改善chatpdf的功能
This commit is contained in:
qingxu fu
parent
613be5509b
commit
8ac9b454e3
@ -76,7 +76,6 @@ def get_crazy_functions():
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from crazy_functions.总结word文档 import 总结word文档
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from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
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from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
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from crazy_functions.理解PDF文档内容 import 理解PDF文档内容
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from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
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from crazy_functions.Latex全文润色 import Latex中文润色
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from crazy_functions.Latex全文翻译 import Latex中译英
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@ -108,11 +107,6 @@ def get_crazy_functions():
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"Color": "stop",
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"Function": HotReload(总结word文档)
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},
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# "[测试功能] 理解PDF文档内容（Tk文件选择接口，仅本地）": {
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# # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
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# "AsButton": False, # 加入下拉菜单中
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# "Function": HotReload(理解PDF文档内容)
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# },
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"[测试功能] 理解PDF文档内容（通用接口，读取文件输入区）": {
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# HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
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"Color": "stop",
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@ -131,7 +125,6 @@ def get_crazy_functions():
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"AsButton": False, # 加入下拉菜单中
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"Function": HotReload(Latex中文润色)
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},
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"[测试功能] Latex项目全文中译英（输入路径或上传压缩包）": {
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# HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
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"Color": "stop",
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@ -360,3 +360,171 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
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# 这个中文的句号是故意的，作为一个标识而存在
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res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False)
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return [r.replace('。\n', '.') for r in res]
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def read_and_clean_pdf_text(fp):
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"""
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这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好
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**输入参数说明**
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- `fp`：需要读取和清理文本的pdf文件路径
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**输出参数说明**
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- `meta_txt`：清理后的文本内容字符串
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- `page_one_meta`：第一页清理后的文本内容列表
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**函数功能**
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读取pdf文件并清理其中的文本内容，清理规则包括：
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- 提取所有块元的文本信息，并合并为一个字符串
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- 去除短块（字符数小于100）并替换为回车符
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- 清理多余的空行
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- 合并小写字母开头的段落块并替换为空格
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- 清除重复的换行
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- 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔
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"""
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import fitz, copy
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import re
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import numpy as np
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from colorful import print亮黄, print亮绿
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fc = 0
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fs = 1
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fb = 2
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REMOVE_FOOT_NOTE = True
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REMOVE_FOOT_FFSIZE_PERCENT = 0.95
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def primary_ffsize(l):
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fsize_statiscs = {}
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for wtf in l['spans']:
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if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
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fsize_statiscs[wtf['size']] += len(wtf['text'])
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return max(fsize_statiscs, key=fsize_statiscs.get)
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def ffsize_same(a,b):
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return abs((a-b)/max(a,b)) < 0.02
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# file_content = ""
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with fitz.open(fp) as doc:
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meta_txt = []
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meta_font = []
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meta_line = []
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meta_span = []
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for index, page in enumerate(doc):
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# file_content += page.get_text()
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text_areas = page.get_text("dict") # 获取页面上的文本信息
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for t in text_areas['blocks']:
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if 'lines' in t:
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pf = 998
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for l in t['lines']:
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txt_line = "".join([wtf['text'] for wtf in l['spans']])
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pf = primary_ffsize(l)
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meta_line.append([txt_line, pf, l['bbox'], l])
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for wtf in l['spans']: # for l in t['lines']:
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meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])
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# meta_line.append(["NEW_BLOCK", pf])
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# 块元提取 for each word segment with in line for each line cross-line words for each block
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meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
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'- ', '') for t in text_areas['blocks'] if 'lines' in t])
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meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
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for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
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if index == 0:
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page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
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'- ', '') for t in text_areas['blocks'] if 'lines' in t]
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# 获取正文主字体
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fsize_statiscs = {}
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for span in meta_span:
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if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
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fsize_statiscs[span[1]] += span[2]
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main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
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if REMOVE_FOOT_NOTE:
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give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
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# 切分和重新整合
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mega_sec = []
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sec = []
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for index, line in enumerate(meta_line):
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if index == 0:
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sec.append(line[fc])
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continue
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if REMOVE_FOOT_NOTE:
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if meta_line[index][fs] <= give_up_fize_threshold:
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continue
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if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):
