import numpy as np
import gradio as gr
import textInput
from BERT_inference import BertClassificationModel


output = []
keys = []

# css = ".output {min-height: 500px}"


with gr.Blocks(css = ".output {min-height: 500px}") as demo:
    #用markdown语法编辑输出一段话
    gr.Markdown("# TSA - 文本整理助手")
    gr.Markdown("请选择要输入的文件或填入文本")
    topic_num = gr.Number(label="主题个数")
    max_length = gr.Number(label="摘要最大长度")
    with gr.Tabs():
        with gr.Tab("文本输入"):
            text_input = gr.TextArea(lines=10)
            text_button = gr.Button("生成")
    
        with gr.Tab("文件输入"):
            gr.Markdown("目前支持的格式有PDF、Word、txt")
            file_input = gr.File(file_types=["text", ".pdf", ".docx"])
            file_button = gr.Button("生成")
    # 设置tab选项卡
    with gr.Tabs():
        with gr.Tab("分类页"):
            text_keys_output = gr.TextArea(lines=30)

        with gr.Tab("摘要页",):
            #Blocks特有组件，设置所有子组件按水平排列
            text_ab_output = gr.TextArea(lines=30)

        with gr.Tab("下载页"):
            file_txt_output = gr.File(label="txt格式")
            file_docx_output = gr.File(label="docx格式")
            file_pdf_output = gr.File(label="pdf格式")

    text_button.click(textInput.text_dump_to_lines, inputs=[text_input,topic_num,max_length], outputs=[text_keys_output,text_ab_output,file_txt_output,file_docx_output,file_pdf_output])
    file_button.click(textInput.file_dump_to_lines,inputs=[file_input,topic_num,max_length], outputs=[text_keys_output,text_ab_output,file_txt_output,file_docx_output,file_pdf_output])


demo.queue().launch()