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Article_summarizer_cnn_large_testing / app.py

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Create app.py

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	#importing the necessary library
	import re
	import nltk
	import torch
	from nltk.tokenize import sent_tokenize
	nltk.download('punkt')
	from IPython.display import HTML, display
	import gradio as gr
	from gradio.mix import Parallel
	from transformers import pipeline
	import numpy as np

	# Defining a function to read in the text file
	def read_in_text(url):
	with open(url, 'r') as file:
	article = file.read()
	return article

	#initailizing the model pipeline
	from transformers import BartTokenizer, BartForConditionalGeneration

	model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
	tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")

	#Defining a function to get the summary of the article
	def final_summary(file):
	#reading in the text and tokenizing it into sentence
	text = read_in_text(file.name)
	chunks = sent_tokenize(text)
	output = []

	#looping through the sentences in a batch of 10 and summarizing them
	for i in range(0,len(chunks), 20):
	sentence = ' '.join(chunks[i:i+20])
	inputs = tokenizer(sentence, max_length=1024, return_tensors="pt")
	summary_ids = model.generate(inputs["input_ids"])
	summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	output.append(summary)

	#joining all the summary output together
	summary = ' '.join(output)
	lines1 = sent_tokenize(summary)
	for i in range(len(lines1)):
	lines1[i] = "* " + lines1[i].strip().replace(' .', '.')

	summ_bullet1 = "\n".join(lines1)
	return summ_bullet1

	#creating an interface for the headline generator using gradio
	demo = gr.Interface(final_summary, inputs=[gr.inputs.File(label="Drop your .txt file here", optional=False)],
	title = "ARTICLE SUMMARIZER",
	outputs=[gr.outputs.Textbox(label="Summary")],
	theme= "darkhuggingface")

	#launching the app
	if __name__ == "__main__":
	demo.launch(debug=True)