Update app.py

app.py

@@ -143,129 +143,6 @@ 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}"
-
-# iface = gr.Interface(
-#     fn=predict,
-#     inputs=gr.Image(label="Upload a dog image", type="numpy"),
-#     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>",
-#     examples=['Border_Collie.jpg',
-#               'Golden_Retriever.jpeg',
-#               'Saint_Bernard.jpeg',
-#               'French_Bulldog.jpeg',
-#               'Samoyed.jpg'],
-#      css = """
-#     .container {
-#         max-width: 900px;
-#         margin: 0 auto;
-#         padding: 20px;
-#         background-color: rgba(255, 255, 255, 0.9);
-#         border-radius: 15px;
-#         box-shadow: 0 0 20px rgba(0, 0, 0, 0.1);
-#     }
-    
-#     .gr-form {
-#         display: flex;
-#         flex-direction: column;
-#         align-items: center;
-#     }
-    
-#     .gr-box {
-#         width: 100%;
-#         max-width: 500px;
-#     }
-    
-#     .output-markdown, .output-image {
-#         margin-top: 20px;
-#         padding: 15px;
-#         background-color: #f5f5f5;
-#         border-radius: 10px;
-#     }
-    
-#     .examples {
-#         display: flex;
-#         justify-content: center;
-#         flex-wrap: wrap;
-#         gap: 10px;
-#         margin-top: 20px;
-#     }
-    
-#     .examples img {
-#         width: 100px;
-#         height: 100px;
-#         object-fit: cover;
-#     }
-#     """,
-#     theme='default')
-
-
-
-# # Launch the app
-# if __name__ == "__main__":
-#     iface.launch()
-
 def predict(image):
     if image is None:
         return "Please upload an image to get started.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
@@ -288,7 +165,7 @@ def predict(image):
                 description = get_dog_description(breed)
                 return format_description(description, breed), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
 
-            elif top1_prob < 0.1:
+            elif top1_prob < 0.2:
                 return ("The image is too unclear or the dog breed is not in the dataset. Please upload a clearer image of the dog.",
                         gr.update(visible=False), gr.update(visible=False), gr.update(visible=False))
             else: