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from transformers import pipeline |
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import gradio as gr |
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model_id = 'okite97/distilbert-base-uncased-najianews' |
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pipe = pipeline('text-classification',model=model_id) |
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sample_title1 = 'Uefa Opens Proceedings against Barcelona, Juventus and Real Madrid Over European Super League Plan' |
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sample_desc1 = 'Uefa has opened disciplinary proceedings against Barcelona, Juventus and Real Madrid over their involvement in the proposed European Super League.' |
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sample_title2 = 'Nigeria’s Parliament Passes Amended Electoral Bill' |
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sample_desc2 = 'Nigeria\'s Senate on Tuesday passed the harmonised Clause 84 of the 2010 Electoral Act (Amendment) Bill 2022, which allows political' |
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def predict_news(title, description): |
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news = title + '. ' + description |
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outputs = pipe(news, top_k=6) |
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labels = {} |
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for val in outputs: |
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labels[val['label']] = val['score'] |
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return labels |
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title = 'News Categorization' |
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description = '''Given a news title and an excerpt from the news article, can we place the news in its proper category?''' |
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article = "To learn more, check out [Github repo](https://github.com/chimaobi-okite/NLP-Projects-Competitions/tree/main/NewsCategorization) that this demo is based off of." |
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examples = [[sample_title1, sample_desc1], [sample_title2, sample_desc2]] |
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textbox1 = gr.Textbox(label="News Title" , lines=1) |
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textbox2 = gr.Textbox(label="Excerpt", lines=2) |
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label = gr.Label() |
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demo = gr.Interface(fn=predict_news, inputs = [textbox1, textbox2], |
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outputs = label, |
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title=title, |
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description=description, |
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article = article, examples = examples) |
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demo.launch() |
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