app.py

import gradio as gr
import PIL.Image as Image
from ultralytics import ASSETS, YOLO


from PIL import Image
import gradio as gr

model_path = '/home/mussie/Videos/mussie_doc/Model_testing_for_uniliver/new_data_improved_object_detector.pt'

model = YOLO(model_path)


def predict_image(img, conf_threshold, iou_threshold):
    """Predicts and plots labeled objects in an image using YOLOv8 model with adjustable confidence and IOU thresholds."""
    # Convert the input image to grayscale
    img = img.convert('L')

    results = model.predict(
        source=img,
        conf=conf_threshold,
        iou=iou_threshold,
        show_labels=True,
        show_conf=True,
        imgsz=640,
    )

    for r in results:
        im_array = r.plot()
        im = Image.fromarray(im_array[..., ::-1])

    return im


iface = gr.Interface(
    
    fn=predict_image,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
    ],
    outputs=gr.Image(type="pil", label="Result"),
    title="Ultralytics Gradio",
    description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.",
    # examples=[
    #     [ASSETS / "bus.jpg", 0.25, 0.45],
    #     [ASSETS / "zidane.jpg", 0.25, 0.45],
    # ],
)

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