app.py

import nltk
import re
import nltkmodule

from newspaper import Article
from newspaper import fulltext
import requests
import itertools
import os


from nltk.tokenize import word_tokenize
from sentence_transformers import SentenceTransformer
import pandas as pd
import numpy as np
from pandas import ExcelWriter
from torch.utils.data import DataLoader
import math
from sentence_transformers import models, losses
from sentence_transformers import SentencesDataset, LoggingHandler, SentenceTransformer
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers.readers import *
from nltk.corpus import stopwords
stop_words = stopwords.words('english')
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics.pairwise import cosine_similarity
import scipy.spatial
import networkx as nx
from nltk.tokenize import sent_tokenize
import scispacy
import spacy
import en_core_sci_lg
import string
from nltk.stem.wordnet import WordNetLemmatizer
import gradio as gr
import inflect
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import silhouette_samples, silhouette_score, davies_bouldin_score
import json
from xml.etree import ElementTree as ET
p = inflect.engine()

nlp = en_core_sci_lg.load()
sp = en_core_sci_lg.load()
all_stopwords = sp.Defaults.stop_words

os.environ["TOKENIZERS_PARALLELISM"] = "false"

def remove_stopwords(sen):
    sen_new = " ".join([i for i in sen if i not in stop_words])
    return sen_new




def keyphrase_generator(article_link, model_1, model_2, max_num_keywords, model_3, max_retrieved, model_4):

  word_embedding_model = models.Transformer(model_3)
  pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
                             pooling_mode_mean_tokens=True,
                             pooling_mode_cls_token=False,
                             pooling_mode_max_tokens=False)

  embedder = SentenceTransformer(modules=[word_embedding_model, pooling_model])

  element=[]
  cluster_list_final=[]
  comb_list=[] 
  comb=[]
  title_list=[]
  titles_list=[]
  abstracts_list=[]
  silhouette_score_list=[]
  final_textrank_list=[]
  document=[]
  text_doc=[]
  final_list=[]
  score_list=[]
  sum_list=[]
  #############################################     Here we first extract the sentences using SBERT and Textrank     ###########################
  model_1 = SentenceTransformer(model_1)
  model_2 = SentenceTransformer(model_2)
  url = article_link
  html = requests.get(url).text
  article = fulltext(html)
  corpus=sent_tokenize(article)
  indicator_list=['concluded','concludes','in a study', 'concluding','conclude','in sum','in a recent study','therefore','thus','so','hence',
          'as a result','accordingly','consequently','in short','proves that','shows that','suggests that','demonstrates that','found that','observed that',
          'indicated that','suggested that','demonstrated that']
  count_dict={}
  for l in corpus:
    c=0
    for l2 in indicator_list:     
       if l.find(l2)!=-1:#then it is a substring
          c=1
          break           
    if c:#
       count_dict[l]=1
    else:
       count_dict[l]=0
  for sent, score in count_dict.items():
    score_list.append(score)
  clean_sentences_new = pd.Series(corpus).str.replace("[^a-zA-Z]", " ", regex = True).tolist()
  corpus_embeddings = model_1.encode(clean_sentences_new)
  sim_mat = np.zeros([len(clean_sentences_new), len(clean_sentences_new)])
  for i in range(len(clean_sentences_new)):
    len_embeddings=(len(corpus_embeddings[i]))
    for j in range(len(clean_sentences_new)):
      if i != j:      
        if(len_embeddings == 1024):
          sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,1024), corpus_embeddings[j].reshape(1,1024))[0,0]
        elif(len_embeddings == 768):
          sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,768), corpus_embeddings[j].reshape(1,768))[0,0]
  nx_graph = nx.from_numpy_array(sim_mat)
  scores = nx.pagerank(nx_graph, max_iter = 1500)
  sentences=((scores[i],s) for i,s in enumerate(corpus))
  for elem in sentences:
    element.append(elem[0])
  for sc, lst in zip(score_list, element):  ########## taking the scores from both the lists 
    sum1=sc+lst
    sum_list.append(sum1)
  x=sorted(((sum_list[i],s) for i,s in enumerate(corpus)), reverse=True)
  for elem in x:
    final_textrank_list.append(elem[1])
    
