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import numpy as np |
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import gradio as gr |
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import textInput |
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from BERT_inference import BertClassificationModel |
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output = [] |
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keys = [] |
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with gr.Blocks(css = ".output {min-height: 500px}") as demo: |
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gr.Markdown("# TSA - 文本整理助手") |
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gr.Markdown("请选择要输入的文件或填入文本") |
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topic_num = gr.Number(label="主题个数") |
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max_length = gr.Number(label="摘要最大长度") |
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with gr.Tabs(): |
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with gr.Tab("文本输入"): |
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text_input = gr.TextArea(lines=10) |
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text_button = gr.Button("生成") |
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with gr.Tab("文件输入"): |
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gr.Markdown("目前支持的格式有PDF、Word、txt") |
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file_input = gr.File(file_types=["text", ".pdf", ".docx"]) |
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file_button = gr.Button("生成") |
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with gr.Tabs(): |
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with gr.Tab("分类页"): |
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text_keys_output = gr.TextArea(lines=30) |
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with gr.Tab("摘要页",): |
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text_ab_output = gr.TextArea(lines=30) |
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with gr.Tab("下载页"): |
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file_txt_output = gr.File(label="txt格式") |
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file_docx_output = gr.File(label="docx格式") |
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file_pdf_output = gr.File(label="pdf格式") |
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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]) |
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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]) |
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demo.queue().launch() |
