app.py

word_embedding_model = models.Transformer('cambridgeltl/SapBERT-from-PubMedBERT-fulltext')
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])

def search(query):
    Entrez.email = '[email protected]'
    handle = Entrez.esearch(db='pubmed', 
                            sort='relevance', 
                            retmax='5',
                            retmode='xml', 
                            term=query)
    results = Entrez.read(handle)
    return results

def fetch_details(id_list):
    ids = ','.join(id_list)
    Entrez.email = '[email protected]'
    
    handle_1 = Entrez.efetch(db='pubmed', retmode='xml', id=ids)
    results_1 = Entrez.read(handle_1)
    return results_1


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):
  element=[]
  final_textrank_list=[]
  document=[]
  text_doc=[]
  final_list=[]
  score_list=[]
  sum_list=[]
  model_1 = SentenceTransformer(model_1)
  model_2 = SentenceTransformer(model_2)
  url = article_link
  if (url == False):
    print("error")
  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]", " ").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)
  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])
 
  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) 

  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)


  #if __name__ == '__main__':
  #for qu in range(len(final_list)):
  results=search(f_1)
  id_list = results['IdList']
  #if(id_list != []):
  papers = fetch_details(id_list)
  abstract_list=[]
  year_list=[]
  journal_list=[]
  title_list=[]
  for i, paper in enumerate(papers['PubmedArticle']):
    x=(json.dumps(papers['PubmedArticle'][i], indent=2))
    t_list=[]
    y = json.loads(x)
    try:
      value_1 = y['MedlineCitation']['Article']['Abstract']['AbstractText']
      value = (y['MedlineCitation']['Article']['ArticleTitle'])
      value_2 = (y['MedlineCitation']['Article']['Journal']['JournalIssue']['PubDate']['Year'])
      value_journal = (y['MedlineCitation']['Article']['Journal']['Title'])
      t_list.append(value)
      title_list.append(t_list)
      year_list.append(value_2)
      abstract_list.append(value_1)
      journal_list.append(value_journal)
    except KeyError:
      value_1 = []
      title_list.append(t_list)
      abstract_list.append(value_1)
      year_list.append(value_2)
      journal_list.append(value_journal)
  mydict={'Title': title_list, 'Abstract':abstract_list, 'Journal Title': journal_list, 'Year': year_list}
  df_new=pd.DataFrame(mydict)
    #print(df_new)        
  #else:
  #  abstract_list=[]
  #  title_list=[]
  #  year_list=[]
  #  journal_list=[]
  #  a=["No result"]
  #  b=["No results"]
  #  abstract_list.append(a)
  #  title_list.append(b)
  #  mydict={'Title': title_list, 'Abstract':abstract_list, 'Journal Title': journal_list, 'Year': year_list}
  #  df_new=pd.DataFrame(mydict)
    #print(df_new)
  return title_list
  
gr.Interface(keyphrase_generator, 
             inputs=[gr.inputs.Textbox(lines=1, placeholder="Provide article web link here",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',                                                 
                                                 '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/all-mpnet-base-v1', 
                                        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=30, step=1, default=10, label="Max Keywords")], 
             outputs=gr.outputs.Textbox(type="auto", label="Stuff"), 
             theme="peach", 
             title="Scientific Article Keyphrase Generator", description="Generates the keyphrases from an article which best describes the article.",
             article= "The work is based on a part of 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 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 default model names are provided which can be changed from the list of pretrained models. "
             "\t The value of output keyphrases can be changed. The default value is 10, minimum is 5 and a maximum value of 30.").launch(share=True,debug=True)