Update app.py

app.py

@@ -37,472 +37,9 @@ from device_manager import DeviceManager, adaptive_gpu
 import asyncio
 import traceback
 
-# model_yolo = YOLO('yolov8l.pt')
-
-# history_manager = UserHistoryManager()
-
-# dog_breeds = ["Afghan_Hound", "African_Hunting_Dog", "Airedale", "American_Staffordshire_Terrier",
-#               "Appenzeller", "Australian_Terrier", "Bedlington_Terrier", "Bernese_Mountain_Dog", "Bichon_Frise",
-#               "Blenheim_Spaniel", "Border_Collie", "Border_Terrier", "Boston_Bull", "Bouvier_Des_Flandres",
-#               "Brabancon_Griffon", "Brittany_Spaniel", "Cardigan", "Chesapeake_Bay_Retriever",
-#               "Chihuahua", "Dachshund", "Dandie_Dinmont", "Doberman", "English_Foxhound", "English_Setter",
-#               "English_Springer", "EntleBucher", "Eskimo_Dog", "French_Bulldog", "German_Shepherd",
-#               "German_Short-Haired_Pointer", "Gordon_Setter", "Great_Dane", "Great_Pyrenees",
-#               "Greater_Swiss_Mountain_Dog","Havanese", "Ibizan_Hound", "Irish_Setter", "Irish_Terrier",
-#               "Irish_Water_Spaniel", "Irish_Wolfhound", "Italian_Greyhound", "Japanese_Spaniel",
-#               "Kerry_Blue_Terrier", "Labrador_Retriever", "Lakeland_Terrier", "Leonberg", "Lhasa",
-#               "Maltese_Dog", "Mexican_Hairless", "Newfoundland", "Norfolk_Terrier", "Norwegian_Elkhound",
-#               "Norwich_Terrier", "Old_English_Sheepdog", "Pekinese", "Pembroke", "Pomeranian",
-#               "Rhodesian_Ridgeback", "Rottweiler", "Saint_Bernard", "Saluki", "Samoyed",
-#               "Scotch_Terrier", "Scottish_Deerhound", "Sealyham_Terrier", "Shetland_Sheepdog", "Shiba_Inu",
-#               "Shih-Tzu", "Siberian_Husky", "Staffordshire_Bullterrier", "Sussex_Spaniel",
-#               "Tibetan_Mastiff", "Tibetan_Terrier", "Walker_Hound", "Weimaraner",
-#               "Welsh_Springer_Spaniel", "West_Highland_White_Terrier", "Yorkshire_Terrier",
-#               "Affenpinscher", "Basenji", "Basset", "Beagle", "Black-and-Tan_Coonhound", "Bloodhound",
-#               "Bluetick", "Borzoi", "Boxer", "Briard", "Bull_Mastiff", "Cairn", "Chow", "Clumber",
-#               "Cocker_Spaniel", "Collie", "Curly-Coated_Retriever", "Dhole", "Dingo",
-#               "Flat-Coated_Retriever", "Giant_Schnauzer", "Golden_Retriever", "Groenendael", "Keeshond",
-#               "Kelpie", "Komondor", "Kuvasz", "Malamute", "Malinois", "Miniature_Pinscher",
-#               "Miniature_Poodle", "Miniature_Schnauzer", "Otterhound", "Papillon", "Pug", "Redbone",
-#               "Schipperke", "Silky_Terrier", "Soft-Coated_Wheaten_Terrier", "Standard_Poodle",
-#               "Standard_Schnauzer", "Toy_Poodle", "Toy_Terrier", "Vizsla", "Whippet",
-#               "Wire-Haired_Fox_Terrier"]
-
-
-# class MultiHeadAttention(nn.Module):
-
-#     def __init__(self, in_dim, num_heads=8):
-#         super().__init__()
-#         self.num_heads = num_heads
-#         self.head_dim = max(1, in_dim // num_heads)
-#         self.scaled_dim = self.head_dim * num_heads
-#         self.fc_in = nn.Linear(in_dim, self.scaled_dim)
-#         self.query = nn.Linear(self.scaled_dim, self.scaled_dim)
-#         self.key = nn.Linear(self.scaled_dim, self.scaled_dim)
-#         self.value = nn.Linear(self.scaled_dim, self.scaled_dim)
-#         self.fc_out = nn.Linear(self.scaled_dim, in_dim)
-
-#     def forward(self, x):
-#         N = x.shape[0]
-#         x = self.fc_in(x)
-#         q = self.query(x).view(N, self.num_heads, self.head_dim)
