Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Project_KeyExtraction-NLP.ipynb
+Automatically generated by Colaboratory.
+Original file is located at
+    https://colab.research.google.com/drive/1adCS5In25XQnEQ53D2H9CjaX7jL9yz6Z
+"""
+
+import pandas
+import nltk
+nltk.download('wordnet')
+
+# load the dataset
+dataset = pandas.read_csv('covid_abstracts.csv')
+dataset.head()
+
+#Fetch wordcount for each abstract
+dataset['word_count'] = dataset['title'].apply(lambda x: len(str(x).split(" ")))
+dataset[['title','word_count']].head()
+
+##Descriptive statistics of word counts
+dataset.word_count.describe()
+
+#Identify common words
+freq = pandas.Series(' '.join(dataset['title'].astype(str)).split()).value_counts()[:20]
+
+#freq = pandas.Series(' '.join(dataset['title']).split()).value_counts()[:20]
+freq
+
+#Identify uncommon words
+freq1 = pandas.Series(' '.join(dataset['title'].astype(str)).split()).value_counts()[-20:]
+
+#freq1 =  pandas.Series(' '.join(dataset
+#         ['title']).split()).value_counts()[-20:]
+freq1
+
+from nltk.stem.porter import PorterStemmer
+from nltk.stem.wordnet import WordNetLemmatizer
+lem = WordNetLemmatizer()
+stem = PorterStemmer()
+word = "cryptogenic"
+print("stemming:",stem.stem(word))
+print("lemmatization:", lem.lemmatize(word, "v"))
+
+import nltk
+nltk.download('wordnet')
+
+# Libraries for text preprocessing
+import re
+import nltk
+nltk.download('stopwords')
+from nltk.corpus import stopwords
+from nltk.stem.porter import PorterStemmer
+from nltk.tokenize import RegexpTokenizer
+#nltk.download('wordnet')
+from nltk.stem.wordnet import WordNetLemmatizer
+
+##Creating a list of stop words and adding custom stopwords
+stop_words = set(stopwords.words("english"))
+##Creating a list of custom stopwords
+new_words = ["using", "show", "result", "large", "also", "iv", "one", "two", "new", "previously", "shown"]
+stop_words = stop_words.union(new_words)
+
+print(stop_words)
+
+print(new_words)
+
+corpus = []
+for i in range(0, 3847):
+    #Remove punctuations
+    text = re.sub('[^a-zA-Z]', ' ', dataset['title'][i])
+
+    #Convert to lowercase
+    text = text.lower()
+
+    #remove tags
+    text=re.sub("</?.*?>"," <> ",text)
+
+    # remove special characters and digits
+    text=re.sub("(\\d|\\W)+"," ",text)
+
+    ##Convert to list from string
+    text = text.split()
+
+    ##Stemming
+    ps=PorterStemmer()
+    #Lemmatisation
+    lem = WordNetLemmatizer()
+    text = [lem.lemmatize(word) for word in text if not word in
+            stop_words]
+    text = " ".join(text)
+    corpus.append(text)
+
+#View corpus item
+corpus[222]
+
+#View corpus item
+corpus[300]
+
+#Word cloud
+from os import path
+from PIL import Image
+from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
+import matplotlib.pyplot as plt
+
+wordcloud = WordCloud(
+                          background_color='white',
+                          stopwords=stop_words,
+                          max_words=100,
+                          max_font_size=50,
+                          random_state=42
+                         ).generate(str(corpus))
+print(wordcloud)
+fig = plt.figure(1)
+plt.imshow(wordcloud)
+plt.axis('off')
+plt.show()
+fig.savefig("word1.png", dpi=900)
+from sklearn.feature_extraction.text import CountVectorizer
+import re
+
+# Assuming you have the 'corpus' defined
+# and 'stop_words' defined as in your previous code
+
+# Create a CountVectorizer with predefined English stop words
+cv = CountVectorizer(max_df=0.8, stop_words='english', max_features=10000, ngram_range=(1, 3))
+X = cv.fit_transform(corpus)
+
+# Alternatively, use your custom stop words
+custom_stop_words = ['same', 'hers', 'they', 'with', 'if', 'y', 'iv', 'new', ...]  # Add your custom stop words
+cv = CountVectorizer(max_df=0.8, stop_words=custom_stop_words, max_features=10000, ngram_range=(1, 3))
+X = cv.fit_transform(corpus)
+
+#from sklearn.feature_extraction.text import CountVectorizer
+#import re
+#cv=CountVectorizer(max_df=0.8,stop_words=stop_words, max_features=10000, ngram_range=(1,3))
+#X=cv.fit_transform(corpus)
+
+from sklearn.feature_extraction.text import CountVectorizer
+
+cv = CountVectorizer(max_df=0.8, stop_words='english', max_features=10000, ngram_range=(1,3))
+X = cv.fit_transform(corpus)
+
+custom_stop_words = ['from', 'to', 'against', 'each', 'own', ...]  # Add your custom stop words
+cv = CountVectorizer(max_df=0.8, stop_words=custom_stop_words, max_features=10000, ngram_range=(1,3))
+X = cv.fit_transform(corpus)
+
+list(cv.vocabulary_.keys())[:10]
+
+#Most frequently occuring words
+def get_top_n_words(corpus, n=None):
+    vec = CountVectorizer().fit(corpus)
+    bag_of_words = vec.transform(corpus)
+    sum_words = bag_of_words.sum(axis=0)
+    words_freq = [(word, sum_words[0, idx]) for word, idx in
+                   vec.vocabulary_.items()]
+    words_freq =sorted(words_freq, key = lambda x: x[1],
+                       reverse=True)
+    return words_freq[:n]
+#Convert most freq words to dataframe for plotting bar plot