 ################################################################    Textrank ends   #################################################
 
 ########################################################   From here we start the keyphrase extraction process   ################################################
 
  a=int((10*len(final_textrank_list))/100.0)
  if(a<5):
    total=5
  else:
    total=int(a)
  for i in range(total):
    document.append(final_textrank_list[i])
  doc=" ".join(document)
  for i in document:
    doc_1=nlp(i)
    text_doc.append([X.text for X in doc_1.ents])
  entity_list = [item for sublist in text_doc for item in sublist]
  entity_list = [word for word in entity_list if not word in all_stopwords]
  entity_list = [word_entity for word_entity in entity_list if(p.singular_noun(word_entity) == False)]
  entity_list=list(dict.fromkeys(entity_list))
  doc_embedding = model_2.encode([doc])
  candidates=entity_list
  candidate_embeddings = model_2.encode(candidates)
  distances = cosine_similarity(doc_embedding, candidate_embeddings)
  top_n = max_num_keywords
  keyword_list = [candidates[index] for index in distances.argsort()[0][-top_n:]]
  keywords = '\n'.join(keyword_list) 
  
  ##############################################################   Keyphrase extraction ends    #############################################
  
  
  ##################################################################  From here we start the clustering and query generation      ##################################

  c_len=(len(keyword_list))
  keyword_embeddings = embedder.encode(keyword_list)
  data_embeddings = embedder.encode(keyword_list)

  for num_clusters in range(1, top_n):
    clustering_model = KMeans(n_clusters=num_clusters)
    clustering_model.fit(keyword_embeddings)
    cluster_assignment = clustering_model.labels_
    clustered_sentences = [[] for i in range(num_clusters)]
    for sentence_id, cluster_id in enumerate(cluster_assignment):
      clustered_sentences[cluster_id].append(keyword_list[sentence_id])
    cl_sent_len=(len(clustered_sentences))
    list_cluster=list(clustered_sentences)
    a=len(list_cluster)
    cluster_list_final.append(list_cluster)
    if (c_len==cl_sent_len and c_len>=3) or cl_sent_len==1:
      silhouette_avg = 0
      silhouette_score_list.append(silhouette_avg)
    elif c_len==cl_sent_len==2:
      silhouette_avg = 1
      silhouette_score_list.append(silhouette_avg)
    else:
      silhouette_avg = silhouette_score(keyword_embeddings, cluster_assignment)
      silhouette_score_list.append(silhouette_avg)
  res_dict = dict(zip(silhouette_score_list, cluster_list_final))
  cluster_items=res_dict[max(res_dict)]

  for i in cluster_items:
    z=' OR '.join(i)
    comb.append("("+z+")")
  comb_list.append(comb)
  combinations = []
  for subset in itertools.combinations(comb, 2):
    combinations.append(subset)
  f1_list=[]
  for s in combinations:
    final = ' AND '.join(s)
    f1_list.append("("+final+")")
  f_1=' OR '.join(f1_list)
  final_list.append(f_1)

########################################################   query generation ends here     #######################################

#######################################   PubeMed abstract extraction starts here    #########################################

  ncbi_url='https://eutils.ncbi.nlm.nih.gov/entrez/eutils/'

  last_url='esearch.fcgi?db=pubmed'+'&term='+f_1
  overall_url=ncbi_url+last_url+'&rettype=json'+'&sort=relevance'
  pubmed_search_request = requests.get(overall_url)
  
  root = ET.fromstring(pubmed_search_request.text)
  levels = root.findall('.//Id')
  search_id_list=[]
  for level in levels:  
    name = level.text
    search_id_list.append(name)
  all_search_ids = ','.join(search_id_list)
  fetch_url='efetch.fcgi?db=pubmed'
  search_id='&id='+all_search_ids
  return_url=ncbi_url+fetch_url+search_id+'&rettype=text'+'&retmode=xml'+'&retmax=500'+'&sort=relevance'
  pubmed_abstract_request = requests.get(return_url)
  root_1 = ET.fromstring(pubmed_abstract_request.text)
  article_title = root_1.findall('.//ArticleTitle')
  for a in article_title:  
    article_title_name = a.text
    titles_list.append(article_title_name)
  article_abstract = root_1.findall('.//AbstractText')
  for b in article_abstract:
    article_abstract_name = b.text
    abstracts_list.append(article_abstract_name)

