Update app.py

app.py

@@ -8,13 +8,15 @@ tokenizer = AutoTokenizer.from_pretrained("MaryahGreene/arch_flava_mod", trust_r
 id2label = model.config.id2label  # make sure this is set during training!
 
 def predict(text):
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-    with torch.no_grad():
+    try:
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
         outputs = model(**inputs)
-    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
-    top_label = torch.argmax(probs, dim=1).item()
-    label_name = id2label[str(top_label)]
-    confidence = probs[0][top_label].item()
-    return f"Prediction: {label_name} ({confidence:.2%} confidence)"
+        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+        top_label = torch.argmax(probs, dim=1).item()
+        label_name = id2label[str(top_label)]
+        confidence = probs[0][top_label].item()
+        return f"Prediction: {label_name} ({confidence:.2%} confidence)"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
 
 gr.Interface(fn=predict, inputs="text", outputs="text", title="ArchFlava Predictor").launch()