Remove flagging

app.py

@@ -11,35 +11,6 @@ model_name = "ellenhp/query2osm-bert-v1"
 tokenizer = AutoTokenizer.from_pretrained(model_name, padding=True)
 model = Hydra.from_pretrained(model_name).to('cpu')
 
-
-class DatasetSaver(gr.FlaggingCallback):
-    inner: Optional[gr.HuggingFaceDatasetSaver] = None
-
-    def __init__(self,  inner):
-        self.inner = inner
-
-    def setup(self, components: list[Component], flagging_dir: str):
-        self.inner.setup(components, flagging_dir)
-
-    def flag(self,
-             flag_data: list[Any],
-             flag_option: str = "",
-             username: str | None = None):
-        flag_data = [flag_data[0], {"label": flag_data[1]['label']}]
-        self.inner.flag(flag_data, flag_option, None)
-
-
-HF_TOKEN = os.getenv('HF_TOKEN')
-if HF_TOKEN is not None:
-    hf_writer = gr.HuggingFaceDatasetSaver(
-        HF_TOKEN, "osm-queries-crowdsourced", True, "data.csv", False)
-else:
-    hf_writer = None
-
-
-flag_callback = DatasetSaver(hf_writer)
-
-
 def predict(input_query):
     with torch.no_grad():
         print(input_query)
@@ -58,9 +29,6 @@ gradio_app = gr.Interface(
     inputs=[textbox],
     outputs=[label],
     title="Query Classification",
-    allow_flagging="manual",
-    flagging_options=["correct classification", "incorrect classification"],
-    flagging_callback=flag_callback,
 )
 
 if __name__ == "__main__":