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import gradio as gr | |
import os | |
from transformers import AutoTokenizer, AutoModel | |
from sentence_transformers import SentenceTransformer | |
import pickle | |
import nltk | |
nltk.download('punkt') # tokenizer | |
nltk.download('averaged_perceptron_tagger') # postagger | |
import time | |
from input_format import * | |
from score import * | |
# load document scoring model | |
torch.cuda.is_available = lambda : False | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
pretrained_model = 'allenai/specter' | |
tokenizer = AutoTokenizer.from_pretrained(pretrained_model) | |
doc_model = AutoModel.from_pretrained(pretrained_model) | |
doc_model.to(device) | |
# load sentence model | |
sent_model = SentenceTransformer('sentence-transformers/gtr-t5-base') | |
sent_model.to(device) | |
def get_similar_paper( | |
abstract_text_input, | |
author_id_input, | |
results={}, # this state variable will be updated and returned | |
# progress=gr.Progress(track_tqdm=True) | |
): | |
num_papers_show = 10 # number of top papers to show from the reviewer | |
print('retrieving similar papers...') | |
start = time.time() | |
input_sentences = sent_tokenize(abstract_text_input) | |
# Get author papers from id | |
#progress(0.1, desc="Retrieving reviewer papers ...") | |
name, papers = get_text_from_author_id(author_id_input) | |
# Compute Doc-level affinity scores for the Papers | |
# print('computing document scores...') | |
#progress(0.5, desc="Computing document scores...") | |
# TODO detect duplicate papers? | |
titles, abstracts, paper_urls, doc_scores = compute_document_score( | |
doc_model, | |
tokenizer, | |
abstract_text_input, | |
papers, | |
batch=50 | |
) | |
results = { | |
'titles': titles, | |
'abstracts': abstracts, | |
'urls': paper_urls, | |
'doc_scores': doc_scores | |
} | |
# Select top K choices of papers to show | |
titles = titles[:num_papers_show] | |
abstracts = abstracts[:num_papers_show] | |
doc_scores = doc_scores[:num_papers_show] | |
paper_urls = paper_urls[:num_papers_show] | |
display_title = ['[ %0.3f ] %s'%(s, t) for t, s in zip(titles, doc_scores)] | |
end = time.time() | |
print('paper retrieval complete in [%0.2f] seconds'%(end - start)) | |
#progress(0.4, desc="Obtaining relevant information from the papers...") | |
print('obtaining highlights..') | |
start = time.time() | |
input_sentences = sent_tokenize(abstract_text_input) | |
num_sents = len(input_sentences) | |
for aa, (tt, ab, ds) in enumerate(zip(titles, abstracts, doc_scores)): | |
# Compute sent-level and phrase-level affinity scores for each papers | |
sent_ids, sent_scores, info, top_pairs_info = get_highlight_info( | |
sent_model, | |
abstract_text_input, | |
ab, | |
K=2 | |
) | |
# get scores for each word in the format for Gradio Interpretation component | |
word_scores = dict() | |
for i in range(num_sents): | |
word_scores[str(i)] = { | |
"original": ab, | |
"interpretation": list(zip(info['all_words'], info[i]['scores'])) | |
} | |
results[display_title[aa]] = { | |
'title': tt, | |
'abstract': ab, | |
'doc_score': '%0.3f'%ds, | |
'source_sentences': input_sentences, | |
'highlight': word_scores, | |
'top_pairs': top_pairs_info | |
} | |
end = time.time() | |
print('done in [%0.2f] seconds'%(end - start)) | |
## Set up output elements | |
# set up elements to show | |
out = [ | |
gr.update(choices=display_title, interactive=True, visible=False), # set of papers (radio) | |
gr.update(choices=input_sentences, interactive=True, visible=False) # submission sentences | |
] | |
# set up elements to visualize upfront | |
top_papers_show = 3 # number of top papers to show upfront | |
top_num_info_show = 2 # number of sentence pairs from each paper to show upfront | |
summary_out = [] | |
for i in range(top_papers_show): | |
out_tmp = [ | |
gr.update(value=titles[i], visible=True), | |
gr.update(value='%0.3f'%doc_scores[i], visible=True) # document affinity | |
] | |
tp = results[display_title[i]]['top_pairs'] | |
for j in range(top_num_info_show): | |
out_tmp += [ | |
gr.update(value='%0.3f'%tp[j]['score'], visible=True), # sentence relevance | |
tp[j]['query']['original'], | |
tp[j]['query'], | |
tp[j]['candidate']['original'], | |
tp[j]['candidate'] | |
] | |
summary_out += out_tmp | |
# add updates to the show more button | |
out = out + summary_out + [gr.update(visible=True)] # make show more button visible | |
