Update app.py

app.py

@@ -13,10 +13,12 @@ model = models.resnet50(weights=None)
 # Revise fully connected layer to output 37 classes (num_classes = 37)
 model.fc = torch.nn.Linear(2048, 37)
 
+# Load Model weights
 model.load_state_dict(torch.load('./resnet50_model_weights.pth', map_location=device))
 
 model.eval()
 
+# Transformation for the input image
 transform = transforms.Compose([
     transforms.Resize((224, 224)),
     transforms.ToTensor(),
@@ -48,7 +50,7 @@ def classify_image(image):
         probabilities, indices = torch.topk(probabilities, k=3)  
         # Return the class names with their corresponding probabilities
         predictions = [(class_names[idx], prob.item()) for idx, prob in zip(indices[0], probabilities[0])]
-        return {class_name: prob for class_name, prob in predictions}  # Return raw float numbers
+        return {class_name: prob for class_name, prob in predictions}  # Return raw float numbers  # Return formatted percentages
 
 # Path to the folder containing example images
 examples_path = './examples'
@@ -66,22 +68,27 @@ examples = [[examples_path + "/" + img] for img in os.listdir(examples_path)]
 # Create dropdown menu for users to see available classes (as reference, no direct connection to prediction)
 dropdown = gr.Dropdown(choices=class_names, label="Recognizable Breeds", type="value")
 
-# Define Gradio Interface
-demo = gr.Interface(
-    fn=classify_image,  
-    inputs=[gr.Image(type="pil")],  # Only image input is used for prediction
-    outputs=[gr.Label(num_top_classes=3, label="Top 3 Predictions")],  # Outputs top 3 predictions with probabilities
-    examples=examples,  
-    title='Oxford Pet 🐈🐕',  
-    description='A ResNet50-based model for classifying 37 different pet breeds.', 
-    article='[Oxford Project](https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/The%20Oxford-IIIT%20Pet%20Project)' 
-)
-
-# Add dropdown to reference the recognizable breeds
+# Use `gr.Blocks()` to define the full interface
 with gr.Blocks() as demo_with_dropdown:
-    gr.Markdown("# Oxford Pet 🐾 Recognizable Breeds")
-    dropdown  # Display the dropdown for reference
-    demo  # Add the existing demo
+    # Display markdown heading
+    gr.Markdown("# Oxford Pet 🐕🐈 Recognizable Breeds")
+    
+    # Dropdown as a reference for users
+    dropdown  
+    
+    # Image classification demo
+    gr.Image(label="Upload an image to classify").style(height="auto", width="100%")
+    
+    # Gradio interface for the image input and label output
+    gr.Interface(
+        fn=classify_image,  
+        inputs=gr.Image(type="pil"),  # Only image input is used for prediction
+        outputs=gr.Label(num_top_classes=3, label="Top 3 Predictions"),  # Outputs top 3 predictions with probabilities
+        examples=examples,  
+        title='Oxford Pet 🐈🐕',  
+        description='A ResNet50-based model for classifying 37 different pet breeds.', 
+        article='[Oxford Project](https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/The%20Oxford-IIIT%20Pet%20Project)' 
+    )
 
-# Launch Gradio demo with dropdown
+# Launch Gradio app
 demo_with_dropdown.launch()