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abdulmatinomotoso
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b69d9b2
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Parent(s):
86cc709
Create app.py
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app.py
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#importing the necessary libraries
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import re
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import nltk
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from nltk.tokenize import sent_tokenize
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nltk.download('punkt')
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import gradio as gr
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from gradio.mix import Parallel
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# Defining a function to read in the text file
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def read_in_text(url):
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with open(url, 'r') as file:
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article = file.read()
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return article
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#Doing some text preprocessing, more will still be needed later
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def clean_text(url):
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text = read_in_text(url)
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text = text.encode("ascii", errors="ignore").decode(
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"ascii"
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) # remove non-ascii, Chinese characters
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text = re.sub('(by[\s\w,|]+ - \d\d\/\d\d\/\d\d\s\d+:\d+\s\w{2}\s\w{2})|(by[\s\w|,]+\d\d,\s\d{4})', "", text)
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text = re.sub(r"\n", " ", text)
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text = re.sub(r"\n\n", " ", text)
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text = re.sub(r"\t", " ", text)
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text = text.strip(" ")
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text = re.sub(
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" +", " ", text
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).strip() # get rid of multiple spaces and replace with a single
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return text
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#importing the model and tokenizer for the headline generator
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from transformers import (
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AutoTokenizer,
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AutoModelForSeq2SeqLM,
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)
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#initializing the tokenizer and the model
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model_type_2 ="chinhon/pegasus-newsroom-headline_writer"
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tokenizer_2 = AutoTokenizer.from_pretrained(model_type_2)
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model_2 = AutoModelForSeq2SeqLM.from_pretrained(model_type_2)
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#Defining a function to generate the headlines
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def headline_generator_2(file):
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input_text = clean_text(file.name)
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with tokenizer_2.as_target_tokenizer():
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batch = tokenizer_2(
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input_text[:1000], truncation=True, padding="longest", return_tensors="pt"
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)
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translated = model_2.generate(**batch)
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summary_2 = tokenizer_2.batch_decode(translated, skip_special_tokens=True)
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return summary_2[0]
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#creating an interface for the headline generator using gradio
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demo = gr.Interface(headline_generator_2, inputs=[gr.inputs.File(label="Drop your .txt file here", optional=False)],
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title = "HEADLINE GENERATOR",
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outputs=[gr.outputs.Textbox(label="Summary")],
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theme= "darkhuggingface")
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#launching the app
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if __name__ == "__main__":
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demo.launch(debug=True)
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