import nltk
import re
import nltkmodule
from newspaper import Article
from newspaper import fulltext
import requests
from nltk.tokenize import word_tokenize
from sentence_transformers import SentenceTransformer, models, losses, LoggingHandler
import pandas as pd
import numpy as np
from torch.utils.data import DataLoader
import math
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers.readers import *
from nltk.corpus import stopwords
stop_words = stopwords.words('english')
from sklearn.metrics.pairwise import cosine_similarity
import networkx as nx
from nltk.tokenize import sent_tokenize
import scispacy
import en_core_sci_lg
import string
import gradio as gr
import inflect

inflect_op = inflect.engine()
nlp = en_core_sci_lg.load()
sp = en_core_sci_lg.load()
all_stopwords = sp.Defaults.stop_words


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=[]
  score_list=[]
  sum_list=[]
  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)
  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(inflect_op.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) 
  return keywords

igen=gr.Interface(keyphrase_generator, 
                  inputs=[gr.components.Textbox(lines=1, placeholder="Provide an online health article web link here",default="", label="Article web link"),
                      gr.components.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/all-roberta-large-v1',
                                                 '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='pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', 
                                        label="Select any SBERT model for TextRank from the list below"),
                     gr.components.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.components.Slider(minimum=5, maximum=30, step=1, default=10, label="Max Keywords")],
             outputs=gr.outputs.Textbox(type="text", label="Output"), theme="peach", 
             title="Health Article Keyphrase Generator", 
             description="Generates the keyphrases from an online health article which best describes the article. ", #Examples are provided below for demo purposes. Choose any one example to see the 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],
                         
             #            ["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],
                         
             #            ["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],
                         
             #           ["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]                      
             #          ],
             article= "The work is based on a part of the paper provided <a href=https://dl.acm.org/doi/10.1145/3487664.3487701>here</a>." 
             "\t It uses the TextRank algorithm with <a href=https://www.sbert.net/>SBERT</a> to first find the top ranked sentences and then extracts the keyphrases" 
             "\t from those sentences using <a href = https://allenai.github.io/scispacy/>scispaCy</a> and SBERT."
             "\t The list of SBERT models provided 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 models available. "
             "\t The value of output keyphrases can be changed. The default value is 10, minimum is 5 and a maximum value of 30.")
             
igen.launch(share=False)
####