Update app.py

app.py

@@ -1,18 +1,31 @@
 import gradio as gr
 import cv2
 import numpy as np
+from ultralytics import YOLO
+from PIL import Image
 
+# Load YOLO model
+model = YOLO("best.pt")  # Ensure 'best.pt' is in the same directory
+
+# Define prediction function
 def predict(image):
-  result =model.predict(source =image,imgsz = 640, conf = 0.25)
-  annotated_image =result[0].plot()
-  annotated_image[:, :, ::-1]
-  return annotated_image
+    # Perform YOLO detection
+    result = model.predict(source=image, imgsz=640, conf=0.25)
+    annotated_image = result[0].plot()
+
+    # Convert image from BGR to RGB
+    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
 
+    return annotated_image
+
+# Gradio interface
 app = gr.Interface(
-    fn = predict,
-    inputs = gr.Image(type = "numpy", label ="Upload an Image"),
-    outputs = gr.Image(type = "numpy", label ="Detect a Tooth Cavity"),
-    title = "Tooth Cavity Detection Using YOLO V10 made by Pulastya 😎",
-    description = "Upload an Image ant eh YOLO V10 model will detect and Annotate the Tooth Decay"
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload an Image"),
+    outputs=gr.Image(type="numpy", label="Detected Tooth Cavity"),
+    title="Tooth Cavity Detection Using YOLO V10 by Pulastya 😎",
+    description="Upload a dental X-ray, and the YOLO V10 model will detect and annotate tooth decay."
 )
-app.launch()
+
+if __name__ == "__main__":
+    app.launch()