Update app.py

app.py

@@ -1,12 +1,9 @@
-import gradio as gr
 import spaces
 import supervision as sv
-from PIL import ImageDraw
-import PIL.Image as Image 
 import PIL.Image as Image
 from ultralytics import YOLO
 from huggingface_hub import hf_hub_download, HfApi
-import random
+
 global repo_id
 
 def download_models(model_id):
@@ -19,9 +16,6 @@ def get_model_filenames(repo_id, file_extension = ".pt"):
     model_filenames = [file for file in files if file.endswith(file_extension)]
     return model_filenames
 
-def random_color():
-    return tuple(random.randint(0, 255) for _ in range(3))
-
 repo_id = "atalaydenknalbant/asl-yolo-models"
 model_filenames = get_model_filenames(repo_id)
 print("Model filenames:", model_filenames)
@@ -39,22 +33,13 @@ def yolo_inference(image, model_id, conf_threshold, iou_threshold, max_detection
     model_path = download_models(model_id)
     model = YOLO(model_path)
     results = model(source=image, imgsz=640, iou=iou_threshold, conf=conf_threshold, verbose=False, max_det=max_detection)[0]
-    
     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 = image.copy()  
-    draw = ImageDraw.Draw(annotated_image)
-
-    for label, (x1, y1, x2, y2) in zip(labels, detections.xyxy):  
-        color = random_color() 
-        draw.rectangle([x1, y1, x2, y2], outline=color, width=3)
-        draw.text((x1, y1), label, fill=color)
+    annotated_image = box_annotator.annotate(image, detections=detections, labels=labels)
 
     return annotated_image