Update app.py

app.py

@@ -502,113 +502,6 @@ async def process_single_dog(image):
 #         return error_msg, None, gr.update(visible=False, choices=[]), None
 
 
-# async def predict(image):
-#     if image is None:
-#         return "Please upload an image to start.", None, gr.update(visible=False, choices=[]), None
-
-#     try:
-#         if isinstance(image, np.ndarray):
-#             image = Image.fromarray(image)
-
-#         dogs = await detect_multiple_dogs(image)
-        
-#         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
-#         buttons = []
-#         annotated_image = image.copy()
-#         draw = ImageDraw.Draw(annotated_image)
-#         font = ImageFont.load_default()
-
-#         dogs_info = ""
-#         buttons_html = ""
-
-#         for i, (cropped_image, detection_confidence, box) in enumerate(dogs):
-#             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
-#             color = color_list[i % len(color_list)]
-#             draw.rectangle(box, outline=color, width=3)
-#             draw.text((box[0] + 5, box[1] + 5), f"Dog {i+1}", fill=color, font=font)
-
-#             combined_confidence = detection_confidence * top1_prob
-#             #dogs_info += f'<div class="dog-info" style="border-left: 5px solid {color};">'
-#             dogs_info += f'<div class="dog-info" style="border-left: 5px solid {color}; margin-bottom: 20px; padding: 15px;">'
-#             dogs_info += f'<h2>Dog {i+1}</h2>'
-
-#             if top1_prob >= 0.45:
-#                 breed = topk_breeds[0]
-#                 description = get_dog_description(breed)
-#                 dogs_info += format_description_html(description, breed)
-
-#             elif combined_confidence >= 0.15:
-#                 dogs_info += f"<p>Top 3 possible breeds:</p><ul>"
-#                 for j, (breed, prob) in enumerate(zip(topk_breeds[:3], topk_probs_percent[:3])):
-#                     dogs_info += f"<li><strong>{breed}</strong> ({prob} confidence)</li>"
-#                 dogs_info += "</ul>"
-#                 buttons_html += '<div class="breed-buttons">' 
-#                 for breed in topk_breeds[:3]:
-#                     button_id = f"Dog {i+1}: More about {breed}"
-#                     buttons_html += f'<button class="breed-button" onclick="handle_button_click(\'{button_id}\')">{breed}</button>'
-#                     buttons.append(button_id)
-#                 buttons_html += '</div>' 
-                
-            
-  
-#             else:
-#                 dogs_info += "<p>The image is unclear or the breed is not in the dataset. Please upload a clearer image.</p>"
-            
-#             dogs_info += '</div>'
-
-#         dogs_info += buttons_html 
-
-        
-#         html_output = f"""
-#         <style>
-#         .dog-info {{ border: 1px solid #ddd; margin-bottom: 20px; padding: 15px; border-radius: 5px; box-shadow: 0 2px 5px rgba(0,0,0,0.1); }}
-#         .dog-info h2 {{ background-color: #f0f0f0; padding: 10px; margin: -15px -15px 15px -15px; border-radius: 5px 5px 0 0; }}
-#         .breed-buttons {{ margin-top: 10px; }}
-#         .breed-button {{ margin-right: 10px; margin-bottom: 10px; padding: 5px 10px; background-color: #4CAF50; color: white; border: none; border-radius: 3px; cursor: pointer; }}
-#         </style>
-#         {dogs_info}
-#         {buttons_html}
-#         """
-
-#         if buttons:
-#             html_output += """
-#             <script>
-#             function handle_button_click(button_id) {
-#                 const radio = document.querySelector('input[type=radio][value="' + button_id + '"]');
-#                 if (radio) {
-#                     radio.click();
-#                 } else {
-#                     console.error("Radio button not found:", button_id);
-#                 }
-#             }
-#             </script>
-#             """
-#             initial_state = {
-#                 "dogs_info": dogs_info,
-#                 "buttons": buttons,
-#                 "show_back": True,
-#                 "image": annotated_image,
-#                 "is_multi_dog": len(dogs) > 1,
-#                 "html_output": html_output  # 儲存完整的 HTML 輸出
-#             }
-#             return html_output, annotated_image, gr.update(visible=True, choices=buttons), initial_state
-#         else:
-#             initial_state = {
-#                 "dogs_info": dogs_info,
-#                 "buttons": [],
-#                 "show_back": False,
-#                 "image": annotated_image,
-#                 "is_multi_dog": len(dogs) > 1,
-#                 "html_output": html_output  # 儲存完整的 HTML 輸出
-#             }
-#             return html_output, annotated_image, gr.update(visible=False, choices=[]), initial_state
-
-#     except Exception as e:
-#         error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-#         print(error_msg)
-#         return error_msg, None, gr.update(visible=False, choices=[]), None
-
-
 async def predict(image):
     if image is None:
         return "Please upload an image to start.", None, gr.update(visible=False, choices=[]), None
@@ -617,17 +510,16 @@ async def predict(image):
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
 
