Update app.py

app.py

@@ -1,8 +1,9 @@
 import gradio as gr
 import torch
 from transformers import AutoImageProcessor, AutoModelForImageClassification
-from torchvision.transforms import Compose, Resize, ToTensor, Normalize,RandomHorizontalFlip, RandomRotation
+from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomHorizontalFlip, RandomRotation
 from PIL import Image
+import traceback
 
 # Load model and processor
 model_name = "riyadifirman/klasifikasiburung"
@@ -20,17 +21,23 @@ transform = Compose([
 ])
 
 def predict(image):
-    image = Image.fromarray(image)
-    inputs = transform(image).unsqueeze(0)
-    outputs = model(inputs)
-    logits = outputs.logits
-    predicted_class_idx = logits.argmax(-1).item()
-    return processor.decode(predicted_class_idx)
+    try:
+        image = Image.fromarray(image)
+        inputs = transform(image).unsqueeze(0)
+        outputs = model(inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+        return processor.decode(predicted_class_idx)
+    except Exception as e:
+        # Menampilkan error
+        print("An error occurred:", e)
+        print(traceback.format_exc())  # Ini akan print error secara detail
+        return "An error occurred while processing your request."
 
 # Create Gradio interface
 interface = gr.Interface(
     fn=predict,
-    inputs=gr.inputs.Image(type="numpy"),
+    inputs=gr.Image(type="numpy"),
     outputs="text",
     title="Bird Classification",
     description="Upload an image of a bird to classify it."