app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModel
from sentence_transformers import SentenceTransformer
import pickle
import nltk
import time

from input_format import *
from score import *

nltk.download('punkt') # tokenizer
nltk.download('averaged_perceptron_tagger') # postagger

## 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'
pretrained_model = 'allenai/specter2'
tokenizer = AutoTokenizer.from_pretrained(pretrained_model)
doc_model = AutoModel.from_pretrained(pretrained_model) 
doc_model.to(device)

## load sentence model 
sent_model = doc_model # have the same model for document and sentence level

# OR specify different model for sentence level
#sent_model = SentenceTransformer('sentence-transformers/gtr-t5-base')
#sent_model.to(device)

NUM_PAPERS_SHOW = 5 # max number of top papers to show from the reviewer upfront
NUM_PAIRS_SHOW = 5 # max number of top sentence pairs to show

def get_similar_paper(
    title_input,
    abstract_text_input, 
    author_id_input, 
    top_paper_slider,
    top_pair_slider,
    results={}, # this state variable will be updated and returned
):
    progress = gr.Progress()
    if title_input == None:
        title_input = '' # if no title is given, just focus on abstract.
    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, paper_years, paper_citations = compute_document_score(
        doc_model, 
        tokenizer,
        title_input,
        abstract_text_input, 
        papers,
        batch=10
    )
    
    results = {
        'name': name,
        'author_url': author_id_input,
        'titles': titles,
        'abstracts': abstracts,
        'urls': paper_urls,
        'doc_scores': doc_scores,
        'years': paper_years,
        'citations': paper_citations,
    }
    
    # Select top 10 papers to show
    titles = titles[:10]
    abstracts = abstracts[:10]
    doc_scores = doc_scores[:10]
    paper_urls = paper_urls[:10]
    paper_years = paper_years[:10]
    paper_citations = paper_citations[:10]
    
    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_input_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, 
            tokenizer,
            abstract_text_input, 
            ab,
            K=None,  
            top_pair_num=10, # top ten sentence pairs at max to show upfront 
        )
        num_cand_sents = sent_ids.shape[1]
        
        # get scores for each word in the format for Gradio Interpretation component
        word_scores = dict()
        for i in range(num_input_sents):
            word_scores[str(i)] = dict()
            for j in range(1, num_cand_sents+1):
                ww, ss = remove_spaces(info['all_words'], info[i][j]['scores'])
                word_scores[str(i)][str(j)] = {
                    "original": ab,
                    "interpretation": list(zip(ww, ss))
                } 

        results[display_title[aa]] = {
            'title': tt,
            'abstract': ab,
            'num_cand_sents': num_cand_sents,
            'doc_score': '%0.3f'%ds,
            'source_sentences': input_sentences,
            'highlight': word_scores,
            'top_pairs': top_pairs_info,
            'url': url,
            'year': paper_years[aa],
            'citations': paper_citations[aa],
        }
    
    end = time.time()
    highlight_time = end - start
    print('done in [%0.2f] seconds'%(highlight_time)) 
    
    ## Set up output elements
    ## Components for Initial Part
    result1_desc_value = """
        <h3>Top %d relevant papers by the reviewer <a href="%s" target="_blank">%s</a></h3>
        
        For each paper, top %d sentence pairs (one from the submission on the left, one from the paper on the right) with the highest relevance scores are shown.
        
        **<span style="color:black;background-color:#65B5E3;">Blue highlights</span>**: phrases that appear in both sentences.
        """%(int(top_paper_slider), author_id_input, results['name'], int(top_pair_slider))

    out1 = [
        gr.update(visible=True), # Explore more button
        gr.update(value=result1_desc_value, visible=True), # result 1 description
        gr.update(value='Done (in %0.1f seconds)'%(retrieval_time+highlight_time), visible=True), # search status
        gr.update(visible=True),  # top paper slider
        gr.update(visible=True) # top pair slider
    ]
    
    ### Components for Results in Initial Part
    top_papers_show = int(top_paper_slider) # number of top papers to show upfront
    top_num_info_show = int(top_pair_slider) # number of sentence pairs from each paper to show upfront
    output = setup_outputs(results, top_papers_show, top_num_info_show)
    out2 = []
    for x in output:
        out2 += x 

