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Hate_speech_Privacy_Detection / app.py

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	"""
	python interactive.py
	"""
	from transformers import AutoTokenizer, AutoModelForSequenceClassification
	from transformers import TextClassificationPipeline
	import gradio as gr

	# global var
	MODEL_NAME = 'momo/KcELECTRA-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"
	)

	MODEL_BUF = {
	"name": MODEL_NAME,
	"tokenizer": tokenizer,
	"model": model,
	}

	def change_model_name(name):
	MODEL_BUF["name"] = name
	MODEL_BUF["tokenizer"] = AutoTokenizer.from_pretrained(name)
	MODEL_BUF["model"] = AutoModelForSequenceClassification.from_pretrained(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"]

	unsmile_labels = ["여성/가족","남성","성소수자","인종/국적","연령","지역","종교","기타 혐오","악플/욕설", "clean", '이름', '전화번호', '주소', '계좌번호', '주민번호']
	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)}

	pipe = TextClassificationPipeline(
	model = model,
	tokenizer = tokenizer,
	return_all_scores=True,
	function_to_apply='sigmoid'
	)
	for result in pipe(text):
	print(result)
	return result

	if __name__ == '__main__':
	exam1 = '경기도 성남시 수정구 태평3동은 우리 동네야!'
	exam2 = '내 핸드폰 번호는 010-3930-8237 이야!'
	exam3 = '아 젠장 너무 짜증난다'

	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='text',
	examples = [
	[MODEL_BUF["name"], exam1],
	[MODEL_BUF["name"], exam2],
	[MODEL_BUF["name"], exam3]
	],
	title="한국어 혐오표현, 개인정보 판별기 (Korean Hate Speech and Privacy Detection)",
	description="Korean Hate Speech and Privacy Detection.\n 15개 label Detection: 여성/가족, 남성, 성소수자, 인종/국적, 연령, 지역, 종교, 기타 혐오, 악플/욕설, clean, '이름', '전화번호', '주소', '계좌번호', '주민번호' \n Current Supported Model:\n1. momo/KcELECTRA-base_Hate_speech_Privacy_Detection\n2. momo/KcBERT-base_Hate_speech_Privacy_Detection."
	)
	app.launch()