Delete app.py

app.py

@@ -1,829 +0,0 @@
-import os
-import numpy as np
-import torch
-import torch.nn as nn
-import gradio as gr
-from torchvision.models import efficientnet_v2_m, EfficientNet_V2_M_Weights
-from torchvision.ops import nms, box_iou
-import torch.nn.functional as F
-from torchvision import transforms
-from PIL import Image, ImageDraw, ImageFont, ImageFilter
-from data_manager import get_dog_description, UserPreferences, get_breed_recommendations, format_recommendation_html
-from history_manager import UserHistoryManager
-from search_history import create_history_tab, create_history_component
-from styles import get_css_styles
-from breed_detection import create_detection_tab
-from breed_comparison import create_comparison_tab
-from breed_recommendation import create_recommendation_tab
-from urllib.parse import quote
-from ultralytics import YOLO
-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",
-              "Blenheim_Spaniel", "Border_Collie", "Border_Terrier", "Boston_Bull", "Bouvier_Des_Flandres",
-              "Brabancon_Griffon", "Brittany_Spaniel", "Cardigan", "Chesapeake_Bay_Retriever",
-              "Chihuahua", "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", "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",
-              "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
-
-
-num_classes = 120
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model = BaseModel(num_classes=num_classes, device=device)
-
-checkpoint = torch.load('best_model_81_dog.pth', map_location=torch.device('cpu'))
-model.load_state_dict(checkpoint['model_state_dict'])
-
-# evaluation mode
-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)
-
-
-def get_akc_breeds_link(breed: str) -> str:
-    """Generate AKC breed page URL with intelligent name handling"""
-    # 基本的字符串處理
-    breed_name = breed.lower()
-    
-    # 處理常見的名稱格式
-    breed_name = breed_name.replace('_', '-')
-    breed_name = breed_name.replace("'", '')
-    breed_name = breed_name.replace(" ", '-')
-    
-    # 處理特殊情況
-    special_cases = {
-        'mexican-hairless': 'xoloitzcuintli',
-        'brabancon-griffon': 'brussels-griffon',
-        'bull-mastiff': 'bullmastiff',
-        'walker-hound': 'treeing-walker-coonhound'
-    }
-    
-    breed_name = special_cases.get(breed_name, breed_name)
-    return f"https://www.akc.org/dog-breeds/{breed_name}/"
-
-
-async def predict_single_dog(image):
-    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]]
-
-        # Calculate relative probabilities for display
-        raw_probs = [prob.item() for prob in topk_probs[0]]
-        sum_probs = sum(raw_probs)
-        relative_probs = [f"{(prob/sum_probs * 100):.2f}%" for prob in raw_probs]
-
-    return top1_prob, topk_breeds, relative_probs
-
-
-async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.45):
-    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
-
-
-async def process_single_dog(image):
-    top1_prob, topk_breeds, relative_probs = await predict_single_dog(image)
-
-    # Case 1: Low confidence - unclear image or breed not in dataset
-    if top1_prob < 0.2:
-        error_message = '''
-            <div class="dog-info-card">
-                <div class="breed-info">
-                    <p class="warning-message">
-                        <span class="icon">⚠️</span>
-                        The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.
