Update app.py

app.py

@@ -2,38 +2,52 @@ import gradio as gr
 from ultralyticsplus import YOLO, render_result
 import cv2
 import torch
-import ultralytics
-import ultralyticsplus
+import time
 
-# Check versions
-print(f"Torch version: {torch.__version__}")
-print(f"Ultralytics version: {ultralytics.__version__}")
-print(f"UltralyticsPlus version: {ultralyticsplus.__version__}")
+# Check GPU availability
+print(f"CUDA available: {torch.cuda.is_available()}")
+print(f"CUDA device count: {torch.cuda.device_count()}")
+if torch.cuda.is_available():
+    print(f"Current device: {torch.cuda.current_device()}")
+    print(f"Device name: {torch.cuda.get_device_name(0)}")
 
-# Load model
-model = YOLO('foduucom/plant-leaf-detection-and-classification')
+# Load model with GPU acceleration
+model = YOLO('foduucom/plant-leaf-detection-and-classification').to('cuda' if torch.cuda.is_available() else 'cpu')
 
 # Model configuration
-model.overrides['conf'] = 0.25  # Confidence threshold
-model.overrides['iou'] = 0.45   # IoU threshold
+model.overrides['conf'] = 0.25
+model.overrides['iou'] = 0.45
 model.overrides['agnostic_nms'] = False
 model.overrides['max_det'] = 1000
 
 def detect_leaves(image):
-    # Convert image format
+    start_time = time.time()
+    
+    # Convert image and check processing time
+    print("Converting image format...")
     img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    print(f"Conversion time: {time.time() - start_time:.2f}s")
     
-    # Perform prediction
-    results = model.predict(img)
+    # Perform prediction with timing
+    print("Starting prediction...")
+    pred_start = time.time()
+    results = model.predict(img, imgsz=640)  # Fixed inference size
+    print(f"Prediction time: {time.time() - pred_start:.2f}s")
     
-    # Get results
+    # Process results
     num_leaves = len(results[0].boxes)
+    print(f"Detected {num_leaves} leaves")
+    
+    # Render results
+    print("Rendering results...")
+    render_start = time.time()
     rendered_img = render_result(model=model, image=img, result=results[0])
+    print(f"Rendering time: {time.time() - render_start:.2f}s")
     
-    # Convert back to RGB for Gradio
+    # Convert back to RGB
     return cv2.cvtColor(rendered_img, cv2.COLOR_BGR2RGB), num_leaves
 
-# Create Gradio interface
+# Create Gradio interface with queue
 interface = gr.Interface(
     fn=detect_leaves,
     inputs=gr.Image(label="Upload Plant Image"),
@@ -41,9 +55,13 @@ interface = gr.Interface(
         gr.Image(label="Detected Leaves"),
         gr.Number(label="Number of Leaves Found")
     ],
-    title="🍃 Plant Leaf Detection & Counting",
-    description="Upload an image of a plant to detect and count its leaves"
+    title="🍃 Plant Leaf Detection",
+    allow_flagging="never"
 )
 
 if __name__ == "__main__":
-    interface.launch(server_port=7860)
+    interface.launch(
+        server_port=7860,
+        share=False,
+        enable_queue=True  # Essential for heavy computations
+    )