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
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from data_manager import get_dog_description
from urllib.parse import quote

dog_breeds = ["Afghan_Hound(阿富汗獵犬)", "African_Hunting_Dog(非洲野犬)", "Airedale(艾爾谷犬)",
            "American_Staffordshire_Terrier(美國斯塔福郡梗)", "Appenzeller(亞賓澤爾犬)",
            "Australian_Terrier(澳大利亞梗)", "Bedlington_Terrier(貝德靈頓梗)",
            "Bernese_Mountain_Dog(伯恩山犬)", "Blenheim_Spaniel(布萊尼姆獵犬)",
            "Border_Collie(邊境牧羊犬)", "Border_Terrier(邊境梗)", "Boston_Bull(波士頓梗)",
            "Bouvier_Des_Flandres(法蘭德斯牧羊犬)", "Brabancon_Griffon(布魯塞爾格里芬犬)",
            "Brittany_Spaniel(布列塔尼獵犬)", "Cardigan(卡迪根威爾士柯基犬)",
            "Chesapeake_Bay_Retriever(切薩皮克灣獵犬)", "Chihuahua(吉娃娃)",
            "Dandie_Dinmont(丹第丁蒙梗)", "Doberman(杜賓犬)", "English_Foxhound(英國獵狐犬)",
            "English_Setter(英國雪達犬)", "English_Springer(英國跳獵犬)",
            "EntleBucher(恩特雷布赫山地犬)", "Eskimo_Dog(愛斯基摩犬)", "French_Bulldog(法國鬥牛犬)",
            "German_Shepherd(德國牧羊犬)", "German_Short-Haired_Pointer(德國短毛指示犬)",
            "Gordon_Setter(戈登雪達犬)", "Great_Dane(大丹犬)", "Great_Pyrenees(大白熊犬)",
            "Greater_Swiss_Mountain_Dog(大瑞士山地犬)", "Ibizan_Hound(依比沙獵犬)",
            "Irish_Setter(愛爾蘭雪達犬)", "Irish_Terrier(愛爾蘭梗)",
            "Irish_Water_Spaniel(愛爾蘭水獵犬)", "Irish_Wolfhound(愛爾蘭獵狼犬)",
            "Italian_Greyhound(義大利灰狗)", "Japanese_Spaniel(日本狆)",
            "Kerry_Blue_Terrier(凱利藍梗)", "Labrador_Retriever(拉布拉多尋回犬)",
            "Lakeland_Terrier(湖畔梗)", "Leonberg(獅毛狗)", "Lhasa(拉薩犬)",
            "Maltese_Dog(馬爾濟斯犬)", "Mexican_Hairless(墨西哥無毛犬)", "Newfoundland(紐芬蘭犬)",
            "Norfolk_Terrier(諾福克梗)", "Norwegian_Elkhound(挪威獵麋犬)",
            "Norwich_Terrier(諾利治梗)", "Old_English_Sheepdog(古代英國牧羊犬)",
            "Pekinese(北京犬)", "Pembroke(威爾士柯基犬)", "Pomeranian(博美犬)",
            "Rhodesian_Ridgeback(羅得西亞脊背犬)", "Rottweiler(羅威納犬)",
            "Saint_Bernard(聖伯納犬)", "Saluki(薩路基獵犬)", "Samoyed(薩摩耶犬)",
            "Scotch_Terrier(蘇格蘭梗)", "Scottish_Deerhound(蘇格蘭獵鹿犬)",
            "Sealyham_Terrier(錫利哈姆梗)", "Shetland_Sheepdog(設得蘭牧羊犬)",
            "Shih-Tzu(西施犬)", "Siberian_Husky(西伯利亞哈士奇)",
            "Staffordshire_Bullterrier(斯塔福郡鬥牛梗)", "Sussex_Spaniel(蘇塞克斯獵犬)",
            "Tibetan_Mastiff(藏獒)", "Tibetan_Terrier(西藏梗)", "Walker_Hound(沃克獵犬)",
            "Weimaraner(威瑪犬)", "Welsh_Springer_Spaniel(威爾士跳獵犬)",
            "West_Highland_White_Terrier(西高地白梗)", "Yorkshire_Terrier(約克夏梗)",
            "Affenpinscher(猴犬)", "Basenji(巴辛吉犬)", "Basset(巴吉度獵犬)", "Beagle(比格犬)",
            "Black-and-Tan_Coonhound(黑褐獵浣熊犬)", "Bloodhound(尋血獵犬)",
            "Bluetick(布魯提克獵犬)", "Borzoi(俄羅斯獵狼犬)", "Boxer(拳師犬)", "Briard(布里亞犬)",
            "Bull_Mastiff(獒犬)", "Cairn(凱恩梗)", "Chow(鬆獅犬)", "Clumber(克倫伯獵犬)",
            "Cocker_Spaniel(可卡獵犬)", "Collie(柯利牧羊犬)", "Curly-Coated_Retriever(捲毛尋回犬)",
            "Dhole(豺)", "Dingo(澳洲野犬)", "Flat-Coated_Retriever(平毛尋回犬)",
            "Giant_Schnauzer(大型雪納瑞犬)", "Golden_Retriever(黃金獵犬)",
            "Groenendael(比利時牧羊犬)", "Keeshond(荷蘭毛獅犬)", "Kelpie(澳洲卡爾比犬)",
            "Komondor(匈牙利牧羊犬)", "Kuvasz(庫瓦茲犬)", "Malamute(阿拉斯加雪橇犬)",
            "Malinois(比利時瑪利諾犬)", "Miniature_Pinscher(迷你杜賓犬)",
            "Miniature_Poodle(迷你貴賓犬)", "Miniature_Schnauzer(迷你雪納瑞犬)",
            "Otterhound(水獺獵犬)", "Papillon(蝴蝶犬)", "Pug(巴哥犬)", "Redbone(紅骨獵浣熊犬)",
            "Schipperke(舒柏奇犬)", "Silky_Terrier(絲毛梗)",
            "Soft-Coated_Wheaten_Terrier(愛爾蘭軟毛梗)", "Standard_Poodle(標準貴賓犬)",
            "Standard_Schnauzer(標準雪納瑞犬)", "Toy_Poodle(玩具貴賓犬)", "Toy_Terrier(玩具梗)",
            "Vizsla(維茲拉犬)", "Whippet(惠比特犬)", "Wire-Haired_Fox_Terrier(硬毛獵狐梗)"]

