Update app.py

app.py

@@ -204,29 +204,6 @@ def get_akc_breeds_link():
 #     except Exception as e:
 #         return f"An error occurred: {e}"
 
-# 返回特定狗品種的描述函數
-def get_breed_info(breed):
-    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
-
-# 原始的預測函數，增加了按鈕來觸發品種描述的顯示
 def predict(image):
     try:
         image_tensor = preprocess_image(image)
@@ -237,37 +214,68 @@ def predict(image):
             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)
-            top1_prob = topk_probs[0][0].item()
 
-            if top1_prob < 0.2:
-                return "The image does not appear to be a dog. Please upload a clearer or different dog image."
+            # 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()]
-                return get_breed_info(breed)
+                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
+
+            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."
 
             else:
-                # 如果模型無法確定，列出 top 3 品種按鈕
+                # 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]]
 
-                # 用按鈕來返回品種描述
-                buttons = [gr.Button(f"Click here to view more about {breed} ({prob})", variant="primary")
-                           for breed, prob in zip(topk_breeds, topk_probs_percent)]
-
-                return f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:", buttons
-
+                # 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
+                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}"
 
-
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(label="Upload a dog image", type="numpy"),
-    outputs=[gr.Markdown(label="Prediction Results"), gr.Button(value="See More Details")],
+    outputs=gr.Markdown(label="Prediction Results"),
     title="<h1 style='font-family:Roboto; font-weight:bold; color:#2C3E50; text-align:center;'>🐶 Dog Breed Classifier 🔍</h1>",
     article= 'For more details on this project and other work, feel free to visit my GitHub [Dog Breed Classifier](https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog%20Breed%20Classifier)',
     description="<p style='font-family:Open Sans; color:#34495E; text-align:center;'>Upload a picture of a dog, and model will predict its breed, provide detailed information, and include an extra information link!</p>",