Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+from ultralytics import YOLO
+from PIL import Image
+from ultralytics.utils.plotting import Annotator,colors
+
+
+model = YOLO('Dental_model.pt')  # Replace 'yolov8n.pt' with your model file if using a custom one
+
+names=model.model.names
+def detect_objects(image):
+    image1=image.copy()
+    results = model.predict(image)
+    whole=results[0].plot()
+    classes=results[0].boxes.cls.cpu().tolist()
+    boxes=results[0].boxes.xyxy.cpu()
+    annotator = Annotator(image, line_width=3)
+    annotator1=Annotator(image1, line_width=3)
+    for box,cls in zip(boxes,classes):
+        annotator.box_label(box, label=names[int(cls)], color=colors(int(cls)))
+        annotator1.box_label(box, label=None, color=colors(int(cls)))
+    return Image.fromarray(annotator.result()),Image.fromarray(annotator1.result())
+
+# Gradio Interface
+title = "YOLOv8 Object Detection"
+description = "Upload an image to detect objects using a YOLOv8 model."
+
+gradio_app =gr.Interface(fn=detect_objects, 
+                     inputs=gr.Image(type="pil"), 
+                     outputs=[gr.Image(type='pil', label="Dental Analysis"),
+                              gr.Image(type='pil', label="Dental Analysis")])
+
+if __name__=="__main__":
+    gradio_app.launch(server_name="0.0.0.0", server_port=7861, share=True, show_error=False)