Update app.py

app.py

@@ -4,15 +4,16 @@ import os
 os.system("pip -qq install yoloxdetect")
 import torch
 import json
+import yoloxdetect2.helpers as yoloxdetectow
+#from yoloxdetect import YoloxDetector
 
-from yoloxdetect import YoloxDetector
 
 # Images
 torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
 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)
+model = yoloxdetectow.YoloxDetector2('kadirnar/yolox_s-v0.1.1', 'configs.yolox_s', device="cpu", hf_model=True)
 
 def yolox_inference(
     image_path: gr.inputs.Image = None,
@@ -49,18 +50,19 @@ def yolox_inference(
       ]
     }
 
-    #print (pred2[3])
-    for i, element in enumerate(pred2[0]):
-      object = {}
-      itemclass = round(pred2[2][i].item())
-      object["classe"] = itemclass
-      object["nome"] = pred2[3][itemclass]
-      object["score"] = pred2[1][i].item()
-      object["x"] = element[0].item()
-      object["y"] = element[1].item()
-      object["w"] = element[2].item()
-      object["h"] = element[3].item()
-      tensor["tensorflow"].append(object)
+    if pred2 is not None:
+        #print (pred2[3])
+        for i, element in enumerate(pred2[0]):
+            object = {}
+            itemclass = round(pred2[2][i].item())
+            object["classe"] = itemclass
+            object["nome"] = pred2[3][itemclass]
+            object["score"] = pred2[1][i].item()
+            object["x"] = element[0].item()
+            object["y"] = element[1].item()
+            object["w"] = element[2].item()
+            object["h"] = element[3].item()
+            tensor["tensorflow"].append(object)
   
     #print(tensor)