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# 尝试识别段落
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if meta_line[index][fc].endswith('.') and\
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(meta_line[index-1][fc] != 'NEW_BLOCK') and \
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(meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:
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sec[-1] += line[fc]
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sec[-1] += "\n\n"
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else:
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sec[-1] += " "
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sec[-1] += line[fc]
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else:
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if (index+1 < len(meta_line)) and \
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meta_line[index][fs] > main_fsize:
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# 单行 + 字体大
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mega_sec.append(copy.deepcopy(sec))
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sec = []
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sec.append("# " + line[fc])
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else:
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# 尝试识别section
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if meta_line[index-1][fs] > meta_line[index][fs]:
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sec.append("\n" + line[fc])
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else:
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sec.append(line[fc])
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mega_sec.append(copy.deepcopy(sec))
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finals = []
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for ms in mega_sec:
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final = " ".join(ms)
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final = final.replace('- ', ' ')
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finals.append(final)
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meta_txt = finals
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def 把字符太少的块清除为回车(meta_txt):
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for index, block_txt in enumerate(meta_txt):
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if len(block_txt) < 100:
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meta_txt[index] = '\n'
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return meta_txt
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meta_txt = 把字符太少的块清除为回车(meta_txt)
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def 清理多余的空行(meta_txt):
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for index in reversed(range(1, len(meta_txt))):
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if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
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meta_txt.pop(index)
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return meta_txt
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meta_txt = 清理多余的空行(meta_txt)
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def 合并小写开头的段落块(meta_txt):
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def starts_with_lowercase_word(s):
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pattern = r"^[a-z]+"
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match = re.match(pattern, s)
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if match:
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return True
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else:
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return False
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for _ in range(100):
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for index, block_txt in enumerate(meta_txt):
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if starts_with_lowercase_word(block_txt):
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if meta_txt[index-1] != '\n':
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meta_txt[index-1] += ' '
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else:
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meta_txt[index-1] = ''
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meta_txt[index-1] += meta_txt[index]
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meta_txt[index] = '\n'
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return meta_txt
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meta_txt = 合并小写开头的段落块(meta_txt)
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meta_txt = 清理多余的空行(meta_txt)
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meta_txt = '\n'.join(meta_txt)
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# 清除重复的换行
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for _ in range(5):
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meta_txt = meta_txt.replace('\n\n', '\n')
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# 换行 -> 双换行
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meta_txt = meta_txt.replace('\n', '\n\n')
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for f in finals:
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print亮黄(f)
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print亮绿('***************************')
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return meta_txt, page_one_meta
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@ -2,174 +2,9 @@ from toolbox import CatchException, report_execption, write_results_to_file
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from toolbox import update_ui
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
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from .crazy_utils import read_and_clean_pdf_text
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from colorful import *
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def read_and_clean_pdf_text(fp):
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"""
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这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好，不建议任何人去读这个函数
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**输入参数说明**
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- `fp`：需要读取和清理文本的pdf文件路径
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**输出参数说明**
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- `meta_txt`：清理后的文本内容字符串
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- `page_one_meta`：第一页清理后的文本内容列表
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**函数功能**
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读取pdf文件并清理其中的文本内容，清理规则包括：
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- 提取所有块元的文本信息，并合并为一个字符串
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- 去除短块（字符数小于100）并替换为回车符
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- 清理多余的空行
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- 合并小写字母开头的段落块并替换为空格
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- 清除重复的换行
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- 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔
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"""
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import fitz, copy
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import re
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import numpy as np
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fc = 0
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fs = 1
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fb = 2
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REMOVE_FOOT_NOTE = True
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REMOVE_FOOT_FFSIZE_PERCENT = 0.95
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def primary_ffsize(l):
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fsize_statiscs = {}
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for wtf in l['spans']:
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if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
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fsize_statiscs[wtf['size']] += len(wtf['text'])
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return max(fsize_statiscs, key=fsize_statiscs.get)
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def ffsize_same(a,b):
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return abs((a-b)/max(a,b)) < 0.02
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# file_content = ""
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with fitz.open(fp) as doc:
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meta_txt = []
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meta_font = []
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meta_line = []
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meta_span = []
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for index, page in enumerate(doc):
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# file_content += page.get_text()
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text_areas = page.get_text("dict") # 获取页面上的文本信息
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for t in text_areas['blocks']:
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if 'lines' in t:
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pf = 998
