Update app.py

app.py

@@ -143,6 +143,67 @@ def preprocess_image(image):
 def get_akc_breeds_link():
     return "https://www.akc.org/dog-breeds/"
 
+# def predict(image):
+#     try:
+#         image_tensor = preprocess_image(image)
+#         with torch.no_grad():
+#             output = model(image_tensor)
+#             if isinstance(output, tuple):
+#                 logits = output[0]
+#             else:
+#                 logits = output
+
+#             # 取得預測的top k結果
+#             probabilities = F.softmax(logits, dim=1)
+#             topk_probs, topk_indices = torch.topk(probabilities, k=3)
+
+#             # 檢查最高的預測機率
+#             top1_prob = topk_probs[0][0].item()
+
+#             if top1_prob >= 0.5:
+#                 # 正確辨識時，返回該品種資訊
+#                 predicted = topk_indices[0][0]
+#                 breed = dog_breeds[predicted.item()]
+#                 description = get_dog_description(breed)
+#                 akc_link = get_akc_breeds_link()
+
+#                 if isinstance(description, dict):
+#                     description_str = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
+#                 else:
+#                     description_str = description
+
+#                 # 添加AKC連結
+#                 description_str += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {breed} to find detailed information."
+
+#                 # 添加免責聲明
+#                 disclaimer = ("\n\n*Disclaimer: The external link provided leads to the American Kennel Club (AKC) dog breeds page. "
+#                               "You may need to search for the specific breed on that page. "
+#                               "I am 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
+
+#             else:
+#                 # 不確定時，返回top 3的預測結果
+#                 topk_breeds = [dog_breeds[idx.item()] for idx in topk_indices[0]]
+#                 topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]
+
+#                 # 用粗體返回品種和機率
+#                 topk_results = "\n\n".join([f"**{i+1}. {breed}** ({prob} confidence)" for i, (breed, prob) in enumerate(zip(topk_breeds, topk_probs_percent))])
+
+#                 # 提供說明
+#                 explanation = (
+#                     f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n{topk_results}\n\n"
+#                     "This can happen if the image quality is low or the breed is rare in the dataset. "
+#                     "Please try uploading a clearer image or a different angle of the dog. "
+#                     "For more accurate results, ensure the dog is the main subject of the photo."
+#                 )
+
+#                 return explanation
+#     except Exception as e:
+#         return f"An error occurred: {e}"
+
 def predict(image):
     try:
         image_tensor = preprocess_image(image)
@@ -160,8 +221,16 @@ def predict(image):
             # 檢查最高的預測機率
             top1_prob = topk_probs[0][0].item()
 
+            # 假設低於 20% 機率為非狗或不確定的圖片
+            if top1_prob < 0.2:
+                return (
+                    "The model couldn't confidently identify a dog breed. "
+                    "It seems like the image may not contain a dog, or the image quality is too low. "
+                    "Please upload a clearer picture or ensure the subject is a dog."
+                )
+
+            # 當信心高於 50% 時，直接返回該品種資訊
             if top1_prob >= 0.5:
-                # 正確辨識時，返回該品種資訊
                 predicted = topk_indices[0][0]
                 breed = dog_breeds[predicted.item()]
                 description = get_dog_description(breed)
@@ -185,12 +254,15 @@ def predict(image):
                 return description_str
 
             else:
-                # 不確定時，返回top 3的預測結果
+                # 信心不足50%，返回top 3的預測結果並附加連結
                 topk_breeds = [dog_breeds[idx.item()] for idx in topk_indices[0]]
                 topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]
 
-                # 用粗體返回品種和機率
-                topk_results = "\n\n".join([f"**{i+1}. {breed}** ({prob} confidence)" for i, (breed, prob) in enumerate(zip(topk_breeds, topk_probs_percent))])
+                # 構建每個品種的連結和預測機率
+                topk_results = "\n\n".join(
+                    [f"**{i+1}. [{breed}](https://www.akc.org/dog-breeds/{quote(breed)})** ({prob} confidence)"
+                     for i, (breed, prob) in enumerate(zip(topk_breeds, topk_probs_percent))]
+                )
 
                 # 提���說明
                 explanation = (