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rom transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
import gradio as gr
from torch.nn import functional as F
import seaborn
import matplotlib
import platform
from transformers.file_utils import ModelOutput
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
# global var
MODEL_NAME = 'yseop/distilbert-base-financial-relation-extraction'
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
}
font_dir = ['./']
for font in fm.findSystemFonts(font_dir):
print(font)
fm.fontManager.addfont(font)
plt.rcParams["font.family"] = 'NanumGothicCoding'
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 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_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])
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__':
text = 'An A-B trust is a joint trust created by a married couple for the purpose of minimizing estate taxes.'
model_name_list = [
'yseop/distilbert-base-financial-relation-extraction'
]
#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]],
title="FReE",
description="Financial relations classifier"
)
app.launch(inline=False)
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