    ### Components for Explore More Section
    # list of top papers, sentences to select from, paper_title, affinity
    title = results[display_title[0]]['title'] # set default title as the top paper
    url = results[display_title[0]]['url']
    aff_score = results[display_title[0]]['doc_score']
    title_out = """<a href="%s" target="_blank"><h5>%s</h5></a>"""%(url, title)
    aff_score_out = '##### Affinity Score: %s'%aff_score
    result2_desc_value = """
        ##### Click a paper by %s (left, sorted by affinity scores), and a sentence from the submission abstract (center), to see which parts of the paper's abstract are relevant (right).
        """%results['name']
    out3 = [
        gr.update(choices=display_title, value=display_title[0],  interactive=True), # set of papers (radio)
        gr.update(choices=input_sentences, value=input_sentences[0], interactive=True), # submission sentences 
        gr.update(value=title_out), # paper_title
        gr.update(value=aff_score_out),  # affinity
        gr.update(value=result2_desc_value), # result 2 description (show more section)
        gr.update(value=2, maximum=len(sent_tokenize(abstracts[0]))), # highlight slider to control
    ]
    
    torch.cuda.empty_cache()
    
    ## Return by adding the State variable info
    return out1 + out2 + out3 + [results]

def setup_outputs(info, top_papers_show, top_num_info_show):
    titles = info['titles']
    doc_scores = info['doc_scores']
    paper_urls = info['urls']
    paper_years = info['years']
    paper_citations = info['citations']
    display_title = ['[ %0.3f ] %s'%(s, t) for t, s in zip(info['titles'], info['doc_scores'])]
    title = []
    affinity = []
    citation_count = []
    sent_pair_score = []
    sent_text_query = []
    sent_text_candidate = []
    sent_hl_query = []
    sent_hl_candidate = []
    demarc_lines = []
    for i in range(top_papers_show):
        if i == 0:
            title.append(
                gr.update(value="""<a href="%s" target="_blank"><h4>%s (%s)</h4></a>"""%(paper_urls[i], titles[i], str(paper_years[i])), visible=True)
            )
            affinity.append(
                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
            )
            citation_count.append(
                 gr.update(value="""#### Citation Count: %d"""%paper_citations[i], visible=True) # document affinity
            )
        else:
            title.append(
                gr.update(value="""<a href="%s" target="_blank"><h4>%s (%s)</h4></a>"""%(paper_urls[i], titles[i], str(paper_years[i])), visible=True)
            )
            affinity.append(
                gr.update(value='#### Affinity Score: %0.3f'%doc_scores[i], visible=True) # document affinity
            )
            citation_count.append(
                 gr.update(value="""#### Citation Count: %d"""%paper_citations[i], visible=True) # document affinity
            )
        demarc_lines.append(gr.Markdown.update(visible=True))

        # fill in the rest as 
        tp = info[display_title[i]]['top_pairs']
        for j in range(top_num_info_show):
            if i == 0 and j == 0:
                # for the first entry add help tip
                sent_pair_score.append(
                    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)
                ) 
            else:
                sent_pair_score.append(
                    gr.Textbox.update(value='Sentence Relevance:\n%0.3f'%tp[j]['score'], visible=True)
                ) 
            sent_text_query.append(gr.Textbox.update(tp[j]['query']['original']))
            sent_text_candidate.append(gr.Textbox.update(tp[j]['candidate']['original']))
            sent_hl_query.append(tp[j]['query'])
            sent_hl_candidate.append(tp[j]['candidate'])
            #row2.append(gr.update(visible=True))
        sent_pair_score += [gr.Markdown.update(visible=False)] * (NUM_PAIRS_SHOW - top_num_info_show)
        sent_text_query += [gr.Textbox.update(value='', visible=False)] * (NUM_PAIRS_SHOW - top_num_info_show)
        sent_text_candidate += [gr.Textbox.update(value='', visible=False)] * (NUM_PAIRS_SHOW - top_num_info_show)
        sent_hl_query += [None] * (NUM_PAIRS_SHOW - top_num_info_show)
        sent_hl_candidate += [None] * (NUM_PAIRS_SHOW - top_num_info_show)

