Update app.py

app.py

@@ -12,6 +12,8 @@ torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master
 torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
 torch.hub.download_url_to_file('https://raw.githubusercontent.com/Megvii-BaseDetection/YOLOX/main/assets/dog.jpg', 'dog.jpg')
 
+model = YoloxDetector('kadirnar/yolox_s-v0.1.1', 'configs.yolox_s', device="cpu", hf_model=True)
+
 def yolox_inference(
     image_path: gr.inputs.Image = None,
     model_path: gr.inputs.Dropdown = 'kadirnar/yolox_s-v0.1.1',
@@ -29,15 +31,17 @@ def yolox_inference(
         Rendered image
     """
 
-    model = YoloxDetector(model_path, config_path=config_path, device="cpu", hf_model=True)
+    #model = YoloxDetector(model_path, config_path=config_path, device="cpu", hf_model=True)
     #pred = model.predict(image_path=image_path, image_size=image_size)
-    model.torchyolo = True
-    pred2 = model.predict(image_path=image_path, image_size=image_size)
-    #text = "Ola"
-    #print (vars(model))
-    #print (pred2[0])
-    #print (pred2[1])
-    #print (pred2[2])
+    pred2 = []
+    if model :
+        model.torchyolo = True
+        pred2 = model.predict(image_path=image_path, image_size=image_size)
+        #text = "Ola"
+        #print (vars(model))
+        #print (pred2[0])
+        #print (pred2[1])
+        #print (pred2[2])
 
     
     tensor = {