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 scores...')
    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
    }
    pickle.dump(tmp, open('paper_info.pkl', 'wb'))
    
    # 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('retrieval complete in [%0.2f] seconds'%(end - start))
    
    return (
        gr.update(choices=display_title, interactive=True, visible=True), # set of papers
        gr.update(choices=input_sentences, interactive=True), # submission sentences
        gr.update(visible=True),    # title row
        gr.update(visible=True),    # abstract row
        gr.update(visible=True)     # button
    )

def get_highlights(
    abstract_text_input,
    pdf_file_input,
    abstract,
    K=2
):
    print('obtaining highlights..')
    start = time.time()
    # Compute sent-level and phrase-level affinity scores for each papers
    sent_ids, sent_scores, info = get_highlight_info(
        sent_model, 
        abstract_text_input, 
        abstract,
        K=K
    )

    input_sentences = sent_tokenize(abstract_text_input)
    num_sents = len(input_sentences)
     
    word_scores = dict()
    
    # different highlights for each input sentence
    for i in range(num_sents):
        word_scores[str(i)] = {
            "original": abstract,
            "interpretation": list(zip(info['all_words'], info[i]['scores']))
        } # format to feed to for Gradio Interpretation component
    
    tmp = {
        'source_sentences': input_sentences,
        'highlight': word_scores
    }
    pickle.dump(tmp, open('highlight_info.pkl', 'wb'))
    end = time.time()
    print('done in [%0.2f] seconds'%(end - start))
    
    # update the visibility of radio choices
    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(source_sent_choice):
    # change the output highlight based on the sentence selected from the submission
    fname = 'highlight_info.pkl'
    if os.path.exists(fname):
        tmp = pickle.load(open(fname, 'rb'))
        source_sents = tmp['source_sentences']
        highlights = tmp['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 = 'paper_info.pkl'
    if os.path.exists(fname):
        tmp = pickle.load(open(fname, 'rb'))
        for title, abstract, aff_score in zip(tmp['titles'], tmp['abstracts'], tmp['doc_scores']):
            display_title = '[ %0.3f ] %s'%(aff_score, title)
            if display_title == selected_papers_radio:
                #print('changing paper')
                return title, abstract, aff_score   # update title, abstract, and affinity score fields
    else:
        return

with gr.Blocks() as demo:
    
    # TODO Text description about the app and disclaimer
    ### TEXT Description
    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 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 "Search Similar Papers" button.
##### Search Similar Papers
- Based on the input information above, the tool will 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 score (ranging from 0 -1), 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. 
##### Show Relevant Parts
- Once you have retrieved the similar papers above, and selected a paper that you are interested in, you will have an option to see what parts of the selected paper may be relevant to the submission abstract. Click on the "Show Relevant Parts" button.
- 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:red">Red</span> highlights: sentences from the reviewer's paper abstract with high semantic similarity to the selected sentence.
- <span style="color:blue">Blue</span> highlights: matching 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 paper above and re-click "Show Relevant Parts" to refresh. 

**Disclaimer.** This tool and its output should not serve as a 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). 
        """
    ) 
    
    ### 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('Search Similar Papers from the Reviewer')  
    
    ### 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='Selected Top 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(visibe=False) as abstract_row:
        paper_abstract = gr.Textbox(label='Abstract', interactive=False, visible=False)
        
    with gr.Row(visible=False) as explain_button_row:
        explain_btn = gr.Button('Show Relevant Parts from Selected Paper')
    
    ### 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
    compute_btn.click(
        fn=get_similar_paper,
        inputs=[
            abstract_text_input, 
            pdf_file_input, 
            author_id_input
        ],
        outputs=[
            selected_papers_radio,
            source_sentences,
            title_row,
            paper_abstract,
            explain_button_row,
        ]
    )      
    
    # get highlights
    explain_btn.click(
        fn=get_highlights,
        inputs=[
            abstract_text_input, 
            pdf_file_input,
            paper_abstract
        ],
        outputs=source_sentences
    )
    
    # change highlight based on selected sentences from submission
    source_sentences.change(
        fn=change_output_highlight,
        inputs=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
        ]
    )

if __name__ == "__main__":
    demo.launch()