+top_words = get_top_n_words(corpus, n=20)
+top_df = pandas.DataFrame(top_words)
+top_df.columns=["Word", "Freq"]
+#Barplot of most freq words
+import seaborn as sns
+sns.set(rc={'figure.figsize':(13,8)})
+g = sns.barplot(x="Word", y="Freq", data=top_df)
+g.set_xticklabels(g.get_xticklabels(), rotation=30)
+
+#Most frequently occuring Bi-grams
+def get_top_n2_words(corpus, n=None):
+    vec1 = CountVectorizer(ngram_range=(2,2),
+            max_features=2000).fit(corpus)
+    bag_of_words = vec1.transform(corpus)
+    sum_words = bag_of_words.sum(axis=0)
+    words_freq = [(word, sum_words[0, idx]) for word, idx in
+                  vec1.vocabulary_.items()]
+    words_freq =sorted(words_freq, key = lambda x: x[1],
+                reverse=True)
+    return words_freq[:n]
+top2_words = get_top_n2_words(corpus, n=20)
+top2_df = pandas.DataFrame(top2_words)
+top2_df.columns=["Bi-gram", "Freq"]
+print(top2_df)
+#Barplot of most freq Bi-grams
+import seaborn as sns
+sns.set(rc={'figure.figsize':(13,8)})
+h=sns.barplot(x="Bi-gram", y="Freq", data=top2_df)
+h.set_xticklabels(h.get_xticklabels(), rotation=45)
+
+#Most frequently occuring Tri-grams
+def get_top_n3_words(corpus, n=None):
+    vec1 = CountVectorizer(ngram_range=(3,3),
+           max_features=2000).fit(corpus)
+    bag_of_words = vec1.transform(corpus)
+    sum_words = bag_of_words.sum(axis=0)
+    words_freq = [(word, sum_words[0, idx]) for word, idx in
+                  vec1.vocabulary_.items()]
+    words_freq =sorted(words_freq, key = lambda x: x[1],
+                reverse=True)
+    return words_freq[:n]
+top3_words = get_top_n3_words(corpus, n=20)
+top3_df = pandas.DataFrame(top3_words)
+top3_df.columns=["Tri-gram", "Freq"]
+print(top3_df)
+#Barplot of most freq Tri-grams
+import seaborn as sns
+sns.set(rc={'figure.figsize':(13,8)})
+j=sns.barplot(x="Tri-gram", y="Freq", data=top3_df)
+j.set_xticklabels(j.get_xticklabels(), rotation=45)
+
+from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
+
+# Assuming you already have the 'corpus' defined
+
+# Create a CountVectorizer
+cv = CountVectorizer(max_df=0.8, stop_words='english', max_features=10000, ngram_range=(1, 3))
+
+# Fit and transform the corpus
+X = cv.fit_transform(corpus)
+
+# Create a TfidfTransformer and fit it to the CountVectorizer output
+tfidf_transformer = TfidfTransformer(smooth_idf=True, use_idf=True)
+tfidf_transformer.fit(X)
+
+# Get feature names from CountVectorizer
+feature_names = cv.get_feature_names_out()
+
+# Fetch document for which keywords need to be extracted
+doc = corpus[82]
+
+# Generate tf-idf for the given document
+tf_idf_vector = tfidf_transformer.transform(cv.transform([doc]))
+
+# Now you can proceed with your further code
+
+#Function for sorting tf_idf in descending order
+from scipy.sparse import coo_matrix
+def sort_coo(coo_matrix):
+    tuples = zip(coo_matrix.col, coo_matrix.data)
+    return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
+
+def extract_topn_from_vector(feature_names, sorted_items, topn=10):
+    """get the feature names and tf-idf score of top n items"""
+
+    #use only top n items from vector
+    sorted_items = sorted_items[:topn]
+
+    score_vals = []
+    feature_vals = []
+
+    # word index and corresponding tf-idf score
+    for idx, score in sorted_items:
+
+        #keep track of feature name and its corresponding score
+        score_vals.append(round(score, 3))
+        feature_vals.append(feature_names[idx])
+
+    #create a tuples of feature,score
+    #results = zip(feature_vals,score_vals)
+    results= {}
+    for idx in range(len(feature_vals)):
+        results[feature_vals[idx]]=score_vals[idx]
+
+    return results
+#sort the tf-idf vectors by descending order of scores
+sorted_items=sort_coo(tf_idf_vector.tocoo())
+#extract only the top n; n here is 10
+keywords=extract_topn_from_vector(feature_names,sorted_items,10)
+
+# now print the results
+print("\nAbstract:")
+print(doc)
+print("\nKeywords:")
+for k in keywords:
+    print(k,keywords[k])
+
+from gensim.models import word2vec
+tokenized_sentences = [sentence.split() for sentence in corpus]
+model = word2vec.Word2Vec(tokenized_sentences, min_count=1)
+
+model.wv.most_similar(positive=["incidence"])
+
+import nltk
+#nltk.download('omw-1.4')
+from nltk.corpus import wordnet as wn
+
+wn.synsets('car')
+
+wn.synset('car.n.01').definition()
+import gradio as gr
+from nltk.corpus import wordnet as wn
+
+# Function to get the definition of the first synset for a given word
+def get_synset_definition(word):
+    synsets = wn.synsets(word)
+    if synsets:
+        first_synset = synsets[0]
+        return first_synset.definition()
+    else:
+        return "No synsets found for the given word."
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=get_synset_definition,
+    inputs=gr.Textbox(),
+    outputs=gr.Textbox(),
+    live=True,
+    title="WordNet Synset Definition",
+    description="Enter a word to get the definition of its first WordNet synset.",
+)
+
+# Launch the Gradio interface
+iface.launch()