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for l in t['lines']:
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txt_line = "".join([wtf['text'] for wtf in l['spans']])
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pf = primary_ffsize(l)
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meta_line.append([txt_line, pf, l['bbox'], l])
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for wtf in l['spans']: # for l in t['lines']:
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meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])
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# meta_line.append(["NEW_BLOCK", pf])
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# 块元提取 for each word segment with in line for each line cross-line words for each block
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meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
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'- ', '') for t in text_areas['blocks'] if 'lines' in t])
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meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
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for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
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if index == 0:
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page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
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'- ', '') for t in text_areas['blocks'] if 'lines' in t]
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# 获取正文主字体
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fsize_statiscs = {}
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for span in meta_span:
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if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
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fsize_statiscs[span[1]] += span[2]
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main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
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if REMOVE_FOOT_NOTE:
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give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
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# 切分和重新整合
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mega_sec = []
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sec = []
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for index, line in enumerate(meta_line):
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if index == 0:
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sec.append(line[fc])
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continue
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if REMOVE_FOOT_NOTE:
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if meta_line[index][fs] <= give_up_fize_threshold:
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continue
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if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):
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# 尝试识别段落
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if meta_line[index][fc].endswith('.') and\
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(meta_line[index-1][fc] != 'NEW_BLOCK') and \
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(meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:
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sec[-1] += line[fc]
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sec[-1] += "\n\n"
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else:
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sec[-1] += " "
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sec[-1] += line[fc]
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else:
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if (index+1 < len(meta_line)) and \
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meta_line[index][fs] > main_fsize:
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# 单行 + 字体大
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mega_sec.append(copy.deepcopy(sec))
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sec = []
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sec.append("# " + line[fc])
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else:
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# 尝试识别section
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if meta_line[index-1][fs] > meta_line[index][fs]:
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sec.append("\n" + line[fc])
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else:
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sec.append(line[fc])
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mega_sec.append(copy.deepcopy(sec))
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finals = []
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for ms in mega_sec:
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final = " ".join(ms)
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final = final.replace('- ', ' ')
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finals.append(final)
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meta_txt = finals
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def 把字符太少的块清除为回车(meta_txt):
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for index, block_txt in enumerate(meta_txt):
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if len(block_txt) < 100:
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meta_txt[index] = '\n'
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return meta_txt
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meta_txt = 把字符太少的块清除为回车(meta_txt)
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def 清理多余的空行(meta_txt):
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for index in reversed(range(1, len(meta_txt))):
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if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
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meta_txt.pop(index)
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return meta_txt
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meta_txt = 清理多余的空行(meta_txt)
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def 合并小写开头的段落块(meta_txt):
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def starts_with_lowercase_word(s):
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pattern = r"^[a-z]+"
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match = re.match(pattern, s)
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if match:
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return True
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else:
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return False
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for _ in range(100):
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for index, block_txt in enumerate(meta_txt):
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if starts_with_lowercase_word(block_txt):
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if meta_txt[index-1] != '\n':
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meta_txt[index-1] += ' '
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else:
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meta_txt[index-1] = ''
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meta_txt[index-1] += meta_txt[index]
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meta_txt[index] = '\n'
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return meta_txt
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meta_txt = 合并小写开头的段落块(meta_txt)
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meta_txt = 清理多余的空行(meta_txt)
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meta_txt = '\n'.join(meta_txt)
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# 清除重复的换行
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for _ in range(5):
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meta_txt = meta_txt.replace('\n\n', '\n')
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# 换行 -> 双换行
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meta_txt = meta_txt.replace('\n', '\n\n')
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for f in finals:
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print亮黄(f)
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print亮绿('***************************')
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return meta_txt, page_one_meta
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@CatchException
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def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
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import glob
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@ -1,142 +1,66 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_execption
|
||||
import re
|
||||
import unicodedata
|
||||
from .crazy_utils import read_and_clean_pdf_text
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
fast_debug = False
|
||||
|
||||
def is_paragraph_break(match):
|
||||
"""
|
||||
根据给定的匹配结果来判断换行符是否表示段落分隔。
|
||||
如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
|
||||
也可以根据之前的内容长度来判断段落是否已经足够长。
|
||||
"""
|
||||
prev_char, next_char = match.groups()
|
||||
|
||||
# 句子结束标志
|
||||
sentence_endings = ".!?"
