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app.py

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+import gradio as gr
+import sahi
+import torch
+from ultralyticsplus import YOLO, render_model_output
+
+# Images
+sahi.utils.file.download_from_url(
+    "https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/zidane.jpg",
+    "zidane.jpg",
+)
+
+
+model_names = [
+    "yolov8m-seg.pt"
+]
+
+current_model_name = "yolov8m-seg.pt"
+model = YOLO(current_model_name)
+
+
+def yolov8_inference(
+    image: gr.inputs.Image = None,
+    model_name: gr.inputs.Dropdown = None,
+    image_size: gr.inputs.Slider = 640,
+    conf_threshold: gr.inputs.Slider = 0.25,
+    iou_threshold: gr.inputs.Slider = 0.45,
+):
+
+    global model
+    global current_model_name
+    if model_name != current_model_name:
+        model = YOLO(model_name)
+        current_model_name = model_name
+    model.overrides["conf"] = conf_threshold
+    model.overrides["iou"] = iou_threshold
+    results = model.predict(image, imgsz=image_size, return_outputs=True)
+    renders = []
+    for image_results in model.predict(image, imgsz=image_size, return_outputs=True):
+        render = render_model_output(
+            model=model, image=image, model_output=image_results
+        )
+        renders.append(render)
+
+    return renders[0]
+
+
+inputs = [
+    gr.Image(type="filepath", label="Input Image"),
+    gr.Dropdown(
+        model_names,
+        value=current_model_name,
+        label="Model type",
+    ),
+    gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"),
+    gr.Slider(
+        minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"
+    ),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
+]
+
+outputs = gr.Image(type="filepath", label="Output Image")
+title = "Ultralytics YOLOv8 Segmentation Demo"
+article = "<p style='text-align: center'><a href='http://claireye.com.tw'>Claireye</a> | 2023</p>"
+examples = [
+    ["zidane.jpg", "yolov8m-seg.pt", 640, 0.6, 0.45]
+
+]
+demo_app = gr.Interface(
+    fn=yolov8_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+	article = article,
+    examples=examples,
+    cache_examples=True,
+    theme="default",
+)
+demo_app.launch(debug=True, enable_queue=True)

requirements.txt

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+sahi
+torch
+ultralytics==8.0.6
+ultralyticsplus==0.0.9