Create app.py

app.py

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+from ultralytics import YOLO
+from PIL import Image
+onn_xmodel=YOLO("/content/best.onnx")
+import gradio as gr
+def detect_fraction(image):
+  results=onn_xmodel.predict([image],stream=False,imgsz=512)
+#results
+  res=results[0].plot()
+  return Image.fromarray(res)
+
+
+demo=gr.Interface(fn=detect_fraction,inputs=gr.Image(type='pil',label='Upload your XRAY'),
+                  outputs=gr.Image(type='pil',label='Results'),
+                  examples=['image1_0_png.rf.99862308d714bff3f9c410adf5ca93ac.jpg',
+                           'image1_1035_png.rf.d7493a5653bc3628f7a1b1ec0eb5de85.jpg',
+                           'image1_1015_png.rf.3b7320c3c40771fa5532bf713a728b83.jpg',
+                           'image1_1084_png.rf.d9da08b77872f6b4282f2700d216b9b3.jpg',
+                           'image1_1311_png.rf.27646e931562cc3823c79f79472b8749.jpg',
+                           'image1_145_png.rf.a69d928d011a93d25a95b7b8380ea25d.jpg'],
+                  #themes=gr.themes.Soft(primary_hue=gr.themes.colors.amber,secondary_hue=gr.themes.colors.blue),
+                  title="Detecting bone fractions")
+if __name__=="__main__":
+    demo.launch(debug=True)