Upload app.py

app.py

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+import gradio as gr
+import torch
+from sahi.prediction import ObjectPrediction
+from sahi.utils.cv import visualize_object_predictions, read_image
+from ultralyticsplus import YOLO, render_result
+
+
+def yolov8_inference(
+    image,
+    model_path,
+    image_size,
+    conf_threshold,
+    iou_threshold,
+):
+    """
+    YOLOv8 inference function
+    Args:
+        image: Input image
+        model_path: Path to the model
+        image_size: Image size
+        conf_threshold: Confidence threshold
+        iou_threshold: IOU threshold
+    Returns:
+        Rendered image
+    """
+    model = YOLO(f'kadirnar/{model_path}-v8.0')
+    # set model parameters
+    model.overrides['conf'] = conf_threshold  # NMS confidence threshold
+    model.overrides['iou'] = iou_threshold  # NMS IoU threshold
+    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+    model.overrides['max_det'] = 1000  # maximum number of detections per image
+    results = model.predict(image, imgsz=image_size)
+    render = render_result(model=model, image=image, result=results[0])
+    return render
+        
+
+inputs = [
+    gr.Image(type="filepath", label="Input Image"),
+    gr.Dropdown(["yolov8n", "yolov8m", "yolov8l", "yolov8x"], 
+                       value="yolov8m", label="Model"),
+    gr.Slider(minimum=320, maximum=1280, value=640, step=320, 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 = "State-of-the-Art YOLO Models for Object detection"
+
+examples = [['demo_01.jpg', 'yolov8n', 640, 0.25, 0.45], ['demo_02.jpg', 'yolov8l', 640, 0.25, 0.45], ['demo_03.jpg', 'yolov8x', 1280, 0.25, 0.45]]
+demo_app = gr.Interface(
+    fn=yolov8_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+)
+demo_app.launch(debug=True)