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()

        # 動態計算 num_heads
        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'])

# 將模型設置為評估模式
model.eval()

# Image preprocessing function
def preprocess_image(image):
    # 如果圖片是 numpy.ndarray 轉換為 PIL.Image
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)

    # 使用 torchvision.transforms 進行預處理
    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 predict(image):
#     try:
#         image_tensor = preprocess_image(image)
#         with torch.no_grad():
#             logits, _ = model(image_tensor)
#             _, predicted = torch.max(logits, 1)

#         breed = dog_breeds[predicted.item()]  # Map label to breed name

#         # Retrieve breed description
#         description = get_dog_description(breed)

#         # Formatting the description for better display
#         if isinstance(description, dict):
#             description_str = f"**Breed**: {description['Breed']}\n\n"
#             description_str += f"**Size**: {description['Size']}\n\n"
#             description_str += f"**Lifespan**: {description['Lifespan']}\n\n"
#             description_str += f"**Temperament**: {description['Temperament']}\n\n"
#             description_str += f"**Care Level**: {description['Care Level']}\n\n"
#             description_str += f"**Good with Children**: {description['Good with Children']}\n\n"
#             description_str += f"**Exercise Needs**: {description['Exercise Needs']}\n\n"
#             description_str += f"**Grooming Needs**: {description['Grooming Needs']}\n\n"
#             description_str += f"**Description**: {description['Description']}\n\n"
#         else:
#             description_str = description

#         return description_str
#     except Exception as e:
#         return f"An error occurred: {e}"

def get_akc_link(breed):
    formatted_breed = breed.lower().replace(" ", "-")
    return f"https://www.akc.org/dog-breeds/{formatted_breed}/"

def predict(image):
    try:
        image_tensor = preprocess_image(image)
        with torch.no_grad():
            output = model(image_tensor)
            if isinstance(output, tuple):
                logits = output[0]
            else:
                logits = output
            _, predicted = torch.max(logits, 1)
        breed = dog_breeds[predicted.item()]  # Map label to breed name
        
        # Retrieve breed description
        description = get_dog_description(breed)
        
        # Generate AKC link
        akc_link = get_akc_link(breed)
        
        # Formatting the description for better display
        if isinstance(description, dict):
            description_str = f"**Breed**: {description['Breed']}\n\n"
            description_str += f"**Size**: {description['Size']}\n\n"
            description_str += f"**Lifespan**: {description['Lifespan']}\n\n"
            description_str += f"**Temperament**: {description['Temperament']}\n\n"
            description_str += f"**Care Level**: {description['Care Level']}\n\n"
            description_str += f"**Good with Children**: {description['Good with Children']}\n\n"
            description_str += f"**Exercise Needs**: {description['Exercise Needs']}\n\n"
            description_str += f"**Grooming Needs**: {description['Grooming Needs']}\n\n"
            description_str += f"**Description**: {description['Description']}\n\n"
        else:
            description_str = description
        
        # Add AKC link
        description_str += f"\n\n[Click here for more information about {breed} on AKC]({akc_link})"
        
        return description_str
    except Exception as e:
        return f"An error occurred: {e}"


iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(label="Upload a dog image", type="numpy"),
    outputs=gr.Markdown(label="Prediction Results"),
    title="<h1 style='font-family:Roboto; font-weight:bold; color:#2C3E50; text-align:center;'>🐶 Dog Breed Classifier 🔍</h1>",
    description="<p style='font-family:Open Sans; color:#34495E; text-align:center;'>Upload a picture of a dog, and AI will predict its breed, provide detailed information, and include a Wikipedia link!</p>",
    examples=['Border_Collie.jpg',
              'Golden_Retriever.jpeg',
              'Saint_Bernard.jpeg',
              'French_Bulldog.jpeg',
              'Samoyed.jpg'],
     css = """
    /* 新增樣式 */
    .container {
        max-width: 900px;
        margin: 0 auto;
        padding: 20px;
        background-color: rgba(255, 255, 255, 0.9);
        border-radius: 15px;
        box-shadow: 0 0 20px rgba(0, 0, 0, 0.1);
    }
    
    .gr-form {
        display: flex;
        flex-direction: column;
        align-items: center;
    }
    
    .gr-box {
        width: 100%;
        max-width: 500px;
    }
    
    .output-markdown, .output-image {
        margin-top: 20px;
        padding: 15px;
        background-color: #f5f5f5;
        border-radius: 10px;
    }
    
    .examples {
        display: flex;
        justify-content: center;
        flex-wrap: wrap;
        gap: 10px;
        margin-top: 20px;
    }
    
    .examples img {
        width: 100px;
        height: 100px;
        object-fit: cover;
    }
    """,
    theme='default')



# Launch the app
if __name__ == "__main__":
    iface.launch()