##################################   PubMed extraction ends here    ########################################################

##########################################    Most relevant abstracts as per news article heading starts here    ##########################################
  
  first_article = Article(url, language='en')
  first_article.download()
  first_article.parse()
  article_heading=(first_article.title)
  article_heading=sent_tokenize(article_heading)
  model_4 = SentenceTransformer(model_4)
  
  my_dict = dict(zip(titles_list,abstracts_list))
  title_embeddings = model_4.encode(titles_list)
  heading_embedding = model_4.encode(article_heading)
  similarities = cosine_similarity(heading_embedding, title_embeddings)
  max_n = max_retrieved
  sorted_titles = [titles_list[index] for index in similarities.argsort()[0][-max_n:]]
  sorted_abstract_list=[]
  for list_elem in sorted_titles:
    sorted_abstract_list.append(my_dict[list_elem])
  sorted_dict = {'Title': sorted_titles, 'Abstract': sorted_abstract_list}
  df_new=pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in sorted_dict.items() ]))  
  df_final = df_new.fillna(' ')
  #fp = df_final.to_csv('title_abstract.csv', index=False)
  

#############################################    Ends here    ####################################################
  
  #return df_final
  #return fp
  return sorted_dict
  

igen_pubmed = gr.Interface(keyphrase_generator, 
             inputs=[gr.inputs.Textbox(lines=1, placeholder="Provide article web link here (Can be chosen from examples below)",default="", label="Article web link"),
                     gr.inputs.Dropdown(choices=['sentence-transformers/all-mpnet-base-v2',
                                                 'sentence-transformers/all-mpnet-base-v1',                       
                                                 'sentence-transformers/all-distilroberta-v1',
                                                 'sentence-transformers/gtr-t5-large',
                                                 'pritamdeka/S-Bluebert-snli-multinli-stsb',
                                                 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
                                                 'pritamdeka/S-BioBert-snli-multinli-stsb',                                                 
                                                 'sentence-transformers/stsb-mpnet-base-v2',
                                                 'sentence-transformers/stsb-roberta-base-v2',
                                                 'sentence-transformers/stsb-distilroberta-base-v2',
                                                 'sentence-transformers/sentence-t5-large',
                                                 'sentence-transformers/sentence-t5-base'], 
                                        type="value", 
                                        default='sentence-transformers/stsb-roberta-base-v2', 
                                        label="Select any SBERT model for TextRank from the list below"),
                     gr.inputs.Dropdown(choices=['sentence-transformers/paraphrase-mpnet-base-v2',
                                                 'sentence-transformers/all-mpnet-base-v1',
                                                 'sentence-transformers/paraphrase-distilroberta-base-v1',
                                                 'sentence-transformers/paraphrase-xlm-r-multilingual-v1',
                                                 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2',
                                                 'sentence-transformers/paraphrase-albert-small-v2',
                                                 'sentence-transformers/paraphrase-albert-base-v2',                                                
                                                 'sentence-transformers/paraphrase-MiniLM-L12-v2',
                                                 'sentence-transformers/paraphrase-MiniLM-L6-v2',
                                                 'sentence-transformers/all-MiniLM-L12-v2',
                                                 'sentence-transformers/all-distilroberta-v1',
                                                 'sentence-transformers/paraphrase-TinyBERT-L6-v2',
                                                 'sentence-transformers/paraphrase-MiniLM-L3-v2',
                                                 'sentence-transformers/all-MiniLM-L6-v2'], 
                                        type="value", 
                                        default='sentence-transformers/all-mpnet-base-v1', 
                                        label="Select any SBERT model for keyphrases from the list below"),                                                     
                     gr.inputs.Slider(minimum=5, maximum=20, step=1, default=10, label="Max Keywords"),
                     gr.inputs.Dropdown(choices=['cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
                                                 'cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token'],
                                        type="value", 
                                        default='cambridgeltl/SapBERT-from-PubMedBERT-fulltext', 
                                        label="Select any SapBERT model for clustering from the list below"),
                     gr.inputs.Slider(minimum=5, maximum=15, step=1, default=10, label="PubMed Max Abstracts"),
                     gr.inputs.Dropdown(choices=['pritamdeka/S-Bluebert-snli-multinli-stsb',
                                                 'pritamdeka/S-BioBert-snli-multinli-stsb',
                                                 'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
                                                 'sentence-transformers/all-mpnet-base-v2'], 
                                        type="value", 
                                        default='sentence-transformers/all-mpnet-base-v2', 
                                        label="Select any SBERT model for abstracts from the list below")], 
             #outputs=gr.outputs.Dataframe(type="auto", label="Retrieved Results from PubMed",max_cols=2, overflow_row_behaviour="paginate"), 
             outputs=gr.outputs.JSON(label="Title and Abstracts"),
             #outputs=gr.outputs.File(label=None),
             theme="peach", layout="horizontal",
             title="PubMed Abstract Retriever", description="Retrieves relevant PubMed abstracts for an online article which can be used as further references. The output is in the form of JSON with <b><i>Title</i></b> and <b><i>Abstract</i></b> as the fields of the JSON output. Please note that it may take sometime for the models to load. Examples are provided below for demo purposes. Choose any one example to see the results. The models can be changed to see different results. ",
             examples=[
                         ["https://www.cancer.news/2021-12-22-mrna-vaccines-weaken-immune-system-cause-cancer.html",                        
                         'sentence-transformers/all-mpnet-base-v1',
                         'sentence-transformers/paraphrase-MiniLM-L12-v2',
                         10,
                         'cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
                         15,
                         'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb'],
                         
