app.py

from ultralytics import YOLO
from PIL import Image
import gradio as gr
from huggingface_hub import snapshot_download
import os


def load_model(repo_id):
    download_dir = snapshot_download(repo_id)
    print(download_dir)
    path  = os.path.join(download_dir, "best_int8_openvino_model")
    print(path)
    detection_model = YOLO(path, task='detect')
    return detection_model


def predict(pilimg):

    source = pilimg
    # x = np.asarray(pilimg)
    # print(x.shape)
    result = detection_model.predict(source, conf=0.6, iou=0.6)
    img_bgr = result[0].plot()
    out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # RGB-order PIL image
    
    return out_pilimg


REPO_ID = "SpadeAce888/Classification_sweets_chocolate_wafer"
detection_model = load_model(REPO_ID)

title = "Sweet or Chocolate Wafer?"
interface = gr.Interface(
                fn=predict,
                inputs=gr.Image(type="pil", label="Input Image"),
                outputs=gr.Image(type="pil", label="Object Detected Image"),
                title=title,
             )

# Launch the interface
interface.launch(share=True)