Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from torchvision import transforms
+from model import load_model, class_names
+import torch
+
+model = load_model()
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+def predict(image):
+    img = image.convert("RGB")
+    tensor = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        output = model(tensor)
+        probs = torch.softmax(output, dim=1).squeeze()
+    return {class_names[i]: float(probs[i]) for i in range(len(class_names))}
+
+demo = gr.Interface(fn=predict,
+                    inputs=gr.Image(type="pil"),
+                    outputs=gr.Label(num_top_classes=2),
+                    title="Fracture X-Ray Classifier",
+                    description="Upload an X-ray image to detect fractures.")
+
+demo.launch()