Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_path = "RUSpam/spam_deberta_v4"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+def predict_spam(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=1)
+        predicted_class = torch.argmax(logits, dim=1).item()
+    
+    spam_probability = probabilities[0][1].item()
+    not_spam_probability = probabilities[0][0].item()
+    
+    result = "Спам" if predicted_class == 1 else "Не спам"
+    
+    return {
+        "Результат": result,
+        "Вероятность спама": f"{spam_probability:.2%}"
+    }
+
+# Создание интерфейса Gradio
+iface = gr.Interface(
+    fn=predict_spam,
+    inputs=gr.Textbox(lines=5, label="Введите текст"),
+    outputs=[
+        gr.Label(label="Результат"),
+        gr.Label(label="Вероятность спама")
+    ],
+    title="Определение спама в русскоязычных текстах"
+)
+
+iface.launch()