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
 
 # Images
 torch.hub.download_url_to_file('https://huggingface.co/spaces/foduucom/table-extraction-yolov8/resolve/main/test/table1.jpg', 'document1.jpg')
@@ -28,35 +28,14 @@ def yolov8_inference(
         Rendered image
     """
     model = YOLO(model_path)
-    model.conf = conf_threshold
-    model.iou = iou_threshold
-    results = model.predict(image, imgsz=image_size)
-    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)
-
+    model.overrides['conf'] = conf_threshold
+    model.overrides['iou']= iou_threshold
+    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+    model.overrides['max_det'] = 1000 
     image = read_image(image)
-    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
-    return output_image['image']
+    render = render_result(model=model, image=image, result=results[0])
+    
+    return render
         
 
 inputs = [