Update app.py

app.py

@@ -3,6 +3,7 @@ import torch
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 from torchvision.transforms import Compose, Resize, ToTensor, Normalize
 from PIL import Image
+import traceback
 
 # Load model and processor
 model_name = "riyadifirman/klasifikasiburung"
@@ -18,16 +19,30 @@ 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:
+        print("An error occurred:", e)
+        print(traceback.format_exc())
+        return "An error occurred while processing your request."
+
+def predict_function(input_data):
+    try: 
+        # model 
+        output = f"Processed input: {input_data}"  # Gantilah dengan model 
+        return output
+    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
-# In newer versions of Gradio, 'inputs' and 'outputs' are directly
-# specified within the gr.Interface constructor.
 interface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy"), # Changed from gr.inputs.Image to gr.Image
@@ -36,5 +51,8 @@ interface = gr.Interface(
     description="Upload an image of a bird to classify it."
 )
 
+iface = gr.Interface(fn=predict_function, inputs="text", outputs="text")
+
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()
+    iface.launch()