Update app.py

app.py

@@ -2,7 +2,7 @@ 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
+from ultralyticsplus import YOLO, render_result
 
 
 def yolov8_inference(
@@ -23,39 +23,15 @@ def yolov8_inference(
     Returns:
         Rendered image
     """
-    model = YOLO(f'{model_path}.pt')
+    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, return_outputs=True)
-    object_prediction_list = []
-    for _, image_results in enumerate(results):
-        if len(image_results)!=0:
-            image_predictions_in_xyxy_format = image_results['det']
-            for pred in image_predictions_in_xyxy_format:
-                x1, y1, x2, y2 = (
-                    int(pred[0]),
-                    int(pred[1]),
-                    int(pred[2]),
-                    int(pred[3]),
-                )
-                bbox = [x1, y1, x2, y2]
-                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)
-
-    image = read_image(image)
-    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
-    return output_image['image']
+    results = model.predict(image)
+    render = render_result(model=model, image=image, result=results[0])
+    return render
         
 
 inputs = [