Save dataset.

app.py

@@ -50,6 +50,9 @@ torch.hub.download_url_to_file('https://i.pinimg.com/originals/c2/ce/e0/c2cee056
 model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True, autoshape=True)  # local model  o google colab
 #model = torch.hub.load('path/to/yolov5', 'custom', path='/content/yolov56.pt', source='local')  # local repo
 
+HF_TOKEN = os.getenv("ZIKA_TOKEN_WRITE")
+hf_writer = gr.HuggingFaceDatasetSaver(hf_foken=HF_TOKEN, dataset_name="demo-iazika-flags")
+
 def getQuantity(string): 
     contador_raw = ''.join(string.split(" ")[3:])
     
@@ -132,16 +135,18 @@ article ="<p style='text-align: center'><a href='https://docs.google.com/present
 examples = [['640',0.45, 0.75,'ejemplo1.jpg'], ['640',0.45, 0.75,'ejemplo2.jpg']]
 
 iface = gr.Interface(yolo, 
-                        inputs=[in1, in2, in3, in4], 
-                        outputs=[out2,out3,out4], title=title, 
-                        description=description, 
-                        article=article, 
-                        examples=examples,
-                        theme="huggingface", 
-                        analytics_enabled=False
-                        ).launch(debug=True)
-
-iface.launch()
+                    inputs=[in1, in2, in3, in4], 
+                    outputs=[out2,out3,out4], title=title, 
+					description=description, 
+					article=article, 
+					examples=examples, 
+					theme="huggingface", 
+					analytics_enabled=False, 
+					allow_flagging="manual",
+					flagging_options=["Correcto", "Incorrecto", "Casi correcto", "Error", "Otro"],
+					flagging_callback=hf_writer)
+
+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).