client.py

import gradio as gr
import torch
import torch.nn as nn
import torchvision.transforms as transforms


class AnimeCNN(nn.Module):
    def __init__(self, num_classes=4):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25),
            
            nn.Conv2d(32, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25)
        )
        
        self.classifier = nn.Sequential(
            nn.Linear(64*16*16, 256),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(256, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model = AnimeCNN()
model.load_state_dict(torch.load('model.pth', map_location=device, weights_only=True))
model.eval()

classes = ["usada_pekora", "aisaka_taiga", "megumin", "minato_aqua"]

transform = transforms.Compose([
    transforms.Resize((64, 64)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

def predict(image):
    image = transform(image).unsqueeze(0)
    
    with torch.no_grad():
        outputs = model(image)
        probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
    
    confidences = {classes[i]: float(probabilities[i]) for i in range(4)}
    return confidences

interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="入力画像"),
    outputs=gr.Label(num_top_classes=4, label="予測結果"),
    title="アニメキャラクター分類器",
    description="うさだぺこら・逢坂大河・めぐみん・湊あくあの画像を分類します。画像をアップロードしてください。",
    examples=[
        ["examples/usada_pekora.jpg"],
        ["examples/aisaka_taiga.jpg"],
        ["examples/megumin.jpg"],
        ["examples/minato_aqua.jpg"]
    ],
)

interface.launch(server_name="0.0.0.0", server_port=7860, share=True)