Update app.py

app.py

@@ -95,7 +95,6 @@ class BaseModel(nn.Module):
         self.feature_dim = self.backbone.classifier[1].in_features
         self.backbone.classifier = nn.Identity()
 
-        # 動態計算 num_heads
         self.num_heads = max(1, min(8, self.feature_dim // 64))
         self.attention = MultiHeadAttention(self.feature_dim, num_heads=self.num_heads)
 
@@ -122,16 +121,16 @@ model = BaseModel(num_classes=num_classes, device=device)
 checkpoint = torch.load('best_model_81_dog.pth', map_location=torch.device('cpu'))
 model.load_state_dict(checkpoint['model_state_dict'])
 
-# 將模型設置為評估模式
+# evaluation mode
 model.eval()
 
 # Image preprocessing function
 def preprocess_image(image):
-    # 如果圖片是 numpy.ndarray 轉換為 PIL.Image
+    # If the image is numpy.ndarray turn into PIL.Image
     if isinstance(image, np.ndarray):
         image = Image.fromarray(image)
 
-    # 使用 torchvision.transforms 進行預處理
+    # Use torchvision.transforms to process images
     transform = transforms.Compose([
         transforms.Resize((224, 224)),
         transforms.ToTensor(),
@@ -140,38 +139,12 @@ def preprocess_image(image):
 
     return transform(image).unsqueeze(0)
 
-# def predict(image):
-#     try:
-#         image_tensor = preprocess_image(image)
-#         with torch.no_grad():
-#             logits, _ = model(image_tensor)
-#             _, predicted = torch.max(logits, 1)
-
-#         breed = dog_breeds[predicted.item()]  # Map label to breed name
-
-#         # Retrieve breed description
-#         description = get_dog_description(breed)
-
-#         # Formatting the description for better display
-#         if isinstance(description, dict):
-#             description_str = f"**Breed**: {description['Breed']}\n\n"
-#             description_str += f"**Size**: {description['Size']}\n\n"
-#             description_str += f"**Lifespan**: {description['Lifespan']}\n\n"
-#             description_str += f"**Temperament**: {description['Temperament']}\n\n"
-#             description_str += f"**Care Level**: {description['Care Level']}\n\n"
-#             description_str += f"**Good with Children**: {description['Good with Children']}\n\n"
-#             description_str += f"**Exercise Needs**: {description['Exercise Needs']}\n\n"
-#             description_str += f"**Grooming Needs**: {description['Grooming Needs']}\n\n"
-#             description_str += f"**Description**: {description['Description']}\n\n"
-#         else:
-#             description_str = description
-
-#         return description_str
-#     except Exception as e:
-#         return f"An error occurred: {e}"
 
 def get_akc_link(breed):
-    formatted_breed = breed.lower().replace(" ", "-")
+    # Remove any non-English characters and convert to lowercase
+    formatted_breed = ''.join(c for c in breed if ord(c) < 128).lower()
+    # Replace spaces with hyphens and remove any remaining special characters
+    formatted_breed = '-'.join(word for word in formatted_breed.split() if word.isalnum())
     return f"https://www.akc.org/dog-breeds/{formatted_breed}/"
 
 def predict(image):
@@ -184,30 +157,23 @@ def predict(image):
             else:
                 logits = output
             _, predicted = torch.max(logits, 1)
-        breed = dog_breeds[predicted.item()]  # Map label to breed name
+        breed = dog_breeds[predicted.item()]
         
-        # Retrieve breed description
         description = get_dog_description(breed)
-        
-        # Generate AKC link
         akc_link = get_akc_link(breed)
         
-        # Formatting the description for better display
         if isinstance(description, dict):
-            description_str = f"**Breed**: {description['Breed']}\n\n"
-            description_str += f"**Size**: {description['Size']}\n\n"
-            description_str += f"**Lifespan**: {description['Lifespan']}\n\n"
-            description_str += f"**Temperament**: {description['Temperament']}\n\n"
-            description_str += f"**Care Level**: {description['Care Level']}\n\n"
-            description_str += f"**Good with Children**: {description['Good with Children']}\n\n"
-            description_str += f"**Exercise Needs**: {description['Exercise Needs']}\n\n"
-            description_str += f"**Grooming Needs**: {description['Grooming Needs']}\n\n"
-            description_str += f"**Description**: {description['Description']}\n\n"
+            description_str = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
         else:
             description_str = description
         
-        # Add AKC link
-        description_str += f"\n\n[Click here for more information about {breed} on AKC]({akc_link})"
+        # Add AKC link as an option
+        description_str += f"\n\n**Want to learn more?** [View detailed information about {breed} on the AKC website]({akc_link})"
+        
+        # Add disclaimer
+        disclaimer = ("\n\n*Disclaimer: The external link provided leads to the American Kennel Club (AKC) website. "
+                      "We are not responsible for the content on external sites. Please refer to the AKC's terms of use and privacy policy.*")
+        description_str += disclaimer
         
         return description_str
     except Exception as e:
@@ -226,7 +192,6 @@ iface = gr.Interface(
               'French_Bulldog.jpeg',
               'Samoyed.jpg'],
      css = """
-    /* 新增樣式 */
     .container {
         max-width: 900px;
         margin: 0 auto;