Update app.py

app.py

@@ -1,67 +1,54 @@
 import gradio as gr
 from ultralyticsplus import YOLO, render_result
 import cv2
-import torch
 import time
 
-# 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 with automatic device detection
+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
-model.overrides['iou'] = 0.45
-model.overrides['agnostic_nms'] = False
-model.overrides['max_det'] = 1000
+# Optimize model configuration
+model.overrides.update({
+    'conf': 0.25,
+    'iou': 0.45,
+    'imgsz': 640,
+    'device': '0' if model.device.type != 'cpu' else 'cpu'
+})
 
 def detect_leaves(image):
     start_time = time.time()
     
-    # Convert image and check processing time
-    print("Converting image format...")
+    # Convert image format
     img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    print(f"Conversion time: {time.time() - start_time:.2f}s")
     
-    # 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")
+    # Predict with optimized settings
+    results = model.predict(
+        source=img,
+        verbose=False,  # Disable unnecessary logging
+        stream=False    # Disable streaming mode
+    )
     
     # 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
+    print(f"Total processing time: {time.time() - start_time:.2f}s")
     return cv2.cvtColor(rendered_img, cv2.COLOR_BGR2RGB), num_leaves
 
-# Create Gradio interface with queue
+# Create lightweight interface
 interface = gr.Interface(
     fn=detect_leaves,
-    inputs=gr.Image(label="Upload Plant Image"),
+    inputs=gr.Image(label="Plant Image"),
     outputs=[
-        gr.Image(label="Detected Leaves"),
-        gr.Number(label="Number of Leaves Found")
+        gr.Image(label="Detection Result", width=600),
+        gr.Number(label="Leaves Count")
     ],
-    title="🍃 Plant Leaf Detection",
+    title="🍃 Leaf Detection",
     allow_flagging="never"
 )
 
 if __name__ == "__main__":
     interface.launch(
         server_port=7860,
-        share=False,
-        enable_queue=True  # Essential for heavy computations
+        show_error=True,
+        enable_queue=True
     )