-#         k = self.key(x).view(N, self.num_heads, self.head_dim)
-#         v = self.value(x).view(N, self.num_heads, self.head_dim)
-
-#         energy = torch.einsum("nqd,nkd->nqk", [q, k])
-#         attention = F.softmax(energy / (self.head_dim ** 0.5), dim=2)
-
-#         out = torch.einsum("nqk,nvd->nqd", [attention, v])
-#         out = out.reshape(N, self.scaled_dim)
-#         out = self.fc_out(out)
-#         return out
-
-# class BaseModel(nn.Module):
-#     def __init__(self, num_classes, device='cuda' if torch.cuda.is_available() else 'cpu'):
-#         super().__init__()
-#         self.device = device
-#         self.backbone = efficientnet_v2_m(weights=EfficientNet_V2_M_Weights.IMAGENET1K_V1)
-#         self.feature_dim = self.backbone.classifier[1].in_features
-#         self.backbone.classifier = nn.Identity()
-
-#         self.num_heads = max(1, min(8, self.feature_dim // 64))
-#         self.attention = MultiHeadAttention(self.feature_dim, num_heads=self.num_heads)
-
-#         self.classifier = nn.Sequential(
-#             nn.LayerNorm(self.feature_dim),
-#             nn.Dropout(0.3),
-#             nn.Linear(self.feature_dim, num_classes)
-#         )
-
-#         self.to(device)
-
-#     def forward(self, x):
-#         x = x.to(self.device)
-#         features = self.backbone(x)
-#         attended_features = self.attention(features)
-#         logits = self.classifier(attended_features)
-#         return logits, attended_features
-
-# # Initialize model
-# num_classes = len(dog_breeds)
-# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-# # Initialize base model
-# model = BaseModel(num_classes=num_classes, device=device).to(device)
-
-# # Load model path
-# model_path = '124_best_model_dog.pth'
-# checkpoint = torch.load(model_path, map_location=device)
-
-# # Load model state
-# model.load_state_dict(checkpoint['base_model'], strict=False)
-# model.eval()
-
-# # Image preprocessing function
-# def preprocess_image(image):
-#     # If the image is numpy.ndarray turn into PIL.Image
-#     if isinstance(image, np.ndarray):
-#         image = Image.fromarray(image)
-
-#     # Use torchvision.transforms to process images
-#     transform = transforms.Compose([
-#         transforms.Resize((224, 224)),
-#         transforms.ToTensor(),
-#         transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-#     ])
-
-#     return transform(image).unsqueeze(0)
-
-# async def predict_single_dog(image):
-#     """
-#     Predicts the dog breed using only the classifier.
-#     Args:
-#         image: PIL Image or numpy array
-#     Returns:
-#         tuple: (top1_prob, topk_breeds, relative_probs)
-#     """
-#     image_tensor = preprocess_image(image).to(device)
-    
-#     with torch.no_grad():
-#         # Get model outputs (只使用logits，不需要features)
-#         logits = model(image_tensor)[0]  # 如果model仍返回tuple，取第一個元素
-#         probs = F.softmax(logits, dim=1)
-        
-#         # Classifier prediction
-#         top5_prob, top5_idx = torch.topk(probs, k=5)
-#         breeds = [dog_breeds[idx.item()] for idx in top5_idx[0]]
-#         probabilities = [prob.item() for prob in top5_prob[0]]
-        
-#         # Calculate relative probabilities
-#         sum_probs = sum(probabilities[:3])  # 只取前三個來計算相對概率
-#         relative_probs = [f"{(prob/sum_probs * 100):.2f}%" for prob in probabilities[:3]]
-        
-#         # Debug output
-#         print("\nClassifier Predictions:")
-#         for breed, prob in zip(breeds[:5], probabilities[:5]):
-#             print(f"{breed}: {prob:.4f}")
