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 = {
        '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 #TODO make this adjustable by the user?
        )
        
        # 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):
        out_tmp = [
            #gr.update(value=titles[i], visible=True), 
            gr.update(value="#### [%s](%s)"%(titles[i], paper_urls[i]), visible=True), 
            gr.update(value='#### Affinity: %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

    # 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():
    # show the interactive part of the app
    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 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):
            #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']
        url = info[selected_papers_radio]['url']
        title_out = '#### [%s](%s)'%(title, url) # output in format of markdown
        aff_score_out = '#### Affinity: %s'%aff_score
        return title_out, abstract, aff_score_out, 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 [this video]() for a quicker 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, sentence from the paper abstract)
- **<span style="color:black;background-color:#65B5E3;">Blue highlights</span>** inidicate phrases that appear 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 of top 10 similar papers with affinity scores.
- You can select different papers from the list to see the title and the abstract 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. The darker the color, the higher the similarity.
- **<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, click on another paper from the list.
-------
        """
    ) 
    
    ### 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?') 

    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():
        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.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.Markdown(value='', visible=False)
        with gr.Column(scale=1):
            #affinity2 = gr.Textbox(label='Affinity', interactive=False, value='', visible=False)
            affinity2 = gr.Markdown(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.Markdown(value='', visible=False)
        with gr.Column(scale=1):
            # affinity3 = gr.Textbox(label='Affinity', interactive=False, value='', visible=False)
            affinity3 = gr.Markdown(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.Markdown(value='')
        with gr.Column(scale=1):
            # affinity= gr.Textbox(label='Affinity', interactive=False, value='')
            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,
            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=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. 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()