Update app.py

app.py

@@ -177,6 +177,7 @@ def get_akc_breeds_link():
 #     except Exception as e:
 #         return f"An error occurred: {e}"
 
+# Prediction function
 def predict(image):
     try:
         image_tensor = preprocess_image(image)
@@ -187,52 +188,39 @@ def predict(image):
             else:
                 logits = output
             
-            # 計算預測的概率分佈
             probabilities = F.softmax(logits, dim=1)
-            
-            # 取得最高的預測分數以及對應的品種
             top_confidence, top_index = torch.max(probabilities, 1)
-            top_confidence = top_confidence.item()  # 轉成 Python 數值
+            top_confidence = top_confidence.item()
             top_breed = dog_breeds[top_index.item()]
 
-            # 如果最高預測分數大於等於 60%，直接返回該品種的資訊
+            # If confidence is higher than 60%, return the top prediction directly
             if top_confidence >= 0.60:
                 description = get_dog_description(top_breed)
                 akc_link = get_akc_breeds_link()
                 description_str = f"**Breed**: {top_breed}\n\n**Description**: {description}\n"
                 description_str += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {top_breed}."
                 return description_str
-            
-            # 如果預測分數小於 60%，返回 Top-3 預測並讓用戶選擇
+
+            # If confidence is lower than 60%, return top 3 results and explain why
             else:
                 top3_confidences, top3_indices = torch.topk(probabilities, 3, dim=1)
                 top3_breeds = [dog_breeds[idx] for idx in top3_indices.squeeze().tolist()]
                 top3_confidences = top3_confidences.squeeze().tolist()
 
-                return {
-                    "top3_breeds": top3_breeds,
-                    "top3_confidences": [f"{conf * 100:.2f}%" for conf in top3_confidences],
-                    "selected_breed": None,
-                    "message": "The confidence score is low. Please select the correct breed from the options or select 'None of the above' if none are correct."
-                }
+                top3_info = "\n\n".join([f"{i+1}. {breed} ({conf*100:.2f}% confidence)" 
+                                         for i, (breed, conf) in enumerate(zip(top3_breeds, top3_confidences))])
+
+                # Return top 3 breeds and an explanation
+                message = (f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
+                           f"{top3_info}\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 message
 
     except Exception as e:
         return f"An error occurred: {e}"
 
-# 處理用戶選擇的結果
-def handle_user_selection(top3_breeds, selected_breed):
-    if selected_breed in top3_breeds:
-        breed_index = top3_breeds.index(selected_breed)
-        description = get_dog_description(selected_breed)
-        
-        akc_link = get_akc_breeds_link()
-        description_str = f"**Breed**: {selected_breed}\n\n**Description**: {description}\n"
-        description_str += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {selected_breed}."
-
-        return description_str
-    else:
-        return "Sorry, the breed could not be identified. Please try uploading a clearer image or another breed."
-
 
 iface = gr.Interface(
     fn=predict,