-                    </p>
-                </div>
-            </div>
-        '''
-        initial_state = {
-            "explanation": error_message,
-            "image": None,
-            "is_multi_dog": False
-        }
-        return error_message, None, initial_state
-
-    breed = topk_breeds[0]
-
-    # Case 2: High confidence - single breed result
-    if top1_prob >= 0.45:
-        description = get_dog_description(breed)
-        formatted_description = format_description_html(description, breed)  # 使用 format_description_html
-        html_content = f'''
-            <div class="dog-info-card">
-                <div class="breed-info">
-                    {formatted_description}
-                </div>
-            </div>
-        '''
-        initial_state = {
-            "explanation": html_content,
-            "image": image,
-            "is_multi_dog": False
-        }
-        return html_content, image, initial_state
-
-    # Case 3: Medium confidence - show top 3 breeds with relative probabilities
-    else:
-        breeds_html = ""
-        for i, (breed, prob) in enumerate(zip(topk_breeds, relative_probs)):
-            description = get_dog_description(breed)
-            formatted_description = format_description_html(description, breed)  # 使用 format_description_html
-            breeds_html += f'''
-                <div class="dog-info-card">
-                    <div class="breed-info">
-                        <div class="breed-header">
-                            <span class="breed-name">Breed {i+1}: {breed}</span>
-                            <span class="confidence-badge">Confidence: {prob}</span>
-                        </div>
-                        {formatted_description}
-                    </div>
-                </div>
-            '''
-
-        initial_state = {
-            "explanation": breeds_html,
-            "image": image,
-            "is_multi_dog": False
-        }
-        return breeds_html, image, initial_state
-
-
-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):
-    if image is None:
-        return "Please upload an image to start.", None, None
-
-    try:
-        if isinstance(image, np.ndarray):
-            image = Image.fromarray(image)
-
-        dogs = await detect_multiple_dogs(image)
-        # 更新顏色組合
-        single_dog_color = '#34C759'  # 清爽的綠色作為單狗顏色
-        color_list = [
-        '#FF5733',  # 珊瑚紅
-        '#28A745',  # 深綠色
-        '#3357FF',  # 寶藍色
-        '#FF33F5',  # 粉紫色
-        '#FFB733',  # 橙黃色
-        '#33FFF5',  # 青藍色
-        '#A233FF',  # 紫色
-        '#FF3333',  # 紅色
-        '#33FFB7',  # 青綠色
-        '#FFE033'   # 金黃色
-        ]
-        annotated_image = image.copy()
-        draw = ImageDraw.Draw(annotated_image)
-
-        try:
-            font = ImageFont.truetype("arial.ttf", 24)
-        except:
-            font = ImageFont.load_default()
-
-        dogs_info = ""
-
-        for i, (cropped_image, detection_confidence, box) in enumerate(dogs):
-            color = single_dog_color if len(dogs) == 1 else color_list[i % len(color_list)]
-
-            # 優化圖片上的標記
-            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]
-
-            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)
-
-            top1_prob, topk_breeds, relative_probs = await predict_single_dog(cropped_image)
-            combined_confidence = detection_confidence * top1_prob
-
-            # 開始資訊卡片
-            dogs_info += f'<div class="dog-info-card" style="border-left: 6px solid {color};">'
-
-            if combined_confidence < 0.2:
-                dogs_info += f'''
-                    <div class="dog-info-header" style="background-color: {color}10;">
-                        <span class="dog-label" style="color: {color};">Dog {i+1}</span>
-                    </div>
-                    <div class="breed-info">
-                        <p class="warning-message">
-                            <span class="icon">⚠️</span>
-                            The image is unclear or the breed is not in the dataset. Please upload a clearer image.
