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#importing the necessary libraries
import re
import nltk
from nltk.tokenize import sent_tokenize
nltk.download('punkt')
import gradio as gr
from gradio.mix import Parallel
# 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
#Doing some text preprocessing, more will still be needed later
def clean_text(url):
text = read_in_text(url)
text = text.encode("ascii", errors="ignore").decode(
"ascii"
) # remove non-ascii, Chinese characters
text = re.sub(r"\n", " ", text)
text = re.sub(r"\n\n", " ", text)
text = re.sub(r"\t", " ", text)
text = text.strip(" ")
text = re.sub(
" +", " ", text
).strip() # get rid of multiple spaces and replace with a single
return text
#importing the model and tokenizer for the headline generator
from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
)
#initializing the tokenizer and the model
model_type_2 ="valurank/pegasus-multi_news-headline"
tokenizer_2 = AutoTokenizer.from_pretrained(model_type_2, use_auth_token='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')
model_2 = AutoModelForSeq2SeqLM.from_pretrained(model_type_2, use_auth_token='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')
#Defining a function to generate the headlines
def headline_generator_2(file):
input_text = clean_text(file.name)
with tokenizer_2.as_target_tokenizer():
batch = tokenizer_2(
input_text, truncation=True, padding="longest", return_tensors="pt"
)
translated = model_2.generate(**batch)
summary_2 = tokenizer_2.batch_decode(translated, skip_special_tokens=True, max_length=50)
return summary_2[0]
#creating an interface for the headline generator using gradio
demo = gr.Interface(headline_generator_2, inputs=[gr.inputs.File(label="Drop your .txt file here", optional=False)],
title = "HEADLINE GENERATOR",
outputs=[gr.outputs.Textbox(label="Headline")],
theme= "darkhuggingface")
#launching the app
if __name__ == "__main__":
demo.launch(debug=True) |