-            
-#         return probabilities[0], breeds[:3], relative_probs
-
-
-# async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55):
-#     results = model_yolo(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
-
-# def non_max_suppression(boxes, iou_threshold):
-#     keep = []
-#     boxes = sorted(boxes, key=lambda x: x[1], reverse=True)
-#     while boxes:
-#         current = boxes.pop(0)
-#         keep.append(current)
-#         boxes = [box for box in boxes if calculate_iou(current[0], box[0]) < iou_threshold]
-#     return keep
-
-
-# def calculate_iou(box1, box2):
-#     x1 = max(box1[0], box2[0])
-#     y1 = max(box1[1], box2[1])
-#     x2 = min(box1[2], box2[2])
-#     y2 = min(box1[3], box2[3])
-
-#     intersection = max(0, x2 - x1) * max(0, y2 - y1)
-#     area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
-#     area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
-
-#     iou = intersection / float(area1 + area2 - intersection)
-#     return iou
-
-
-
-# def create_breed_comparison(breed1: str, breed2: str) -> dict:
-#     breed1_info = get_dog_description(breed1)
-#     breed2_info = get_dog_description(breed2)
-
-#     # 標準化數值轉換
-#     value_mapping = {
-#         'Size': {'Small': 1, 'Medium': 2, 'Large': 3, 'Giant': 4},
-#         'Exercise_Needs': {'Low': 1, 'Moderate': 2, 'High': 3, 'Very High': 4},
-#         'Care_Level': {'Low': 1, 'Moderate': 2, 'High': 3},
-#         'Grooming_Needs': {'Low': 1, 'Moderate': 2, 'High': 3}
-#     }
-
-#     comparison_data = {
-#         breed1: {},
-#         breed2: {}
-#     }
-
-#     for breed, info in [(breed1, breed1_info), (breed2, breed2_info)]:
-#         comparison_data[breed] = {
-#             'Size': value_mapping['Size'].get(info['Size'], 2),  # 預設 Medium
-#             'Exercise_Needs': value_mapping['Exercise_Needs'].get(info['Exercise Needs'], 2),  # 預設 Moderate
-#             'Care_Level': value_mapping['Care_Level'].get(info['Care Level'], 2),
-#             'Grooming_Needs': value_mapping['Grooming_Needs'].get(info['Grooming Needs'], 2),
-#             'Good_with_Children': info['Good with Children'] == 'Yes',
-#             'Original_Data': info
-#         }
-
-#     return comparison_data
-
-
-# async 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 = await 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 = await 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，包含錯誤處理
-#                     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
-
-
-# def show_details_html(choice, previous_output, initial_state):
-#     """
-#     Generate detailed HTML view for a selected breed.
-
-#     Args:
-#         choice: str, Selected breed option
-#         previous_output: str, Previous HTML output
-#         initial_state: dict, Current state information
-
-#     Returns:
-#         tuple: (html_output, gradio_update, updated_state)
-#     """
-#     if not choice:
-#         return previous_output, gr.update(visible=True), initial_state
-
-#     try:
-#         breed = choice.split("More about ")[-1]
-#         description = get_dog_description(breed)
-#         html_output = format_breed_details_html(description, breed)
-
-#         # Update state
-#         initial_state["current_description"] = html_output
-#         initial_state["original_buttons"] = initial_state.get("buttons", [])
-
-#         return html_output, gr.update(visible=True), initial_state
-
-#     except Exception as e:
-#         error_msg = f"An error occurred while showing details: {e}"
