Update app.py

app.py

@@ -1,46 +1,51 @@
 import gradio as gr
 from ultralyticsplus import YOLO, render_result
 import cv2
+import torch
 
-# Load the YOLO model
-model = YOLO('foduucom/plant-leaf-detection-and-classification')
+# Verify torch version
+print(f"Using torch version: {torch.__version__}")
 
-# Set model parameters
-model.overrides['conf'] = 0.25  # NMS confidence threshold
-model.overrides['iou'] = 0.45  # NMS IoU threshold
-model.overrides['agnostic_nms'] = False  # NMS class-agnostic
-model.overrides['max_det'] = 1000  # maximum detections per image
+# Load model with compatibility fix
+def load_model():
+    try:
+        model = YOLO('foduucom/plant-leaf-detection-and-classification')
+        model.overrides['conf'] = 0.25
+        model.overrides['iou'] = 0.45
+        model.overrides['agnostic_nms'] = False
+        model.overrides['max_det'] = 1000
+        return model
+    except Exception as e:
+        raise RuntimeError("Error loading model. Please check the requirements versions.") from e
+
+model = load_model()
 
 def detect_leaves(image):
-    # Convert from Gradio's numpy array to image file
+    # Convert image format
     image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
     cv2.imwrite('temp_image.jpg', image)
     
     # Perform prediction
     results = model.predict('temp_image.jpg')
     
-    # Count number of leaves detected
+    # Process results
     num_leaves = len(results[0].boxes)
-    
-    # Render results on image
     render = render_result(model=model, image='temp_image.jpg', result=results[0])
     
     return render, num_leaves
 
 # Create Gradio interface
-with gr.Blocks(title="Leaf Detection & Classification") as demo:
-    gr.Markdown("# 🍃 Plant Leaf Detection & Classification")
-    gr.Markdown("Upload a plant image to detect and count leaves")
+with gr.Blocks(theme=gr.themes.Soft(), title="Leaf Detection") as demo:
+    gr.Markdown("## 🍃 Plant Leaf Detection & Counter")
+    gr.Markdown("Upload a plant image to analyze leaf count and species")
     
     with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(label="Upload Plant Image")
-            submit_btn = gr.Button("Detect Leaves")
-        
-        with gr.Column():
-            output_image = gr.Image(label="Detection Results")
-            leaf_count = gr.Number(label="Number of Leaves Detected")
+        input_image = gr.Image(label="Input Image", type="numpy")
+        output_image = gr.Image(label="Detected Leaves", interactive=False)
+    
+    leaf_count = gr.Number(label="Total Leaves Detected", precision=0)
     
+    submit_btn = gr.Button("Analyze Image", variant="primary")
     submit_btn.click(
         fn=detect_leaves,
         inputs=[input_image],
@@ -48,4 +53,4 @@ with gr.Blocks(title="Leaf Detection & Classification") as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch(server_port=7860, share=False)
+    demo.launch(server_port=7860, show_error=True)