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
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

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]", " ").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)):
    for j in range(len(clean_sentences_new)):
      if i != j:
        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=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.inputs.Textbox(lines=3, placeholder="Provide article link here", label="Article link"),gr.inputs.Textbox(lines=1, placeholder="SBERT model",default="all-mpnet-base-v2", label="SBERT model for TextRank (e.g. all-mpnet-base-v2)"),gr.inputs.Textbox(lines=1, placeholder="SBERT model",default="all-distilroberta-v1",label="SBERT model for Keyphrases (e.g. all-distilroberta-v1)"),gr.inputs.Slider(minimum=5, maximum=30, step=1, default=10, label="Max number of keyphrases to show")], 
             outputs="text", theme="huggingface", 
             title="Health Article Keyphrase Generator", 
             description="Generates the keyphrases from an online health 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>Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact Checking</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.")
igen.launch(share=True)