from textwrap3 import wrap

text = """A Lion lay asleep in the forest, his great head resting on his paws. A timid little Mouse came upon him unexpectedly, and in her fright and haste to
get away, ran across the Lion's nose. Roused from his nap, the Lion laid his huge paw angrily on the tiny creature to kill her.  "Spare me!" begged
the poor Mouse. "Please let me go and some day I will surely repay you."  The Lion was much amused to think that a Mouse could ever help him. But he
was generous and finally let the Mouse go.  Some days later, while stalking his prey in the forest, the Lion was caught in the toils of a hunter's
net. Unable to free himself, he filled the forest with his angry roaring. The Mouse knew the voice and quickly found the Lion struggling in the net.
Running to one of the great ropes that bound him, she gnawed it until it parted, and soon the Lion was free.  "You laughed when I said I would repay
you," said the Mouse. "Now you see that even a Mouse can help a Lion." """
for wrp in wrap(text, 150):
  print (wrp)
print ("\n")

import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
summary_model = T5ForConditionalGeneration.from_pretrained('t5-base')
summary_tokenizer = T5Tokenizer.from_pretrained('t5-base')

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
summary_model = summary_model.to(device)

import random
import numpy as np

def set_seed(seed: int):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

set_seed(42)

import nltk
nltk.download('punkt')
nltk.download('brown')
nltk.download('wordnet')
from nltk.corpus import wordnet as wn
from nltk.tokenize import sent_tokenize

def postprocesstext (content):
  final=""
  for sent in sent_tokenize(content):
    sent = sent.capitalize()
    final = final +" "+sent
  return final


def summarizer(text,model,tokenizer):
  text = text.strip().replace("\n"," ")
  text = "summarize: "+text
  # print (text)
  max_len = 512
  encoding = tokenizer.encode_plus(text,max_length=max_len, pad_to_max_length=False,truncation=True, return_tensors="pt").to(device)

  input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]

  outs = model.generate(input_ids=input_ids,
                                  attention_mask=attention_mask,
                                  early_stopping=True,
                                  num_beams=3,
                                  num_return_sequences=1,
                                  no_repeat_ngram_size=2,
                                  min_length = 75,
                                  max_length=300)


  dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]
  summary = dec[0]
  summary = postprocesstext(summary)
  summary= summary.strip()

  return summary


summarized_text = summarizer(text,summary_model,summary_tokenizer)


print ("\noriginal Text >>")
for wrp in wrap(text, 150):
  print (wrp)
print ("\n")
print ("Summarized Text >>")
for wrp in wrap(summarized_text, 150):
  print (wrp)
print ("\n")

total = 10

"""# **Answer Span Extraction (Keywords and Noun Phrases)**"""

import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
import string
import pke
import traceback


def get_nouns_multipartite(content):
    out=[]
    try:
        # extractor = spacy.load("en_core_web_sm")
        extractor = pke.unsupervised.MultipartiteRank()

        extractor.load_document(input=content,language='en')


        #    not contain punctuation marks or stopwords as candidates.
        pos = {'PROPN','NOUN'}
        #pos = {'PROPN','NOUN'}
        stoplist = list(string.punctuation)
        stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']
        stoplist += stopwords.words('english')
        # extractor.candidate_selection(pos=pos, stoplist=stoplist)
        extractor.candidate_selection(pos=pos)
        # 4. build the Multipartite graph and rank candidates using random walk,
        #    alpha controls the weight adjustment mechanism, see TopicRank for
        #    threshold/method parameters.
        extractor.candidate_weighting(alpha=1.1,
                                      threshold=0.75,
                                      method='average')
        keyphrases = extractor.get_n_best(n=15)
        

        for val in keyphrases:
            out.append(val[0])
    except:
        out = []
        traceback.print_exc()

    return out

from flashtext import KeywordProcessor

def get_keywords(originaltext,summarytext,total):
  keywords = get_nouns_multipartite(originaltext)
  print ("keywords unsummarized: ",keywords)
  keyword_processor = KeywordProcessor()
  for keyword in keywords:
    keyword_processor.add_keyword(keyword)

  keywords_found = keyword_processor.extract_keywords(summarytext)
  keywords_found = list(set(keywords_found))
  print ("keywords_found in summarized: ",keywords_found)

  important_keywords =[]
  for keyword in keywords:
    if keyword in keywords_found:
      important_keywords.append(keyword)

  return important_keywords[:total]


imp_keywords = get_keywords(text,summarized_text,total)
print (imp_keywords)

"""# **Question generation using T5**"""

question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')
question_model = question_model.to(device)

def get_question(context,answer,model,tokenizer):
  text = "context: {} answer: {}".format(context,answer)
  encoding = tokenizer.encode_plus(text,max_length=384, pad_to_max_length=False,truncation=True, return_tensors="pt").to(device)
  input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]

  outs = model.generate(input_ids=input_ids,
                                  attention_mask=attention_mask,
                                  early_stopping=True,
                                  num_beams=5,
                                  num_return_sequences=1,
                                  no_repeat_ngram_size=2,
                                  max_length=72)


  dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]


  Question = dec[0].replace("question:","")
  Question= Question.strip()
  return Question



for wrp in wrap(summarized_text, 150):
  print (wrp)
print ("\n")

for answer in imp_keywords:
  ques = get_question(summarized_text,answer,question_model,question_tokenizer)
  print (ques)
  print (answer.capitalize())
  print ("\n")

