app.py

import os
import numpy as np
import torch
import torch.nn as nn
import gradio as gr
import time
import traceback
import spaces
from torchvision.models import convnext_base, ConvNeXt_Base_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
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_unknown_breed_message,
    format_not_dog_message,
    format_hint_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
from functools import wraps


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

        # 1. 初始化 backbone
        self.backbone = convnext_base(weights=ConvNeXt_Base_Weights.IMAGENET1K_V1)
        self.backbone.classifier = nn.Identity()  # 移除原始分類器

        # 2. 使用測試數據確定實際的特徵維度
        with torch.no_grad():  # 不需要計算梯度
            dummy_input = torch.randn(1, 3, 224, 224)  # 創建示例輸入
            features = self.backbone(dummy_input)
            if len(features.shape) > 2:  # 如果特徵是多維的
                features = features.mean([-2, -1])  # 進行全局平均池化
            self.feature_dim = features.shape[1]  # 獲取正確的特徵維度

        print(f"Feature Dim: {self.feature_dim}")  # 幫助調試

        # 3. 設置多頭注意力層
        self.num_heads = max(1, min(8, self.feature_dim // 64))
        self.attention = MultiHeadAttention(self.feature_dim, num_heads=self.num_heads)

        # 4. 設置分類器
        self.classifier = nn.Sequential(
            nn.LayerNorm(self.feature_dim),
            nn.Dropout(0.3),
            nn.Linear(self.feature_dim, num_classes)
        )

    def forward(self, x):
        """
        模型的前向傳播過程
        Args:
            x (Tensor): 輸入圖像張量，形狀為 [batch_size, channels, height, width]
        Returns:
            Tuple[Tensor, Tensor]: 分類邏輯值和注意力特徵
        """
        x = x.to(self.device)

        # 1. 提取基礎特徵
        features = self.backbone(x)

        # 2. 處理特徵維度
        if len(features.shape) > 2:
            # 如果特徵維度是 [batch_size, channels, height, width]
            # 轉換為 [batch_size, channels]
            features = features.mean([-2, -1])  # 使用全局平均池化

        # 3. 應用注意力機制
        attended_features = self.attention(features)

        # 4. 最終分類
        logits = self.classifier(attended_features)

        return logits, attended_features


class ModelManager:
    """
    模型管理器：負責模型的初始化、設備管理和資源控制(CPU, GPU)
    """
    _instance = None
    _initialized = False
    _yolo_model = None
    _breed_model = None
    _device = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance

    def __init__(self):
        # 避免重複初始化
        if not ModelManager._initialized:
            # 初始化設備，這會在第一次創建實例時執行
            self._device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
            ModelManager._initialized = True
    
    @property
    def device(self):
        """
        提供對設備的訪問
        確保在需要時設備已經被初始化
        """
        if self._device is None:
            self._device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        return self._device

    @property
    def yolo_model(self):
        """
        延遲初始化YOLO
        只有在第一次使用時才會創建實例
        """
        if self._yolo_model is None:
            self._yolo_model = YOLO('yolov8x.pt')
        return self._yolo_model

    @property
    def breed_model(self):
        """
        延遲初始化品種分類模型
        只有在第一次使用時才會創建實例並移動到正確的設備上
        """
        if self._breed_model is None:
            self._breed_model = BaseModel(
                num_classes=len(dog_breeds), 
                device=self.device  
            ).to(self.device)
            
            checkpoint = torch.load(
                'ConvNextBase_best_model_dog.pth', 
                map_location=self.device  # 確保checkpoint加載到正確的設備
            )
            self._breed_model.load_state_dict(checkpoint['base_model'], strict=False)
            self._breed_model.eval()
        return self._breed_model


model_manager = ModelManager()
            

# 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)
    
@spaces.GPU
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(model_manager.device)
    
    with torch.no_grad():
        # Get model outputs (只使用logits，不需要features)
        logits = model_manager.breed_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
        
