Update app.py

app.py

@@ -99,14 +99,14 @@ def yolo(size, iou, conf, im):
 
 
 
-in1 = gr.Radio(['640', '1280'], label="Tamaño de la imagen", type='value')
-in2 = gr.Slider(minimum=0, maximum=1, step=0.05, label='NMS IoU threshold')
-in3 = gr.Slider(minimum=0, maximum=1, step=0.05, label='Umbral o  threshold')
-in4 = gr.Image(type='pil', label="Original Image")
-
-out2 = gr.Image(type="pil", label="YOLOv5")
-out3 = gr.Dataframe(label="Cantidad_especie", headers=['Cantidad','Especie'], type="pandas")  
-out4 = gr.JSON(label="JSON")
+in1 = gr.inputs.Radio(['640', '1280'], label="Tamaño de la imagen", type='value')
+in2 = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, label='NMS IoU threshold')
+in3 = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, label='Umbral o  threshold')
+in4 = gr.inputs.Image(type='pil', label="Original Image")
+
+out2 = gr.outputs.Image(type="pil", label="YOLOv5")
+out3 = gr.outputs.Dataframe(label="Cantidad_especie", headers=['Cantidad','Especie'], type="pandas")  
+out4 = gr.outputs.JSON(label="JSON")
 #-------------- Text-----
 title = 'Trampas Barceló'
 description = """
@@ -133,7 +133,7 @@ iface = gr.Interface(yolo,
 					#flagging_callback=hf_writer
                     )
 					
-iface.launch(debug=True)
+iface.launch(enable_queue=True, debug=True)
 
 """For YOLOv5 PyTorch Hub inference with **PIL**, **OpenCV**, **Numpy** or **PyTorch** inputs please see the full [YOLOv5 PyTorch Hub Tutorial](https://github.com/ultralytics/yolov5/issues/36).
 ## Citation