assert(len(out) == (top_num_info_show * 5 + 2) * top_papers_show + 3) | |
out += [gr.update(visible=True), gr.update(visible=True)] # demarcation line between results | |
# add the search results to pass on to the Gradio State varaible | |
out += [results] | |
return tuple(out) | |
def show_more(): | |
return ( | |
gr.update(visible=True), # set of papers | |
gr.update(visible=True), # submission sentences | |
gr.update(visible=True), # title row | |
gr.update(visible=True), # abstract row | |
) | |
def update_name(author_id_input): | |
# update the name of the author based on the id input | |
name, _ = get_text_from_author_id(author_id_input) | |
return gr.update(value=name) | |
def change_output_highlight(selected_papers_radio, source_sent_choice, info={}): | |
# change the output highlight based on the sentence selected from the submission | |
if len(info.keys()) != 0: # if the info is not empty | |
source_sents = info[selected_papers_radio]['source_sentences'] | |
highlights = info[selected_papers_radio]['highlight'] | |
for i, s in enumerate(source_sents): | |
#print('changing highlight') | |
if source_sent_choice == s: | |
return highlights[str(i)] | |
else: | |
return | |
def change_paper(selected_papers_radio, info={}): | |
if len(info.keys()) != 0: # if the info is not empty | |
title = info[selected_papers_radio]['title'] | |
abstract = info[selected_papers_radio]['abstract'] | |
aff_score = info[selected_papers_radio]['doc_score'] | |
highlights = info[selected_papers_radio]['highlight'] | |
return title, abstract, aff_score, highlights['0'] | |
else: | |
return | |
with gr.Blocks() as demo: | |
info = gr.State({}) # cached search results as a State variable shared throughout | |
# Text description about the app and disclaimer | |
### TEXT Description | |
# TODO add instruction video link | |
gr.Markdown( | |
""" | |
# Paper Matching Helper | |
This is a tool designed to help match an academic paper (submission) to a potential peer reviewer, by presenting information that may be relevant to the users. | |
Below we describe how to use the tool. Also feel free to check out the [video]() for a more detailed rundown. | |
##### Input | |
- The tool requires two inputs: (1) an academic paper's abstract in a text format, (2) and a potential reviewer's [Semantic Scholar](https://www.semanticscholar.org/) profile link. Once you put in a valid profile link, the reviewer's name will be displayed. | |
- Once the name is confirmed, press the `What Makes This a Good Match?` button. | |
- Based on the input information, the tool will first search for similar papers from the reviewer's previous publications using [Semantic Scholar API](https://www.semanticscholar.org/product/api). | |
##### Relevant Parts from Top Papers | |
- You will be shown three most relevant papers from the reviewer with high **affinity scores** (ranging from 0 to 1) computed using text representations from a [language model](https://github.com/allenai/specter/tree/master/specter). | |
- For each of the paper, we present relevant pieces of information from the submission and the paper: two pairs of (sentence relevance score, sentence from the submission abstract, sentnece from the paper abstract) | |
- **<span style="color:black;background-color:#65B5E3;">Blue highlights</span>** inidicate phrases that are included in both sentences. | |
##### More Relevant Parts | |
- If the information above is not enough, click `See more relevant parts from other papers` button. | |
- You will see a list top 10 similar papers along with the affinity scores for each. | |
- You can select different papers from the list to see title, abstract, and affinity scores in detail. | |
- Below the list of papers, we highlight relevant parts from the selected paper to different sentences of the submission abstract. | |
- On the left, you will see individual sentences from the submission abstract to select from. | |
- On the right, you will see the abstract of the selected paper, with **highlights** incidating relevant parts to the selected sentence. | |
- **<span style="color:black;background-color:#DB7262;">Red highlights</span>**: sentences with high semantic similarity to the selected sentence. | |
- **<span style="color:black;background-color:#65B5E3;">Blue highlights</span>**: phrases included in the selected sentence. | |
- To see relevant parts in a different paper from the reviewer, select the new paper. | |
------- | |
""" | |
) | |
### INPUT | |
with gr.Row() as input_row: | |