-                        </p>
-                    </div>
-                '''
-            elif top1_prob >= 0.45:
-                breed = topk_breeds[0]
-                description = get_dog_description(breed)
-                dogs_info += f'''
-                    <div class="dog-info-header" style="background-color: {color}10;">
-                        <span class="dog-label" style="color: {color};">
-                            <span class="icon">🐾</span> {breed}
-                        </span>
-                    </div>
-                    <div class="breed-info">
-                        <h2 class="section-title">
-                            <span class="icon">📋</span> BASIC INFORMATION
-                        </h2>
-                        <div class="info-section">
-                            <div class="info-item">
-                                <span class="tooltip tooltip-left">
-                                    <span class="icon">📏</span>
-                                    <span class="label">Size:</span>
-                                    <span class="tooltip-icon">ⓘ</span>
-                                    <span class="tooltip-text">
-                                        <strong>Size Categories:</strong><br>
-                                        • Small: Under 20 pounds<br>
-                                        • Medium: 20-60 pounds<br>
-                                        • Large: Over 60 pounds<br>
-                                        • Giant: Over 100 pounds<br>
-                                        • Varies: Depends on variety
-                                    </span>
-                                </span>
-                                <span class="value">{description['Size']}</span>
-                            </div>
-                            <div class="info-item">
-                                <span class="tooltip">
-                                    <span class="icon">⏳</span>
-                                    <span class="label">Lifespan:</span>
-                                    <span class="tooltip-icon">ⓘ</span>
-                                    <span class="tooltip-text">
-                                        <strong>Average Lifespan:</strong><br>
-                                        • Short: 6-8 years<br>
-                                        • Average: 10-15 years<br>
-                                        • Long: 12-20 years<br>
-                                        • Varies by size: Larger breeds typically have shorter lifespans
-                                    </span>
-                                </span>
-                                <span class="value">{description['Lifespan']}</span>
-                            </div>
-                        </div>
-                        <h2 class="section-title">
-                            <span class="icon">🐕</span> TEMPERAMENT & PERSONALITY
-                        </h2>
-                        <div class="temperament-section">
-                            <span class="tooltip">
-                                <span class="value">{description['Temperament']}</span>
-                                <span class="tooltip-icon">ⓘ</span>
-                                <span class="tooltip-text">
-                                    <strong>Temperament Guide:</strong><br>
-                                    • Describes the dog's natural behavior and personality<br>
-                                    • Important for matching with owner's lifestyle<br>
-                                    • Can be influenced by training and socialization
-                                </span>
-                            </span>
-                        </div>
-                        <h2 class="section-title">
-                            <span class="icon">💪</span> CARE REQUIREMENTS
-                        </h2>
-                        <div class="care-section">
-                            <div class="info-item">
-                                <span class="tooltip tooltip-left">
-                                    <span class="icon">🏃</span>
-                                    <span class="label">Exercise:</span>
-                                    <span class="tooltip-icon">ⓘ</span>
-                                    <span class="tooltip-text">
-                                        <strong>Exercise Needs:</strong><br>
-                                        • Low: Short walks and play sessions<br>
-                                        • Moderate: 1-2 hours of daily activity<br>
-                                        • High: Extensive exercise (2+ hours/day)<br>
-                                        • Very High: Constant activity and mental stimulation needed
-                                    </span>
-                                </span>
-                                <span class="value">{description['Exercise Needs']}</span>
-                            </div>
-                            <div class="info-item">
-                                <span class="tooltip">
-                                    <span class="icon">✂️</span>
-                                    <span class="label">Grooming:</span>
-                                    <span class="tooltip-icon">ⓘ</span>
-                                    <span class="tooltip-text">
-                                        <strong>Grooming Requirements:</strong><br>
-                                        • Low: Basic brushing, occasional baths<br>
-                                        • Moderate: Weekly brushing, occasional grooming<br>
-                                        • High: Daily brushing, frequent professional grooming needed<br>
-                                        • Professional care recommended for all levels
-                                    </span>
-                                </span>
-                                <span class="value">{description['Grooming Needs']}</span>
-                            </div>
-                            <div class="info-item">
-                                <span class="tooltip">
