Create app.py

app.py

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+import gradio as gr
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# 1. Load your model (Ensure this matches your training architecture)
+# Change 'models.resnet18' if you used a different one
+from torchvision import models
+model = models.resnet18()
+model.fc = torch.nn.Linear(model.fc.in_features, 2) 
+model.load_state_dict(torch.load("tile_model.pt", map_location="cpu"))
+model.eval()
+
+# 2. Define labels based on your dataset folders
+labels = ["Defect", "Normal"]
+
+def predict(img):
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+    ])
+    img = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        prediction = torch.nn.functional.softmax(model(img)[0], dim=0)
+        confidences = {labels[i]: float(prediction[i]) for i in range(2)}
+    return confidences
+
+# 3. Create the Interface
+interface = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="pil"), 
+    outputs=gr.Label(num_top_classes=2),
+    title="Wall/Floor Tile Defect Inspector"
+)
+
+interface.launch()