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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoConfig
import re
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/FNP_T5_D2T_complete'
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.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"] = AutoModelForSeq2SeqLM.from_pretrained(name)
MODEL_BUF["config"] = AutoConfig.from_pretrained(name)
def generate(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"]
model.eval()
input_ids = tokenizer.encode("AFA:{}".format(text), return_tensors="pt")
outputs = model.generate(input_ids, max_length=200, num_beams=2, repetition_penalty=2.5, top_k=50, top_p=0.98, length_penalty=1.0, early_stopping=True)
output = tokenizer.decode(outputs[0])
#return ".".join(output.split(".")[:-1]) + "."
sent = ".".join(output.split(".")[:-1]) + "."
return re.match(r'<pad> ([^<>]*)', sent).group(1)
output_text = gr.outputs.Textbox()
if __name__ == '__main__':
text = ['Group profit | valIs | € 115.7 million && € 115.7 million | dTime | in 2019']
example = [['Net income | valIs | $48 million && $48 million | diGeo | in France && Net income | jPose | the interest rate && the interest rate | valIs | 0.6%'],
['The retirement age | incBy | 7 years && 7 years | cTime | 2018 && The retirement age | jpose | life expectancy && life expectancy | incBy | 10 years'],
['sales | incBy | € 115.7 million && € 115.7 million | dTime | in 2019 && € 115.7 million | diGeo | Europe']]
model_name_list = [
'yseop/FNP_T5_D2T_complete',
'yseop/FNP_T5_D2T_simple'
]
app = gr.Interface(
fn=generate,
inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs=output_text,
examples = [[MODEL_BUF["name"], text]],
title="FTG",
description="Financial Text Generation"
)
app.launch(inline=False)
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