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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 | |
| 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.""" | |
| sample = [['Europe'], ['Great Depression'], ['Crocodile Dundee']] | |
| iface = Parallel(gr.Interface(fn=get_wiki_original_text, inputs=gr.inputs.Textbox(label="Requested Topic from Wikipedia : "), outputs="text"), | |
| gr.Interface(fn=get_wiki_summary_by_lem, inputs=gr.inputs.Textbox(label="Requested Topic from Wikipedia : "), outputs="text"), | |
| gr.Interface(fn=get_wiki_summary_by_pegasus, inputs=gr.inputs.Textbox(label="Requested Topic from Wikipedia : "), outputs="text"), | |
| # get_wiki_original_text,get_wiki_summary_by_lem, get_wiki_summary_by_pegasus, | |
| title= 'Text Summarizer', | |
| description = desc, | |
| examples=sample, | |
| inputs = gr.inputs.Textbox(label="Text")) | |
| iface.launch(inline = False) |