Update app.py

app.py

@@ -24,6 +24,7 @@ import uuid
 import filelock
 import csv
 
+# TODO move to separate file for cleaner code
 class HuggingFaceDatasetSaver(FlaggingCallback):
     """
     A callback that saves each flagged sample (both the input and output data) to a HuggingFace dataset.
@@ -311,6 +312,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 hf_writer = HuggingFaceDatasetSaver(hf_token, "crowdsourced-sentiment_analysis")
 
 # Prepare model
+# TODO convert the model to ONNX
 tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base", token=hf_token)
 model = AutoModelForSequenceClassification.from_pretrained("arcleife/roberta-sentiment-id", num_labels=3, token=hf_token).to(device)
 
@@ -343,20 +345,4 @@ io = gr.Interface(fn=text_classification,
                   # flagging_callback=hf_writer
                  )
 
-io.launch(inline=False)
-
-# with gr.Blocks() as main_interface:
-#     gr.LoginButton()
-    
-#     gr.Markdown("# 人格否定検知")
-#     gr.Markdown("**Input**にテキストを入力し、**実行**をクリックしてください。")
-#     with gr.Row():
-#         with gr.Column():
-#             inp = gr.Textbox(placeholder="テキストを入力してください。", label="Input", lines=4)
-#         with gr.Column():
-#             out = gr.Label(label="Result")
-#             flag = gr.Button("Flag")
-#     btn = gr.Button("実行")
-#     btn.click(fn=text_classification, inputs=inp, outputs=out)
-
-# main_interface.launch()
+io.launch(inline=False)