    # mark others not visible
    title += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show)
    affinity += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show)
    citation_count += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show)
    demarc_lines += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show)
    sent_pair_score += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW
    sent_text_query += [gr.Textbox.update(value='', visible=False)] * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW
    sent_text_candidate += [gr.Textbox.update(value='', visible=False)]  * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW
    sent_hl_query += [None] * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW
    sent_hl_candidate += [None] * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW

    assert(len(title) == NUM_PAPERS_SHOW)
    assert(len(affinity) == NUM_PAPERS_SHOW)
    assert(len(sent_pair_score) == NUM_PAIRS_SHOW * NUM_PAPERS_SHOW)

    return title, affinity, citation_count, demarc_lines, sent_pair_score, sent_text_query, sent_text_candidate, sent_hl_query, sent_hl_candidate

def show_more(info):
    # show the interactive part of the app
    return (
        gr.update(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), # affinity row
        gr.update(visible=True), # citation row
        gr.update(visible=True), # highlight legend
        gr.update(visible=True), # highlight slider
        gr.update(visible=True), # highlight abstract
    )

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_sentence(
    selected_papers_radio, 
    source_sent_choice, 
    highlight_slider, 
    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']
        idx = source_sents.index(source_sent_choice)
        return highlights[str(idx)][str(highlight_slider)]
    else:
        return

def change_paper(
    selected_papers_radio, 
    source_sent_choice, 
    highlight_slider,
    info={}
): 
    if len(info.keys()) != 0: # if the info is not empty
        source_sents = info[selected_papers_radio]['source_sentences']
        title = info[selected_papers_radio]['title']
        year = info[selected_papers_radio]['year']
        citation_count = info[selected_papers_radio]['citations']
        num_sents = info[selected_papers_radio]['num_cand_sents']
        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"><h5>%s (%s)</h5></a>"""%(url, title, str(year))
        aff_score_out = '##### Affinity Score: %s'%aff_score
        citation_count_out = '##### Citation Count: %s'%citation_count
        idx = source_sents.index(source_sent_choice)
        if highlight_slider <= num_sents:
            return title_out, abstract, aff_score_out, citation_count_out, highlights[str(idx)][str(highlight_slider)], gr.update(value=highlight_slider, maximum=num_sents)
        else: # if the slider is set to more than the current number of sentences, show the max number of highlights
            return title_out, abstract, aff_score_out, citation_count_out, highlights[str(idx)][str(num_sents)], gr.update(value=num_sents, maximum=num_sents)
    else:
        return

def change_num_highlight(
    selected_papers_radio, 
    source_sent_choice,
    highlight_slider, 
    info={}
):
    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']
        idx = source_sents.index(source_sent_choice)
        return highlights[str(idx)][str(highlight_slider)] 
    else:
        return 

def change_top_output(top_paper_slider, top_pair_slider, info={}):
    top_papers_show = int(top_paper_slider)
    top_num_info_show = int(top_pair_slider)
    
    result1_desc_value = """
    <h3>Top %d relevant papers by the reviewer <a href="%s" target="_blank">%s</a></h3>
    
    For each paper, top %d sentence pairs (one from the submission on the left, one from the paper on the right) with the highest relevance scores are shown.
    
    **<span style="color:black;background-color:#65B5E3;">Blue highlights</span>**: phrases that appear in both sentences.
    """%(int(top_paper_slider), info['author_url'], info['name'], int(top_pair_slider))
    if len(info.keys()) != 0:
        tmp = setup_outputs(info, top_papers_show, top_num_info_show)
        x = []
        for t in tmp:
            x += t
        return x + [gr.update(value=result1_desc_value)]
    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

    # General instruction
    general_instruction = """
# R2P2: An Assistance Tool for Reviewer-Paper Matching in Peer Review 

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

<center><img src="file/tool-img.jpeg" width="70%" alt="general workflow"></center>

#### How does it help?
A typical meta-reviewer workflow lacks supportive information on **what makes the pre-selected candidate reviewers a good fit** for the submission. Only affinity scores between the reviewer and the paper are shown, without additional details on what makes them similar/different. 

