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