Initial Commit

app.py

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+import gradio as gr
+import os
+import torch.nn.functional as F
+import torch
+from torchvision import transforms
+
+model = torch.load("/content/drive/MyDrive/Mask Detection/model.pth", map_location=torch.device("cpu"))
+IMG_SIZE = 224
+MASK_LABEL = ["Mask worn properly.", "Mask not worn properly: nose out", "Mask not worn properly: chin and nose out", "Didn't wear mask."]
+
+transforms_test = transforms.Compose(
+    [
+        transforms.Resize((IMG_SIZE, IMG_SIZE)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+    ]
+)
+
+MASK_LABEL = ["Mask worn properly.", "Mask not worn properly: nose out", "Mask not worn properly: chin and nose out", "Didn't wear mask."]
+
+def predict_image(image):
+    transformed_tensor = torch.unsqueeze(transforms_test(image), 0)
+    logits = model(transformed_tensor)
+    probability = torch.flatten(F.softmax(logits, dim=1)).detach().cpu().numpy()
+    print(probability)
+    labels = {A: B.item() for A, B in zip(MASK_LABEL, probability)}
+    sorted_labels = dict(sorted(labels.items(), key=lambda item: item[1], reverse=True))
+    print(sorted_labels)
+    return sorted_labels
+
+title = "ViT Mask Detection"
+description = "Gradio demo for ViT-16 Mask Image Classification created by <a href='https://github.com/stevenlimcorn'>Steven Limcorn</a>"
+article = "An Application made by stevenlimcorn. Notebook access at: <a href='https://github.com/stevenlimcorn/Mask-Classification'></a>"
+
+demo = gr.Interface(predict_image, 
+                    inputs=gr.Image(label="Input Image", type="pil", source="webcam"),
+                    outputs=gr.Label(), title=title, description=description, article=article
+                    )
+
+demo.launch()

model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c2cee68dc4777f9133fe97ccca2414e66d20f628fdef4efcef99bfac9408259b
+size 343285383

requirements.txt

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+timm==0.4.12
+torch==1.10.1
+gradio
+numpy
+torchvision