import torch import gradio as gr import torch.nn as nn import torch.nn.functional as F from torch.nn import init import torchvision.transforms as transforms from PIL import Image # MobileNetV3 Model Definition (keep this exactly as in your original code) class hswish(nn.Module): def forward(self, x): return x * F.relu6(x + 3) / 6 class hsigmoid(nn.Module): def forward(self, x): return F.relu6(x + 3) / 6 class SeModule(nn.Module): def __init__(self, in_size, reduction=4): super().__init__() self.se = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(in_size, in_size//reduction, 1, bias=False), nn.BatchNorm2d(in_size//reduction), nn.ReLU(inplace=True), nn.Conv2d(in_size//reduction, in_size, 1, bias=False), nn.BatchNorm2d(in_size), hsigmoid() ) def forward(self, x): return x * self.se(x) class Block(nn.Module): def __init__(self, kernel_size, in_size, expand_size, out_size, nolinear, semodule, stride): super().__init__() self.stride = stride self.se = semodule self.conv1 = nn.Conv2d(in_size, expand_size, 1, 1, 0, bias=False) self.bn1 = nn.BatchNorm2d(expand_size) self.nolinear1 = nolinear self.conv2 = nn.Conv2d(expand_size, expand_size, kernel_size, stride, kernel_size//2, groups=expand_size, bias=False) self.bn2 = nn.BatchNorm2d(expand_size) self.nolinear2 = nolinear self.conv3 = nn.Conv2d(expand_size, out_size, 1, 1, 0, bias=False) self.bn3 = nn.BatchNorm2d(out_size) self.shortcut = nn.Sequential() if stride == 1 and in_size != out_size: self.shortcut = nn.Sequential( nn.Conv2d(in_size, out_size, 1, 1, 0, bias=False), nn.BatchNorm2d(out_size), ) def forward(self, x): out = self.nolinear1(self.bn1(self.conv1(x))) out = self.nolinear2(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) if self.se: out = self.se(out) return out + self.shortcut(x) if self.stride==1 else out class MobileNetV3_Small(nn.Module): def __init__(self, num_classes=30): super().__init__() self.conv1 = nn.Conv2d(3, 16, 3, 2, 1, bias=False) self.bn1 = nn.BatchNorm2d(16) self.hs1 = hswish() self.bneck = nn.Sequential( Block(3, 16, 16, 16, nn.ReLU(), SeModule(16), 2), Block(3, 16, 72, 24, nn.ReLU(), None, 2), Block(3, 24, 88, 24, nn.ReLU(), None, 1), Block(5, 24, 96, 40, hswish(), SeModule(40), 2), Block(5, 40, 240, 40, hswish(), SeModule(40), 1), Block(5, 40, 240, 40, hswish(), SeModule(40), 1), Block(5, 40, 120, 48, hswish(), SeModule(48), 1), Block(5, 48, 144, 48, hswish(), SeModule(48), 1), Block(5, 48, 288, 96, hswish(), SeModule(96), 2), Block(5, 96, 576, 96, hswish(), SeModule(96), 1), Block(5, 96, 576, 96, hswish(), SeModule(96), 1), ) self.conv2 = nn.Conv2d(96, 576, 1, 1, 0, bias=False) self.bn2 = nn.BatchNorm2d(576) self.hs2 = hswish() self.linear3 = nn.Linear(576, 1280) self.bn3 = nn.BatchNorm1d(1280) self.hs3 = hswish() self.linear4 = nn.Linear(1280, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): init.normal_(m.weight, std=0.001) if m.bias is not None: init.constant_(m.bias, 0) def forward(self, x): x = self.hs1(self.bn1(self.conv1(x))) x = self.bneck(x) x = self.hs2(self.bn2(self.conv2(x))) x = F.avg_pool2d(x, x.size()[2:]) x = x.view(x.size(0), -1) x = self.hs3(self.bn3(self.linear3(x))) return self.linear4(x) # Initialize Model model = MobileNetV3_Small().cpu() model.load_state_dict(torch.load("MobileNet3_small_StateDictionary.pth", map_location='cpu')) model.eval() # Class Labels classes = [ 'antelope', 'buffalo', 'chimpanzee', 'cow', 'deer', 'dolphin', 'elephant', 'fox', 'giantpanda', 'giraffe', 'gorilla', 'grizzlybear', 'hamster', 'hippopotamus', 'horse', 'humpbackwhale', 'leopard', 'lion', 'moose', 'otter', 'ox', 'pig', 'polarbear', 'rabbit', 'rhinoceros', 'seal', 'sheep', 'squirrel', 'tiger', 'zebra' ] # Preprocessing preprocess = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) def predict(images): """Process multiple images and return predictions""" predictions = [] # Batch processing batch = torch.stack([preprocess(Image.open(img).convert('RGB')) for img in images]) with torch.inference_mode(): outputs = model(batch) _, preds = torch.max(outputs, 1) return ", ".join([classes[p] for p in preds.cpu().numpy()]) # Gradio Interface with gr.Blocks(title="Animal Classifier") as demo: gr.Markdown("## 🐾 Animal Classifier") gr.Markdown("Upload multiple animal images to get predictions!") gr.Markdown("Detectable Classes: antelope, buffalo, chimpanzee, cow, deer, dolphin, elephant, fox, giantpanda, giraffe, gorilla, grizzlybear, hamster, hippopotamus, horse, humpbackwhale, leopard, lion, moose, otter, ox, pig, polarbear, rabbit, rhinoceros, seal, sheep, squirrel, tiger, zebra") with gr.Row(): inputs = gr.File( file_count="multiple", file_types=["image"], label="Upload Animal Images" ) submit = gr.Button("Classify 🚀", variant="primary") with gr.Row(): gallery = gr.Gallery(label="Upload Preview", columns=4) outputs = gr.Textbox(label="Predictions", lines=5) submit.click( fn=lambda files: ( [f.name for f in files], # Update gallery predict([f.name for f in files]) # Get predictions ), inputs=inputs, outputs=[gallery, outputs] ) if __name__ == "__main__": demo.launch(show_error=True)