app.py

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
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 < 0.15:
        initial_state = {
            "explanation": "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.",
            "image": None,
            "is_multi_dog": False
        }
        return initial_state["explanation"], 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(description, breed)
        initial_state = {
            "explanation": formatted_description,
            "image": image,
            "is_multi_dog": False
        }
        return formatted_description, image, initial_state
        
    # Case 3: Medium confidence - show top 3 breeds with relative probabilities
    else:
        breeds_info = ""
        for i, (breed, prob) in enumerate(zip(topk_breeds, relative_probs)):
            description = get_dog_description(breed)
            formatted_description = format_description(description, breed)
            breeds_info += f"\n\nBreed {i+1}: **{breed}** (Confidence: {prob})\n{formatted_description}"

        initial_state = {
            "explanation": breeds_info,
            "image": image,
            "is_multi_dog": False
        }
        return breeds_info, image, initial_state
        


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
        '#33FF57',  # 薄荷綠
        '#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'<div class="dog-info-card" style="border-left: 6px solid {color};">'
            
            if combined_confidence < 0.15:
                dogs_info += f'''
                    <div class="dog-info-header" style="background-color: {color}10;">
                        <span class="dog-label" style="color: {color};">Dog {i+1}</span>
                    </div>
                    <div class="breed-info">
                        <p class="warning-message">
                            <span class="icon">⚠️</span>
                            The image is unclear or the breed is not in the dataset. Please upload a clearer image.
                        </p>
                    </div>
                '''
            elif top1_prob >= 0.45:
                breed = topk_breeds[0]
                description = get_dog_description(breed)
                dogs_info += f'''
                    <div class="dog-info-header" style="background-color: {color}10;">
                        <span class="dog-label" style="color: {color};">
                            <span class="icon">🏆</span> {breed}
                        </span>
                    </div>
                    <div class="breed-info">
                        <h2 class="section-title">
                            <span class="icon">📋</span> BASIC INFORMATION
                        </h2>
                        <div class="info-section">
                            <div class="info-item">
                                <span class="icon">📏</span>
                                <span class="label">Size:</span>
                                <span class="value">{description['Size']}</span>
                            </div>
                            <div class="info-item">
                                <span class="icon">⏳</span>
                                <span class="label">Lifespan:</span>
                                <span class="value">{description['Lifespan']}</span>
                            </div>
                        </div>

                        <h2 class="section-title">
                            <span class="icon">🐕</span> TEMPERAMENT & PERSONALITY
                        </h2>
                        <div class="temperament-section">
                            <span class="value">{description['Temperament']}</span>
                        </div>

                        <h2 class="section-title">
                            <span class="icon">💪</span> CARE REQUIREMENTS
                        </h2>
                        <div class="care-section">
                            <div class="info-item">
                                <span class="icon">🏃</span>
                                <span class="label">Exercise:</span>
                                <span class="value">{description['Exercise Needs']}</span>
                            </div>
                            <div class="info-item">
                                <span class="icon">✂️</span>
                                <span class="label">Grooming:</span>
                                <span class="value">{description['Grooming Needs']}</span>
                            </div>
                            <div class="info-item">
                                <span class="icon">⭐</span>
                                <span class="label">Care Level:</span>
                                <span class="value">{description['Care Level']}</span>
                            </div>
                        </div>

                        <h2 class="section-title">
                            <span class="icon">👨‍👩‍👧‍👦</span> FAMILY COMPATIBILITY
                        </h2>
                        <div class="family-section">
                            <div class="info-item">
                                <span class="icon">👶</span>
                                <span class="label">Good with Children:</span>
                                <span class="value">{description['Good with Children']}</span>
                            </div>
                        </div>

                        <h2 class="section-title">
                            <span class="icon">📝</span> DESCRIPTION
                        </h2>
                        <div class="description-section">
                            <p>{description.get('Description', '')}</p>
                        </div>

                        <div class="action-section">
                            <a href="{get_akc_breeds_link(breed)}" target="_blank" class="akc-button">
                                <span class="icon">🌐</span>
                                Learn more about {breed} on AKC website
                            </a>
                        </div>
                    </div>
                '''
            else:
                dogs_info += f'''
                    <div class="dog-info-header" style="background-color: {color}10;">
                        <span class="dog-label" style="color: {color};">Dog {i+1}</span>
                    </div>
                    <div class="breed-info">
                        <div class="model-uncertainty-note">
                            <span class="icon">ℹ️</span>
                            Note: The model is showing some uncertainty in its predictions. 
                            Here are the most likely breeds based on the available visual features.
                        </div>
                        <div class="breeds-list">
                '''
                
