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()

        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():
    return "https://www.akc.org/dog-breeds/"

# 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

#             # 取得預測的top k結果
#             probabilities = F.softmax(logits, dim=1)
#             topk_probs, topk_indices = torch.topk(probabilities, k=3)

#             # 檢查最高的預測機率
#             top1_prob = topk_probs[0][0].item()

#             if top1_prob >= 0.5:
#                 # 正確辨識時，返回該品種資訊
#                 predicted = topk_indices[0][0]
#                 breed = dog_breeds[predicted.item()]
#                 description = get_dog_description(breed)
#                 akc_link = get_akc_breeds_link()

#                 if isinstance(description, dict):
#                     description_str = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
#                 else:
#                     description_str = description

#                 # 添加AKC連結
#                 description_str += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {breed} to find detailed information."

#                 # 添加免責聲明
#                 disclaimer = ("\n\n*Disclaimer: The external link provided leads to the American Kennel Club (AKC) dog breeds page. "
#                               "You may need to search for the specific breed on that page. "
#                               "I am not responsible for the content on external sites. "
#                               "Please refer to the AKC's terms of use and privacy policy.*")
#                 description_str += disclaimer

#                 return description_str

#             else:
#                 # 不確定時，返回top 3的預測結果
#                 topk_breeds = [dog_breeds[idx.item()] for idx in topk_indices[0]]
#                 topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]

#                 # 用粗體返回品種和機率
#                 topk_results = "\n\n".join([f"**{i+1}. {breed}** ({prob} confidence)" for i, (breed, prob) in enumerate(zip(topk_breeds, topk_probs_percent))])

#                 # 提供說明
#                 explanation = (
#                     f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n{topk_results}\n\n"
#                     "This can happen if the image quality is low or the breed is rare in the dataset. "
#                     "Please try uploading a clearer image or a different angle of the dog. "
#                     "For more accurate results, ensure the dog is the main subject of the photo."
#                 )

#                 return explanation
#     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>",
#     article= 'For more details on this project and other work, feel free to visit my GitHub [Dog Breed Classifier](https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog%20Breed%20Classifier)',
#     description="<p style='font-family:Open Sans; color:#34495E; text-align:center;'>Upload a picture of a dog, and model will predict its breed, provide detailed information, and include an extra information link!</p>",
#     examples=['Border_Collie.jpg',
#               'Golden_Retriever.jpeg',
#               'Saint_Bernard.jpeg',
#               'French_Bulldog.jpeg',
#               'Samoyed.jpg'],
#      css = """
#     .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()

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

            # 取得預測的top k結果
            probabilities = F.softmax(logits, dim=1)
            topk_probs, topk_indices = torch.topk(probabilities, k=3)

            # 檢查最高的預測機率
            top1_prob = topk_probs[0][0].item()

            if top1_prob >= 0.5:
                # 正確辨識時，返回該品種資訊
                predicted = topk_indices[0][0]
                breed = dog_breeds[predicted.item()]
                description = get_dog_description(breed)
                akc_link = get_akc_breeds_link()

                if isinstance(description, dict):
                    description_str = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
                else:
                    description_str = description

                # 添加AKC連結
                description_str += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {breed} to find detailed information."

                # 添加免責聲明
                disclaimer = ("\n\n*Disclaimer: The external link provided leads to the American Kennel Club (AKC) dog breeds page. "
                              "You may need to search for the specific breed on that page. "
                              "I am not responsible for the content on external sites. "
                              "Please refer to the AKC's terms of use and privacy policy.*")
                description_str += disclaimer

                return description_str, gr.Button.update(visible=False), gr.Button.update(visible=False), gr.Button.update(visible=False)

            elif top1_prob < 0.1:
                # 如果信心度低於 0.1，返回提示請上傳更清晰的圖片
                return "The image is too unclear or the dog breed is not in the dataset. Please upload a clearer image of the dog.", gr.Button.update(visible=False), gr.Button.update(visible=False), gr.Button.update(visible=False)

            else:
                # 不確定時，返回top 3的預測結果，並且允許點擊查看詳細資訊
                topk_breeds = [dog_breeds[idx.item()] for idx in topk_indices[0]]
                topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]

                # 提供說明
                explanation = (
                    f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n"
                    f"1. {topk_breeds[0]} ({topk_probs_percent[0]} confidence)\n"
                    f"2. {topk_breeds[1]} ({topk_probs_percent[1]} confidence)\n"
                    f"3. {topk_breeds[2]} ({topk_probs_percent[2]} confidence)\n\n"
                    "Click on a button to view more information about the breed."
                )

                return explanation, gr.Button.update(visible=True, value=f"More about {topk_breeds[0]}"), gr.Button.update(visible=True, value=f"More about {topk_breeds[1]}"), gr.Button.update(visible=True, value=f"More about {topk_breeds[2]}")
    except Exception as e:
        return f"An error occurred: {e}", gr.Button.update(visible=False), gr.Button.update(visible=False), gr.Button.update(visible=False)

def show_details(breed):
    description = get_dog_description(breed)
    akc_link = get_akc_breeds_link()
    
    if isinstance(description, dict):
        description_str = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
    else:
        description_str = description

    # 添加AKC連結
    description_str += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {breed} to find detailed information."

    # 添加免責聲明
    disclaimer = ("\n\n*Disclaimer: The external link provided leads to the American Kennel Club (AKC) dog breeds page. "
                  "You may need to search for the specific breed on that page. "
                  "I am not responsible for the content on external sites. "
                  "Please refer to the AKC's terms of use and privacy policy.*")
    description_str += disclaimer

    return description_str

# Gradio Interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(label="Upload a dog image", type="numpy"),
    outputs=[gr.Markdown(label="Prediction Results"), gr.Button(value="View More 1", visible=False), gr.Button(value="View More 2", visible=False), gr.Button(value="View More 3", visible=False)],
    title="<h1 style='font-family:Roboto; font-weight:bold; color:#2C3E50; text-align:center;'>🐶 Dog Breed Classifier 🔍</h1>",
    article= 'For more details on this project and other work, feel free to visit my GitHub [Dog Breed Classifier](https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog%20Breed%20Classifier)',
    description="<p style='font-family:Open Sans; color:#34495E; text-align:center;'>Upload a picture of a dog, and model will predict its breed, provide detailed information, and include an extra information link!</p>",
    examples=['Border_Collie.jpg',
              'Golden_Retriever.jpeg',
              'Saint_Bernard.jpeg',
              'French_Bulldog.jpeg',
              'Samoyed.jpg'],
     css = """
    .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'
)

# 定義按鈕的點擊事件
iface.add_event_listener('View More 1', fn=lambda: show_details(topk_breeds[0]))
iface.add_event_listener('View More 2', fn=lambda: show_details(topk_breeds[1]))
iface.add_event_listener('View More 3', fn=lambda: show_details(topk_breeds[2]))

# 啟動 Gradio 應用
if __name__ == "__main__":
    iface.launch()