                         ["https://www.cancer.news/2022-02-04-doctors-testifying-covid-vaccines-causing-cancer-aids.html#",                        
                         'sentence-transformers/all-mpnet-base-v1',
                         'sentence-transformers/all-mpnet-base-v1',
                         12,
                         'cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
                         11,
                         'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb'],
                         
                         ["https://www.medicalnewstoday.com/articles/alzheimers-addressing-sleep-disturbance-may-alleviate-symptoms",
                         'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
                         'sentence-transformers/all-mpnet-base-v1',
                         10,
                         'cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
                         10,
                         'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb'],
                         
                        ["https://www.medicalnewstoday.com/articles/omicron-what-do-we-know-about-the-stealth-variant",
                         'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
                         'sentence-transformers/all-mpnet-base-v1',
                         15,
                         'cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
                         10,
                         'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb']                       
                       ],                     
             article= "This work is based on the paper <a href=https://dl.acm.org/doi/10.1145/3487664.3487701>provided here</a>." 
             "\t It uses the TextRank algorithm with SBERT to first find the top sentences and then extracts the keyphrases from those sentences using scispaCy and SBERT."
             "\t The application then uses a UMLS based BERT model, <a href=https://arxiv.org/abs/2010.11784>SapBERT</a> to cluster the keyphrases using K-means clustering method and finally create a boolean query. After that the top k titles and abstracts are retrieved from PubMed database and displayed according to relevancy. The SapBERT models can be changed as per the list provided. "
             "\t The list of SBERT models required in the textboxes can be found in <a href=www.sbert.net/docs/pretrained_models.html>SBERT Pre-trained models hub</a>."
             "\t The model names can be changed from the list of pre-trained models provided. "
             "\t The value of keyphrases can be changed. The default value is 10, minimum is 5 and a maximum value of 20. "
             "\t The value of maximum abstracts to be retrieved can be changed. The minimum is 5, default is 10 and a maximum of 15.")

igen_pubmed.launch(share=False,server_name='0.0.0.0',show_error=True)