Update app.py

app.py

@@ -128,127 +128,6 @@ def preprocess_image(image):
 def get_akc_breeds_link():
     return "https://www.akc.org/dog-breeds/"
 
-# 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)
-    
-#     try:
-#         image_tensor = preprocess_image(image)
-#         with torch.no_grad():
-#             output = model(image_tensor)
-#             logits = output[0] if isinstance(output, tuple) else output
-
-#             probabilities = F.softmax(logits, dim=1)
-#             topk_probs, topk_indices = torch.topk(probabilities, k=3)
-
-#             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:
-#                 breed = topk_breeds[0]
-#                 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 < 0.2:
-#                 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:
-#                 explanation = (
-#                     f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
-#                     f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n"
-#                     f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]} confidence)\n"
-#                     f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)\n\n"
-#                     "Click on a button to view more information about the breed."
-#                 )
-#                 return explanation, gr.update(visible=True, value=f"More about {topk_breeds[0]}"), gr.update(visible=True, value=f"More about {topk_breeds[1]}"), gr.update(visible=True, value=f"More about {topk_breeds[2]}")
-
-#     except Exception as e:
-#         return f"An error occurred: {e}", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-
-# def format_description(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
-
-# def show_details(breed):
-#     breed_name = breed.split("More about ")[-1]
-#     description = get_dog_description(breed_name)
-#     return format_description(description, breed_name)
-
-# with gr.Blocks(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;
-#     }
-#     """) as iface:
-
-#     gr.HTML("<h1 style='font-family:Roboto; font-weight:bold; color:#2C3E50; text-align:center;'>🐶 Dog Breed Classifier 🔍</h1>")
-#     gr.HTML("<p style='font-family:Open Sans; color:#34495E; text-align:center;'>Upload a picture of a dog, and the model will predict its breed, provide detailed information, and include an extra information link!</p>")
-    
-#     with gr.Row():
-#         input_image = gr.Image(label="Upload a dog image", type="numpy")
-#         output = gr.Markdown(label="Prediction Results")
-    
-#     with gr.Row():
-#         btn1 = gr.Button("View More 1", visible=False)
-#         btn2 = gr.Button("View More 2", visible=False)
-#         btn3 = gr.Button("View More 3", visible=False)
-
-#     input_image.change(predict, inputs=input_image, outputs=[output, btn1, btn2, btn3])
-    
-#     btn1.click(show_details, inputs=btn1, outputs=output)
-#     btn2.click(show_details, inputs=btn2, outputs=output)
-#     btn3.click(show_details, inputs=btn3, outputs=output)
-
-#     gr.Examples(
-#         examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'],
-#         inputs=input_image
-#     )
-
-#     gr.HTML('For more details on this project and other work, feel free to visit my GitHub <a href="https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog%20Breed%20Classifier">Dog Breed Classifier</a>')
-
-# # launch the program
-# if __name__ == "__main__":
-#     iface.launch()
-
 
 def format_description(description, breed):
     if isinstance(description, dict):
@@ -284,7 +163,7 @@ def _predict_single_dog(image):
     return top1_prob, topk_breeds, topk_probs_percent
 
 
-async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.5):
+async def detect_multiple_dogs(image, conf_threshold=0.25, iou_threshold=0.3):
     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
     dogs = []
     for box in results.boxes:
@@ -304,7 +183,7 @@ async def predict(image):
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
 
-        dogs = await detect_multiple_dogs(image, conf_threshold=0.25, iou_threshold=0.4)
+        dogs = await detect_multiple_dogs(image, conf_threshold=0.25, iou_threshold=0.3)
         
         if len(dogs) <= 1:
             return await process_single_dog(image)
@@ -362,6 +241,7 @@ async def predict(image):
         error_msg = f"An error occurred: {str(e)}"
         print(error_msg)  # 添加日誌輸出
         return error_msg, None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), None
+        
 
 async def process_single_dog(image):
     top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)