all add

app.py

@@ -2,52 +2,76 @@
 python interactive.py
 """
 import torch
-from transformers import AutoTokenizer, BertForSequenceClassification
+from transformers import AutoTokenizer, BertForSequenceClassification, AutoModelForSequenceClassification, AutoConfig
 from transformers import TextClassificationPipeline
 import gradio as gr
 
-model_name = 'momo/KcBERT-base_Hate_speech_Privacy_Detection'
-
-model_name_list = [
-    'momo/KcELECTRA-base_Hate_speech_Privacy_Detection',
-    "momo/KcBERT-base_Hate_speech_Privacy_Detection",
-]
-
-model = BertForSequenceClassification.from_pretrained(
-    model_name,
+# global var
+MODEL_NAME = 'momo/KcBERT-base_Hate_speech_Privacy_Detection'
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(
+    MODEL_NAME,
     num_labels= 15,
     problem_type="multi_label_classification"
 )
+config = AutoConfig.from_pretrained(MODEL_NAME)
+
+MODEL_BUF = {
+    "name": MODEL_NAME,
+    "tokenizer": tokenizer,
+    "model": model,
+    "config": config
+}
+
+def change_model_name(name):
+    MODEL_BUF["name"] = name
+    MODEL_BUF["tokenizer"] = AutoTokenizer.from_pretrained(name)
+    MODEL_BUF["model"] = AutoModelForSequenceClassification.from_pretrained(name)
+    MODEL_BUF["config"] = AutoConfig.from_pretrained(name)
+
 
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+def predict(model_name, text):
+    if model_name != MODEL_BUF["name"]:
+        change_model_name(model_name)
+    
+    tokenizer = MODEL_BUF["tokenizer"]
+    model = MODEL_BUF["model"]
+    config = MODEL_BUF["config"]
 
-unsmile_labels = ["여성/가족","남성","성소수자","인종/국적","연령","지역","종교","기타 혐오","악플/욕설","clean", 'name', 'number', 'address', 'bank', 'person']
-num_labels = len(unsmile_labels)
+    unsmile_labels = ["여성/가족","남성","성소수자","인종/국적","연령","지역","종교","기타 혐오","악플/욕설","clean", 'name', 'number', 'address', 'bank', 'person']
+    num_labels = len(unsmile_labels)
 
-model.config.id2label = {i: label for i, label in zip(range(num_labels), unsmile_labels)}
-model.config.label2id = {label: i for i, label in zip(range(num_labels), unsmile_labels)}
+    model.config.id2label = {i: label for i, label in zip(range(num_labels), unsmile_labels)}
+    model.config.label2id = {label: i for i, label in zip(range(num_labels), unsmile_labels)}
 
-pipe = TextClassificationPipeline(
+    pipe = TextClassificationPipeline(
     model = model,
     tokenizer = tokenizer,
     return_all_scores=True,
     function_to_apply='sigmoid'
     )
-    
-def dectection(input):
-    for result in pipe(input)[0]:
-        return result
 
-#Create a gradio app with a button that calls predict()
-app = gr.Interface(
-    fn=dectection,
-    inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs=['label'], 
-    title="한국어 혐오표현, 개인정보 판별기 (Korean Hate Speech and Privacy Detection)",
-    description="Korean Hate Speech and Privacy Detection."
-    )
-app.launch(share=True)
+    for result in pipe(text)[0]:
 
+        return result
 
+if __name__ == '__main__':
+    text = '읿딴걸 홍볿글 읿랉곭 쌑젩낄고 앉앟있냩'
+
+    model_name_list = [
+        'momo/KcELECTRA-base_Hate_speech_Privacy_Detection',
+        "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', 'plot'], 
+        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(inline=False)