-                                    <span class="icon">⭐</span>
-                                    <span class="label">Care Level:</span>
-                                    <span class="tooltip-icon">ⓘ</span>
-                                    <span class="tooltip-text">
-                                        <strong>Care Level Explained:</strong><br>
-                                        • Low: Basic care and attention needed<br>
-                                        • Moderate: Regular care and routine needed<br>
-                                        • High: Significant time and attention needed<br>
-                                        • Very High: Extensive care, training and attention required
-                                    </span>
-                                </span>
-                                <span class="value">{description['Care Level']}</span>
-                            </div>
-                        </div>
-                        <h2 class="section-title">
-                            <span class="icon">👨‍👩‍👧‍👦</span> FAMILY COMPATIBILITY
-                        </h2>
-                        <div class="family-section">
-                            <div class="info-item">
-                                <span class="tooltip">
-                                    <span class="icon"></span>
-                                    <span class="label">Good with Children:</span>
-                                    <span class="tooltip-icon">ⓘ</span>
-                                    <span class="tooltip-text">
-                                        <strong>Child Compatibility:</strong><br>
-                                        • Yes: Excellent with kids, patient and gentle<br>
-                                        • Moderate: Good with older children<br>
-                                        • No: Better suited for adult households
-                                    </span>
-                                </span>
-                                <span class="value">{description['Good with Children']}</span>
-                            </div>
-                        </div>
-                        <h2 class="section-title">
-                            <span class="icon">📝</span> DESCRIPTION
-                        </h2>
-                        <div class="description-section">
-                            <p>{description.get('Description', '')}</p>
-                        </div>
-                        <div class="action-section">
-                            <a href="{get_akc_breeds_link(breed)}" target="_blank" class="akc-button">
-                                <span class="icon">🌐</span>
-                                Learn more about {breed} on AKC website
-                            </a>
-                        </div>
-                    </div>
-                '''
-            else:
-                dogs_info += f'''
-                    <div class="dog-info-header" style="background-color: {color}10;">
-                        <span class="dog-label" style="color: {color};">Dog {i+1}</span>
-                    </div>
-                    <div class="breed-info">
-                        <div class="model-uncertainty-note">
-                            <span class="icon">ℹ️</span>
-                            Note: The model is showing some uncertainty in its predictions.
-                            Here are the most likely breeds based on the available visual features.
-                        </div>
-                        <div class="breeds-list">
-                '''
-
-                for j, (breed, prob) in enumerate(zip(topk_breeds, relative_probs)):
-                    description = get_dog_description(breed)
-                    dogs_info += f'''
-                        <div class="breed-option uncertainty-mode">
-                            <div class="breed-header">
-                                <span class="option-number">Option {j+1}</span>
-                                <span class="breed-name">{breed}</span>
-                                <span class="confidence-badge" style="background-color: {color}20; color: {color};">
-                                    Confidence: {prob}
-                                </span>
-                            </div>
-                            <div class="breed-content">
-                                {format_description_html(description, breed)}
-                            </div>
-                        </div>
-                    '''
-                dogs_info += '</div></div>'
-
-            dogs_info += '</div>'
-
-
-        html_output = f"""
-            <div class="dog-info-card">
-                {dogs_info}
-            </div>
-        """
-
-        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 error_msg, None, None
-
-
-
-def show_details_html(choice, previous_output, initial_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)
-        formatted_description = format_description_html(description, breed)
-
-        html_output = f"""
-        <div class="dog-info">
-            <h2>{breed}</h2>
-            {formatted_description}
-        </div>
-        """
-
-        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 f"<p style='color: red;'>{error_msg}</p>", gr.update(visible=True), initial_state
-
-
-def format_description_html(description, breed):
-    html = "<ul style='list-style-type: none; padding-left: 0;'>"
-    if isinstance(description, dict):
-        for key, value in description.items():
-            if key != "Breed":  # 跳過重複的品種顯示
-                if key == "Size":
-                    html += f'''
-                        <li style='margin-bottom: 10px;'>
-                            <span class="tooltip">
-                                <strong>{key}:</strong>
-                                <span class="tooltip-icon">ⓘ</span>
-                                <span class="tooltip-text">
-                                    <strong>Size Categories:</strong><br>
-                                    • Small: Under 20 pounds<br>
-                                    • Medium: 20-60 pounds<br>
