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app.py

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+from transformers import pipeline
+
+# Загрузка модели через pipeline
+classifier = pipeline("text-classification", model="data-silence/news_classifier")
+
+# Словарь для преобразования меток
+category_mapper = {
+    'LABEL_0': 'climate',
+    'LABEL_1': 'conflicts',
+    'LABEL_2': 'culture',
+    'LABEL_3': 'economy',
+    'LABEL_4': 'gloss',
+    'LABEL_5': 'health',
+    'LABEL_6': 'politics',
+    'LABEL_7': 'science',
+    'LABEL_8': 'society',
+    'LABEL_9': 'sports',
+    'LABEL_10': 'travel'
+}
+
+def classify(text):
+    result = classifier(text)
+    category = category_mapper[result[0]['label']]
+    score = result[0]['score']
+    return {"category": category, "confidence": score}
+
+# Для Gradio интерфейса
+def run_inference(text):
+    result = classify(text)
+    return f"Predicted category: {result['category']} (confidence: {result['confidence']:.2f})"

requirements.txt

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+transformers
+torch