Update username in app.py

app.py

@@ -3,15 +3,22 @@ from transformers import AutoModelForImageClassification
 import torch
 import torchvision.transforms as transforms
 from PIL import Image
+import traceback
+import sys
 
 # Load model from Hub instead of local file
 def load_model():
-    model = AutoModelForImageClassification.from_pretrained(
-        "nragrawal/resnet-imagenet",
-        trust_remote_code=True
-    )
-    model.eval()
-    return model
+    try:
+        model = AutoModelForImageClassification.from_pretrained(
+            "nragrawal/resnet-imagenet",
+            trust_remote_code=True
+        )
+        model.eval()
+        return model
+    except Exception as e:
+        print(f"Error loading model: {str(e)}")
+        print(traceback.format_exc())
+        raise e
 
 # Preprocessing
 transform = transforms.Compose([
@@ -24,28 +31,39 @@ transform = transforms.Compose([
 
 # Inference function
 def predict(image):
-    model = load_model()
-    
-    # Preprocess image
-    img = Image.fromarray(image)
-    img = transform(img).unsqueeze(0)
-    
-    # Inference
-    with torch.no_grad():
-        output = model(img)
-        probabilities = torch.nn.functional.softmax(output[0], dim=0)
+    try:
+        model = load_model()
         
-    # Get top 5 predictions
-    top5_prob, top5_catid = torch.topk(probabilities, 5)
-    return {f"Class {i}": float(prob) for i, prob in zip(top5_catid, top5_prob)}
+        # Preprocess image
+        img = Image.fromarray(image)
+        img = transform(img).unsqueeze(0)
+        
+        # Inference
+        with torch.no_grad():
+            output = model(img)
+            probabilities = torch.nn.functional.softmax(output[0], dim=0)
+            
+        # Get top 5 predictions
+        top5_prob, top5_catid = torch.topk(probabilities, 5)
+        return {f"Class {i}": float(prob) for i, prob in zip(top5_catid, top5_prob)}
+    except Exception as e:
+        print(f"Error during prediction: {str(e)}")
+        print(traceback.format_exc())
+        return {"error": str(e)}
 
-# Create Gradio interface
+# Create Gradio interface with error handling
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(),
     outputs=gr.Label(num_top_classes=5),
     title="ResNet Image Classification",
-    description="Upload an image to classify it using ResNet"
+    description="Upload an image to classify it using ResNet",
+    allow_flagging="never"
 )
 
-iface.launch() 
+# Add error handling to launch
+try:
+    iface.launch()
+except Exception as e:
+    print(f"Error launching interface: {str(e)}")
+    print(traceback.format_exc())