app.py

from transformers import (
  EncoderDecoderModel,
  AutoTokenizer
)
import torch
import streamlit as st

PRETRAINED = "raynardj/wenyanwen-chinese-translate-to-ancient"

def inference(text):
    tk_kwargs = dict(
      truncation=True,
      max_length=128,
      padding="max_length",
      return_tensors='pt')
   
    inputs = tokenizer([text,],**tk_kwargs)
    with torch.no_grad():
        return tokenizer.batch_decode(
            model.generate(
            inputs.input_ids,
            attention_mask=inputs.attention_mask,
            num_beams=3,
            bos_token_id=101,
            eos_token_id=tokenizer.sep_token_id,
            pad_token_id=tokenizer.pad_token_id,
        ), skip_special_tokens=True)[0].replace(" ","")

st.title("🪕古朴 ❄️清雅 🌊壮丽")
st.markdown("""
> Translate from Chinese to Ancient Chinese / 还你古朴清雅壮丽的文言文, 
* 一个transformer神经网络的现代文向文言文的自动翻译引擎。训练的代码在[这里](https://github.com/raynardj/yuan)， 喜欢加⭐️
* 最多100个中文字符
""")

@st.cache(allow_output_mutation=True)
def load_model():
    tokenizer = AutoTokenizer.from_pretrained(PRETRAINED)
    model = EncoderDecoderModel.from_pretrained(PRETRAINED)
    return tokenizer, model

tokenizer, model = load_model()

text = st.text_area(value="轻轻地我走了，正如我轻轻地来。我挥一挥衣袖，不带走一片云彩。", label="输入文本")

if st.button("曰"):
    if len(text) > 100:
        st.error("无过百字，若过则当答此言。")
    else:
        st.write(inference(text))