Update app.py

app.py

@@ -26,7 +26,6 @@ from html_templates import (
     format_description_html,
     format_single_dog_result,
     format_multiple_breeds_result,
-    format_error_message,
     format_unknown_breed_message,
     format_not_dog_message,
     format_warning_html,
@@ -240,37 +239,6 @@ def predict_single_dog(image):
             
         return probabilities[0], breeds[:3], relative_probs
         
-# @spaces.GPU
-# def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55):
-    
-#     results = model_manager.yolo_model(image, conf=conf_threshold, 
-#                                      iou=iou_threshold)[0]
-    
-#     dogs = []
-#     boxes = []
-#     for box in results.boxes:
-#         if box.cls == 16:  # COCO dataset class for dog is 16
-#             xyxy = box.xyxy[0].tolist()
-#             confidence = box.conf.item()
-#             boxes.append((xyxy, confidence))
-
-#     if not boxes:
-#         dogs.append((image, 1.0, [0, 0, image.width, image.height]))
-#     else:
-#         nms_boxes = non_max_suppression(boxes, iou_threshold)
-
-#         for box, confidence in nms_boxes:
-#             x1, y1, x2, y2 = box
-#             w, h = x2 - x1, y2 - y1
-#             x1 = max(0, x1 - w * 0.05)
-#             y1 = max(0, y1 - h * 0.05)
-#             x2 = min(image.width, x2 + w * 0.05)
-#             y2 = min(image.height, y2 + h * 0.05)
-#             cropped_image = image.crop((x1, y1, x2, y2))
-#             dogs.append((cropped_image, confidence, [x1, y1, x2, y2]))
-
-#     return dogs
-
 @spaces.GPU
 def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55):
     """
@@ -374,131 +342,13 @@ def create_breed_comparison(breed1: str, breed2: str) -> dict:
 
     return comparison_data
 
-    
-# def predict(image):
-#     """
-#     Main prediction function that handles both single and multiple dog detection.
-
-#     Args:
-#         image: PIL Image or numpy array
-
-#     Returns:
-#         tuple: (html_output, annotated_image, initial_state)
-#     """
-    
-#     if image is None:
-#         return format_warning_html("Please upload an image to start."), None, None
-
-#     try:
-#         if isinstance(image, np.ndarray):
-#             image = Image.fromarray(image)
-
-#         # Detect dogs in the image
-#         dogs = detect_multiple_dogs(image)
-#         color_scheme = get_color_scheme(len(dogs) == 1)
-
-#         # Prepare for annotation
-#         annotated_image = image.copy()
-#         draw = ImageDraw.Draw(annotated_image)
-
-#         try:
-#             font = ImageFont.truetype("arial.ttf", 24)
-#         except:
-#             font = ImageFont.load_default()
-
-#         dogs_info = ""
-
-#         # Process each detected dog
-#         for i, (cropped_image, detection_confidence, box) in enumerate(dogs):
-#             color = color_scheme if len(dogs) == 1 else color_scheme[i % len(color_scheme)]
-
-#             # Draw box and label on image
-#             draw.rectangle(box, outline=color, width=4)
-#             label = f"Dog {i+1}"
-#             label_bbox = draw.textbbox((0, 0), label, font=font)
-#             label_width = label_bbox[2] - label_bbox[0]
-#             label_height = label_bbox[3] - label_bbox[1]
-
-#             # Draw label background and text
-#             label_x = box[0] + 5
-#             label_y = box[1] + 5
-#             draw.rectangle(
-#                 [label_x - 2, label_y - 2, label_x + label_width + 4, label_y + label_height + 4],
-#                 fill='white',
-#                 outline=color,
-#                 width=2
-#             )
-#             draw.text((label_x, label_y), label, fill=color, font=font)
-
-#             # Predict breed
-#             top1_prob, topk_breeds, relative_probs = predict_single_dog(cropped_image)
-#             combined_confidence = detection_confidence * top1_prob
-
-#             # Format results based on confidence with error handling
-#             try:
-#                 if combined_confidence < 0.2:
-#                     dogs_info += format_error_message(color, i+1)
-#                 elif top1_prob >= 0.45:
-#                     breed = topk_breeds[0]
-#                     description = get_dog_description(breed)
-#                     # Handle missing breed description
-#                     if description is None:
-#                         # 如果沒有描述，創建一個基本描述
-#                         description = {
-#                             "Name": breed,
-#                             "Size": "Unknown",
-#                             "Exercise Needs": "Unknown",
-#                             "Grooming Needs": "Unknown",
-#                             "Care Level": "Unknown",
-#                             "Good with Children": "Unknown",
-#                             "Description": f"Identified as {breed.replace('_', ' ')}"
-#                         }
-#                     dogs_info += format_single_dog_result(breed, description, color)
-#                 else:
-#                     # 修改format_multiple_breeds_result的調用，包含錯誤���理
-#                     dogs_info += format_multiple_breeds_result(
-#                         topk_breeds,
-#                         relative_probs,
-#                         color,
-#                         i+1,
-#                         lambda breed: get_dog_description(breed) or {
-#                             "Name": breed,
-#                             "Size": "Unknown",
-#                             "Exercise Needs": "Unknown",
-#                             "Grooming Needs": "Unknown",
-#                             "Care Level": "Unknown",
-#                             "Good with Children": "Unknown",
-#                             "Description": f"Identified as {breed.replace('_', ' ')}"
-#                         }
-#                     )
-#             except Exception as e:
-#                 print(f"Error formatting results for dog {i+1}: {str(e)}")
-#                 dogs_info += format_error_message(color, i+1)
-
-#         # Wrap final HTML output
-#         html_output = format_multi_dog_container(dogs_info)
-
-#         # Prepare initial state
-#         initial_state = {
-#             "dogs_info": dogs_info,
-#             "image": annotated_image,
-#             "is_multi_dog": len(dogs) > 1,
-#             "html_output": html_output
-#         }
-
-#         return html_output, annotated_image, initial_state
-
-#     except Exception as e:
-#         error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-#         print(error_msg)
-#         return format_warning_html(error_msg), None, None
-
 
 @spaces.GPU
 def predict(image):
     """
     主要的預測函數，負責處理狗的檢測和品種辨識。
     它整合了YOLO的物體檢測和專門的品種分類模型。
+    實施雙層檢測，非狗會直接忽略．
     
     Args:
         image: PIL Image 或 numpy array