Update app.py

app.py

@@ -209,69 +209,69 @@ def predict(image):
         image_tensor = preprocess_image(image)
         with torch.no_grad():
             output = model(image_tensor)
-            if isinstance(output, tuple):
-                logits = output[0]
-            else:
-                logits = output
+            logits = output[0] if isinstance(output, tuple) else 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()
+            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]]
 
             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()]
+                # High confidence prediction
+                breed = topk_breeds[0]
                 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()])
+                    formatted_description = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
                 else:
-                    description_str = description
+                    formatted_description = 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."
+                akc_link = get_akc_breeds_link()
+                formatted_description += 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
+                formatted_description += disclaimer
 
-                return description_str
+                return formatted_description
 
             elif top1_prob < 0.1:
-                # 如果信心度低於 0.1，返回提示請上傳更清晰的圖片
-                return "The image is too unclear or the dog breed is not in the dataset. Please upload a clearer image of the dog."
-
+                # Very low confidence prediction
+                return ("The model couldn't confidently identify the breed. The image might not be clear enough or might not contain a dog. "
+                        "Please try uploading a clearer image of a dog.")
             else:
-                # 不確定時，返回top 3的預測結果，並且允許點擊查看詳細資訊
-                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，並為每個品種添加點擊連結
-                topk_results = "\n\n".join([
-                    f"**{i+1}. [Click here to view more about {breed}]()** ({prob} confidence)" 
-                    for i, (breed, prob) in enumerate(zip(topk_breeds, topk_probs_percent))
-                ])
-
-                # 提供說明
+                # Medium confidence prediction
+                topk_results = "\n\n".join([f"**{i+1}. {breed}** (Confidence: {prob})" for i, (breed, prob) in enumerate(zip(topk_breeds, topk_probs_percent))])
                 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."
+                    "Please select one of the breeds above to see more information:"
                 )
+                return explanation, gr.update(choices=topk_breeds, visible=True)
 
-                return explanation
     except Exception as e:
         return f"An error occurred: {e}"
 
+def show_breed_info(breed):
+    description = get_dog_description(breed)
+    if isinstance(description, dict):
+        formatted_description = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
+    else:
+        formatted_description = description
+
+    akc_link = get_akc_breeds_link()
+    formatted_description += 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."
+
+    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.*")
+    formatted_description += disclaimer
+
+    return formatted_description
+
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(label="Upload a dog image", type="numpy"),