Update app.py

app.py

@@ -1,13 +1,14 @@
 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
-import glob 
-model = YOLO('Dental_model.pt')  # Replace 'yolov8n.pt' with your model file if using a custom one
-pic_files=glob.glob('*.jpg')
-names=model.model.names
+from ultralytics.utils.plotting import Annotator, colors
+import glob
+
+# Load model and data
+model = YOLO('Dental_model.pt')
+pic_files = glob.glob('*.jpg')
+names = model.model.names
+
 def detect_objects(image):
     image1 = image.copy()
     results = model.predict(image)
@@ -15,24 +16,36 @@ def detect_objects(image):
     boxes = results[0].boxes.xyxy.cpu()
     annotator = Annotator(image, line_width=3)
     annotator1 = Annotator(image1, line_width=3)
-    cloud_sdk='<iframe src="https://cloudhand-sdk-xlsh.vercel.app/" width="100%" height="600px" style="border:none;"></iframe>'
+    cloud_sdk = '<iframe src="https://cloudhand-sdk-xlsh.vercel.app/" width="100%" height="600px" style="border:none;"></iframe>'
+    
     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()),cloud_sdk
 
+    return Image.fromarray(annotator.result()), Image.fromarray(annotator1.result())
+
+# Gradio Blocks App
+with gr.Blocks() as demo:
+    gr.Markdown("## Dental Analysis")
+    gr.Markdown("Analyze your Dental XRAY image with our AI object Detection model")
 
-# Gradio Interface
-title = "YOLOv8 Object Detection"
-description = "Upload an image to detect objects using a YOLOv8 model."
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Image")
+            run_button = gr.Button("Run Detection")
+            example_images = gr.Examples(
+                examples=pic_files,
+                inputs=image_input,
+                label="Examples"
+            )
+        with gr.Column():
+            image_output_1 = gr.Image(type="pil", label="Dental Analysis")
+            image_output_2 = gr.Image(type="pil", label="Without Labels")
+            html_output = gr.HTML(cloud_sdk)
 
-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"),
-                            gr.HTML() ],
-                        examples=pic_files)
+    run_button.click(fn=detect_objects, 
+                     inputs=image_input, 
+                     outputs=[image_output_1, image_output_2])
 
-if __name__=="__main__":
-    gradio_app.launch()
+if __name__ == "__main__":
+    demo.launch()