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
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, 
    pdf_file_input, 
    author_id_input, 
    num_papers_show=10
):
    print('retrieving similar papers...')
    start = time.time()
    input_sentences = sent_tokenize(abstract_text_input)
    
    # TODO handle pdf file input
    if pdf_file_input is not None:
        name = None
        papers = []
        raise ValueError('Use submission abstract instead.')
    else:
        # Get author papers from id
        name, papers = get_text_from_author_id(author_id_input)
    
    # Compute Doc-level affinity scores for the Papers 
    print('computing document scores...') 
    # TODO detect duplicate papers?
    titles, abstracts, doc_scores = compute_document_score(
        doc_model, 
        tokenizer,
        abstract_text_input, 
        papers,
        batch=50
    )
    
    tmp = {
        'titles': titles,
        'abstracts': abstracts,
        '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]
    
    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))
    
    print('obtaining highlights..')
    start = time.time()
    input_sentences = sent_tokenize(abstract_text_input)
    num_sents = len(input_sentences)
    
    summary_info = dict() # elements to visualize upfront
    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']))
            }

        tmp[display_title[aa]] = {
            'title': tt,
            'abstract': ab,
            'doc_score': ds,
            'source_sentences': input_sentences,
            'highlight': word_scores,
            'top_pairs': top_pairs_info
        }
       
    # TODO better ways of saving intermediate results? user identifiers per session? 
    pickle.dump(tmp, open('info.pkl', 'wb')) 
    end = time.time()
    print('done in [%0.2f] seconds'%(end - start)) 
    
    # 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=doc_scores[i], visible=True)
        ]
        tp = tmp[display_title[i]]['top_pairs']
        for j in range(top_num_info_show):
            out_tmp += [
                gr.update(value=tp[j]['score'], visible=True), 
                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)] # show more button
    assert(len(out) == (top_num_info_show * 5 + 2) * top_papers_show + 3)
    
    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):
    # change the output highlight based on the sentence selected from the submission
    fname = 'info.pkl'
    if os.path.exists(fname):
        tmp = pickle.load(open(fname, 'rb'))
        source_sents = tmp[selected_papers_radio]['source_sentences']
        highlights = tmp[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):
    # change the paper to show based on the paper selected
    fname = 'info.pkl'
    if os.path.exists(fname):
        tmp = pickle.load(open(fname, 'rb'))
        title = tmp[selected_papers_radio]['title']
        abstract = tmp[selected_papers_radio]['abstract']
        aff_score = tmp[selected_papers_radio]['doc_score']
        highlights = tmp[selected_papers_radio]['highlight']
        return title, abstract, aff_score, highlights['0']

    else:
        return

with gr.Blocks() as demo:
    
    # Text description about the app and disclaimer
    ### TEXT Description
    # TODO add instruction video link
    # TODO udpate instruction based on new changes
    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.
##### Similar Papers From the Reviewer
- Based on the input information above, the tool will first search for similar papers from the reviewer's previous publications using [Semantic Scholar API](https://www.semanticscholar.org/product/api). 
- It will list top 10 similar papers along with the **affinity scores** (ranging from 0 -1) for each, computed using text representations from a [language model](https://github.com/allenai/specter/tree/master/specter).
- You can click on different papers to see title, abstract, and affinity scores in detail. 
##### Relevant Parts
- Below the list of papers, we highlight relevant parts in the selected paper compared to the submission abstract.
- On the left, you will see individual sentences from the submission abstract you can select from.
- On the right, you will see the abstract of the selected paper, with **highlights**.
- **<span style="color:black;background-color:#DB7262;">Red highlights</span>**: sentences from the reviewer's paper abstract with high semantic similarity to the selected sentence.
- **<span style="color:black;background-color:#5296D5;">Blue highlights</span>**: phrases from the reviewer's paper abstract that is 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():
            pdf_file_input = gr.File(label='OR upload a submission PDF File')
        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?')  
        
        
    ### OVERVIEW
    # Paper title, score, and top-ranking sentence pairs -- two sentence pairs per paper, three papers
    # TODO blockfy similar components together and simplify 
    ## ONE BLOCK OF INFO FOR A SINGLE PAPER
    ## PAPER1 
    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.Number(label='Affinity', interactive=False, value=0, visible=False)
    with gr.Row() as rel1_1:
        with gr.Column(scale=1):
            sent_pair_score1_1 = gr.Number(label='Sentence Relevance', interactive=False, value=0, 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.Number(label='Sentence Relevance', interactive=False, value=0, 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)
            
    ## 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.Number(label='Affinity', interactive=False, value=0, visible=False)
    with gr.Row() as rel2_1:
        with gr.Column(scale=1):
            sent_pair_score2_1 = gr.Number(label='Sentence Relevance', interactive=False, value=0, 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 Submission', 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.Number(label='Sentence Relevance', interactive=False, value=0, 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 Submission', visible=False)
            sent_pair_candidate2_2_hl = gr.components.Interpretation(sent_pair_candidate2_2)

    ## 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.Number(label='Affinity', interactive=False, value=0, visible=False)
    with gr.Row() as rel3_1:
        with gr.Column(scale=1):
            sent_pair_score3_1 = gr.Number(label='Sentence Relevance', interactive=False, value=0, 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 Submission', 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.Number(label='Sentence Relevance', interactive=False, value=0, 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 Submission', 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 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.Number(label='Affinity', interactive=False, value=0)
    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, 
            pdf_file_input, 
            author_id_input
        ],
        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
        ]
    )      
    
    # 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
        ],
        outputs=highlight
    )
    
    # change paper to show based on selected papers
    selected_papers_radio.change(
        fn=change_paper,
        inputs=selected_papers_radio,
        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 user 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()