Update app.py

app.py

@@ -4,6 +4,9 @@ from sahi.prediction import ObjectPrediction
 from sahi.utils.cv import visualize_object_predictions, read_image
 from ultralyticsplus import YOLO
 
+from ultralyticsplus import render_result
+import requests
+import cv2
 
 image_path = [['test_images/2a998cfb0901db5f8210.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/2ce19ce0191acb44920b.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
              ['test_images/2daab6ea3310e14eb801.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/4a137deefb14294a7005 (1).jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
@@ -30,32 +33,35 @@ def yolov8_img_inference(
     model.conf = conf_threshold
     model.iou = iou_threshold
     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)
+    # 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']
+    # 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_image = [
@@ -80,4 +86,9 @@ interface_image = gr.Interface(
     theme='huggingface'
 )
 
-interface_image.launch(debug=True, enable_queue=True)
+gr.TabbedInterface(
+    [interface_image],
+    tab_names=['Image inference']
+).queue().launch()
+
+# interface_image.launch(debug=True, enable_queue=True)