R2P2 provides more information about each reviewer. Given a paper and a reviewer, it searches for the **most relevant papers** among the reviewer's previous publications and **highlights relevant parts** within them. 
    """
    # More details (video, addendum)
    more_details_instruction = """Check out <a href="https://drive.google.com/file/d/1Ex_-cOplBitO7riNGliecFc8H3chXUN-/view?usp=share_link", target="_blank">this video</a> for an overview of what R2P2 is and <a href="https://drive.google.com/file/d/1C_fadFNdJkbrgeaoeoqeJdVCvg5iJi5V/view?usp=share_link", target="_blank">this video</a> for how to use it. You can find more details <a href="file/details.html", target="_blank">here</a>, along with our privacy policy and disclaimer."""

    gr.Markdown(general_instruction)
    gr.HTML(more_details_instruction)
    gr.Markdown("""---""")
    
    # Add main example
    example_title ="The Toronto Paper Matching System: An automated paper-reviewer assignment system"
    example_submission = """One of the most important tasks of conference organizers is the assignment of papers to reviewers. Reviewers' assessments of papers is a crucial step in determining the conference program, and in a certain sense to shape the direction of a field. However this is not a simple task: large conferences typically have to assign hundreds of papers to hundreds of reviewers, and time constraints make the task impossible for one person to accomplish. Furthermore other constraints, such as reviewer load have to be taken into account, preventing the process from being completely distributed. We built the first version of a system to suggest reviewer assignments for the NIPS 2010 conference, followed, in 2012, by a release that better integrated our system with Microsoft's popular Conference Management Toolkit (CMT). Since then our system has been widely adopted by the leading conferences in both the machine learning and computer vision communities. This paper provides an overview of the system, a summary of learning models and methods of evaluation that we have been using, as well as some of the recent progress and open issues."""
    example_reviewer = "https://www.semanticscholar.org/author/Nihar-B.-Shah/1737249"
    
    ## Add other examples for the task

    # match 1
    # example1_title = "VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures"
    # example1_submission = """Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. Results For the first time we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows to efficiently introduce both convolution and pooling operations of the network. We trained our model, called VoroCNN, to predict local qualities of 3D protein folds. The prediction results are competitive to the state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in the recognition of protein binding interfaces. Availability The model, data, and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/. Contact ceslovas.venclovas@bti.vu.lt, sergei.grudinin@inria.fr"""
    # example1_reviewer = "https://www.semanticscholar.org/author/2025052385"

    # # match 2
    # example2_title = "Model-based Policy Optimization with Unsupervised Model Adaptation"
    # example2_submission = """Model-based reinforcement learning methods learn a dynamics model with real data sampled from the environment and leverage it to generate simulated data to derive an agent. However, due to the potential distribution mismatch between simulated data and real data, this could lead to degraded performance. Despite much effort being devoted to reducing this distribution mismatch, existing methods fail to solve it explicitly. In this paper, we investigate how to bridge the gap between real and simulated data due to inaccurate model estimation for better policy optimization. To begin with, we first derive a lower bound of the expected return, which naturally inspires a bound maximization algorithm by aligning the simulated and real data distributions. To this end, we propose a novel model-based reinforcement learning framework AMPO, which introduces unsupervised model adaptation to minimize the integral probability metric (IPM) between feature distributions from real and simulated data. Instantiating our framework with Wasserstein-1 distance gives a practical model-based approach. Empirically, our approach achieves state-of-the-art performance in terms of sample efficiency on a range of continuous control benchmark tasks."""
    # example2_reviewer = "https://www.semanticscholar.org/author/144974941"

    # # match 3
    # example3_title = "Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance"
    # example3_submission = """The Wasserstein distance between two probability measures on a metric space is a measure of closeness with applications in statistics, probability, and machine learning. In this work, we consider the fundamental question of how quickly the empirical measure obtained from $n$ independent samples from $\mu$ approaches $\mu$ in the Wasserstein distance of any order. We prove sharp asymptotic and finite-sample results for this rate of convergence for general measures on general compact metric spaces. Our finite-sample results show the existence of multi-scale behavior, where measures can exhibit radically different rates of convergence as $n$ grows."""
    # example3_reviewer = "https://www.semanticscholar.org/author/27911143"
    
