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app.py

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+from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
+import torch
+import joblib
+
+# Load the model and tokenizer
+model = DistilBertForSequenceClassification.from_pretrained(".")
+tokenizer = DistilBertTokenizer.from_pretrained(".")
+
+# Load the label mapping
+label_mapping = joblib.load("label_mapping.joblib")
+
+def predict(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    
+    # Make prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Get the predicted class
+    predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    
+    # Map the predicted class to its label
+    predicted_label = label_mapping[predicted_class]
+    
+    return predicted_label
+
+# Test the function
+print(predict("Your test text here"))