Spaces:
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add app
Browse files
app.py
CHANGED
@@ -6,73 +6,97 @@ from transformers import AutoTokenizer, BertForSequenceClassification, AutoModel
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from transformers import TextClassificationPipeline
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import gradio as gr
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MODEL_NAME = 'momo/KcBERT-base_Hate_speech_Privacy_Detection'
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForSequenceClassification.from_pretrained(
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num_labels= 15,
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problem_type="multi_label_classification"
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)
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# config = AutoConfig.from_pretrained(MODEL_NAME)
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MODEL_BUF = {
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"name": MODEL_NAME,
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"tokenizer": tokenizer,
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"model": model,
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# "config": config
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}
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def predict(model_name, text):
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app.launch()
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# # global var
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from transformers import TextClassificationPipeline
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import gradio as gr
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tokenizer = AutoTokenizer.from_pretrained('momo/KcELECTRA-base_Hate_speech_Privacy_Detection')
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model = AutoModelForSequenceClassification.from_pretrained(
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'momo/KcELECTRA-base_Hate_speech_Privacy_Detection',
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num_labels= 15,
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problem_type="multi_label_classification"
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)
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pipe = TextClassificationPipeline(
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model = model,
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tokenizer = tokenizer,
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return_all_scores=True,
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function_to_apply='sigmoid'
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)
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def predict(text):
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return pipe(text)[0]["translation_text"]
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iface = gr.Interface(
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fn=predict,
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inputs='text',
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outputs='text',
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examples=[["Hello! My name is Omar"]]
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)
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iface.launch()
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# # global var
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# MODEL_NAME = 'momo/KcBERT-base_Hate_speech_Privacy_Detection'
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# tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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# model = AutoModelForSequenceClassification.from_pretrained(
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# MODEL_NAME,
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# num_labels= 15,
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# problem_type="multi_label_classification"
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# )
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# MODEL_BUF = {
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# "name": MODEL_NAME,
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# "tokenizer": tokenizer,
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# "model": model,
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# }
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# def change_model_name(name):
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# MODEL_BUF["name"] = name
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# MODEL_BUF["tokenizer"] = AutoTokenizer.from_pretrained(name)
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# MODEL_BUF["model"] = AutoModelForSequenceClassification.from_pretrained(name)
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# def predict(model_name, text):
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# if model_name != MODEL_BUF["name"]:
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# change_model_name(model_name)
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# tokenizer = MODEL_BUF["tokenizer"]
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# model = MODEL_BUF["model"]
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# unsmile_labels = ["์ฌ์ฑ/๊ฐ์กฑ","๋จ์ฑ","์ฑ์์์","์ธ์ข
/๊ตญ์ ","์ฐ๋ น","์ง์ญ","์ข
๊ต","๊ธฐํ ํ์ค","์
ํ/์์ค","clean", 'name', 'number', 'address', 'bank', 'person']
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# num_labels = len(unsmile_labels)
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# model.config.id2label = {i: label for i, label in zip(range(num_labels), unsmile_labels)}
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# model.config.label2id = {label: i for i, label in zip(range(num_labels), unsmile_labels)}
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# pipe = TextClassificationPipeline(
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# model = model,
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# tokenizer = tokenizer,
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# return_all_scores=True,
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# function_to_apply='sigmoid'
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# )
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# for result in pipe(text)[0]:
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# output = result
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# return output
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# if __name__ == '__main__':
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# text = '์ฟ๋ด๊ฑธ ํ๋ณฟ๊ธ ์ฟ๋๊ณญ ์์ ฉ๋๊ณ ์์์๋ฉ'
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# model_name_list = [
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# 'momo/KcELECTRA-base_Hate_speech_Privacy_Detection',
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# "momo/KcBERT-base_Hate_speech_Privacy_Detection",
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# ]
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# #Create a gradio app with a button that calls predict()
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# app = gr.Interface(
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# fn=predict,
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# inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs=['label'],
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# examples = [[MODEL_BUF["name"], text], [MODEL_BUF["name"], "4=๏ฟฝ๏ฟฝ๏ฟฝ๏ฟฝ 4โ ๐ฆ"]],
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# title="ํ๊ตญ์ด ํ์คํํ, ๊ฐ์ธ์ ๋ณด ํ๋ณ๊ธฐ (Korean Hate Speech and Privacy Detection)",
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# description="Korean Hate Speech and Privacy Detection."
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# )
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# app.launch()
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# # global var
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