with gr.Column(): | |
abstract_text_input = gr.Textbox(label='Submission Abstract') | |
with gr.Column(): | |
with gr.Row(): | |
author_id_input = gr.Textbox(label='Reviewer Link or ID (Semantic Scholar)') | |
with gr.Row(): | |
name = gr.Textbox(label='Confirm Reviewer Name', interactive=False) | |
author_id_input.change(fn=update_name, inputs=author_id_input, outputs=name) | |
with gr.Row(): | |
compute_btn = gr.Button('What Makes This a Good Match?') | |
# TODO indicate the progress when pressed | |
with gr.Row(): | |
search_status = gr.Textbox(label='Search Status', interactive=False, visible=True) | |
### OVERVIEW | |
# Paper title, score, and top-ranking sentence pairs -- two sentence pairs per paper, three papers | |
## ONE BLOCK OF INFO FOR A SINGLE PAPER | |
## PAPER1 | |
# TODO add link to each paper | |
with gr.Row(): | |
with gr.Column(scale=3): | |
paper_title1 = gr.Textbox(label="From the reviewer's paper:", interactive=False, visible=False) | |
with gr.Column(scale=1): | |
affinity1 = gr.Textbox(label='Affinity', interactive=False, value='', visible=False) | |
with gr.Row() as rel1_1: | |
with gr.Column(scale=1): | |
sent_pair_score1_1 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False) | |
with gr.Column(scale=4): | |
sent_pair_source1_1 = gr.Textbox(label='Sentence from Submission', visible=False) | |
sent_pair_source1_1_hl = gr.components.Interpretation(sent_pair_source1_1) | |
with gr.Column(scale=4): | |
sent_pair_candidate1_1 = gr.Textbox(label='Sentence from Paper', visible=False) | |
sent_pair_candidate1_1_hl = gr.components.Interpretation(sent_pair_candidate1_1) | |
with gr.Row() as rel1_2: | |
with gr.Column(scale=1): | |
sent_pair_score1_2 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False) | |
with gr.Column(scale=4): | |
sent_pair_source1_2 = gr.Textbox(label='Sentence from Submission', visible=False) | |
sent_pair_source1_2_hl = gr.components.Interpretation(sent_pair_source1_2) | |
with gr.Column(scale=4): | |
sent_pair_candidate1_2 = gr.Textbox(label='Sentence from Paper', visible=False) | |
sent_pair_candidate1_2_hl = gr.components.Interpretation(sent_pair_candidate1_2) | |
with gr.Row(visible=False) as demarc1: | |
gr.Markdown( | |
"""---""" | |
) | |
## PAPER 2 | |
with gr.Row(): | |
with gr.Column(scale=3): | |
paper_title2 = gr.Textbox(label="From the reviewer's paper:", interactive=False, visible=False) | |
with gr.Column(scale=1): | |
affinity2 = gr.Textbox(label='Affinity', interactive=False, value='', visible=False) | |
with gr.Row() as rel2_1: | |
with gr.Column(scale=1): | |
sent_pair_score2_1 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False) | |
with gr.Column(scale=4): | |
sent_pair_source2_1 = gr.Textbox(label='Sentence from Submission', visible=False) | |
sent_pair_source2_1_hl = gr.components.Interpretation(sent_pair_source2_1) | |
with gr.Column(scale=4): | |
sent_pair_candidate2_1 = gr.Textbox(label='Sentence from Paper', visible=False) | |
sent_pair_candidate2_1_hl = gr.components.Interpretation(sent_pair_candidate2_1) | |
with gr.Row() as rel2_2: | |
with gr.Column(scale=1): | |
sent_pair_score2_2 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False) | |
with gr.Column(scale=4): | |
sent_pair_source2_2 = gr.Textbox(label='Sentence from Submission', visible=False) | |
sent_pair_source2_2_hl = gr.components.Interpretation(sent_pair_source2_2) | |
with gr.Column(scale=4): | |
sent_pair_candidate2_2 = gr.Textbox(label='Sentence from Paper', visible=False) | |
sent_pair_candidate2_2_hl = gr.components.Interpretation(sent_pair_candidate2_2) | |
with gr.Row(visible=False) as demarc2: | |
gr.Markdown( | |
"""---""" | |
) | |
## PAPER 3 | |
with gr.Row(): | |
with gr.Column(scale=3): | |
paper_title3 = gr.Textbox(label="From the reviewer's paper:", interactive=False, visible=False) | |
with gr.Column(scale=1): | |
affinity3 = gr.Textbox(label='Affinity', interactive=False, value='', visible=False) | |
with gr.Row() as rel3_1: | |
with gr.Column(scale=1): | |
sent_pair_score3_1 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False) | |
with gr.Column(scale=4): | |
sent_pair_source3_1 = gr.Textbox(label='Sentence from Submission', visible=False) | |
sent_pair_source3_1_hl = gr.components.Interpretation(sent_pair_source3_1) | |
with gr.Column(scale=4): | |
sent_pair_candidate3_1 = gr.Textbox(label='Sentence from Paper', visible=False) | |
sent_pair_candidate3_1_hl = gr.components.Interpretation(sent_pair_candidate3_1) | |
