Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""McqGenerator.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1_1AYqOfr649dp8QiMuV3GPiNH_bMdpUA
+
+## Installation of libraries
+
+## Example 1
+"""
+
+from textwrap3 import wrap
+
+text = """Elon Musk has shown again he can influence the digital currency market with just his tweets. After saying that his electric vehicle-making company
+Tesla will not accept payments in Bitcoin because of environmental concerns, he tweeted that he was working with developers of Dogecoin to improve
+system transaction efficiency. Following the two distinct statements from him, the world's largest cryptocurrency hit a two-month low, while Dogecoin
+rallied by about 20 percent. The SpaceX CEO has in recent months often tweeted in support of Dogecoin, but rarely for Bitcoin.  In a recent tweet,
+Musk put out a statement from Tesla that it was “concerned” about the rapidly increasing use of fossil fuels for Bitcoin (price in India) mining and
+transaction, and hence was suspending vehicle purchases using the cryptocurrency.  A day later he again tweeted saying, “To be clear, I strongly
+believe in crypto, but it can't drive a massive increase in fossil fuel use, especially coal”.  It triggered a downward spiral for Bitcoin value but
+the cryptocurrency has stabilised since.   A number of Twitter users welcomed Musk's statement. One of them said it's time people started realising
+that Dogecoin “is here to stay” and another referred to Musk's previous assertion that crypto could become the world's future currency."""
+
+for wrp in wrap(text, 150):
+  print (wrp)
+print ("\n")
+
+"""## Example 2"""
+
+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")
+
+"""# **Answer Span Extraction (Keywords and Noun Phrases)**"""
+
+total = 10
+
+import nltk
+nltk.download('stopwords')
+from nltk.corpus import stopwords
+import string
+import pke
+import traceback
+
+def get_nouns_multipartite(content):
+    out=[]
+    try:
+        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 with 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")
+
+"""# **Gradio UI Visualization**"""
+
+import numpy as np
+from sense2vec import Sense2Vec
+s2v = Sense2Vec().from_disk('../s2v_old')
+
+from sentence_transformers import SentenceTransformer
+# paraphrase-distilroberta-base-v1
+sentence_transformer_model = SentenceTransformer('msmarco-distilbert-base-v3')
+
+from similarity.normalized_levenshtein import NormalizedLevenshtein
+normalized_levenshtein = NormalizedLevenshtein()
+
+def filter_same_sense_words(original,wordlist):
+  filtered_words=[]
+  base_sense =original.split('|')[1] 
+  print (base_sense)
+  for eachword in wordlist:
+    if eachword[0].split('|')[1] == base_sense:
+      filtered_words.append(eachword[0].split('|')[0].replace("_", " ").title().strip())
+  return filtered_words
+
+def get_highest_similarity_score(wordlist,wrd):
+  score=[]
+  for each in wordlist:
+    score.append(normalized_levenshtein.similarity(each.lower(),wrd.lower()))
+  return max(score)
+
+def sense2vec_get_words(word,s2v,topn,question):
+    output = []
+    print ("word ",word)
+    try:
+      sense = s2v.get_best_sense(word, senses= ["NOUN", "PERSON","PRODUCT","LOC","ORG","EVENT","NORP","WORK OF ART","FAC","GPE","NUM","FACILITY"])
+      most_similar = s2v.most_similar(sense, n=topn)
+      # print (most_similar)
+      output = filter_same_sense_words(sense,most_similar)
+      print ("Similar ",output)
+    except:
+      output =[]
+
+    threshold = 0.6
+    final=[word]
+    checklist =question.split()
+    for x in output:
+      if get_highest_similarity_score(final,x)<threshold and x not in final and x not in checklist:
+        final.append(x)
+    
+    return final[1:]
+
+def mmr(doc_embedding, word_embeddings, words, top_n, lambda_param):
+
+    # Extract similarity within words, and between words and the document
+    word_doc_similarity = cosine_similarity(word_embeddings, doc_embedding)
+    word_similarity = cosine_similarity(word_embeddings)
+
+    # Initialize candidates and already choose best keyword/keyphrase
+    keywords_idx = [np.argmax(word_doc_similarity)]
+    candidates_idx = [i for i in range(len(words)) if i != keywords_idx[0]]
+
+    for _ in range(top_n - 1):
+        # Extract similarities within candidates and
+        # between candidates and selected keywords/phrases
+        candidate_similarities = word_doc_similarity[candidates_idx, :]
+        target_similarities = np.max(word_similarity[candidates_idx][:, keywords_idx], axis=1)
+
+        # Calculate MMR
+        mmr = (lambda_param) * candidate_similarities - (1-lambda_param) * target_similarities.reshape(-1, 1)
+        mmr_idx = candidates_idx[np.argmax(mmr)]
+
+        # Update keywords & candidates
+        keywords_idx.append(mmr_idx)
+        candidates_idx.remove(mmr_idx)
+
+    return [words[idx] for idx in keywords_idx]
+
+from collections import OrderedDict
+from sklearn.metrics.pairwise import cosine_similarity
+
+def get_distractors_wordnet(word):
+    distractors=[]
+    try:
+      syn = wn.synsets(word,'n')[0]
+      
+      word= word.lower()
+      orig_word = word
+      if len(word.split())>0:
+          word = word.replace(" ","_")
+      hypernym = syn.hypernyms()
+      if len(hypernym) == 0: 
+          return distractors
+      for item in hypernym[0].hyponyms():
+          name = item.lemmas()[0].name()
+          #print ("name ",name, " word",orig_word)
+          if name == orig_word:
+              continue
+          name = name.replace("_"," ")
+          name = " ".join(w.capitalize() for w in name.split())
+          if name is not None and name not in distractors:
+              distractors.append(name)
+    except:
+      print ("Wordnet distractors not found")
+    return distractors
+
+def get_distractors (word,origsentence,sense2vecmodel,sentencemodel,top_n,lambdaval):
+  distractors = sense2vec_get_words(word,sense2vecmodel,top_n,origsentence)
+  print ("distractors ",distractors)
+  if len(distractors) ==0:
+    return distractors
+  distractors_new = [word.capitalize()]
+  distractors_new.extend(distractors)
+  # print ("distractors_new .. ",distractors_new)
+
+  embedding_sentence = origsentence+ " "+word.capitalize()
+  # embedding_sentence = word
+  keyword_embedding = sentencemodel.encode([embedding_sentence])
+  distractor_embeddings = sentencemodel.encode(distractors_new)
+
+  # filtered_keywords = mmr(keyword_embedding, distractor_embeddings,distractors,4,0.7)
+  max_keywords = min(len(distractors_new),5)
+  filtered_keywords = mmr(keyword_embedding, distractor_embeddings,distractors_new,max_keywords,lambdaval)
+  # filtered_keywords = filtered_keywords[1:]
+  final = [word.capitalize()]
+  for wrd in filtered_keywords:
+    if wrd.lower() !=word.lower():
+      final.append(wrd.capitalize())
+  final = final[1:]
+  return final
+
+sent = "What cryptocurrency did Musk rarely tweet about?"
