Delete app.py

app.py

@@ -1,558 +0,0 @@
-import os
-import numpy as np
-import torch
-import torch.nn as nn
-import gradio as gr
-import time
-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, calculate_compatibility_score
-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 breed_visualization import create_visualization_tab
-# from style_transfer import DogStyleTransfer, create_style_transfer_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 model_architecture import BaseModel, dog_breeds
-from urllib.parse import quote
-from ultralytics import YOLO
-import asyncio
-import traceback
-
-history_manager = UserHistoryManager()
-
-class ModelManager:
-    """
-    Singleton class for managing model instances and device allocation
-    specifically designed for Hugging Face Spaces deployment.
-    """
-    _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):
-        if not ModelManager._initialized:
-            self._device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-            ModelManager._initialized = True
-
-    @property
-    def device(self):
-        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):
-        if self._yolo_model is None:
-            self._yolo_model = YOLO('yolov8x.pt')
-        return self._yolo_model
-
-    @property
-    def breed_model(self):
-        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.pth',
-                map_location=self.device
-            )
-            if 'base_model' in checkpoint:
-                
-                self._breed_model.load_state_dict(checkpoint['base_model'], strict=False)
-            elif 'model' in checkpoint:
-                
-                self._breed_model.load_state_dict(checkpoint['model'], strict=False)
-            elif 'state_dict' in checkpoint:
-               
-                self._breed_model.load_state_dict(checkpoint['state_dict'], strict=False)
-            else:
-                
-                try:
-                    self._breed_model.load_state_dict(checkpoint, strict=False)
-                except Exception as e:
-                    print(f"嘗試載入模型權重時出錯: {e}")
-                    print(f"checkpoint鍵: {list(checkpoint.keys()) if isinstance(checkpoint, dict) else 'Not a dictionary'}")                    
-            self._breed_model.eval()
-        return self._breed_model
-
-# Initialize model manager
-model_manager = ModelManager()
-
-def preprocess_image(image):
-    """Preprocesses images for model input"""
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-
-    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 dog breed for a single image"""
-    image_tensor = preprocess_image(image).to(model_manager.device)
-
-    with torch.no_grad():
-        logits = model_manager.breed_model(image_tensor)[0]
-        probs = F.softmax(logits, dim=1)
-
-        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]]
-
-        sum_probs = sum(probabilities[:3])
-        relative_probs = [f"{(prob/sum_probs * 100):.2f}%" for prob in probabilities[:3]]
-
-        return probabilities[0], breeds[:3], relative_probs
-
-def enhanced_preprocess(image, is_standing=False, has_overlap=False):
-    """
-    Enhanced image preprocessing function with special handling for different poses
-    and overlapping cases.
-    """
-    target_size = 224
-    w, h = image.size
-
-    if is_standing:
-        if h > w * 1.5:
-            new_h = target_size
-            new_w = min(target_size, int(w * (target_size / h)))
-            new_w = max(new_w, int(target_size * 0.6))
-    elif has_overlap:
-        scale = min(target_size/w, target_size/h) * 0.95
-        new_w = int(w * scale)
-        new_h = int(h * scale)
-    else:
-        scale = min(target_size/w, target_size/h)
-        new_w = int(w * scale)
-        new_h = int(h * scale)
-
-    resized = image.resize((new_w, new_h), Image.Resampling.LANCZOS)
-    final_image = Image.new('RGB', (target_size, target_size), (240, 240, 240))
-    paste_x = (target_size - new_w) // 2
-    paste_y = (target_size - new_h) // 2
-    final_image.paste(resized, (paste_x, paste_y))
-
-    return final_image
-
-@spaces.GPU
-def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.3):
-    """
-    Enhanced multiple dog detection with improved bounding box handling and
-    intelligent boundary adjustments.
