Create app.py

app.py

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+from torchvision import transforms
+import torch
+import urllib
+from PIL import Image
+import gradio as gr
+import torch
+
+# Images
+torch.hub.download_url_to_file('https://static.scientificamerican.com/sciam/cache/file/7A715AD8-449D-4B5A-ABA2C5D92D9B5A21_source.png', 'bird.png')
+
+model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True,
+                       **{'topN': 6, 'device':'cpu', 'num_classes': 200})
+
+transform_test = transforms.Compose([
+    transforms.Resize((600, 600), Image.BILINEAR),
+    transforms.CenterCrop((448, 448)),
+    # transforms.RandomHorizontalFlip(),  # only if train
+    transforms.ToTensor(),
+    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+])
+
+
+model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})
+
+def birds(img):
+  scaled_img = transform_test(img)
+  torch_images = scaled_img.unsqueeze(0)
+
+  with torch.no_grad():
+      top_n_coordinates, concat_out, raw_logits, concat_logits, part_logits, top_n_index, top_n_prob = model(torch_images)
+
+      _, predict = torch.max(concat_logits, 1)
+      pred_id = predict.item()
+      return model.bird_classes[pred_id].split('.')[1]
+
+inputs = gr.inputs.Image(type='pil', label="Original Image")
+outputs = gr.outputs.Textbox(label="bird class")
+
+title = "ntsnet"
+description = "demo for ntsnet to classify birds. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='http://artelab.dista.uninsubria.it/res/research/papers/2019/2019-IVCNZ-Nawaz-Birds.pdf'>Are These Birds Similar: Learning Branched Networks for Fine-grained Representations</a> | <a href='https://github.com/nicolalandro/ntsnet-cub200'>Github Repo</a></p>"
+
+examples = [
+            ['bird.png']
+]
+gr.Interface(birds, inputs, outputs, title=title, description=description, 
+             article=article, examples=examples, analytics_enabled=False).launch()