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| import gradio as gr | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline | |
| tokenizer = AutoTokenizer.from_pretrained("coppercitylabs/uzbek-news-category-classifier") | |
| model = AutoModelForSequenceClassification.from_pretrained("coppercitylabs/uzbek-news-category-classifier") | |
| def prediction(news): | |
| # create pipeline | |
| clasifer = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model, return_all_scores=True) | |
| preds = clasifer(news) | |
| preds_dict={} | |
| for pred in preds[0]: | |
| preds_dict[pred['label']] = pred['score'] | |
| return preds_dict | |
| gradio_ui = gr.Interface( | |
| fn=prediction, | |
| title="O'zbek Yangiliklari Klassifikatsiyasi", | |
| description=f"", | |
| inputs=gr.inputs.Textbox(lines=10, label="Yangilik matnini kiriting"), | |
| outputs=gr.outputs.Label(num_top_classes=5, type="auto", label="Yangiliklar ko'rsatkichi"), | |
| theme="huggingface", | |
| ) | |
| gradio_ui.launch() |