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# coding=utf-8
# author: xusong <xusong28@jd.com>
# time: 2022/8/23 17:08
"""
https://github.com/gradio-app/gradio/blob/299ba1bd1aed8040b3087c06c10fedf75901f91f/gradio/external.py#L484
interface = gr.Interface.load(
"models/bert-base-uncased", api_key=None, alias="fill-mask"
)
## TODO:
1. json_output
2. 百分数换成小数
3.
"""
import gradio as gr
from transformers import FillMaskPipeline
from transformers import BertTokenizer
from kplug.modeling_kplug import KplugForMaskedLM
model_dir = "models/pretrain/"
tokenizer = BertTokenizer.from_pretrained(model_dir)
model = KplugForMaskedLM.from_pretrained(model_dir)
# fill mask
def fill_mask(text):
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
outputs = fill_masker(text)
return {i["token_str"]: i["score"] for i in outputs}
mlm_examples = [
"这款连[MASK]裙真漂亮",
"这是杨[MASK]同款包包，精选优质皮料制作",
"美颜去痘洁面[MASK]",
]
mlm_iface = gr.Interface(
fn=fill_mask,
inputs=gr.inputs.Textbox(
label="输入文本",
default="这款连[MASK]裙真漂亮"),
# outputs=gr.Label(
outputs=gr.outputs.Label(
label="填词",
),
examples=mlm_examples,
title="文本填词（Fill Mask）",
description='基于KPLUG预训练语言模型，'
'<a href=""> K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding'
' and Generation in E-Commerce (Findings of EMNLP 2021) </a>。'
)
if __name__ == "__main__":
mlm_iface.launch()
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