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| from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig | |
| import gradio as gr | |
| from torch.nn import functional as F | |
| import seaborn | |
| import matplotlib | |
| import platform | |
| if platform.system() == "Darwin": | |
| print("MacOS") | |
| matplotlib.use('Agg') | |
| import matplotlib.pyplot as plt | |
| import io | |
| from PIL import Image | |
| import matplotlib.font_manager as fm | |
| import util | |
| font_path = r'NanumGothicCoding.ttf' | |
| fontprop = fm.FontProperties(fname=font_path, size=18) | |
| plt.rcParams["font.family"] = 'NanumGothic' | |
| def visualize_attention(sent, attention_matrix, n_words=10): | |
| def draw(data, x, y, ax): | |
| seaborn.heatmap(data, | |
| xticklabels=x, square=True, yticklabels=y, vmin=0.0, vmax=1.0, | |
| cbar=False, ax=ax) | |
| # make plt figure with 1x6 subplots | |
| fig = plt.figure(figsize=(16, 8)) | |
| # fig.subplots_adjust(hspace=0.7, wspace=0.2) | |
| for i, layer in enumerate(range(1, 12, 2)): | |
| ax = fig.add_subplot(2, 3, i+1) | |
| ax.set_title("Layer {}".format(layer)) | |
| draw(attention_matrix[layer], sent if layer > 6 else [], sent if layer in [1,7] else [], ax=ax) | |
| fig.tight_layout() | |
| plt.close() | |
| return fig | |
| def predict(model_name, text): | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| config = AutoConfig.from_pretrained(model_name) | |
| print(config.id2label) | |
| tokenized_text = tokenizer([text], return_tensors='pt') | |
| input_tokens = tokenizer.convert_ids_to_tokens(tokenized_text.input_ids[0]) | |
| print(input_tokens) | |
| input_tokens = util.bytetokens_to_unicdode(input_tokens) if config.model_type in ['roberta', 'gpt', 'gpt2'] else 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__': | |
| model_name = 'jason9693/SoongsilBERT-beep-base' | |
| text = '읿딴걸 홍볿글 읿랉곭 쌑젩낄고 앉앟있냩' | |
| # output = predict(model_name, text) | |
| # print(output) | |
| model_name_list = [ | |
| 'jason9693/SoongsilBERT-beep-base' | |
| ] | |
| #Create a gradio app with a button that calls predict() | |
| app = gr.Interface( | |
| fn=predict, | |
| server_port=26899, | |
| server_name='0.0.0.0', | |
| inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs=['label', 'plot'], | |
| examples = [[model_name, text]], | |
| title="한국어 혐오성 발화 분류기 (Korean Hate Speech Classifier)", | |
| description="Korean Hate Speech Classifier with Several Pretrained LM\nCurrent Supported Model:\n1. SoongsilBERT" | |
| ) | |
| app.launch(inline=False) | |