-#         print(error_msg)
-#         return format_warning_html(error_msg), gr.update(visible=True), initial_state
-
-# def main():
-#     with gr.Blocks(css=get_css_styles()) as iface:
-#         # Header HTML
-
-#         gr.HTML("""
-#         <header style='text-align: center; padding: 20px; margin-bottom: 20px;'>
-#             <h1 style='font-size: 2.5em; margin-bottom: 10px; color: #2D3748;'>
-#                 🐾 PawMatch AI
-#             </h1>
-#             <h2 style='font-size: 1.2em; font-weight: normal; color: #4A5568; margin-top: 5px;'>
-#                 Your Smart Dog Breed Guide
-#             </h2>
-#             <div style='width: 50px; height: 3px; background: linear-gradient(90deg, #4299e1, #48bb78); margin: 15px auto;'></div>
-#             <p style='color: #718096; font-size: 0.9em;'>
-#                 Powered by AI • Breed Recognition • Smart Matching • Companion Guide
-#             </p>
-#         </header>
-#         """)
-
-#         # 先創建歷史組件實例（但不創建標籤頁）
-#         history_component = create_history_component()
-
-#         with gr.Tabs():
-#             # 1. 品種檢測標籤頁
-#             example_images = [
-#                 'Border_Collie.jpg',
-#                 'Golden_Retriever.jpeg',
-#                 'Saint_Bernard.jpeg',
-#                 'Samoyed.jpg',
-#                 'French_Bulldog.jpeg'
-#             ]
-#             detection_components = create_detection_tab(predict, example_images)
-
-#             # 2. 品種比較標籤頁
-#             comparison_components = create_comparison_tab(
-#                 dog_breeds=dog_breeds,
-#                 get_dog_description=get_dog_description,
-#                 breed_health_info=breed_health_info,
-#                 breed_noise_info=breed_noise_info
-#             )
-
-#             # 3. 品種推薦標籤頁
-#             recommendation_components = create_recommendation_tab(
-#                 UserPreferences=UserPreferences,
-#                 get_breed_recommendations=get_breed_recommendations,
-#                 format_recommendation_html=format_recommendation_html,
-#                 history_component=history_component
-#             )
-
-
-#             # 4. 最後創建歷史記錄標籤頁
-#             create_history_tab(history_component)
-
-#         # Footer
-#         gr.HTML('''
-#             <div style="
-#                 display: flex;
-#                 align-items: center;
-#                 justify-content: center;
-#                 gap: 20px;
-#                 padding: 20px 0;
-#             ">
-#                 <p style="
-#                     font-family: 'Arial', sans-serif;
-#                     font-size: 14px;
-#                     font-weight: 500;
-#                     letter-spacing: 2px;
-#                     background: linear-gradient(90deg, #555, #007ACC);
-#                     -webkit-background-clip: text;
-#                     -webkit-text-fill-color: transparent;
-#                     margin: 0;
-#                     text-transform: uppercase;
-#                     display: inline-block;
-#                 ">EXPLORE THE CODE →</p>
-#                 <a href="https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/PawMatchAI" style="text-decoration: none;">
-#                     <img src="https://img.shields.io/badge/GitHub-PawMatch_AI-007ACC?logo=github&style=for-the-badge">
-#                 </a>
-#             </div>
-#         ''')
-
-#     return iface
-
-# if __name__ == "__main__":
-#     iface = main()
-#     iface.launch()
-
+model_yolo = YOLO('yolov8l.pt')
 
