fix

app.py

@@ -127,22 +127,19 @@ def predict(image: Union[np.ndarray, None]) -> tuple[str, dict]:
             return "Error: Failed to preprocess image", {}
             
         with torch.no_grad():
-            # Move input to same device as model
             input_tensor = input_tensor.to(DEVICE)
             output = model(input_tensor)
-            # Apply softmax to get probabilities
             probabilities = torch.nn.functional.softmax(output[0], dim=0)
             
         # Get predictions and confidences
         top_5_probs, top_5_indices = torch.topk(probabilities, k=5)
         
-        # Convert to percentages and round to 2 decimal places
+        # Format confidences with exactly 2 decimal places
         confidences = {
-            CLASS_NAMES[idx.item()]: float(round(prob.item() * 100, 2))
+            CLASS_NAMES[idx.item()]: "{:.2f}".format(float(prob.item() * 100))
             for prob, idx in zip(top_5_probs, top_5_indices)
         }
         
-        # Get top prediction
         predicted_class = CLASS_NAMES[top_5_indices[0].item()]
         
         return predicted_class, confidences
@@ -166,23 +163,40 @@ def get_example_list() -> list:
 
 # Create Gradio interface with error handling
 try:
-    iface = gr.Interface(
-        fn=predict,
-        inputs=gr.Image(type="numpy", label="Upload Image"),
-        outputs=[
-            gr.Label(label="Predicted Class", num_top_classes=1),
-            gr.Label(label="Top 5 Predictions", num_top_classes=5)
-        ],
-        title="Image Classification with ResNet50",
-        description=(
-            "Upload an image to classify:\n"
-            "The model will predict the class and show top 5 confidence scores."
-        ),
-        examples=get_example_list(),
-        cache_examples=True,
-        theme=gr.themes.Base()
-    )
-    
+    with gr.Blocks(theme=gr.themes.Base()) as iface:
+        gr.Markdown("# Image Classification with ResNet50")
+        gr.Markdown("Upload an image to classify. The model will predict the class and show top 5 confidence scores.")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                input_image = gr.Image(type="numpy", label="Upload Image")
+                predict_btn = gr.Button("Predict")
+            
+            with gr.Column(scale=1):
+                output_label = gr.Label(label="Predicted Class", num_top_classes=1)
+                confidence_label = gr.Label(label="Top 5 Predictions", num_top_classes=5)
+
+        # Add examples
+        gr.Examples(
+            examples=get_example_list(),
+            inputs=input_image,
+            outputs=[output_label, confidence_label],
+            fn=predict,
+            cache_examples=True
+        )
+
+        # Set up prediction event
+        predict_btn.click(
+            fn=predict,
+            inputs=input_image,
+            outputs=[output_label, confidence_label]
+        )
+        input_image.change(
+            fn=predict,
+            inputs=input_image,
+            outputs=[output_label, confidence_label]
+        )
+
 except Exception as e:
     logger.error(f"Error creating Gradio interface: {str(e)}")
     raise