with gr.Row() as rel3_2: | |
with gr.Column(scale=1): | |
sent_pair_score3_2 = gr.Textbox(label='Sentence Relevance', interactive=False, value='', visible=False) | |
with gr.Column(scale=4): | |
sent_pair_source3_2 = gr.Textbox(label='Sentence from Submission', visible=False) | |
sent_pair_source3_2_hl = gr.components.Interpretation(sent_pair_source3_2) | |
with gr.Column(scale=4): | |
sent_pair_candidate3_2 = gr.Textbox(label='Sentence from Paper', visible=False) | |
sent_pair_candidate3_2_hl = gr.components.Interpretation(sent_pair_candidate3_2) | |
## Show more button | |
with gr.Row(): | |
see_more_rel_btn = gr.Button('See more relevant parts from other papers', visible=False) | |
### PAPER INFORMATION | |
# show multiple papers in radio check box to select from | |
with gr.Row(): | |
selected_papers_radio = gr.Radio( | |
choices=[], # will be udpated with the button click | |
visible=False, # also will be updated with the button click | |
label='Top Relevant Papers from the Reviewer' | |
) | |
# selected paper information | |
with gr.Row(visible=False) as title_row: | |
with gr.Column(scale=3): | |
paper_title = gr.Textbox(label='Title', interactive=False) | |
with gr.Column(scale=1): | |
affinity= gr.Textbox(label='Affinity', interactive=False, value='') | |
with gr.Row(): | |
paper_abstract = gr.Textbox(label='Abstract', interactive=False, visible=False) | |
### RELEVANT PARTS (HIGHLIGHTS) | |
with gr.Row(): | |
with gr.Column(scale=2): # text from submission | |
source_sentences = gr.Radio( | |
choices=[], | |
visible=False, | |
label='Sentences from Submission Abstract', | |
) | |
with gr.Column(scale=3): # highlighted text from paper | |
highlight = gr.components.Interpretation(paper_abstract) | |
### EVENT LISTENERS | |
# retrieve similar papers and show top results | |
compute_btn.click( | |
fn=get_similar_paper, | |
inputs=[ | |
abstract_text_input, | |
author_id_input, | |
info | |
], | |
outputs=[ | |
selected_papers_radio, | |
source_sentences, | |
paper_title1, # paper info | |
affinity1, | |
sent_pair_score1_1, | |
sent_pair_source1_1, | |
sent_pair_source1_1_hl, | |
sent_pair_candidate1_1, | |
sent_pair_candidate1_1_hl, | |
sent_pair_score1_2, | |
sent_pair_source1_2, | |
sent_pair_source1_2_hl, | |
sent_pair_candidate1_2, | |
sent_pair_candidate1_2_hl, | |
paper_title2, | |
affinity2, | |
sent_pair_score2_1, | |
sent_pair_source2_1, | |
sent_pair_source2_1_hl, | |
sent_pair_candidate2_1, | |
sent_pair_candidate2_1_hl, | |
sent_pair_score2_2, | |
sent_pair_source2_2, | |
sent_pair_source2_2_hl, | |
sent_pair_candidate2_2, | |
sent_pair_candidate2_2_hl, | |
paper_title3, | |
affinity3, | |
sent_pair_score3_1, | |
sent_pair_source3_1, | |
sent_pair_source3_1_hl, | |
sent_pair_candidate3_1, | |
sent_pair_candidate3_1_hl, | |
sent_pair_score3_2, | |
sent_pair_source3_2, | |
sent_pair_source3_2_hl, | |
sent_pair_candidate3_2, | |
sent_pair_candidate3_2_hl, | |
see_more_rel_btn, | |
demarc1, | |
demarc2, | |
info, | |
] | |
) | |
# Get more info (move to more interactive portion) | |
see_more_rel_btn.click( | |
fn=show_more, | |
inputs=None, | |
outputs=[ | |
selected_papers_radio, | |
source_sentences, | |
title_row, | |
paper_abstract | |
] | |
) | |
# change highlight based on selected sentences from submission | |
source_sentences.change( | |
fn=change_output_highlight, | |
inputs=[ | |
selected_papers_radio, | |
source_sentences, | |
info | |
], | |
outputs=highlight | |
) | |
# change paper to show based on selected papers | |
selected_papers_radio.change( | |
fn=change_paper, | |
inputs=[ | |
selected_papers_radio, | |
info, | |
], | |
outputs= [ | |
paper_title, | |
paper_abstract, | |
affinity, | |
highlight | |
] | |
) | |
gr.Markdown( | |
""" | |
--------- | |
**Disclaimer.** This tool and its output should not serve as the sole justification for confirming a match for the submission. It is intended as a supplementary tool that the users may use at their discretion; the correctness of the output of the tool is not guaranteed. This may be improved by updating the internal models used to compute the affinity scores and sentence relevance, which may require additional research independently. The tool does not compromise the privacy of the reviewers as it relies only on their publicly-available information (e.g., names and list of previously published papers). | |
""" | |
) | |
if __name__ == "__main__": | |
demo.launch() | |