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import gradio as gr
from gradio.mix import Parallel, Series
import wikipedia
import spacy
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
import nltk
nltk.download('wordnet', quiet=True)
nltk.download('punkt', quiet=True)
from nltk.stem import WordNetLemmatizer
from heapq import nlargest
import warnings
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
#from transformers import PegasusForConditionalGeneration, PegasusTokenizer
warnings.filterwarnings("ignore")
def get_wiki_original_text(inp):
text = wikipedia.summary(inp)
return text
"""
def get_wiki_summary_by_pegasus(inp):
text = wikipedia.summary(inp)
tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-xsum')
tokens = tokenizer(text, truncation=True, padding="longest", return_tensors="pt")
model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
summary = model.generate(**tokens)
return tokenizer.decode(summary)
"""
def get_wiki_summary_by_lem(inp):
text = wikipedia.summary(inp)
print(text)
stopwords = list(STOP_WORDS)
lemmatizer = WordNetLemmatizer()
tokens = [lemmatizer.lemmatize(str(token).lower()) for token in nltk.word_tokenize(text) if str(token) not in punctuation and str(token).lower() not in stopwords and len(token) >1]
word_counts = {}
for token in tokens:
if token in word_counts.keys():
word_counts[token] += 1
else:
word_counts[token] = 1
sentence_scores = {}
for sentence in nltk.sent_tokenize(text):
sentence_scores[sentence] = 0
for wrd in nltk.word_tokenize(sentence):
if lemmatizer.lemmatize(str(wrd).lower()) in word_counts.keys():
sentence_scores[sentence] += word_counts[lemmatizer.lemmatize(str(wrd).lower())]
summary_length = 0
if len(sentence_scores) > 5 :
summary_length = int(len(sentence_scores)*0.20)
else:
summary_length = int(len(sentence_scores)*0.50)
summary = str()
for sentence in nltk.sent_tokenize(text):
for i in range(0,summary_length):
if str(sentence).find(str(nlargest(summary_length, sentence_scores, key = sentence_scores.get)[i])) == 0:
summary += str(sentence).replace('\n','')
summary += ' '
print('\033[1m' + "Summarized Text" + '\033[0m')
return summary
def get_wiki_summary_by_tfidf(inp):
text = wikipedia.summary(inp)
tfidf_vectorizer = TfidfVectorizer(ngram_range=(1,3))
all_sentences = [str(sent) for sent in nltk.sent_tokenize(text)]
sentence_vectors = tfidf_vectorizer.fit_transform(all_sentences)
sentence_scores_vector = np.hstack(np.array(sentence_vectors.sum(axis=1)))
sentence_scores = dict(zip(all_sentences, sentence_scores_vector))
summary_length = 0
if len(sentence_scores) > 5 :
summary_length = int(len(sentence_scores)*0.20)
else:
summary_length = int(len(sentence_scores)*0.50)
summary = str()
for sentence in nltk.sent_tokenize(text):
for i in range(0,summary_length):
if str(sentence).find(str(nlargest(summary_length, sentence_scores, key = sentence_scores.get)[i])) == 0:
summary += str(sentence).replace('\n','')
summary += ' '
return summary
desc = """This interface allows you to summarize Wikipedia explanations. Only requirement is to write the topic. For summarization this model uses extractive summarization method and the number of sentences in the output depends on the length of the original text."""
x = """ Europe """
y = ''' Great Depression '''
z = ''' Crocodile Dundee '''
sample = [[x],[y],[z]]
iface = Parallel(gr.Interface(fn=get_wiki_original_text, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Original Text'),
gr.Interface(fn=get_wiki_summary_by_lem, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Summary 1'),
gr.Interface(fn=get_wiki_summary_by_tfidf, inputs=gr.inputs.Textbox(label="Text"), outputs="text", description='Summary 2'),
title= 'Text Summarizer',
description = desc,
examples=sample,
inputs = gr.inputs.Textbox(label="Text"))
iface.launch(inline = False) |