import gradio as gr
import PIL.Image as Image

from ultralytics import YOLO

model = YOLO("best.pt")


def predict_image(img, conf_threshold, iou_threshold):
    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="Olive Disease Detector",
    description="Upload images for inference.",
    examples=[
        ["examples/healthy.jpg", 0.5, 0.45],
        ["examples/Screenshot_20240916_162647.png", 0.5, 0.4],
        ["examples/unnamed.jpg", 0.5, 0.4],
        ["examples/images (1).jpeg", 0.5, 0.4],
        ["examples/photo_2017_07_30_19_20_37.jpg", 0.5, 0.4],
        ["examples/aculus_2.jpg", 0.25, 0.45],
        ["examples/aculus_1.jpg", 0.25, 0.45],
        ["examples/peacock_2.jpg", 0.25, 0.45],
        ["examples/peacock_3.jpg", 0.25, 0.45],
    ],
)

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