add app

app.py

@@ -6,34 +6,6 @@ from transformers import AutoTokenizer, BertForSequenceClassification, AutoModel
 from transformers import TextClassificationPipeline
 import gradio as gr
 
-# tokenizer = AutoTokenizer.from_pretrained('momo/KcELECTRA-base_Hate_speech_Privacy_Detection')
-# model = AutoModelForSequenceClassification.from_pretrained(
-#     'momo/KcELECTRA-base_Hate_speech_Privacy_Detection',
-#     num_labels= 15,
-#     problem_type="multi_label_classification"
-# )
-
-# pipe = TextClassificationPipeline(
-# model = model,
-# tokenizer = tokenizer,
-# return_all_scores=True,
-# function_to_apply='sigmoid'
-# )
-
-# def predict(text):
-#   return pipe(text)[0]
-
-# iface = gr.Interface(
-#   fn=predict, 
-#   inputs='text',
-#   outputs='text',
-#   examples=[["Hello! My name is Omar"]]
-# )
-
-# iface.launch()
-
-
-
 # global var
 MODEL_NAME = 'momo/KcBERT-base_Hate_speech_Privacy_Detection'
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
@@ -87,7 +59,7 @@ if __name__ == '__main__':
     #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=['text'], 
+        inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs='text', 
         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."