Update app.py

app.py

@@ -31,19 +31,19 @@ model = YOLO('linhcuem/cham_diem_yolov8')
 
 ###################################################
 def yolov8_img_inference(
-    image: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider  = 640,
-    conf_threshold: gr.inputs.Slider  = 0.25,
-    iou_threshold: gr.inputs.Slider  = 0.45,
+    image
+    model_path= None,
+    image_size= 640,
+    conf_threshold= 0.25,
+    iou_threshold = 0.45,
 ):
     # model = YOLO(model_path)
-    # model.conf = conf_threshold
-    # model.iou = iou_threshold
-    model.overrides['conf'] = conf_threshold
-    model.overrides['iou'] = iou_threshold
-    model.overrides['agnostic_nms'] = False
-    model.overrides['max_det'] = 1000
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    # model.overrides['conf'] = conf_threshold
+    # model.overrides['iou'] = iou_threshold
+    # model.overrides['agnostic_nms'] = False
+    # model.overrides['max_det'] = 1000
     # image = read_image
     results = model.predict(image)
     render = render_result(model=model, image=image, result=results[0])
@@ -112,9 +112,11 @@ inputs_image = [
     gr.Image(type="pil"),
     gr.Dropdown(["linhcuem/linhcuem/cham_diem_yolov8"], 
                         default="linhcuem/cham_diem_yolov8", label="Model"),
-    gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-    gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+    gr.Slider(maximum=1280, step=32, value = 640 label="Image Size"),
+    gr.Slider(maximum=1.0 , step=0.05, value = 0.25, label="Confidence Threshold"),
+    gr.Slider(maximum=1.0, step=0.05, value = 0.45, label="IOU Threshold"),
+    
+    
 ]
 
 # outputs_image =gr.outputs.Image(type="filepath", label="Output Image")