app.py

from ultralytics import YOLO
import gradio as gr

# Load the pretrained model directly using YOLOvv8.from_pretrained
model = YOLO("foduucom/plant-leaf-detection-and-classification")

def predict_leaves(image_path):
    """
    Given an image file path, run prediction on it using the YOLOvv8 model.
    The model will automatically save the result if configured.
    """
    # Run the prediction; you can pass additional kwargs as needed (like save=True)
    results = model.predict(source=image_path, save=True)
    
    # Optionally, count the detected leaves using the first result
    count = len(results[0].boxes)
    
    return f"Detected leaves: {count}"

# Build a Gradio Interface for the leaf detection app
iface = gr.Interface(
    fn=predict_leaves,
    inputs=gr.Image(type="filepath"),  # Users can upload an image file
    outputs="text",
    title="Leaf Detection & Classification",
    description="Upload an image to detect and count leaves using the YOLOvv8 model."
)

if __name__ == "__main__":
    iface.launch()