                for j, (breed, prob) in enumerate(zip(topk_breeds, relative_probs)):
                    description = get_dog_description(breed)
                    dogs_info += f'''
                        <div class="breed-option">
                            <div class="breed-header">
                                <span class="option-number">Option {j+1}</span>
                                <span class="breed-name">{breed}</span>
                                <span class="confidence-badge" style="background-color: {color}20; color: {color};">
                                    Confidence: {prob}
                                </span>
                            </div>
                            <div class="breed-content">
                                {format_description_html(description, breed)}
                            </div>
                        </div>
                    '''
                dogs_info += '</div></div>'
            
            dogs_info += '</div>'


        html_output = f"""
        <style>
        .dog-info-card {{ 
            border: 1px solid #e1e4e8; 
            margin: 32px 0;
            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: 20px 24px;
            margin: 0;
            font-size: 22px;
            font-weight: bold;
            border-bottom: 1px solid #e1e4e8;
        }}
        
        .breed-info {{
            padding: 24px;
            line-height: 1.6;
        }}
        
        .section-title {{
            font-size: 1.2em;
            font-weight: 700;
            color: #2c3e50;
            margin: 28px 0 16px 0;
            padding-bottom: 8px;
            border-bottom: 2px solid #e1e4e8;
            text-transform: uppercase;
            letter-spacing: 0.5px;
            display: flex;
            align-items: center;
            gap: 8px;
        }}
        
        .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: 20px;
        }}
        
        .info-item {{
            background: #f8f9fa;
            padding: 12px 16px;
            border-radius: 8px;
            display: flex;
            align-items: center;
            gap: 8px;
            box-shadow: 0 1px 3px rgba(0,0,0,0.05);
        }}
        
        .label {{
            color: #666;
            font-weight: 600;
        }}
        
        .value {{
            color: #2c3e50;
        }}
        
        .temperament-section {{
            background: #f8f9fa;
            padding: 16px;
            border-radius: 8px;
            margin-bottom: 20px;
            color: #444;
        }}
        
        .description-section {{
            background: #f8f9fa;
            padding: 16px;
            border-radius: 8px;
            margin: 20px 0;
            line-height: 1.8;
            color: #444;
        }}
        
        .action-section {{
            margin-top: 24px;
            text-align: center;
        }}
        
        .akc-button {{
            display: inline-flex;
            align-items: center;
            padding: 12px 24px;
            background-color: #00509E; 
            color: white;
            border-radius: 8px;
            text-decoration: none;
            gap: 8px;
            transition: all 0.3s ease;
            font-weight: 500;
        }}
        
        .akc-button:hover {{
            background-color: #003F7F;
            transform: translateY(-1px);
            color: white;
        }}
        
        .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: 1.1em;
            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;
        }}
        
        ul {{
            padding-left: 0;
            margin: 0;
            list-style-type: none;
        }}
        
        li {{
            margin-bottom: 8px;
            display: flex;
            align-items: center;
            gap: 8px;
        }}

        .akc-link {{
            color: #357ABD;
            text-decoration: none;
            font-weight: 500;
            transition: all 0.3s ease;
        }}
        
        .akc-link:hover {{
            text-decoration: underline;
            color: #2C6AA0;
        }}
        </style>
        {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"""
        <div class="dog-info">
            <h2>{breed}</h2>
            {formatted_description}
        </div>
        """
        
        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"<p style='color: red;'>{error_msg}</p>", gr.update(visible=True), initial_state


def format_description_html(description, breed):
    html = "<ul style='list-style-type: none; padding-left: 0;'>"
    if isinstance(description, dict):
        for key, value in description.items():
            # 跳過 Breed 資訊
            if key != "Breed":
                html += f"<li style='margin-bottom: 10px;'><strong>{key}:</strong> {value}</li>"
    elif isinstance(description, str):
        html += f"<li>{description}</li>"
    else:
        html += f"<li>No description available for {breed}</li>"
    html += "</ul>"
    akc_link = get_akc_breeds_link(breed)
    html += f'<p><a href="{akc_link}" target="_blank">Learn more about {breed} on the AKC website</a></p>'
    return html


with gr.Blocks() as iface:
    gr.HTML("<h1 style='text-align: center;'>🐶 Dog Breed Classifier 🔍</h1>")
    gr.HTML("<p style='text-align: center;'>Upload a picture of a dog, and the model will predict its breed and provide detailed information!</p>")
    gr.HTML("<p style='text-align: center; color: #666; font-size: 0.9em;'>Note: The model's predictions may not always be 100% accurate, and it is recommended to use the results as a reference.</p>")
    
    
    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
    )
    
    gr.HTML('For more details on this project and other work, feel free to visit my GitHub <a href="https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog_Breed_Classifier">Dog Breed Classifier</a>')


if __name__ == "__main__":
    iface.launch()