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 breed_health_info import breed_health_info, default_health_note from urllib.parse import quote from ultralytics import YOLO import asyncio import traceback model_yolo = YOLO('yolov8l.pt') 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): base_url = "https://www.akc.org/dog-breeds/" breed_url = breed.lower().replace('_', '-') return f"{base_url}{breed_url}/" 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 = '''

⚠️ The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.

''' 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'''
{formatted_description}
''' 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'''
Breed {i+1}: {breed} Confidence: {prob}
{formatted_description}
''' 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: """比較兩個狗品種的特性""" 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'
' if combined_confidence < 0.2: dogs_info += f'''
Dog {i+1}

⚠️ The image is unclear or the breed is not in the dataset. Please upload a clearer image.

''' elif top1_prob >= 0.45: breed = topk_breeds[0] description = get_dog_description(breed) dogs_info += f'''
🐾 {breed}

📋 BASIC INFORMATION

📏 Size: ⓘ Size Categories:
• Small: Under 20 pounds
• Medium: 20-60 pounds
• Large: Over 60 pounds
• Giant: Over 100 pounds
• Varies: Depends on variety {description['Size']}
⏳ Lifespan: ⓘ Average Lifespan:
• Short: 6-8 years
• Average: 10-15 years
• Long: 12-20 years
• Varies by size: Larger breeds typically have shorter lifespans {description['Lifespan']}

🐕 TEMPERAMENT & PERSONALITY

{description['Temperament']} ⓘ Temperament Guide:
• Describes the dog's natural behavior and personality
• Important for matching with owner's lifestyle
• Can be influenced by training and socialization

💪 CARE REQUIREMENTS

🏃 Exercise: ⓘ Exercise Needs:
• Low: Short walks and play sessions
• Moderate: 1-2 hours of daily activity
• High: Extensive exercise (2+ hours/day)
• Very High: Constant activity and mental stimulation needed {description['Exercise Needs']}
✂️ Grooming: ⓘ Grooming Requirements:
• Low: Basic brushing, occasional baths
• Moderate: Weekly brushing, occasional grooming
• High: Daily brushing, frequent professional grooming needed
• Professional care recommended for all levels {description['Grooming Needs']}
⭐ Care Level: ⓘ Care Level Explained:
• Low: Basic care and attention needed
• Moderate: Regular care and routine needed
• High: Significant time and attention needed
• Very High: Extensive care, training and attention required {description['Care Level']}

👨‍👩‍👧‍👦 FAMILY COMPATIBILITY

Good with Children: ⓘ Child Compatibility:
• Yes: Excellent with kids, patient and gentle
• Moderate: Good with older children
• No: Better suited for adult households {description['Good with Children']}

📝 DESCRIPTION

{description.get('Description', '')}

🌐 Learn more about {breed} on AKC website
''' else: dogs_info += f'''
Dog {i+1}
ℹ️ Note: The model is showing some uncertainty in its predictions. Here are the most likely breeds based on the available visual features.
''' for j, (breed, prob) in enumerate(zip(topk_breeds, relative_probs)): description = get_dog_description(breed) dogs_info += f'''
Option {j+1} {breed} Confidence: {prob}
{format_description_html(description, breed)}
''' dogs_info += '
' dogs_info += '
' html_output = f"""
{dogs_info}
""" 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"""

{breed}

{formatted_description}
""" 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"

