add app

app.py

@@ -0,0 +1,122 @@
+"""
+python interactive.py
+"""
+import torch
+from transformers import AutoTokenizer, BertForSequenceClassification
+from transformers import TextClassificationPipeline
+import gradio as gr
+
+model_name = 'momo/KcELECTRA-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,
+    num_labels=15,
+    problem_type="multi_label_classification"
+).cuda()
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model.cuda()
+
+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,
+    device=0,
+    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(inline=False)
+
+
+
+
+
+# # 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)