Update app.py

app.py

@@ -3,15 +3,15 @@ from ultralyticsplus import YOLO, render_result
 import cv2
 import time
 
-# Load model with automatic device detection
+# Load model
 model = YOLO('foduucom/plant-leaf-detection-and-classification')
 
-# Optimize model configuration
+# Model configuration
 model.overrides.update({
     'conf': 0.25,
     'iou': 0.45,
     'imgsz': 640,
-    'device': '0' if model.device.type != 'cpu' else 'cpu'
+    'device': '0' if next(model.model.parameters()).is_cuda else 'cpu'
 })
 
 def detect_leaves(image):
@@ -20,21 +20,17 @@ def detect_leaves(image):
     # Convert image format
     img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
     
-    # Predict with optimized settings
-    results = model.predict(
-        source=img,
-        verbose=False,  # Disable unnecessary logging
-        stream=False    # Disable streaming mode
-    )
+    # Predict
+    results = model.predict(img, verbose=False)
     
     # Process results
     num_leaves = len(results[0].boxes)
     rendered_img = render_result(model=model, image=img, result=results[0])
     
-    print(f"Total processing time: {time.time() - start_time:.2f}s")
+    print(f"Processing time: {time.time() - start_time:.2f}s")
     return cv2.cvtColor(rendered_img, cv2.COLOR_BGR2RGB), num_leaves
 
-# Create lightweight interface
+# Create interface with queue support
 interface = gr.Interface(
     fn=detect_leaves,
     inputs=gr.Image(label="Plant Image"),
@@ -43,12 +39,12 @@ interface = gr.Interface(
         gr.Number(label="Leaves Count")
     ],
     title="🍃 Leaf Detection",
-    allow_flagging="never"
+    flagging_mode="never"  # Updated from allow_flagging
 )
 
 if __name__ == "__main__":
     interface.launch(
         server_port=7860,
-        show_error=True,
-        enable_queue=True
+        share=False,
+        # Removed enable_queue parameter
     )