update model path

app.py

@@ -1,11 +1,12 @@
 import gradio as gr
 from ultralyticsplus import YOLO
+from ultralytics.yolo.utils.plotting import Annotator
 
 def yolov8_inference(
-    image: gr.inputs.Image = None,
+    image: gr.Image = None,
     model_path = "eeshawn11/naruto_hand_seal_detection",
-    conf_threshold: gr.inputs.Slider = 0.50,
-    iou_threshold: gr.inputs.Slider = 0.45,
+    conf_threshold: gr.Slider = 0.50,
+    iou_threshold: gr.Slider = 0.45,
 ):
     """
     YOLOv8 inference function
@@ -17,11 +18,13 @@ def yolov8_inference(
     Returns:
         Rendered image
     """
-    model = YOLO(model_path)
+    # model = YOLO(model_path)
+    model = YOLO("ultralyticsplus/yolov8s")
     model.conf = conf_threshold
     model.iou = iou_threshold
     results = model.predict(image, return_outputs=True)
     object_prediction_list = []
+    annotator = Annotator(image)
     for _, image_results in enumerate(results):
         if len(image_results)!=0:
             image_predictions_in_xyxy_format = image_results['det']
@@ -36,27 +39,30 @@ def yolov8_inference(
                 score = pred[4]
                 category_name = model.model.names[int(pred[5])]
                 category_id = pred[5]
-                object_prediction = ObjectPrediction(
-                    bbox=bbox,
-                    category_id=int(category_id),
-                    score=score,
-                    category_name=category_name,
-                )
-                object_prediction_list.append(object_prediction)
+                annotator.box_label(bbox, f"{category_name} {score}")
+                # object_prediction = ObjectPrediction(
+                #     bbox=bbox,
+                #     category_id=int(category_id),
+                #     score=score,
+                #     category_name=category_name,
+                # )
+                # object_prediction_list.append(object_prediction)
 
-    image = read_image(image)
-    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
-    return output_image['image']
+    # image = read_image(image)
+    # output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
+    
+    # return output_image['image']
+    return annotator.result()
         
 
 inputs = [
     # gr.inputs.Image(type="filepath", label="Input Image"),
     gr.Image(source="upload", type="pil", label="Image Upload", interactive=True),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.5, step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.5, 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.outputs.Image(type="filepath", label="Output Image")
+outputs = gr.Image(type="filepath", label="Output Image")
 title = "Naruto Hand Seal Detection with YOLOv8"
 
 myapp = gr.Interface(