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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)
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