Update app.py

app.py

@@ -24,14 +24,15 @@ def yolov8_inference(
     image: gr.inputs.Image = None,
     model_name: gr.inputs.Dropdown = None,
     image_size: gr.inputs.Slider = 1280,
-    conf_threshold: gr.inputs.Slider = 0.25,
+    conf_threshold: gr.inputs.Slider = 0.5,
     iou_threshold: gr.inputs.Slider = 0.45,
 ):
 
-
+    image=image[:, :, ::-1].astype(np.uint8)
     model = YOLO("https://huggingface.co/spaces/devisionx/Amazon_demo/blob/main/amazon.pt")
 
     results = model(image,conf=conf_threshold,iou=iou_threshold ,imgsz=1280)[0]
+    image=image[:, :, ::-1].astype(np.uint8)
     detections = sv.Detections.from_yolov8(results)
     annotated_image = annotatorbbox.annotate(scene=image, detections=detections)
     # annotated_image = annotatormask.annotate(scene=annotated_image, detections=detections)
@@ -44,7 +45,7 @@ def yolov8_inference(
 image_input = gr.inputs.Image()  # Adjust the shape according to your requirements
 
 inputs = [
-    gr.inputs.Image(label="Input Image"),
+    gr.Image(label="Input Image"),
     gr.Slider(
         minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"
     ),
@@ -52,11 +53,11 @@ inputs = [
 ]
 
 outputs = gr.Image(type="filepath", label="Output Image")
-title = "Ultralytics YOLOv8 Segmentation Demo"
+title = "YOLOv8 Segmentation Demo"
 import os
 examples = [
-    ["tu2.png", 0.25, 0.45],
-    ["tu3.jpg", 0.25, 0.45],
+    ["tu2.png", 0.5, 0.45],
+    ["tu3.jpg", 0.5, 0.45],
 ]
 demo_app = gr.Interface(examples=examples,
     fn=yolov8_inference,