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app.py

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+import gradio as gr
+import torch
+from PIL import Image
+
+# Load the trained model
+model_path = model_scripted.pt
+net = torch.jit.load(model_path)
+net.eval()
+
+# Define a prediction function
+def classify_image(image)
+    transform = transforms.Compose([
+        transforms.Resize((128, 128)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5,), (0.5,))
+    ])
+    image = transform(image).unsqueeze(0)  # Add batch dimension
+    with torch.no_grad()
+        output = net(image)
+        _, predicted = torch.max(output, 1)
+        return target_classes[predicted.item()]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type=pil),
+    outputs=text,
+    title=Mechanical Tools Classifier
+)
+
+# Launch the app
+interface.launch()

model_scripted.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:856f4e6ff54992e11599e0df1d9b3926a0e536de0f5e3d052e92b41c8945787d
+size 67854