+history_manager = UserHistoryManager()
 
 dog_breeds = ["Afghan_Hound", "African_Hunting_Dog", "Airedale", "American_Staffordshire_Terrier",
               "Appenzeller", "Australian_Terrier", "Bedlington_Terrier", "Bernese_Mountain_Dog", "Bichon_Frise",
@@ -532,10 +69,6 @@ dog_breeds = ["Afghan_Hound", "African_Hunting_Dog", "Airedale", "American_Staff
               "Wire-Haired_Fox_Terrier"]
 
 
-device_mgr = DeviceManager()
-
-history_manager = UserHistoryManager()
-
 class MultiHeadAttention(nn.Module):
 
     def __init__(self, in_dim, num_heads=8):
@@ -565,9 +98,9 @@ class MultiHeadAttention(nn.Module):
         return out
 
 class BaseModel(nn.Module):
-    
     def __init__(self, num_classes, device='cuda' if torch.cuda.is_available() else 'cpu'):
         super().__init__()
+        self.device = device
         self.backbone = efficientnet_v2_m(weights=EfficientNet_V2_M_Weights.IMAGENET1K_V1)
         self.feature_dim = self.backbone.classifier[1].in_features
         self.backbone.classifier = nn.Identity()
@@ -581,6 +114,8 @@ class BaseModel(nn.Module):
             nn.Linear(self.feature_dim, num_classes)
         )
 
+        self.to(device)
+
     def forward(self, x):
         x = x.to(self.device)
         features = self.backbone(x)
@@ -590,21 +125,19 @@ class BaseModel(nn.Module):
 
 # Initialize model
 num_classes = len(dog_breeds)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 # Initialize base model
-model = BaseModel(num_classes=num_classes)
-model = device_mgr.to_device(model)
+model = BaseModel(num_classes=num_classes, device=device).to(device)
+
 # Load model path
 model_path = '124_best_model_dog.pth'
-checkpoint = torch.load(model_path, map_location=device_mgr.get_device(), weights_only=True)
+checkpoint = torch.load(model_path, map_location=device)
 
 # Load model state
 model.load_state_dict(checkpoint['base_model'], strict=False)
 model.eval()
 
-model_yolo = YOLO('yolov8l.pt')
-model_yolo = device_mgr.to_device(model_yolo)
-
 # Image preprocessing function
 def preprocess_image(image):
     # If the image is numpy.ndarray turn into PIL.Image
@@ -619,35 +152,40 @@ def preprocess_image(image):
     ])
 
     return transform(image).unsqueeze(0)
-       
 
-@adaptive_gpu(duration=30)
 async def predict_single_dog(image):
-    """單獨的狗預測函數"""
-    image_tensor = preprocess_image(image)
-    image_tensor = device_mgr.to_device(image_tensor)
+    """
+    Predicts the dog breed using only the classifier.
+    Args:
+        image: PIL Image or numpy array
+    Returns:
+        tuple: (top1_prob, topk_breeds, relative_probs)
+    """
+    image_tensor = preprocess_image(image).to(device)
     
     with torch.no_grad():
-        outputs = model(image_tensor)
-        logits = outputs[0] if isinstance(outputs, tuple) else outputs
+        # Get model outputs (只使用logits，不需要features)
+        logits = model(image_tensor)[0]  # 如果model仍返回tuple，取第一個元素
         probs = F.softmax(logits, dim=1)
         
+        # Classifier prediction
         top5_prob, top5_idx = torch.topk(probs, k=5)
         breeds = [dog_breeds[idx.item()] for idx in top5_idx[0]]
         probabilities = [prob.item() for prob in top5_prob[0]]
         
-        sum_probs = sum(probabilities[:3])
+        # Calculate relative probabilities
+        sum_probs = sum(probabilities[:3])  # 只取前三個來計算相對概率
         relative_probs = [f"{(prob/sum_probs * 100):.2f}%" for prob in probabilities[:3]]
         
+        # Debug output
         print("\nClassifier Predictions:")
         for breed, prob in zip(breeds[:5], probabilities[:5]):
             print(f"{breed}: {prob:.4f}")
             
         return probabilities[0], breeds[:3], relative_probs
-        
-@adaptive_gpu(duration=30)      
+
+
 async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55):
-    """複數狗預測函數"""
     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
     dogs = []
     boxes = []
@@ -673,7 +211,6 @@ async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55):
             dogs.append((cropped_image, confidence, [x1, y1, x2, y2]))
 
     return dogs
-        
 
 def non_max_suppression(boxes, iou_threshold):
     keep = []