{error_msg}

", gr.update(visible=True), initial_state def format_description_html(description, breed): html = "" # 添加AKC連結 html += f'''
🌐 Learn more about {breed} on AKC website
''' return html with gr.Blocks(css=""" .dog-info-card { border: 1px solid #e1e4e8; margin: 40px 0; /* 增加卡片間距 */ padding: 0; border-radius: 12px; box-shadow: 0 2px 12px rgba(0,0,0,0.08); overflow: hidden; transition: all 0.3s ease; background: white; } .dog-info-card:hover { box-shadow: 0 4px 16px rgba(0,0,0,0.12); } .dog-info-header { padding: 24px 28px; /* 增加內距 */ margin: 0; font-size: 22px; font-weight: bold; border-bottom: 1px solid #e1e4e8; } .breed-info { padding: 28px; /* 增加整體內距 */ line-height: 1.6; } .section-title { font-size: 1.3em; font-weight: 700; color: #2c3e50; margin: 32px 0 20px 0; padding: 12px 0; border-bottom: 2px solid #e1e4e8; text-transform: uppercase; letter-spacing: 0.5px; display: flex; align-items: center; gap: 8px; position: relative; } .icon { font-size: 1.2em; display: inline-flex; align-items: center; justify-content: center; } .info-section, .care-section, .family-section { display: flex; flex-wrap: wrap; gap: 16px; margin-bottom: 28px; /* 增加底部間距 */ padding: 20px; /* 增加內距 */ background: #f8f9fa; border-radius: 12px; border: 1px solid #e1e4e8; /* 添加邊框 */ } .info-item { background: white; /* 改為白色背景 */ padding: 14px 18px; /* 增加內距 */ border-radius: 8px; display: flex; align-items: center; gap: 10px; box-shadow: 0 2px 4px rgba(0,0,0,0.05); border: 1px solid #e1e4e8; flex: 1 1 auto; min-width: 200px; } .label { color: #666; font-weight: 600; font-size: 1.1rem; } .value { color: #2c3e50; font-weight: 500; font-size: 1.1rem; } .temperament-section { background: #f8f9fa; padding: 20px; /* 增加內距 */ border-radius: 12px; margin-bottom: 28px; /* 增加間距 */ color: #444; border: 1px solid #e1e4e8; /* 添加邊框 */ } .description-section { background: #f8f9fa; padding: 24px; /* 增加內距 */ border-radius: 12px; margin: 28px 0; /* 增加上下間距 */ line-height: 1.8; color: #444; border: 1px solid #e1e4e8; /* 添加邊框 */ fontsize: 1.1rem; } .description-section p { margin: 0; padding: 0; text-align: justify; /* 文字兩端對齊 */ word-wrap: break-word; /* 確保長單字會換行 */ white-space: pre-line; /* 保留換行但合併空白 */ max-width: 100%; /* 確保不會超出容器 */ } .action-section { margin-top: 24px; text-align: center; } .akc-button, .breed-section .akc-link, .breed-option .akc-link { display: inline-flex; align-items: center; padding: 14px 28px; background: linear-gradient(145deg, #00509E, #003F7F); color: white; border-radius: 12px; /* 增加圓角 */ text-decoration: none; gap: 12px; /* 增加圖標和文字間距 */ transition: all 0.3s ease; font-weight: 600; font-size: 1.1em; box-shadow: 0 2px 4px rgba(0,0,0,0.1), inset 0 1px 1px rgba(255,255,255,0.1); border: 1px solid rgba(255,255,255,0.1); } .akc-button:hover, .breed-section .akc-link:hover, .breed-option .akc-link:hover { background: linear-gradient(145deg, #003F7F, #00509E); transform: translateY(-2px); color: white; box-shadow: 0 6px 12px rgba(0,0,0,0.2), inset 0 1px 1px rgba(255,255,255,0.2); border: 1px solid rgba(255,255,255,0.2); } .icon { font-size: 1.3em; filter: drop-shadow(0 1px 1px rgba(0,0,0,0.2)); } .warning-message { display: flex; align-items: center; gap: 8px; color: #ff3b30; font-weight: 500; margin: 0; padding: 16px; background: #fff5f5; border-radius: 8px; } .model-uncertainty-note { display: flex; align-items: center; gap: 12px; padding: 16px; background-color: #f8f9fa; border-left: 4px solid #6c757d; margin-bottom: 20px; color: #495057; border-radius: 4px; } .breeds-list { display: flex; flex-direction: