Create app.py

app.py

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+import torch
+model = torch.hub.load('pytorch/vision:v0.9.0', 'fcn_resnet101', pretrained=True)
+model.eval()
+# Download an example image from the pytorch website
+import urllib
+url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+# sample execution (requires torchvision)
+from PIL import Image
+from torchvision import transforms
+import gradio as gr
+import matplotlib.pyplot as plt
+
+
+def inference(input_image):
+    preprocess = transforms.Compose([
+        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
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model.to('cuda')
+
+    with torch.no_grad():
+        output = model(input_batch)['out'][0]
+    output_predictions = output.argmax(0)
+    # create a color pallette, selecting a color for each class
+    palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
+    colors = torch.as_tensor([i for i in range(21)])[:, None] * palette
+    colors = (colors % 255).numpy().astype("uint8")
+
+    # plot the semantic segmentation predictions of 21 classes in each color
+    r = Image.fromarray(output_predictions.byte().cpu().numpy()).resize(input_image.size)
+    r.putpalette(colors)
+    plt.imshow(r)
+    return plt
+
+
+title = "FCN-RESNET101"
+description = "Gradio demo for FCN-RESNET101, Fully-Convolutional Network model with a ResNet-101 backbone. 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='https://arxiv.org/abs/1605.06211'>Fully Convolutional Networks for Semantic Segmentation</a> | <a href='https://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/fcn.py'>Github Repo</a></p>"
+
+gr.Interface(
+    inference, 
+    gr.inputs.Image(type="pil", label="Input"), 
+    gr.outputs.Image(type="plot", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[
+        ["dog.jpg"]
+    ]).launch()