-                                    • Large: Over 60 pounds
-                                </span>
-                            </span> {value}
-                        </li>
-                    '''
-                elif key == "Exercise Needs":
-                    html += f'''
-                        <li style='margin-bottom: 10px;'>
-                            <span class="tooltip">
-                                <strong>{key}:</strong>
-                                <span class="tooltip-icon">ⓘ</span>
-                                <span class="tooltip-text">
-                                    <strong>Exercise Needs:</strong><br>
-                                    • High: 2+ hours of daily exercise<br>
-                                    • Moderate: 1-2 hours of daily activity<br>
-                                    • Low: Short walks and play sessions
-                                </span>
-                            </span> {value}
-                        </li>
-                    '''
-                elif key == "Grooming Needs":
-                    html += f'''
-                        <li style='margin-bottom: 10px;'>
-                            <span class="tooltip">
-                                <strong>{key}:</strong>
-                                <span class="tooltip-icon">ⓘ</span>
-                                <span class="tooltip-text">
-                                    <strong>Grooming Requirements:</strong><br>
-                                    • High: Daily brushing, regular professional care<br>
-                                    • Moderate: Weekly brushing, occasional grooming<br>
-                                    • Low: Minimal brushing, basic maintenance
-                                </span>
-                            </span> {value}
-                        </li>
-                    '''
-                elif key == "Care Level":
-                    html += f'''
-                        <li style='margin-bottom: 10px;'>
-                            <span class="tooltip">
-                                <strong>{key}:</strong>
-                                <span class="tooltip-icon">ⓘ</span>
-                                <span class="tooltip-text">
-                                    <strong>Care Level Explained:</strong><br>
-                                    • High: Needs significant training and attention<br>
-                                    • Moderate: Regular care and routine needed<br>
-                                    • Low: More independent, basic care sufficient
-                                </span>
-                            </span> {value}
-                        </li>
-                    '''
-                elif key == "Good with Children":
-                    html += f'''
-                        <li style='margin-bottom: 10px;'>
-                            <span class="tooltip">
-                                <strong>{key}:</strong>
-                                <span class="tooltip-icon">ⓘ</span>
-                                <span class="tooltip-text">
-                                    <strong>Child Compatibility:</strong><br>
-                                    • Yes: Excellent with kids, patient and gentle<br>
-                                    • Moderate: Good with older children<br>
-                                    • No: Better suited for adult households
-                                </span>
-                            </span> {value}
-                        </li>
-                    '''
-                elif key == "Lifespan":
-                    html += f'''
-                        <li style='margin-bottom: 10px;'>
-                            <span class="tooltip">
-                                <strong>{key}:</strong>
-                                <span class="tooltip-icon">ⓘ</span>
-                                <span class="tooltip-text">
-                                    <strong>Average Lifespan:</strong><br>
-                                    • Short: 6-8 years<br>
-                                    • Average: 10-15 years<br>
-                                    • Long: 12-20 years
-                                </span>
-                            </span> {value}
-                        </li>
-                    '''
-                elif key == "Temperament":
-                    html += f'''
-                        <li style='margin-bottom: 10px;'>
-                            <span class="tooltip">
-                                <strong>{key}:</strong>
-                                <span class="tooltip-icon">ⓘ</span>
-                                <span class="tooltip-text">
-                                    <strong>Temperament Guide:</strong><br>
-                                    • Describes the dog's natural behavior<br>
-                                    • Important for matching with owner
-                                </span>
-                            </span> {value}
-                        </li>
-                    '''
-                else:
-                    # 其他欄位保持原樣顯示
-                    html += f"<li style='margin-bottom: 10px;'><strong>{key}:</strong> {value}</li>"
-    else:
-        html += f"<li>{description}</li>"
-    html += "</ul>"
-
-    # 添加AKC連結
-    html += f'''
-        <div class="action-section">
-            <a href="{get_akc_breeds_link(breed)}" target="_blank" class="akc-button">
-                <span class="icon">🌐</span>
-                Learn more about {breed} on AKC website
-            </a>
-        </div>
-    '''
-    return html
-
-with gr.Blocks(css=get_css_styles()) as iface:
-
-    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>
-    """)
-
-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
-            )
-
-            # 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('''
-            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_Breed_Classifier">
-                Dog Breed Classifier
-            </a>
-        ''')
-
-    return iface
-
-if __name__ == "__main__":
-    iface = main()
-    iface.launch(share=True, debug=True)