Remove option to flag

app.py

@@ -3,7 +3,6 @@ import re
 import gradio as gr
 import nltk
 import torch
-# from gradio import HuggingFaceDatasetSaver
 from transformers import AutoTokenizer, AutoModelForMaskedLM
 
 
@@ -50,9 +49,7 @@ cjvt/SloBERTa-slo-word-spelling-annotator</a>.</p>
 <p>Given an input text: </p>
 <p>1. The input is segmented into sentences and tokenized using NLTK to prepare the model input.</p>
 <p>2. The model makes predictions on the sentence level. </p>
-<b>The model does not work perfectly and can make mistakes, please check the output!</b> <br />
-If the model is performing poorly for an example, you may click the <i>Flag</i> button which will save the example 
-to a log and help us improve the next iterations of the model. <br/>
+<b>The model does not work perfectly and can make mistakes, please check the output!</b>
 """
 
 demo = gr.Interface(
@@ -68,9 +65,8 @@ demo = gr.Interface(
         show_legend=True,
         color_map={"error": "red"}),
     theme=gr.themes.Base(),
-    # flagging_callback=HuggingFaceDatasetSaver(os.environ["ACCES_TOKEN_FOR_WRITING_MISTAKES"],
-    #                                           "slonspell-space-flags", private=True),
-    description=_description
+    description=_description,
+    allow_flagging="never"  # RIP flagging to HuggingFace dataset
 )
 
 if __name__ == "__main__":
@@ -79,6 +75,5 @@ if __name__ == "__main__":
     model = AutoModelForMaskedLM.from_pretrained(model_name)
     mask_token = tokenizer.mask_token
     DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    print(gr.__version__)
 
     demo.launch()