column; gap: 20px; } .breed-option { background: white; border: 1px solid #e1e4e8; border-radius: 8px; overflow: hidden; } .breed-header { display: flex; align-items: center; padding: 16px; background: #f8f9fa; gap: 12px; border-bottom: 1px solid #e1e4e8; } .option-number { font-weight: 600; color: #666; padding: 4px 8px; background: #e1e4e8; border-radius: 4px; } .breed-name { font-size: 1.5em; font-weight: bold; color: #2c3e50; flex-grow: 1; } .confidence-badge { padding: 4px 12px; border-radius: 20px; font-size: 0.9em; font-weight: 500; } .breed-content { padding: 20px; } .breed-content li { margin-bottom: 8px; display: flex; align-items: flex-start; /* 改為頂部對齊 */ gap: 8px; flex-wrap: wrap; /* 允許內容換行 */ } .breed-content li strong { flex: 0 0 auto; /* 不讓標題縮放 */ min-width: 100px; /* 給標題一個固定最小寬度 */ } ul { padding-left: 0; margin: 0; list-style-type: none; } li { margin-bottom: 8px; display: flex; align-items: center; gap: 8px; } .akc-link { color: white; text-decoration: none; font-weight: 600; font-size: 1.1em; transition: all 0.3s ease; } .akc-link:hover { text-decoration: underline; color: #D3E3F0; } .tooltip { position: relative; display: inline-flex; align-items: center; gap: 4px; cursor: help; } .tooltip .tooltip-icon { font-size: 14px; color: #666; } .tooltip .tooltip-text { visibility: hidden; width: 250px; background-color: rgba(44, 62, 80, 0.95); color: white; text-align: left; border-radius: 8px; padding: 8px 10px; position: absolute; z-index: 100; bottom: 150%; left: 50%; transform: translateX(-50%); opacity: 0; transition: all 0.3s ease; font-size: 14px; line-height: 1.3; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2); border: 1px solid rgba(255, 255, 255, 0.1) margin-bottom: 10px; } .tooltip.tooltip-left .tooltip-text { left: 0; transform: translateX(0); } .tooltip.tooltip-right .tooltip-text { left: auto; right: 0; transform: translateX(0); } .tooltip-text strong { color: white !important; background-color: transparent !important; display: block; /* 讓標題獨立一行 */ margin-bottom: 2px; /* 增加標題下方間距 */ padding-bottom: 2px; /* 加入小間距 */ border-bottom: 1px solid rgba(255,255,255,0.2); } .tooltip-text { font-size: 13px; /* 稍微縮小字體 */ } /* 調整列表符號和文字的間距 */ .tooltip-text ul { margin: 0; padding-left: 15px; /* 減少列表符號的縮進 */ } .tooltip-text li { margin-bottom: 1px; /* 減少列表項目間的間距 */ } .tooltip-text br { line-height: 1.2; /* 減少行距 */ } .tooltip .tooltip-text::after { content: ""; position: absolute; top: 100%; left: 20%; /* 調整箭頭位置 */ margin-left: -5px; border-width: 5px; border-style: solid; border-color: rgba(44, 62, 80, 0.95) transparent transparent transparent; } .tooltip-left .tooltip-text::after { left: 20%; } /* 右側箭頭 */ .tooltip-right .tooltip-text::after { left: 80%; } .tooltip:hover .tooltip-text { visibility: visible; opacity: 1; } .tooltip .tooltip-text::after { content: ""; position: absolute; top: 100%; left: 50%; transform: translateX(-50%); border-width: 8px; border-style: solid; border-color: rgba(44, 62, 80, 0.95) transparent transparent transparent; } .uncertainty-mode .tooltip .tooltip-text { position: absolute; left: 100%; bottom: auto; top: 50%; transform: translateY(-50%); margin-left: 10px; z-index: 1000; /* 確保提示框在最上層 */ } .uncertainty-mode .tooltip .tooltip-text::after { content: ""; position: absolute; top: 50%; right: 100%; transform: translateY(-50%); border-width: 5px; border-style: solid; border-color: transparent rgba(44, 62, 80, 0.95) transparent transparent; } .uncertainty-mode .breed-content { font-size: 1.1rem; /* 增加字體大小 */ } .description-section, .description-section