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app.py

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+import torch
+import torch.nn as nn
+from PIL import Image
+import torchvision.transforms as transforms
+import gradio as gr
+from huggingface_hub import hf_hub_download
+
+CIFAR100_CLASSES = [
+    'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle',
+    'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel',
+    'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock',
+    'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur',
+    'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster',
+    'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion',
+    'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse',
+    'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear',
+    'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine',
+    'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea',
+    'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider',
+    'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank',
+    'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip',
+    'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm'
+]
+
+class BasicBlock(nn.Module):
+    expansion = 1
+    def __init__(self, in_channels, out_channels, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_channels != self.expansion * out_channels:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion * out_channels)
+            )
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out += self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+class ResNet(nn.Module):
+    def __init__(self, block, num_blocks, num_classes=100):
+        super(ResNet, self).__init__()
+        self.in_channels = 64
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
+        self.linear = nn.Linear(512 * block.expansion, num_classes)
+    def _make_layer(self, block, out_channels, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_channels, out_channels, stride))
+            self.in_channels = out_channels * block.expansion
+        return nn.Sequential(*layers)
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+        out = torch.nn.functional.avg_pool2d(out, 4)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+        return out
+
+def ResNet18():
+    return ResNet(BasicBlock, [2, 2, 2, 2])
+
+print("Loading model...")
+model_path = hf_hub_download(repo_id="ivantv/cifar100-resnet18", filename="best_model.pth")
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = ResNet18().to(device)
+model.load_state_dict(torch.load(model_path, map_location=device))
+model.eval()
+print("Model loaded!")
+
+transform = transforms.Compose([
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))
+])
+
+def classify_image(image):
+    if image is None:
+        return {}
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    
+    img_tensor = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        output = model(img_tensor)
+        probs = torch.nn.functional.softmax(output, dim=1)[0]
+        top5_prob, top5_idx = torch.topk(probs, 5)
+    
+    return {CIFAR100_CLASSES[idx]: prob.item() for prob, idx in zip(top5_prob, top5_idx)}
+
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=5),
+    title="CIFAR-100 Classifier",
+    description="ResNet-18 model trained on CIFAR-100 (75.84% accuracy)"
+)
+
+iface.launch()
+