app.py

import gradio as gr
from ultralyticsplus import YOLO, render_result
import cv2
import torch

# Verify torch version
print(f"Using torch version: {torch.__version__}")

# 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 image format
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    cv2.imwrite('temp_image.jpg', image)
    
    # Perform prediction
    results = model.predict('temp_image.jpg')
    
    # Process results
    num_leaves = len(results[0].boxes)
    render = render_result(model=model, image='temp_image.jpg', result=results[0])
    
    return render, num_leaves

# Create Gradio interface
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():
        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],
        outputs=[output_image, leaf_count]
    )

if __name__ == "__main__":
    demo.launch(server_port=7860, show_error=True)