adding yoloxl

app.py

@@ -21,12 +21,14 @@ COLORS = [
 ]
 
 def make_prediction(img, feature_extractor, model, model_name):
-    inputs = feature_extractor(img, return_tensors="pt")
-    outputs = model(**inputs)
     if 'yolox' in model_name:
+        inputs = feature_extractor(img)
+        outputs = model(**inputs)
         processed_outputs = {}
         processed_outputs['boxes'], processed_outputs['labels'], processed_outputs['scores'] = model.decode_predictions(outputs)[0]
     else:
+        inputs = feature_extractor(img, return_tensors="pt")
+        outputs = model(**inputs)
         img_size = torch.tensor([tuple(reversed(img.size))])
         processed_outputs = feature_extractor.post_process(outputs, img_size)[0]
     return processed_outputs
@@ -112,7 +114,7 @@ demo = gr.Blocks(css=css)
 
 with demo:
     gr.Markdown(title)
-    gr.Markdown(description)
+    #gr.Markdown(description)
     options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
     slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.5,label='Prediction Threshold')