app.py

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  # uncomment to test with CPU only
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()
):
    
    progress = gr.Progress()
    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=10
    )
    
    results = {
        'name': name,
        '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()
    retrieval_time = end - start
    print('paper retrieval complete in [%0.2f] seconds'%(retrieval_time))
    
    progress(0.9, 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, url) in enumerate(zip(titles, abstracts, doc_scores, paper_urls)):
        # 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 # top two sentences from the candidate 
        )
        
        # get scores for each word in the format for Gradio Interpretation component
        word_scores = dict()
        for i in range(num_sents):
            ww, ss = remove_spaces(info['all_words'], info[i]['scores'])
            word_scores[str(i)] = {
                "original": ab,
                "interpretation": list(zip(ww, ss))
            }

        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,
            'url': url
        }
       
    end = time.time()
    highlight_time = end - start
    print('done in [%0.2f] seconds'%(highlight_time)) 
    
    ## 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):
        if i == 0:
            out_tmp = [
                gr.update(value="""<a href="%s" target="_blank"><h4>%s</h4></a>"""%(paper_urls[i], titles[i]), visible=True),
                gr.update(value="""#### Affinity Score: %0.3f 
                          <div class="help-tip">
                                <p>Measures how similar the paper's abstract is to the submission abstract.</p>
                          </div>
                          """%doc_scores[i],  
                          visible=True) # document affinity
            ]
        else:
            out_tmp = [
                gr.update(value="""<a href="%s" target="_blank"><h4>%s</h4></a>"""%(paper_urls[i], titles[i]), visible=True),
                gr.update(value='#### Affinity Score: %0.3f'%doc_scores[i], visible=True) # document affinity
            ]
        tp = results[display_title[i]]['top_pairs']
        for j in range(top_num_info_show):
            if i == 0 and j == 0:
                out_tmp += [
                    gr.update(value="""Sentence Relevance:\n%0.3f
                            <div class="help-tip">
                                <p>Measures how similar the sentence pairs are.</p>
                            </div>"""%tp[j]['score'], visible=True), # sentence relevance
                    tp[j]['query']['original'],
                    tp[j]['query'],
                    tp[j]['candidate']['original'],
                    tp[j]['candidate']
                ]
            else:
                out_tmp += [
                    gr.update(value='Sentence Relevance:\n%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(value="""
                            <h3>Top three relevant papers by the reviewer <a href="%s" target="_blank">%s</a></h3>
                            
                            For each paper, two sentence pairs (one from the submission, one from the paper) with the highest relevance scores are shown.
                            
                            **<span style="color:black;background-color:#65B5E3;">Blue highlights</span>**: phrases that appear in both sentences.
                            """%(author_id_input, results['name']),
                            visible=True)] # result 1 description

    out += [gr.update(visible=True), gr.update(visible=True)] # demarcation line between results

    # progress status
    out += [gr.update(value='Done (in %0.1f seconds)'%(retrieval_time+highlight_time), visible=True)]
    
    # add the search results to pass on to the Gradio State varaible
    out += [results]
    
    return tuple(out)
        
def show_more(info):
    # show the interactive part of the app
    return (
        gr.update(value="""
            ### Click on different papers by %s below (sorted by their affinity scores) to see other relevant parts!
            """%info['name'], visible=True), # description
        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 show_status():
    # show search status field when search button is clicked
    return gr.update(visible=True)

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):
            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']
        url = info[selected_papers_radio]['url']
        title_out = """<a href="%s" target="_blank"><h4>%s</h4></a>"""%(url, title)
        aff_score_out = '#### Affinity Score: %s'%aff_score
        return title_out, abstract, aff_score_out, highlights['0']

    else:
        return

with gr.Blocks(css='style.css') 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(
        """
# R2P2: Matching Reviewers to Papers in Peer Review

#### Who is R2P2 for?
It is for meta-reviewers, area chairs, program chairs, or anyone who oversees the submission-reviewer matching process in peer review for acadmeic conferences, journals, and grants. 

#### How does it help?
A typical meta-reviewer workflow lacks supportive information on what makes the pre-selected candidate reviewers good fit for the submission. Only the **affinity scores** between the reviewer and the paper are provided, with no additional detail. 

R2P2 provide more information about each reviewer. It searches for the most relevant papers among the reviewer's previous publications and highlights relevant parts within them. 

Check out [this video]() for a quick demo of what R2P2 is, and how it can help!

-------
        """
    ) 
    
    ### INPUT
    with gr.Row() as input_row:
        with gr.Column():
            abstract_text_input = gr.Textbox(label='Submission Abstract', info='Paste in the abstract of the submission.')
        with gr.Column():
            with gr.Row():
                author_id_input = gr.Textbox(label='Reviewer Profile Link (Semantic Scholar)', info="Paste in the reviewer's Semantic Scholar link")
            with gr.Row():
                name = gr.Textbox(label='Confirm Reviewer Name', info='This will be automatically updated based on the reviewer profile link above', 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?') 

    with gr.Row():
        search_status = gr.Textbox(label='Search Status', interactive=False, visible=False)
        
    ### 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 
    with gr.Row():
        result1_desc = gr.Markdown(value='', visible=False)
    # TODO hovering instructions
    with gr.Row():
        with gr.Column(scale=3):
            paper_title1 = gr.Markdown(value='', visible=False)
        with gr.Column(scale=1):
            affinity1 = gr.Markdown(value='', visible=False)
    with gr.Row() as rel1_1:
        with gr.Column(scale=1):
            sent_pair_score1_1 = gr.Markdown(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.Markdown(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.Markdown(value='', visible=False)
        with gr.Column(scale=1):
            affinity2 = gr.Markdown(value='', visible=False)
    with gr.Row() as rel2_1:
        with gr.Column(scale=1):
            sent_pair_score2_1 = gr.Markdown(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.Markdown(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.Markdown(value='', visible=False)
        with gr.Column(scale=1):
            affinity3 = gr.Markdown(value='', visible=False)
    with gr.Row() as rel3_1:
        with gr.Column(scale=1):
            sent_pair_score3_1 = gr.Markdown(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.Markdown(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('Explore more in other papers', visible=False)
    
    ### PAPER INFORMATION
    
    # Description
    with gr.Row():
        result2_desc = gr.Markdown(
            value=''
            ,visible=False
        )

    # 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.Markdown(value='')
        with gr.Column(scale=1):
            affinity = gr.Markdown(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
    
    compute_btn.click(
        fn=show_status,
        inputs=[],
        outputs=search_status
    )
    
    # 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,
            result1_desc,
            demarc1,
            demarc2,
            search_status,
            info,
        ],
        show_progress=True,
        scroll_to_output=True
    )      
    
    # Get more info (move to more interactive portion)
    see_more_rel_btn.click(
        fn=show_more,
        inputs=info,
        outputs=[
            result2_desc,
            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. The search results may be improved by updating the internal models used to compute the affinity scores and sentence relevance, which may require additional independent research. The tool does not compromise the privacy of the reviewers --- it relies only on their publicly-available information (e.g., names and list of previously published papers). All input information will only be temporarily used for internal computation, will not be saved externally, and will be removed when the session is refreshed or closed.
        """
    )
    
if __name__ == "__main__":
    #demo.queue().launch(share=True) 
    demo.queue().launch() 
    # TODO how to disable dark mode?