Update app.py

app.py

@@ -2,48 +2,47 @@ import gradio as gr
 from ultralyticsplus import YOLO, render_result
 import cv2
 import torch
+import ultralytics
+import ultralyticsplus
 
 # Check versions
 print(f"Torch version: {torch.__version__}")
-print(f"Ultralytics version: {YOLO.__version__}")
+print(f"Ultralytics version: {ultralytics.__version__}")
+print(f"UltralyticsPlus version: {ultralyticsplus.__version__}")
 
-# Load model with safe serialization
-def load_model():
-    model = YOLO('foduucom/plant-leaf-detection-and-classification')
-    model.overrides.update({
-        'conf': 0.25,
-        'iou': 0.45,
-        'agnostic_nms': False,
-        'max_det': 1000
-    })
-    return model
+# Load model
+model = YOLO('foduucom/plant-leaf-detection-and-classification')
 
-model = load_model()
+# Model configuration
+model.overrides['conf'] = 0.25  # Confidence threshold
+model.overrides['iou'] = 0.45   # IoU threshold
+model.overrides['agnostic_nms'] = False
+model.overrides['max_det'] = 1000
 
 def detect_leaves(image):
     # Convert image format
-    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    cv2.imwrite('temp_image.jpg', image)
+    img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
     
-    # Predict with numpy fix
-    results = model.predict(source='temp_image.jpg', imgsz=640)
+    # Perform prediction
+    results = model.predict(img)
     
-    # Process results
+    # Get results
     num_leaves = len(results[0].boxes)
-    render = render_result(model=model, image='temp_image.jpg', result=results[0])
+    rendered_img = render_result(model=model, image=img, result=results[0])
     
-    return render, num_leaves
+    # Convert back to RGB for Gradio
+    return cv2.cvtColor(rendered_img, cv2.COLOR_BGR2RGB), num_leaves
 
-# Create simplified interface
+# Create Gradio interface
 interface = gr.Interface(
     fn=detect_leaves,
     inputs=gr.Image(label="Upload Plant Image"),
     outputs=[
-        gr.Image(label="Detection Result"),
-        gr.Number(label="Leaves Count")
+        gr.Image(label="Detected Leaves"),
+        gr.Number(label="Number of Leaves Found")
     ],
-    title="🍃 Plant Leaf Detector",
-    description="Upload a plant image to detect and count leaves"
+    title="🍃 Plant Leaf Detection & Counting",
+    description="Upload an image of a plant to detect and count its leaves"
 )
 
 if __name__ == "__main__":