+keyword = "Bitcoin"
+
+# sent = "What did Musk say he was working with to improve system transaction efficiency?"
+# keyword= "Dogecoin"
+
+
+# sent = "What company did Musk say would not accept bitcoin payments?"
+# keyword= "Tesla"
+
+
+# sent = "What has Musk often tweeted in support of?"
+# keyword = "Cryptocurrency"
+
+print (get_distractors(keyword,sent,s2v,sentence_transformer_model,40,0.2))
+
+"""# **Gradio Visualization with MCQs**"""
+
+import mysql.connector
+import datetime;
+
+mydb = mysql.connector.connect(
+  host="qtechdb-1.cexugk1h8rui.ap-northeast-1.rds.amazonaws.com",
+  user="admin",
+  password="F3v2vGWzb8vaniE3nqzi",
+  database="spring_social"
+)
+
+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.HTML(  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 = getnounphrases(summary_text,sentence_transformer_model,3)
+    np =  get_keywords(context,summary_text,total)
+    print ("\n\nNoun phrases",np)
+
+    output="<b style='color:black;'>Select the correct answer.</b><br><br>"
+    i = 1 
+    for answer in np:
+      ques = get_question(summary_text,answer,question_model,question_tokenizer)
+      distractors = get_distractors(answer.capitalize(),ques,s2v,sentence_transformer_model,40,0.2)
+      # output= output + ques + "\n" + "Ans: "+answer.capitalize() + "\n\n"
+      output = output + "<b style='color:black;'>Q"+ str(i) + ") " + ques + "</b>"
+      # output = output + "<br>"
+      i += 1
+      output = output + "<br><b> ▪ " +answer.capitalize()+  "</b>"
+      if len(distractors)>0:
+        for distractor in distractors[:3]:
+          output = output + "<p> ▪  "  + distractor+  "</p>"
+      output = output + "<br>"
+
+    output = output + "<b style='color:black;'>" + "Correct Answer Key</b><br>"
+
+    i = 1
+    for answer in np:
+      i += 1
+      output = output + "<b style='color:green;'> ▪ " +answer.capitalize()+  "</b>"
+      output = output + "<br>"
+
+    # mycursor = mydb.cursor()
+    # timedate = datetime.datetime.now()
+
+    # sql = "INSERT INTO mcqstexts (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, share=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 = getnounphrases(summary_text,sentence_transformer_model,3)
+  np =  get_keywords(context,summary_text,total)
+  print ("\n\nNoun phrases",np)
+
+  output="<b style='color:black;'>Select the correct answer.</b><br><br>"
+  i = 1 
+  for answer in np:
+    ques = get_question(summary_text,answer,question_model,question_tokenizer)
+    distractors = get_distractors(answer.capitalize(),ques,s2v,sentence_transformer_model,40,0.2)
+    # output= output + ques + "\n" + "Ans: "+answer.capitalize() + "\n\n"
+    output = output + "<b style='color:black;'>Q"+ str(i) + ") " + ques + "</b>"
+    # output = output + "<br>"
+    i += 1
+    output = output + "<br><b> ▪ " +answer.capitalize()+  "</b>"
+    if len(distractors)>0:
+      for distractor in distractors[:3]:
+        output = output + "<p> ▪  "  + distractor+  "</p>"
+    output = output + "<br>"
+
+  output = output + "<b style='color:black;'>" + "Correct Answer Key</b><br>"
+
+  i = 1
+  for answer in np:
+    i += 1
+    output = output + "<b style='color:green;'> ▪ " +answer.capitalize()+  "</b>"
+    output = output + "<br>"
+
+  return output
+
+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)
+      # return text
+      # mycursor = mydb.cursor()
+
+      # timedate= datetime.datetime.now()
+
+      # sql = "INSERT INTO mcqsfiles (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
+
+# filecreate(file,2)
+
+import gradio as gr
+
+context = gr.HTML(label="Text")
+file = gr.File(label="Upload your file (File must contain 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).")
+
+
+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)