    # # match 4
    # example4_title = "Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands"
    # example4_submission = """We study deep neural networks and their use in semiparametric inference. We prove valid inference after first-step estimation with deep learning, a result new to the literature. We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal), obtain valid semiparametric inference. Our estimation rates and semiparametric inference results handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-common rectified linear unit activation function and a depth explicitly diverging with the sample size. We discuss other architectures as well, including fixed-width, very deep networks. We establish nonasymptotic bounds for these deep nets for nonparametric regression, covering the standard least squares and logistic losses in particular. We then apply our theory to develop semiparametric inference, focusing on treatment effects, expected welfare, and decomposition effects for concreteness. Inference in many other semiparametric contexts can be readily obtained. We demonstrate the effectiveness of deep learning with a Monte Carlo analysis and an empirical application to direct mail marketing."""
    # example4_reviewer = "https://www.semanticscholar.org/author/3364789"
    
    ### INPUT
    with gr.Row() as input_row:
        with gr.Column(scale=3):
            with gr.Row():
                title_input = gr.Textbox(label='Submission Title', info='Paste in the title of the submission.')
            with gr.Row():
                abstract_text_input = gr.Textbox(label='Submission Abstract', info='Paste in the abstract of the submission.')
        with gr.Column(scale=2):
            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)
                
    gr.Examples(
        examples=[
            [example_title, example_submission, example_reviewer],
            # [example1_title, example1_submission, example1_reviewer],
            # [example2_title, example2_submission, example2_reviewer],
            # [example3_title, example3_submission, example3_reviewer],
            # [example4_title, example4_submission, example4_reviewer],
        ],
        inputs=[title_input, abstract_text_input, author_id_input],
        cache_examples=False,
        label="Try out the following example input. Click on a row to fill in the input fields accordingly."
    )
    
    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 RESULTS
    # Paper title, score, and top-ranking sentence pairs 
    # a knob for controlling the number of output displayed
    with gr.Row():
        with gr.Column(scale=3):
            result1_desc = gr.Markdown(value='', visible=False)
        with gr.Column(scale=2):
        #    with gr.Row():
            top_paper_slider = gr.Slider(label='Number of papers to show', value=3, minimum=3, step=1, maximum=NUM_PAPERS_SHOW, visible=False)
        with gr.Column(scale=2):
            #with gr.Row():
            top_pair_slider = gr.Slider(label='Number of sentence pairs to show', value=2, minimum=2, step=1, maximum=NUM_PAIRS_SHOW, visible=False)

    paper_title_up = []
    paper_affinity_up = []
    citation_count = []
    sent_pair_score = []
    sent_text_query = []
    sent_text_candidate = []
    sent_hl_query = []
    sent_hl_candidate = []
    demarc_lines = []
    
    row_elems1 = []
    row_elems2 = []
    
    for i in range(NUM_PAPERS_SHOW):
        with gr.Row():
            with gr.Column(scale=3):
                tt = gr.Markdown(value='', visible=False)
                paper_title_up.append(tt)
            with gr.Column(scale=1):
                cc = gr.Markdown(value='', visible=False)
                citation_count.append(cc)
            with gr.Column(scale=1):
                aff = gr.Markdown(value='', visible=False)
                paper_affinity_up.append(aff)
        for j in range(NUM_PAIRS_SHOW):
            with gr.Row():
                with gr.Column(scale=1):
                    sps = gr.Markdown(value='', visible=False)
                    sent_pair_score.append(sps)
                with gr.Column(scale=5):
                    #stq = gr.Textbox(label='Sentence from Submission', visible=False)
                    stq = gr.Textbox(label='', visible=False)
                    shq = gr.components.Interpretation(stq, visible=False)
                    sent_text_query.append(stq)
                    sent_hl_query.append(shq)
                with gr.Column(scale=5):
                    #stc = gr.Textbox(label="Sentence from Reviewer's Paper", visible=False)
                    stc = gr.Textbox(label="", visible=False)
                    shc = gr.components.Interpretation(stc, visible=False)
                    sent_text_candidate.append(stc)
                    sent_hl_candidate.append(shc)
        with gr.Row():
            dml = gr.Markdown("""---""", visible=False)
            demarc_lines.append(dml)
      
    ## Show more button
    with gr.Row():
        see_more_rel_btn = gr.Button('Not Enough Information? Explore More', visible=False)
    
    ### PAPER INFORMATION
    
    # Description for Explore More Section
    with gr.Row():
        result2_desc = gr.Markdown(value='', visible=False)
        
    # Highlight description 
    hl_desc = """
    <font size="2">**<span style="color:black;background-color:#DB7262;">Red</span>**: sentences simiar to the selected sentence from submission. Darker = more similar.</font>
    
