add initial files

app.py

@@ -0,0 +1,32 @@
+import torch
+import gradio as gr
+import json
+from torchvision import transforms
+import torch.nn.functional as F
+
+TORCHSCRIPT_PATH = "res/screensim-resnet-uda+web350k.torchscript"
+IMG_SIZE = (256, 128)
+
+model = torch.jit.load(TORCHSCRIPT_PATH)
+    
+img_transforms = transforms.Compose([
+    transforms.Resize(IMG_SIZE),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+    
+def predict(img1, img2, thresh=0.35):
+    img_input1 = img_transforms(img1).unsqueeze(0)
+    img_input2 = img_transforms(img2).unsqueeze(0)
+    diff = torch.linalg.norm(model(img_input1) - model(img_input2))
+
+    return "{:.3f}".format(diff), "same screen" if float(diff) < thresh else "different screens"
+    
+example_imgs = [
+    ["res/example_pair1.jpg", "res/example_pair2.jpg", 0.35],
+    ["res/example_pair1.jpg", "res/example.jpg", 0.35]
+]
+
+interface = gr.Interface(fn=predict, inputs=[gr.Image(type="pil"), gr.Image(type="pil"), gr.Slider(0.2, 0.5, step=0.05, value=0.35)], outputs=["text", "text"], examples=example_imgs)
+
+interface.launch()