copy

app.py

@@ -1,44 +1,12 @@
 import gradio as gr
-import PIL.Image as Image
 
-from ultralytics import ASSETS, YOLO
+def greet(name, intensity):
+    return "Hello, " + name + "!" * int(intensity)
 
-model = None
-
-
-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
-
-
-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"),
-        gr.Radio(choices=["yolo11n", "yolo11s", "yolo11n-seg", "yolo11s-seg", "yolo11n-pose", "yolo11s-pose"], label="Model Name", value="yolo11n"),
-    ],
-    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"],
-    ],
+demo = gr.Interface(
+    fn=greet,
+    inputs=["text", "slider"],
+    outputs=["text"],
 )
-iface.launch(share=True)
+
+demo.launch()