# !/usr/bin/python
# -*- coding: utf-8 -*-
# @time    : 2021/2/29 21:41
# @author  : Mo
# @function: transformers直接加载bert类模型测试


import traceback
import time
import sys
import os
os.environ["MACRO_CORRECT_FLAG_CSC_TOKEN"] = "1"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
os.environ["USE_TORCH"] = "1"

from macro_correct.pytorch_textcorrection.tcTools import cut_sent_by_stay
from macro_correct import correct_basic
from macro_correct import correct_long
from macro_correct import correct
import gradio as gr



# pretrained_model_name_or_path = "shibing624/macbert4csc-base-chinese"
pretrained_model_name_or_path = "Macadam/macbert4mdcspell_v2"
# pretrained_model_name_or_path = "Macropodus/macbert4mdcspell_v1"
# pretrained_model_name_or_path = "Macropodus/macbert4csc_v1"
# pretrained_model_name_or_path = "Macropodus/macbert4csc_v2"
# pretrained_model_name_or_path = "Macropodus/bert4csc_v1"
# device = torch.device("cpu")
# device = torch.device("cuda")


def macro_correct(text):
    print(text)
    text_csc = correct_long(text)
    print(text_csc)
    print("#"*128)
    text_out = ""
    for t in text_csc:
        for k, v in t.items():
            text_out += f"{k}: {v}\n"
        text_out += "\n"
    return text_out


if __name__ == '__main__':
    print(macro_correct('少先队员因该为老人让坐'))

    examples = [
        "机七学习是人工智能领遇最能体现智能的一个分知",
        "我是练习时长两念半的鸽仁练习生蔡徐坤",
        "真麻烦你了。希望你们好好的跳无",
        "他法语说的很好，的语也不错",
        "遇到一位很棒的奴生跟我疗天",
        "我们为这个目标努力不解",
    ]
    gr.Interface(
        macro_correct,
        inputs='text',
        outputs='text',
        title="Chinese Spelling Correction Model Macropodus/macbert4csc_v2",
        description="Copy or input error Chinese text. Submit and the machine will correct text.",
        article="Link to <a href='https://github.com/yongzhuo/macro-correct' style='color:blue;' target='_blank\'>Github REPO: macro-correct</a>",
        examples=examples
    ).launch()
    # ).launch(server_name="0.0.0.0", server_port=8066, share=False, debug=True)