Update app.py

app.py

@@ -26,7 +26,6 @@ clf_2 = pipeline("image-classification", model=model_2_path)
 class_names_1 = ['artificial', 'real']
 class_names_2 = ['AI Image', 'Real Image']  # Adjust if the second model has different classes
 
-
 def predict_image(img, confidence_threshold):
     # Ensure the image is a PIL Image
     if not isinstance(img, Image.Image):
@@ -94,10 +93,11 @@ image = gr.Image(label="Image to Analyze", sources=['upload'], type='pil')  # En
 confidence_slider = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Confidence Threshold")
 label = gr.JSON(label="Model Predictions")
 
-gr.Interface(
+# Launch the interface with queuing enabled
+iface = gr.Interface(
     fn=predict_image,
     inputs=[image, confidence_slider],
     outputs=label,
-    title="AI Generated Classification",
-    queue=True  # Enable queuing to handle multiple predictions efficiently
-).launch()
+    title="AI Generated Classification"
+)
+iface.launch(queue=True)