Update app.py

app.py

@@ -1,7 +1,30 @@
+
 import gradio as gr
 from ultralytics import YOLO
 import spaces
-import cv2
+import supervision as sv
+
+box_annotator = sv.BoxAnnotator()
+
+category_dict = {
+    0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
+    6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
+    11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
+    16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
+    22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
+    27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
+    32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
+    36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
+    40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
+    46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
+    51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
+    56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
+    61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
+    67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
+    72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
+    77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
+}
+
 
 def attempt_download_from_hub(repo_id, hf_token=None):
     # https://github.com/fcakyon/yolov5-pip/blob/main/yolov5/utils/downloads.py
@@ -21,15 +44,23 @@ def attempt_download_from_hub(repo_id, hf_token=None):
     except (RepositoryNotFoundError, HFValidationError):
         return None
 
+
 @spaces.GPU(duration=200)
 def LeYOLO_inference(image, model_id, image_size, conf_threshold, iou_threshold):
     MODEL_PATH = attempt_download_from_hub(model_id)
     model = model = YOLO(MODEL_PATH)
     results = model(source=image, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0]
-    annotated_image = results[0].plot()
-    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR)
+    detections = sv.Detections.from_ultralytics(results)
+    
+    labels = [
+        f"{category_dict[class_id]} {confidence:.2f}"
+        for class_id, confidence in zip(detections.class_id, detections.confidence)
+    ]
+    annotated_image = box_annotator.annotate(image, detections=detections, labels=labels)
+
     return annotated_image
     
+
 def app():
     with gr.Blocks():
         with gr.Row():