-        # 創建一個新的圖像副本用於註釋
+        dogs = await detect_multiple_dogs(image)
+        
+        color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
+        buttons = []
         annotated_image = image.copy()
         draw = ImageDraw.Draw(annotated_image)
         font = ImageFont.load_default()
 
-        dogs = await detect_multiple_dogs(image)
-        
-        color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
         dogs_info = ""
-        all_breeds = set()  # 使用集合來避免重複
-        buttons = []  # 初始化 buttons 列表
+        buttons_html = ""
 
         for i, (cropped_image, detection_confidence, box) in enumerate(dogs):
             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
@@ -636,33 +528,37 @@ async def predict(image):
             draw.text((box[0] + 5, box[1] + 5), f"Dog {i+1}", fill=color, font=font)
 
             combined_confidence = detection_confidence * top1_prob
-            dogs_info += f'''
-            <div class="dog-info" style="border-left: 5px solid {color}; margin-bottom: 20px; padding: 15px; border: 1px solid #ddd; border-radius: 5px;">
-                <h2 style="background-color: #f0f0f0; padding: 10px; margin: -15px -15px 15px -15px; border-radius: 5px 5px 0 0;">Dog {i+1}</h2>
-            '''
+            #dogs_info += f'<div class="dog-info" style="border-left: 5px solid {color};">'
+            dogs_info += f'<div class="dog-info" style="border-left: 5px solid {color}; margin-bottom: 20px; padding: 15px;">'
+            dogs_info += f'<h2>Dog {i+1}</h2>'
 
             if top1_prob >= 0.45:
                 breed = topk_breeds[0]
-                all_breeds.add(breed)
                 description = get_dog_description(breed)
                 dogs_info += format_description_html(description, breed)
+
             elif combined_confidence >= 0.15:
-                dogs_info += "<p>Top 3 possible breeds:</p><ul>"
+                dogs_info += f"<p>Top 3 possible breeds:</p><ul>"
                 for j, (breed, prob) in enumerate(zip(topk_breeds[:3], topk_probs_percent[:3])):
                     dogs_info += f"<li><strong>{breed}</strong> ({prob} confidence)</li>"
-                    all_breeds.add(breed)
                 dogs_info += "</ul>"
-                dogs_info += '<div class="breed-buttons">'
+                buttons_html += '<div class="breed-buttons">' 
                 for breed in topk_breeds[:3]:
                     button_id = f"Dog {i+1}: More about {breed}"
-                    dogs_info += f'<button class="breed-button" onclick="handle_button_click(\'{button_id}\')">{breed}</button>'
+                    buttons_html += f'<button class="breed-button" onclick="handle_button_click(\'{button_id}\')">{breed}</button>'
                     buttons.append(button_id)
-                dogs_info += '</div>'
+                buttons_html += '</div>' 
+                
+            
+  
             else:
                 dogs_info += "<p>The image is unclear or the breed is not in the dataset. Please upload a clearer image.</p>"
             
             dogs_info += '</div>'
 
+        dogs_info += buttons_html 
+
+        
         html_output = f"""
         <style>
         .dog-info {{ border: 1px solid #ddd; margin-bottom: 20px; padding: 15px; border-radius: 5px; box-shadow: 0 2px 5px rgba(0,0,0,0.1); }}
@@ -671,6 +567,7 @@ async def predict(image):
         .breed-button {{ margin-right: 10px; margin-bottom: 10px; padding: 5px 10px; background-color: #4CAF50; color: white; border: none; border-radius: 3px; cursor: pointer; }}
         </style>
         {dogs_info}
+        {buttons_html}
         """
 
         if buttons:
@@ -692,7 +589,7 @@ async def predict(image):
                 "show_back": True,
                 "image": annotated_image,
                 "is_multi_dog": len(dogs) > 1,
-                "html_output": html_output
+                "html_output": html_output  # 儲存完整的 HTML 輸出
             }
             return html_output, annotated_image, gr.update(visible=True, choices=buttons), initial_state
         else:
@@ -702,7 +599,7 @@ async def predict(image):
                 "show_back": False,
                 "image": annotated_image,
                 "is_multi_dog": len(dogs) > 1,
-                "html_output": html_output
+                "html_output": html_output  # 儲存完整的 HTML 輸出
             }
             return html_output, annotated_image, gr.update(visible=False, choices=[]), initial_state
 
@@ -710,7 +607,6 @@ async def predict(image):
         error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
         print(error_msg)
         return error_msg, None, gr.update(visible=False, choices=[]), None
-
         
 
 def show_details_html(choice, previous_output, initial_state):