change clip history policy
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@ -404,7 +404,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
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result_pdf = pj(work_folder_modified, f'merge_diff.pdf') # get pdf path
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promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot) # promote file to web UI
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if modified_pdf_success:
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yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history) # 刷新Gradio前端界面
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yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 正在尝试生成对比PDF, 请稍后 ...', chatbot, history) # 刷新Gradio前端界面
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result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
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origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
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if os.path.exists(pj(work_folder, '..', 'translation')):
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@ -1007,14 +1007,19 @@ def clip_history(inputs, history, tokenizer, max_token_limit):
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def get_token_num(txt):
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return len(tokenizer.encode(txt, disallowed_special=()))
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input_token_num = get_token_num(inputs)
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if max_token_limit < 5000: output_token_expect = 256 # 4k & 2k models
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elif max_token_limit < 9000: output_token_expect = 512 # 8k models
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else: output_token_expect = 1024 # 16k & 32k models
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if input_token_num < max_token_limit * 3 / 4:
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# 当输入部分的token占比小于限制的3/4时,裁剪时
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# 1. 把input的余量留出来
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max_token_limit = max_token_limit - input_token_num
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# 2. 把输出用的余量留出来
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max_token_limit = max_token_limit - 128
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max_token_limit = max_token_limit - output_token_expect
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# 3. 如果余量太小了,直接清除历史
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if max_token_limit < 128:
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if max_token_limit < output_token_expect:
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history = []
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return history
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else:
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