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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
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
# 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 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')
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)
return result
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'],
examples = [[MODEL_BUF["name"], text]],
title="FReE",
description="Financial relations classifier"
)
app.launch(inline=False)
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