Upload TextGapPro.py

pages/TextGapPro.py

@@ -0,0 +1,132 @@
+import streamlit as st
+import textwrap
+import json
+import requests
+import string
+import re
+import nltk
+import string
+import itertools
+
+import pke
+from nltk.corpus import stopwords
+from nltk.corpus import wordnet
+import traceback
+from nltk.tokenize import sent_tokenize
+from flashtext import KeywordProcessor
+from pprint import pprint
+import random
+
+st.header(" TextGapPro")
+st.subheader("TextGapPro is a cutting-edge Natural Language Processing (NLP) application designed to empower users with the ability to generate fill-in-the-blank sentences effortlessly. In a world increasingly reliant on effective communication, TextGapPro stands out as a powerful tool for writers, educators, and content creators seeking to enhance their content's readability and engagement.")
+text = st.text_area("Input the text to get the fill in the blanks",placeholder="Enter the text", height=200)
+button = st.button("Generate Fill-in-The-Blank")
+
+def tokenize_sentences(text):
+    sentences = sent_tokenize(text)
+    sentences = [sentence.strip() for sentence in sentences if len(sentence) > 20]
+    return sentences
+
+def get_noun_adj_verb(text):
+    out=[]
+    try:
+        extractor = pke.unsupervised.MultipartiteRank()
+        extractor.load_document(input=text,language='en')
+        #    not contain punctuation marks or stopwords as candidates.
+        pos = {'VERB', 'ADJ', '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=30)
+
+
+        for val in keyphrases:
+            out.append(val[0])
+    except:
+        out = []
+        traceback.print_exc()
+
+    return out
+
+
+def get_sentences_for_keyword(keywords, sentences):
+    keyword_processor = KeywordProcessor()
+    keyword_sentences = {}
+    for word in keywords:
+        keyword_sentences[word] = []
+        keyword_processor.add_keyword(word)
+    for sentence in sentences:
+        keywords_found = keyword_processor.extract_keywords(sentence)
+        for key in keywords_found:
+            keyword_sentences[key].append(sentence)
+
+    for key in keyword_sentences.keys():
+        values = keyword_sentences[key]
+        values = sorted(values, key=len, reverse=True)
+        keyword_sentences[key] = values
+    return keyword_sentences
+
+def get_fill_in_the_blanks(sentence_mapping):
+    out={"title":"Fill in the blanks for these sentences with matching words at the top"}
+    blank_sentences = []
+    processed = []
+    keys=[]
+    for key in sentence_mapping:
+        if len(sentence_mapping[key])>0:
+            sent = sentence_mapping[key][0]
+            # Compile a regular expression pattern into a regular expression object, which can be used for matching and other methods
+            insensitive_sent = re.compile(re.escape(key), re.IGNORECASE)
+            no_of_replacements =  len(re.findall(re.escape(key),sent,re.IGNORECASE))
+            line = insensitive_sent.sub(' _________ ', sent)
+            if (sentence_mapping[key][0] not in processed) and no_of_replacements<2:
+                blank_sentences.append(line)
+                processed.append(sentence_mapping[key][0])
+                keys.append(key)
+    out["sentences"]=blank_sentences[:10]
+    out["keys"]=keys[:10]
+    return out
+
+if text and button:
+    wrapper = textwrap.TextWrapper(width=150)
+    word_list = wrapper.wrap(text=text)
+    #for element in word_list:
+    #print(element)
+    #st.write(word_list)
+    sentences = tokenize_sentences(text)
+    #st.write(sentences)
+    noun_verbs_adj = get_noun_adj_verb(text)
+    #st.write(noun_verbs_adj)
+    keyword_sentence_mapping_noun_verbs_adj = get_sentences_for_keyword(noun_verbs_adj, sentences)
+    #st.write(keyword_sentence_mapping_noun_verbs_adj)
+    fill_in_the_blanks = get_fill_in_the_blanks(keyword_sentence_mapping_noun_verbs_adj)
+    #st.write(fill_in_the_blanks)
+    
+    # Need to show the shuffle the answer 
+    # all_answers = []
+    # for keys in fill_in_the_blanks['keys']:
+    #     all_answers.append(keys)
+    #random.shuffle(all_answers)
+    
+    # list_answer = list(all_answers)
+    # random.shuffle(list_answer)
+
+    st.header("Fill in the blanks from Input")
+    # for ans in list_answer:
+    #     st.write(ans)
+    count =0 
+    for sentence in fill_in_the_blanks['sentences']:
+        count = count + 1
+        st.write(count,sentence)
+
+    st.header(" Correct Answer")
+    #st.write(all_answers)
+    for key in fill_in_the_blanks['keys']:
+        st.write(key)