all add

app.py

@@ -64,17 +64,26 @@ if __name__ == '__main__':
         "momo/KcBERT-base_Hate_speech_Privacy_Detection",
     ]
 
-    #Create a gradio app with a button that calls predict()
-    app = gr.Interface(
-        fn=predict,
-        inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs=['label'], 
-        examples = [[MODEL_BUF["name"], text], [MODEL_BUF["name"], "4=🦀 4≠🦀"]],
-        title="한국어 혐오표현, 개인정보 판별기 (Korean Hate Speech and Privacy Detection)",
-        description="Korean Hate Speech and Privacy Detection."
-        )
-    app.launch()
+    # #Create a gradio app with a button that calls predict()
+    # app = gr.Interface(
+    #     fn=predict,
+    #     inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs=['label'], 
+    #     examples = [[MODEL_BUF["name"], text], [MODEL_BUF["name"], "4=🦀 4≠🦀"]],
+    #     title="한국어 혐오표현, 개인정보 판별기 (Korean Hate Speech and Privacy Detection)",
+    #     description="Korean Hate Speech and Privacy Detection."
+    #     )
+    # app.launch()
 
 
+    pipe = TextClassificationPipeline(
+    model = model,
+    tokenizer = tokenizer,
+    return_all_scores=True,
+    function_to_apply='sigmoid'
+    )
+    
+    gr.Interface.from_pipeline(pipe).launch()
+
 
 # # global var
 # MODEL_NAME = 'jason9693/SoongsilBERT-base-beep'