Update app.py

app.py

@@ -13,12 +13,10 @@ 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(),
@@ -50,7 +48,7 @@ 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 formatted percentages
+        return {class_name: prob for class_name, prob in predictions}  # Return raw float numbers
 
 # Path to the folder containing example images
 examples_path = './examples'
@@ -61,34 +59,23 @@ if os.path.exists(examples_path):
 else:
     print(f"[ERROR] Examples folder not found at {examples_path}")
 
-# Gradio interface
 # Load example images from the folder
 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")
 
-# Use `gr.Blocks()` to define the full interface
+# Define Gradio Interface
 with gr.Blocks() as demo_with_dropdown:
-    # 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", scale=0.5)
-    
-    # Gradio interface for the image input and label output
-    gr.Interface(
+    gr.Markdown("# Oxford Pet 🐾 Recognizable Breeds")
+    dropdown  # 顯示下拉選單
+    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
+        inputs=gr.Image(type="pil"),  # 只使用圖片輸入進行預測
+        outputs=gr.Label(num_top_classes=3, label="Top 3 Predictions"),  # 輸出前三個預測
         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 app
-demo_with_dropdown.launch()
+    demo.launch()