# import os
# import numpy as np
# import torch
# import torch.nn as nn
# import gradio as gr
# import time
# 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 breed_health_info import breed_health_info
# from breed_noise_info import breed_noise_info
# from dog_database import get_dog_description, dog_data
# from scoring_calculation_system import UserPreferences
# from recommendation_html_format import format_recommendation_html, get_breed_recommendations
# 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 html_templates import (
# format_description_html,
# format_single_dog_result,
# format_multiple_breeds_result,
# format_error_message,
# format_warning_html,
# format_multi_dog_container,
# format_breed_details_html,
# get_color_scheme,
# get_akc_breeds_link
# )
# 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", "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("""
#
# """)
# # 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('''
#
#
EXPLORE THE CODE →
#
# 
#
#
EXPLORE THE CODE →
''')
return iface
if __name__ == "__main__":
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"Current device: {torch.cuda.current_device()}")
print(f"Device name: {torch.cuda.get_device_name()}")
iface = main()
iface.launch(share=True, debug=True)