lean

app.py

@@ -1,28 +1,13 @@
 import gradio as gr
-import PIL.Image as Image
 
-from ultralytics import ASSETS, YOLO
 
-model = None
 
+model = None
 
+# ./darknet detect cfg/yolov2.cfg yolov2.weights ~/OneDrive\ -\ One\ Thousand\ GmbH/3.jpg
 def predict_image(img, conf_threshold, iou_threshold, model_name):
     """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
-    model = YOLO(model_name)
-    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
+    pass
 
 
 iface = gr.Interface(
@@ -36,9 +21,5 @@ iface = gr.Interface(
     outputs=gr.Image(type="pil", label="Result"),
     title="Ultralytics Gradio Application 🚀",
     description="Upload images for inference. The Ultralytics YOLO11n model is used by default.",
-    examples=[
-        [ASSETS / "bus.jpg", 0.25, 0.45, "yolo11n.pt"],
-        [ASSETS / "zidane.jpg", 0.25, 0.45, "yolo11n.pt"],
-    ],
 )
 iface.launch(share=True)