Delete app.py

app.py

@@ -1,635 +0,0 @@
-import os
-import numpy as np
-import torch
-import torch.nn as nn
-import gradio as gr
-import time
-import traceback
-import spaces
-import timm
-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):
-        """
-        Initializes the MultiHeadAttention module.
-        Args:
-            in_dim (int): Dimension of the input features.
-            num_heads (int): Number of attention heads. Defaults to 8.
-        """
-        super().__init__()
-        self.num_heads = num_heads
-        self.head_dim = max(1, in_dim // num_heads)  # Compute dimension per head
-        self.scaled_dim = self.head_dim * num_heads  # Scaled dimension after splitting into heads
-        self.fc_in = nn.Linear(in_dim, self.scaled_dim)  # Linear layer to project input to scaled_dim
-        self.query = nn.Linear(self.scaled_dim, self.scaled_dim)  # Query projection
-        self.key = nn.Linear(self.scaled_dim, self.scaled_dim)  # Key projection
-        self.value = nn.Linear(self.scaled_dim, self.scaled_dim)  # Value projection
-        self.fc_out = nn.Linear(self.scaled_dim, in_dim)  # Linear layer to project output back to in_dim
-
-    def forward(self, x):
-        """
-        Forward pass for multi-head attention mechanism.
-        Args:
-            x (Tensor): Input tensor of shape (batch_size, input_dim).
-        Returns:
-            Tensor: Output tensor after applying attention mechanism.
-        """
-        N = x.shape[0]  # Batch size
-        x = self.fc_in(x)  # Project input to scaled_dim
-        q = self.query(x).view(N, self.num_heads, self.head_dim)  # Compute queries
-        k = self.key(x).view(N, self.num_heads, self.head_dim)  # Compute keys
-        v = self.value(x).view(N, self.num_heads, self.head_dim)  # Compute values
-
-        # Calculate attention scores
-        energy = torch.einsum("nqd,nkd->nqk", [q, k])  # Dot product between queries and keys
-        attention = F.softmax(energy / (self.head_dim ** 0.5), dim=2)  # Apply softmax with scaling
-
-        # Compute weighted sum of values based on attention scores
-        out = torch.einsum("nqk,nvd->nqd", [attention, v])
-        out = out.reshape(N, self.scaled_dim)  # Concatenate all heads
-        out = self.fc_out(out)  # Project back to original input dimension
-        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. Initialize backbone, num_classes=0 to remove classifier layer
-        self.backbone = timm.create_model(
-                'convnextv2_base',
-                pretrained=True,
-                num_classes=0
-        )
-
-        # 2. Use test data to determine actual feature dimensions
-        with torch.no_grad():   # No need to compute gradients
-            dummy_input = torch.randn(1, 3, 224, 224) # Create example input
-            features = self.backbone(dummy_input)
-
-            if len(features.shape) > 2:  # If features are multi-dimensional
-                features = features.mean([-2, -1]) # Apply global average pooling
-
-            self.feature_dim = features.shape[1] # Get correct feature dimension
-
-        print(f"Feature Dimension from V2 backbone: {self.feature_dim}")
-
-        # 3. Setup multi-head attention layer
-        self.num_heads = max(1, min(8, self.feature_dim // 64))
-        self.attention = MultiHeadAttention(self.feature_dim, num_heads=self.num_heads)
-
-        # 4. Setup classifier
-        self.classifier = nn.Sequential(
-            nn.LayerNorm(self.feature_dim),
-            nn.Dropout(0.3),
-            nn.Linear(self.feature_dim, num_classes)
-        )
-
-    def forward(self, x):
-        """
-        The forward propagation process combines V2's FCCA and the multi-head attention mechanism.
-        Args:
-            x (Tensor): Input image tensor with shape [batch_size, channels, height, width]
-        Returns:
-            Tuple[Tensor, Tensor]: Classification logits and attention features.
-        """
-        x = x.to(self.device)
-
-        # 1. Extract base features
-        features = self.backbone(x)
-
-        # 2. Process feature dimensions
-        if len(features.shape) > 2:
-            # If feature dimensions are [batch_size, channels, height, width]
-            # Convert to [batch_size, channels]
-            features = features.mean([-2, -1])  # Use global average pooling
-
-        # 3. Apply attention mechanism
-        attended_features = self.attention(features)
-
-        # 4. Final classification
-        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(
-                'ConvNextV2Base_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=0.3):
-    """
-    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 * 0.01)
-        y1 = max(0, y1 - h * 0.01)
-        x2 = min(image.width, x2 + w * 0.01)
-        y2 = min(image.height, y2 + h * 0.01)
-        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 < 0.15:
-                    dogs_info += format_unknown_breed_message(color, i+1)
-                elif top1_prob >= 0.4:
-                    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���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.jpeg',
-                '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()