    <font size="2">**<span style="color:black;background-color:#65B5E3;">Blue</span>**: phrases that appear in both sentences.</font>
    """ 
    #---"""
    # show multiple papers in radio check box to select from
    paper_abstract = gr.Textbox(label='', interactive=False, visible=False)
    with gr.Row():
        with gr.Column(scale=1):
            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'
            )
        with gr.Column(scale=2):
            # sentences from submission
            source_sentences = gr.Radio(
                choices=[], 
                visible=False, 
                label='Sentences from Submission Abstract',
            )
        with gr.Column(scale=3):
            # selected paper and highlight
            with gr.Row():
                # slider for highlight amount
                highlight_slider = gr.Slider(
                    label='Number of Highlighted Sentences', 
                    minimum=1, 
                    maximum=15, 
                    step=1, 
                    value=2, 
                    visible=False
                )
            with gr.Row():
                # highlight legend
                highlight_legend = gr.Markdown(value=hl_desc, visible=False)
            with gr.Row(visible=False) as title_row:
                # selected paper title
                paper_title = gr.Markdown(value='')
            with gr.Row(visible=False) as aff_row:
                # selected paper's affinity score
                affinity = gr.Markdown(value='')
            with gr.Row(visible=False) as cite_row:
                # selected paper's citation count 
                citation = gr.Markdown(value='')
            with gr.Row(visible=False) as hl_row: 
                # highlighted text from paper
                highlight = gr.components.Interpretation(paper_abstract) 
                
    ### EVENT LISTENERS
    
    # components to work with
    init_components = [
        see_more_rel_btn, # explore more button
        result1_desc, # description for first results
        search_status, # search status
        top_paper_slider,
        top_pair_slider
    ]
    
    init_result_components = \
        paper_title_up + paper_affinity_up + citation_count + demarc_lines + sent_pair_score + \
            sent_text_query + sent_text_candidate + sent_hl_query + sent_hl_candidate 
    
    explore_more_components = [
        selected_papers_radio, # list of papers for show more section
        source_sentences, # list of sentences for show more section
        paper_title, # paper title for show more section
        affinity, # affinity for show more section
        result2_desc, # description for explore more
        highlight_slider, # highlight slider
    ]
    
    compute_btn.click(
        fn=show_status,
        inputs=[],
        outputs=search_status
    )
    
    compute_btn.click(
        fn=get_similar_paper,
        inputs=[
            title_input,
            abstract_text_input, 
            author_id_input,
            top_paper_slider,
            top_pair_slider,
            info
        ],
        outputs=init_components + init_result_components + explore_more_components + [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,
            aff_row,
            cite_row,
            highlight_legend,
            highlight_slider,
            hl_row,
        ]
    )
    
    # change highlight based on selected sentences from submission
    source_sentences.change(
        fn=change_sentence,
        inputs=[
            selected_papers_radio,
            source_sentences,
            highlight_slider,
            info
        ],
        outputs=highlight
    )
    
    # change paper to show based on selected papers
    selected_papers_radio.change(
        fn=change_paper,
        inputs=[
            selected_papers_radio,
            source_sentences,
            highlight_slider,
            info,
        ],
        outputs= [
            paper_title,
            paper_abstract,
            affinity,
            citation,
            highlight, 
            highlight_slider
        ]
    )
    
    # change number of higlights to show
    highlight_slider.change(
        fn=change_num_highlight,
        inputs=[
            selected_papers_radio,
            source_sentences,
            highlight_slider,
            info
        ],
        outputs=[
            highlight
        ]
    )
    
    # change number of top papers to show initially
    top_paper_slider.change(
        fn=change_top_output,
        inputs=[
            top_paper_slider,
            top_pair_slider,
            info   
        ],
        outputs=init_result_components+[result1_desc]
    ) 
    
    # change number of top sentence pairs to show initially
    top_pair_slider.change(
        fn=change_top_output,
        inputs=[
            top_paper_slider,
            top_pair_slider,
            info
        ],
        outputs=init_result_components+[result1_desc]
    )
    
if __name__ == "__main__":
    demo.queue().launch() # add ?__theme=light to force light mode
    #demo.queue().launch(share=True)  # add ?__theme=light to force light mode