app.py

"""
python interactive.py
"""
import torch
from transformers import AutoTokenizer, BertForSequenceClassification, AutoModelForSequenceClassification, AutoConfig
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]["translation_text"]

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)
# 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", '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)}

#     pipe = TextClassificationPipeline(
#     model = model,
#     tokenizer = tokenizer,
#     return_all_scores=True,
#     function_to_apply='sigmoid'
#     )

#     for result in pipe(text)[0]:
#         output = result

#     return output

# 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'], 
#         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()
    

# # global var
# MODEL_NAME = 'jason9693/SoongsilBERT-base-beep'
# tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
# model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
# 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)


# 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"]

#     tokenized_text = tokenizer([text], return_tensors='pt')

#     input_tokens = tokenizer.convert_ids_to_tokens(tokenized_text.input_ids[0])
#     try:
#         input_tokens = util.bytetokens_to_unicdode(input_tokens) if config.model_type in ['roberta', 'gpt', 'gpt2'] else input_tokens
#     except KeyError:
#         input_tokens = input_tokens

#     model.eval()
#     output, attention = model(**tokenized_text, output_attentions=True, return_dict=False)
#     output = F.softmax(output, dim=-1)
#     result = {}
    
#     for idx, label in enumerate(output[0].detach().numpy()):
#         result[config.id2label[idx]] = float(label)

#     fig = visualize_attention(input_tokens, attention[0][0].detach().numpy())
#     return result, fig#.logits.detach()#.numpy()#, output.attentions.detach().numpy()


# if __name__ == '__main__':
#     text = '읿딴걸 홍볿글 읿랉곭 쌑젩낄고 앉앟있냩'

#     model_name_list = [
#         'jason9693/SoongsilBERT-base-beep',
#         "beomi/beep-klue-roberta-base-hate",
#         "beomi/beep-koelectra-base-v3-discriminator-hate",
#         "beomi/beep-KcELECTRA-base-hate"
#     ]

#     #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 혐오성 발화 분류기 (Korean Hate Speech Classifier)",
#         description="Korean Hate Speech Classifier with Several Pretrained LM\nCurrent Supported Model:\n1. SoongsilBERT\n2. KcBERT(+KLUE)\n3. KcELECTRA\n4.KoELECTRA."
#         )
#     app.launch(inline=False)