Update app.py

app.py

@@ -40,15 +40,15 @@ class_names = ['Abyssinian (阿比西尼亞貓)', 'American Bulldog (美國鬥
 
 # 定義預測函數
 def classify_image(image):
-    image = transform(image).unsqueeze(0).to(device)  # 確保影像資料處理在 CPU 上
+    image = transform(image).unsqueeze(0).to(device)
     with torch.no_grad():
         outputs = model(image)
-        probabilities, indices = torch.topk(outputs, k=3)  # 取得前3個預測
-        probabilities = torch.nn.functional.softmax(probabilities, dim=1)  # 將結果轉換為機率
+        probabilities, indices = torch.topk(outputs, k=3)
+        probabilities = torch.nn.functional.softmax(probabilities, dim=1)
         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: f"{prob:.2f}" for class_name, prob in predictions}
 
-# 設置 examples 路徑
+# 設定 examples 路徑
 examples_path = './examples'
 
 if os.path.exists(examples_path):
@@ -56,23 +56,21 @@ if os.path.exists(examples_path):
 else:
     print(f"[ERROR] Examples folder not found at {examples_path}")
 
-# Gradio 介面
+# 設定範例圖片
 examples = [[examples_path + "/" + img] for img in os.listdir(examples_path)]
 
-# 使用 Markdown 顯示 37 個品種
-breed_list = "### Recognizable Breeds\n" + "\n".join([f"- {breed}" for breed in class_names])
-
-demo = gr.Blocks()
+# 新增下拉選單，顯示所有品種
+dropdown = gr.Dropdown(choices=class_names, label="Select a breed", type="value")
 
-with demo:
-    gr.Markdown("# Oxford Pet 🐕🐈 Recognizable Breeds")
-    gr.Markdown(breed_list)  # 用 Markdown 顯示品種列表
-    gr.Interface(fn=classify_image,  
-                 inputs=gr.Image(type="pil"),  # 只需要圖片輸入
-                 outputs=[gr.Label(num_top_classes=3, label="Top 3 Predictions")],  
-                 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 介面
+demo = gr.Interface(
+    fn=classify_image,  
+    inputs=[gr.Image(type="pil")],  # 移除掉 dropdown 作為輸入
+    outputs=[gr.Label(num_top_classes=3, label="Top 3 Predictions")],  
+    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)'
+)
 
 demo.launch()