p, .temperament-section, .temperament-section .value, .info-item, .info-item .value, .breed-content { font-size: 1.1rem !important; /* 使用 !important 確保覆蓋其他樣式 */ } .recommendation-card { margin-bottom: 40px; } .compatibility-scores { background: #f8f9fa; padding: 24px; border-radius: 12px; margin: 20px 0; } .score-item { margin: 15px 0; } .progress-bar { height: 12px; background-color: #e9ecef; border-radius: 6px; overflow: hidden; margin: 8px 0; } .progress { height: 100%; background: linear-gradient(90deg, #34C759, #30B350); border-radius: 6px; transition: width 0.6s ease; } .percentage { float: right; color: #34C759; font-weight: 600; } .breed-details-section { margin: 30px 0; } .subsection-title { font-size: 1.2em; color: #2c3e50; margin-bottom: 20px; display: flex; align-items: center; gap: 8px; } .details-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 20px; background: #f8f9fa; padding: 20px; border-radius: 12px; border: 1px solid #e1e4e8; } .detail-item { background: white; padding: 15px; border-radius: 8px; border: 1px solid #e1e4e8; } .description-text { line-height: 1.8; color: #444; margin: 0; padding: 24px 30px; /* 調整內部間距，從 20px 改為 24px 30px */ background: #f8f9fa; border-radius: 12px; border: 1px solid #e1e4e8; text-align: justify; /* 添加文字對齊 */ word-wrap: break-word; /* 確保長文字會換行 */ word-spacing: 1px; /* 加入字間距 */ } /* 工具提示改進 */ .tooltip { position: relative; display: inline-flex; align-items: center; gap: 4px; cursor: help; padding: 5px 0; } .tooltip .tooltip-text { visibility: hidden; width: 280px; background-color: rgba(44, 62, 80, 0.95); color: white; text-align: left; border-radius: 8px; padding: 12px 15px; position: absolute; z-index: 1000; bottom: calc(100% + 15px); left: 50%; transform: translateX(-50%); opacity: 0; transition: all 0.3s ease; font-size: 14px; line-height: 1.4; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2); white-space: normal; } .tooltip:hover .tooltip-text { visibility: visible; opacity: 1; } .score-badge { background-color: #34C759; color: white; padding: 6px 12px; border-radius: 20px; font-size: 0.9em; margin-left: 10px; font-weight: 500; box-shadow: 0 2px 4px rgba(52, 199, 89, 0.2); } .bonus-score .tooltip-text { width: 250px; line-height: 1.4; padding: 10px; } .bonus-score .progress { background: linear-gradient(90deg, #48bb78, #68d391); } .health-section { margin: 25px 0; padding: 24px; background-color: #f8f9fb; border-radius: 12px; border: 1px solid #e1e4e8; } .health-section .subsection-title { font-size: 1.3em; font-weight: 600; margin-bottom: 20px; display: flex; align-items: center; gap: 8px; color: #2c3e50; } .health-info { background-color: white; padding: 24px; border-radius: 8px; margin: 15px 0; border: 1px solid #e1e4e8; } .health-details { font-size: 1.1rem; line-height: 1.6; } .health-details h4 { color: #2c3e50; font-size: 1.15rem; font-weight: 600; margin: 20px 0 15px 0; } .health-details h4:first-child { margin-top: 0; } .health-details ul { list-style-type: none; padding-left: 0; margin: 0 0 25px 0; } .health-details ul li { margin-bottom: 12px; padding-left: 20px; position: relative; } .health-details ul li:before { content: "•"; position: absolute; left: 0; color: #2c3e50; } .health-disclaimer { margin-top: 20px; color: #666; font-size: 1.05rem; line-height: 1.6; padding-top: 20px; border-top: 1px solid #e1e4e8; } .health-disclaimer p { margin: 8px 0; padding-left: 15px; position: relative; } .health-disclaimer p:before { content: "»"; position: absolute; left: 0; color: #666; } """) as iface: gr.HTML("""

