initial

app.py

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+import cv2
+import gradio as gr
+import torch
+from torchvision.transforms import Resize, ToTensor
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+model = torch.nn.Module()
+model.load_state_dict('model.pt', map_location=device)
+
+resize = Resize((224))
+to_tensor = ToTensor()
+transforms = [to_tensor, resize]
+
+
+def test(image):
+    for transform in transforms:
+        image = transform(image)
+    return [image]
+
+
+interface = gr.Interface(
+    title = "OAM Autoencoder",
+    description = "Select a image",
+    allow_flagging="never",
+    fn = test, 
+    inputs = [
+        gr.Image(label = "x", shape = [224, 224]),
+    ],
+    outputs = [
+        gr.Image(label = "pred"),
+    ],
+    examples = [
+        ["img.jpg"],
+    ]
+)
+
+interface.launch(share = False)

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:414ba68af052135ea99bf7ad45d4bd4f9f8e2a215a95dae4de489a46e11d7f7b
+size 323870496