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.gitattributes

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app.py

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+import gradio as gr
+import os
+from PIL import Image
+import torch
+from torchvision import transforms
+
+import torch
+model = torch.hub.load('pytorch/vision:v0.10.0', 'wide_resnet50_2', pretrained=True)
+model = torch.hub.load('pytorch/vision:v0.10.0', 'wide_resnet101_2', pretrained=True)
+model.eval()
+os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
+torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+
+def inference(input_image):
+    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]),
+    ])
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+    if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model.to('cuda')
+    with torch.no_grad():
+        output = model(input_batch)
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+    with open("imagenet_classes.txt", "r") as f:
+        categories = [s.strip() for s in f.readlines()]
+    top5_prob, top5_catid = torch.topk(probabilities, 5)
+    result = {}
+    for i in range(top5_prob.size(0)):
+        result[categories[top5_catid[i]]] = top5_prob[i].item()
+    return result
+
+inputs = gr.inputs.Image(type='pil')
+outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
+
+title = "WRN - Wide Residual Networks"
+description = "ResNet blocks based architecture where depth is decreased and width of residual networks is increased."
+article = "<p style='text-align: center'><a href='https://paperswithcode.com/paper/wide-residual-networks'>Wide Residual Networks on Papers With Code</a></p>"
+examples = [
+            ['1.jpg'],
+            ['2.jpg'],
+            ['3.jpg'],
+            ['4.jpg'],
+            ['5.jpg'],
+]
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()

requirements.txt

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+Pillow
+torch
+torchvision