-    """
-    results = model_manager.yolo_model(image, conf=conf_threshold, iou=iou_threshold)[0]
-    img_width, img_height = image.size
-    detected_boxes = []
-
-    # Phase 1: Initial detection and processing
-    for box in results.boxes:
-        if box.cls.item() == 16:  # Dog class
-            xyxy = box.xyxy[0].tolist()
-            confidence = box.conf.item()
-            x1, y1, x2, y2 = map(int, xyxy)
-            w = x2 - x1
-            h = y2 - y1
-
-            detected_boxes.append({
-                'coords': [x1, y1, x2, y2],
-                'width': w,
-                'height': h,
-                'center_x': (x1 + x2) / 2,
-                'center_y': (y1 + y2) / 2,
-                'area': w * h,
-                'confidence': confidence,
-                'aspect_ratio': w / h if h != 0 else 1
-            })
-
-    if not detected_boxes:
-        return [(image, 1.0, [0, 0, img_width, img_height], False)]
-
-    # Phase 2: Analysis of detection relationships
-    avg_height = sum(box['height'] for box in detected_boxes) / len(detected_boxes)
-    avg_width = sum(box['width'] for box in detected_boxes) / len(detected_boxes)
-    avg_area = sum(box['area'] for box in detected_boxes) / len(detected_boxes)
-
-    def calculate_iou(box1, box2):
-        x1 = max(box1['coords'][0], box2['coords'][0])
-        y1 = max(box1['coords'][1], box2['coords'][1])
-        x2 = min(box1['coords'][2], box2['coords'][2])
-        y2 = min(box1['coords'][3], box2['coords'][3])
-
-        if x2 <= x1 or y2 <= y1:
-            return 0.0
-
-        intersection = (x2 - x1) * (y2 - y1)
-        area1 = box1['area']
-        area2 = box2['area']
-        return intersection / (area1 + area2 - intersection)
-
-    # Phase 3: Processing each detection
-    processed_boxes = []
-    overlap_threshold = 0.2
-
-    for i, box_info in enumerate(detected_boxes):
-        x1, y1, x2, y2 = box_info['coords']
-        w = box_info['width']
-        h = box_info['height']
-        center_x = box_info['center_x']
-        center_y = box_info['center_y']
-
-        # Check for overlaps
-        has_overlap = False
-        for j, other_box in enumerate(detected_boxes):
-            if i != j and calculate_iou(box_info, other_box) > overlap_threshold:
-                has_overlap = True
-                break
-
-        # Adjust expansion strategy
-        base_expansion = 0.03
-        max_expansion = 0.05
-
-        is_standing = h > 1.5 * w
-        is_sitting = 0.8 <= h/w <= 1.2
-        is_abnormal_size = (h * w) > (avg_area * 1.5) or (h * w) < (avg_area * 0.5)
-
-        if has_overlap:
-            h_expansion = w_expansion = base_expansion * 0.8
-        else:
-            if is_standing:
-                h_expansion = min(base_expansion * 1.2, max_expansion)
-                w_expansion = base_expansion
-            elif is_sitting:
-                h_expansion = w_expansion = base_expansion
-            else:
-                h_expansion = w_expansion = base_expansion * 0.9
-
-        # Position compensation
-        if center_x < img_width * 0.2 or center_x > img_width * 0.8:
-            w_expansion *= 0.9
-
-        if is_abnormal_size:
-            h_expansion *= 0.8
-            w_expansion *= 0.8
-
-        # Calculate final bounding box
-        expansion_w = w * w_expansion
-        expansion_h = h * h_expansion
-
-        new_x1 = max(0, center_x - (w + expansion_w)/2)
-        new_y1 = max(0, center_y - (h + expansion_h)/2)
-        new_x2 = min(img_width, center_x + (w + expansion_w)/2)
-        new_y2 = min(img_height, center_y + (h + expansion_h)/2)
-
-        # Crop and process image
-        cropped_image = image.crop((int(new_x1), int(new_y1),
-                                  int(new_x2), int(new_y2)))
-
-        processed_image = enhanced_preprocess(
-            cropped_image,
-            is_standing=is_standing,
-            has_overlap=has_overlap
-        )
-
-        processed_boxes.append((
-            processed_image,
-            box_info['confidence'],
-            [new_x1, new_y1, new_x2, new_y2],
-            True
-        ))
-
-    return processed_boxes
-
-@spaces.GPU
-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_hint_html("Please upload an image to start."), None, None
-
-    try:
-        if isinstance(image, np.ndarray):
-            image = Image.fromarray(image)
-
-        # 檢測圖片中的物體
-        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):
-            print(f"Predict processing - Object {i+1}:")
-            print(f"    Is dog: {is_dog}")
-            print(f"    Detection confidence: {detection_confidence:.4f}")
-
-            # 如果是狗且進行品種預測，在這裡也加入打印語句
-            if is_dog:
-                top1_prob, topk_breeds, relative_probs = predict_single_dog(cropped_image)
-                print(f"    Breed prediction - Top probability: {top1_prob:.4f}")
-                print(f"    Top breeds: {topk_breeds[:3]}")
-            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:
-        # 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()
-
-        # # Initialize style transfor
-        # dog_style_transfer = DogStyleTransfer()
-
-        with gr.Tabs():
-            # 1. breed detection
-            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. breed comparison
-            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. breed recommendation
-            recommendation_components = create_recommendation_tab(
-                UserPreferences=UserPreferences,
-                get_breed_recommendations=get_breed_recommendations,
-                format_recommendation_html=format_recommendation_html,
-                history_component=history_component
-            )
-
-            # 4. Visualization Analysis
-            with gr.Tab("Visualization Analysis"):
-                create_visualization_tab(
-                    dog_breeds=dog_breeds,
-                    get_dog_description=get_dog_description,
-                    calculate_compatibility_score=calculate_compatibility_score,
-                    UserPreferences=UserPreferences
-                )
-
-            # # 5. Style Transfer tab
-            # with gr.Tab("Style Transfer"):
-            #     style_transfer_components = create_style_transfer_tab(dog_style_transfer)
-
-
-            # 6. History Search
-            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()