🐾 PawMatch AI

Your Smart Dog Breed Guide

Powered by AI • Breed Recognition • Smart Matching • Companion Guide

""") # 使用 Tabs 來分隔兩個功能 with gr.Tabs(): # 第一個 Tab：原有的辨識功能 with gr.TabItem("Breed Detection"): gr.HTML("

Upload a picture of a dog, and the model will predict its breed and provide detailed information!

") gr.HTML("

Note: The model's predictions may not always be 100% accurate, and it is recommended to use the results as a reference.

") with gr.Row(): input_image = gr.Image(label="Upload a dog image", type="pil") output_image = gr.Image(label="Annotated Image") output = gr.HTML(label="Prediction Results") initial_state = gr.State() input_image.change( predict, inputs=input_image, outputs=[output, output_image, initial_state] ) gr.Examples( examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'], inputs=input_image ) # 第二個 Tab：品種比較功能 with gr.TabItem("Breed Comparison"): gr.HTML("

Select two dog breeds to compare their characteristics and care requirements.

") with gr.Row(): breed1_dropdown = gr.Dropdown( choices=dog_breeds, label="Select First Breed", value="Golden_Retriever" ) breed2_dropdown = gr.Dropdown( choices=dog_breeds, label="Select Second Breed", value="Border_Collie" ) compare_btn = gr.Button("Compare Breeds") comparison_output = gr.HTML(label="Comparison Results") def show_comparison(breed1, breed2): if not breed1 or not breed2: return "Please select two breeds to compare" breed1_info = get_dog_description(breed1) breed2_info = get_dog_description(breed2) html_output = f"""

🐕 {breed1.replace('_', ' ')}

📏 Size: {breed1_info['Size']}
🏃 Exercise Needs: {breed1_info['Exercise Needs']}
✂️ Grooming: {breed1_info['Grooming Needs']}
👨‍👩‍👧‍👦 Good with Children: {breed1_info['Good with Children']}

🐕 {breed2.replace('_', ' ')}

📏 Size: {breed2_info['Size']}
🏃 Exercise Needs: {breed2_info['Exercise Needs']}
✂️ Grooming: {breed2_info['Grooming Needs']}
👨‍👩‍👧‍👦 Good with Children: {breed2_info['Good with Children']}
""" return html_output compare_btn.click( show_comparison, inputs=[breed1_dropdown, breed2_dropdown], outputs=comparison_output ) # 第三個 Tab：品種推薦功能 with gr.TabItem("Breed Recommendation"): gr.HTML("

Tell us about your lifestyle, and we'll recommend the perfect dog breeds for you!

") with gr.Row(): with gr.Column(): living_space = gr.Radio( choices=["apartment", "house_small", "house_large"], label="What type of living space do you have?", info="Choose your current living situation", value="apartment" ) exercise_time = gr.Slider( minimum=0, maximum=180, value=60, label="Daily exercise time (minutes)", info="Consider walks, play time, and training" ) grooming_commitment = gr.Radio( choices=["low", "medium", "high"], label="Grooming commitment level", info="Low: monthly, Medium: weekly, High: daily", value="medium" ) with gr.Column(): experience_level = gr.Radio( choices=["beginner", "intermediate", "advanced"], label="Dog ownership experience", info="Be honest - this helps find the right match", value="beginner" ) has_children = gr.Checkbox( label="Have children at home", info="Helps recommend child-friendly breeds" ) noise_tolerance = gr.Radio( choices=["low", "medium", "high"], label="Noise tolerance level", info="Some breeds are more vocal than others", value="medium" ) # 設置按鈕的點擊事件 get_recommendations_btn = gr.Button("Find My Perfect Match! 🔍", variant="primary") recommendation_output = gr.HTML(label="Breed Recommendations") def process_recommendations(living_space, exercise_time, grooming_commitment, experience_level, has_children, noise_tolerance): try: user_prefs = UserPreferences( living_space=living_space, exercise_time=exercise_time, grooming_commitment=grooming_commitment, experience_level=experience_level, has_children=has_children, noise_tolerance=noise_tolerance, space_for_play=True if living_space != "apartment" else False, other_pets=False, climate="moderate" ) recommendations = get_breed_recommendations(user_prefs) return format_recommendation_html(recommendations) except Exception as e: print(f"Error in process_recommendations: {str(e)}") return f"An error occurred: {str(e)}" # 這行是關鍵 - 確保按鈕點擊事件有正確連接到處理函數 get_recommendations_btn.click( fn=process_recommendations, # 處理函數 inputs=[ living_space, exercise_time, grooming_commitment, experience_level, has_children, noise_tolerance ], outputs=recommendation_output # 輸出結果的位置 ) gr.HTML('For more details on this project and other work, feel free to visit my GitHub Dog Breed Classifier') if __name__ == "__main__": iface.launch(share=True)