@spaces.GPU
def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55):
    """
    使用YOLO模型檢測圖片中的狗。
    只保留被識別為狗（class 16）的物體，並標記它們的狀態。
    
    Args:
        image: PIL Image
        conf_threshold: YOLO檢測的信心度閾值
        iou_threshold: 非極大值抑制的IoU閾值
    
    Returns:
        list: 包含檢測到的狗的列表，每個元素是(cropped_image, confidence, box, is_dog)的元組
    """
    results = model_manager.yolo_model(image, conf=conf_threshold, 
                                     iou=iou_threshold)[0]
    
    dogs = []
    boxes = []
    
    # 只處理被識別為狗的物體
    for box in results.boxes:
        class_id = box.cls.item()
        if class_id == 16:  # COCO dataset中狗的類別是16
            xyxy = box.xyxy[0].tolist()
            confidence = box.conf.item()
            boxes.append((xyxy, confidence, True))  # 加入is_dog標記

    if not boxes:
        # 如果沒有檢測到狗，返回整張圖片並標記為非狗
        return [(image, 1.0, [0, 0, image.width, image.height], False)]
    
    nms_boxes = non_max_suppression(boxes, iou_threshold)
    detected_objects = []

    # 處理每個檢測到的狗
    for box, confidence, is_dog in nms_boxes:
        x1, y1, x2, y2 = box
        w, h = x2 - x1, y2 - y1
        # 擴大檢測框範圍以包含完整的狗
        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))
        detected_objects.append((cropped_image, confidence, [x1, y1, x2, y2], is_dog))

    return detected_objects

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


@spaces.GPU
def predict(image):
    """
    主要的預測函數，負責處理狗的檢測和品種辨識。
    它整合了YOLO的物體檢測和專門的品種分類模型。
    實施雙層檢測，非狗會直接忽略．
    
    Args:
        image: PIL Image 或 numpy array
        
    Returns:
        tuple: (html_output, annotated_image, initial_state)
    """
    if image is None:
        return format_hint_html("Please upload an image to start."), None, None

    try:
        if isinstance(image, np.ndarray):
            image = Image.fromarray(image)

        # 檢測圖片中的狗
        dogs = detect_multiple_dogs(image)
        color_scheme = get_color_scheme(len(dogs) == 1)

        # 準備標註
        annotated_image = image.copy()
        draw = ImageDraw.Draw(annotated_image)

        try:
            font = ImageFont.truetype("arial.ttf", 24)
        except:
            font = ImageFont.load_default()

        dogs_info = ""

        # 處理每個檢測到的物體
        for i, (cropped_image, detection_confidence, box, is_dog) in enumerate(dogs):
            color = color_scheme if len(dogs) == 1 else color_scheme[i % len(color_scheme)]

            # 繪製框和標籤
            draw.rectangle(box, outline=color, width=4)
            label = f"Dog {i+1}" if is_dog else f"Object {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]

            # 繪製標籤背景和文字
            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)

            try:
                # 首先檢查是否為狗
                if not is_dog:
                    dogs_info += format_not_dog_message(color, i+1)
                    continue

                # 如果是狗，進行品種預測
                top1_prob, topk_breeds, relative_probs = predict_single_dog(cropped_image)
                combined_confidence = detection_confidence * top1_prob

                # 根據信心度決定輸出格式
                if combined_confidence < 0.2:
                    dogs_info += format_unknown_breed_message(color, i+1)
                elif top1_prob >= 0.45:
                    breed = topk_breeds[0]
                    description = get_dog_description(breed)
                    if description is None:
                        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:
                    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_unknown_breed_message(color, i+1)

        # 包裝最終的HTML輸出
        html_output = format_multi_dog_container(dogs_info)

        # 準備初始狀態
        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_hint_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_hint_html(error_msg), gr.update(visible=True), initial_state

def main():
    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>
        """)

        # 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()