title = gr.Textbox(label="Paper title")
abstract = gr.Textbox(label="Paper abstract")
b2 = gr.Button("Classify Paper", variant='primary')
b1.click(fn=get_arxiv_title_and_abstract, inputs=arxiv_link, outputs=[title, abstract], api_name="parse")
with gr.Column():
out = gr.Label(label="Topics")
b2.click(classify_paper, inputs=[title, abstract], outputs=out)
gr.Markdown('## Examples')
gr.Examples(
examples=[['https://arxiv.org/abs/1706.03762'], ['https://arxiv.org/abs/1503.04376'], ['https://arxiv.org/abs/2201.06601']],
inputs=arxiv_link,
outputs=[title, abstract],
fn=get_arxiv_title_and_abstract,
cache_examples=True,
)
demo.launch(share=True) |