"""# **UI by using Gradio**"""

import gradio as gr
import re


context = gr.Textbox(lines=10, placeholder="Enter paragraph/content here...", label="Enter your content (words input must be more than 150 words).")
total = gr.Slider(1,10, value=1,step=1, label="Total Number Of Questions")
subject = gr.Textbox(placeholder="Enter subject/title here...", label="Enter your title (title must contain 1 word)")

output = gr.Markdown(  label="Question and Answers")


def generate_question_text(context,subject,total):

  words_text = len(re.findall(r'\w+', context))
  words_subject = len(re.findall(r'\w+', subject))

  if (words_text < 150):
    raise gr.Error("Invalid Input (Words limit must be more than 150 words).")
        # print("Number of words:", words)
    
  elif (words_subject < 1):
      raise gr.Error("Invalid Input (Title must be one or more than one word).")
    
  else:
    summary_text = summarizer(context,summary_model,summary_tokenizer)
    for wrp in wrap(summary_text, 150):
      print (wrp)
    np =  get_keywords(context,summary_text,total)
    random.shuffle(np)

    print ("\n\nNoun phrases",np)
    output="<b style='color:black;'>Answer the following short questions.</b><br><br>"
    i=1
    for answer in np:
      ques = get_question(summary_text,answer,question_model,question_tokenizer)
      # output= output + ques + "\n" + "Ans: "+answer.capitalize() + "\n\n"
      output = output + "<b style='color:black;'>Q"+ str(i) + ") " + ques + "</b><br>"
      # output = output + "<br>"
      output = output + "<br>"
      i += 1
      
    output = output + "<br><b style='color:black;'>" + "Correct Answer Key:</b><br>"

    i=1
    for answer in np:
      output = output + "<b style='color:green;'>" + "Ans"+ str(i) + ": " +answer.capitalize()+  "</b>"
      output = output + "<br>"
      i += 1

    # mycursor = mydb.cursor()
    # timedate = datetime.datetime.now()

    # sql = "INSERT INTO shorttexts (subject, input, output, timedate) VALUES (%s,%s, %s,%s)"
    # val = (subject, context, output, timedate)
    # mycursor.execute(sql, val)

    # mydb.commit()

    # print(mycursor.rowcount, "record inserted.")

    return output

iface = gr.Interface(
  fn=generate_question_text, 
  inputs=[context,subject, total], 
  outputs=output, 
  allow_flagging="never",flagging_options=["Save Data"])
  
# iface.launch(debug=True)

def generate_question(context,subject,total):
  summary_text = summarizer(context,summary_model,summary_tokenizer)
  for wrp in wrap(summary_text, 150):
    print (wrp)
  np =  get_keywords(context,summary_text,total)
  random.shuffle(np)

  print ("\n\nNoun phrases",np)
  output="<b style='color:black;'>Answer the following short questions.</b><br><br>"
  i=1
  for answer in np:
    ques = get_question(summary_text,answer,question_model,question_tokenizer)
    # output= output + ques + "\n" + "Ans: "+answer.capitalize() + "\n\n"
    output = output + "<b style='color:black;'>Q"+ str(i) + ") " + ques + "</b><br>"
    # output = output + "<br>"
    output = output + "<br>"
    i += 1
    
  output = output + "<br><b style='color:black;'>" + "Correct Answer Key:</b><br>"

  i=1
  for answer in np:
    output = output + "<b style='color:green;'>" + "Ans"+ str(i) + ": " +answer.capitalize()+  "</b>"
    output = output + "<br>"
    i += 1

  return output

import glob
import os.path
import pandas as pd

file =None

def filecreate(x,subject,total):

    with open(x.name) as fo:  
      text = fo.read()
    #   print(text)

    words_text = len(re.findall(r'\w+', text))
    words_subject = len(re.findall(r'\w+', subject))


    if (words_text < 150):
        raise gr.Error("Invalid Input (Words limit must be more than 150 words).")
        # print("Number of words:", words)
    
    elif (words_subject < 1):
        raise gr.Error("Invalid Input (Title must be one or more than one word).")
    
    else:
       
      generated = generate_question(text,subject, total)

      # mycursor = mydb.cursor()

      # timedate= datetime.datetime.now()

      # sql = "INSERT INTO shortfiles (subject, input, output, timedate) VALUES (%s,%s, %s,%s)"
      # val = (subject, text, generated, timedate)
      # mycursor.execute(sql, val)

      # mydb.commit()

      # print(mycursor.rowcount, "record inserted.")

      return generated

import gradio as gr

context = gr.HTML(label="Text")
file = gr.File(label="Upload your *.txt file (File must contain more than 150 words).")
subject = gr.Textbox(placeholder="Enter subject/title here...", label="Enter your title (title must contain 1 word).")
total = gr.Slider(1,10, value=1,step=1, label="Total Number Of Questions")


fface = gr.Interface(  
  fn=filecreate, 
  inputs=[file,subject,total], 
  outputs=context, 
  # css=".gradio-container {background-image: url('file=blue.jpg')}",
  allow_flagging="never",flagging_options=["Save Data"])


# fface.launch(debug=True, share=True)

demo = gr.TabbedInterface([iface, fface], ["Text", "Upload File"])
demo.launch(debug=True, show_api=False)