Update app.py

app.py

@@ -2,12 +2,8 @@ import gradio as gr
 import torch
 from sahi.prediction import ObjectPrediction
 from sahi.utils.cv import visualize_object_predictions, read_image
-from ultralytics import YOLO
+from ultralyticsplus import YOLO
 
-# Images
-# torch.hub.download_url_to_file('https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg', 'highway.jpg')
-# torch.hub.download_url_to_file('https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg', 'highway1.jpg')
-# torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
 
 def yolov8_inference(
     image: gr.inputs.Image = None,
@@ -28,8 +24,11 @@ def yolov8_inference(
         Rendered image
     """
     model = YOLO(f'{model_path}.pt')
-    model.conf = conf_threshold
-    model.iou = iou_threshold
+    # 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, return_outputs=True)
     object_prediction_list = []
     for _, image_results in enumerate(results):
@@ -62,7 +61,7 @@ def yolov8_inference(
 inputs = [
     gr.inputs.Image(type="filepath", label="Input Image"),
     gr.inputs.Dropdown(["yolov8n", "yolov8m", "yolov8l", "yolov8x"], 
-                       default="kyolov8m", label="Model"),
+                       default="yolov8m", label="Model"),
     gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
@@ -71,14 +70,14 @@ inputs = [
 outputs = gr.outputs.Image(type="filepath", label="Output Image")
 title = "State-of-the-Art YOLO Models for Object detection"
 
-# examples = [['highway.jpg', 'kadirnar/yolov8m-v8.0', 640, 0.25, 0.45], ['highway1.jpg', 'kadirnar/yolov8l-v8.0', 640, 0.25, 0.45], ['small-vehicles1.jpeg', 'kadirnar/yolov8x-v8.0', 1280, 0.25, 0.45]]
+# 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,
+    examples=examples,
+    cache_examples=True,
     theme='huggingface',
 )
 demo_app.launch(debug=True, enable_queue=True)