|
||||
|
||||
# 设定一个最小段落长度阈值
|
||||
min_paragraph_length = 140
|
||||
|
||||
if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
|
||||
return "\n\n"
|
||||
else:
|
||||
return " "
|
||||
|
||||
def normalize_text(text):
|
||||
"""
|
||||
通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
|
||||
例如，将连字 "fi" 转换为 "f" 和 "i"。
|
||||
"""
|
||||
# 对文本进行归一化处理，分解连字
|
||||
normalized_text = unicodedata.normalize("NFKD", text)
|
||||
|
||||
# 替换其他特殊字符
|
||||
cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)
|
||||
|
||||
return cleaned_text
|
||||
|
||||
def clean_text(raw_text):
|
||||
"""
|
||||
对从 PDF 提取出的原始文本进行清洗和格式化处理。
|
||||
1. 对原始文本进行归一化处理。
|
||||
2. 替换跨行的连词，例如 “Espe-\ncially” 转换为 “Especially”。
|
||||
3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换。
|
||||
"""
|
||||
# 对文本进行归一化处理
|
||||
normalized_text = normalize_text(raw_text)
|
||||
|
||||
# 替换跨行的连词
|
||||
text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text)
|
||||
|
||||
# 根据前后相邻字符的特点，找到原文本中的换行符
|
||||
newlines = re.compile(r'(\S)\n(\S)')
|
||||
|
||||
# 根据 heuristic 规则，用空格或段落分隔符替换原换行符
|
||||
final_text = re.sub(newlines, lambda m: m.group(1) + is_paragraph_break(m) + m.group(2), text)
|
||||
|
||||
return final_text.strip()
|
||||
|
||||
def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import time, glob, os, fitz
|
||||
import tiktoken
|
||||
print('begin analysis on:', file_name)
|
||||
file_content, page_one = read_and_clean_pdf_text(file_name)
|
||||
|
||||
with fitz.open(file_name) as doc:
|
||||
file_content = ""
|
||||
for page in doc:
|
||||
file_content += page.get_text()
|
||||
file_content = clean_text(file_content)
|
||||
# print(file_content)
|
||||
split_number = 10000
|
||||
split_group = (len(file_content)//split_number)+1
|
||||
for i in range(0,split_group):
|
||||
if i==0:
|
||||
prefix = "接下来请你仔细分析下面的论文，学习里面的内容（专业术语、公式、数学概念）.并且注意：由于论文内容较多，将分批次发送，每次发送完之后，你只需要回答“接受完成”"
|
||||
i_say = prefix + f'文件名是{file_name}，文章内容第{i+1}部分是 ```{file_content[i*split_number:(i+1)*split_number]}```'
|
||||
i_say_show_user = f'文件名是：\n{file_name},\n由于论文内容过长，将分批请求（共{len(file_content)}字符，将分为{split_group}批，每批{split_number}字符）。\n当前发送{i+1}/{split_group}部分'
|
||||
elif i==split_group-1:
|
||||
i_say = f'你只需要回答“所有论文接受完成，请进行下一步”。文章内容第{i+1}/{split_group}部分是 ```{file_content[i*split_number:]}```'
|
||||
i_say_show_user = f'当前发送{i+1}/{split_group}部分'
|
||||
else:
|
||||
i_say = f'你只需要回答“接受完成”。文章内容第{i+1}/{split_group}部分是 ```{file_content[i*split_number:(i+1)*split_number]}```'
|
||||
i_say_show_user = f'当前发送{i+1}/{split_group}部分'
|
||||
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt="") # 带超时倒计时
|
||||
while "完成" not in gpt_say:
|
||||
i_say = f'你只需要回答“接受完成”。文章内容第{i+1}/{split_group}部分是 ```{file_content[i*split_number:(i+1)*split_number]}```'
|
||||
i_say_show_user = f'出现error，重新发送{i+1}/{split_group}部分'
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt="") # 带超时倒计时
|
||||
time.sleep(1)
|
||||
chatbot[-1] = (i_say_show_user, gpt_say)
|
||||
history.append(i_say_show_user); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
time.sleep(2)
|
||||
# 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
|
||||
# 的长度必须小于 2500 个 Token
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
|
||||
i_say = f'接下来，请你扮演一名专业的学术教授，利用你的所有知识并且结合这篇文章，回答我的问题。（请牢记：1.直到我说“退出”，你才能结束任务；2.所有问题需要紧密围绕文章内容;3.如果有公式，请使用tex渲染)'
|
||||
chatbot.append((i_say, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
|
||||
from toolbox import get_conf
|
||||
enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL'))
|
||||
def get_token_num(txt): return len(enc.encode(txt))
|
||||
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
|
||||
page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
|
||||
txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
|
||||
# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
|
||||
paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
|
||||
|
||||
# ** gpt request **
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot, history=history, sys_prompt="") # 带超时倒计时
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history.append(i_say); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
############################## <第一步，从摘要中提取高价值信息，放到history中> ##################################
|
||||
final_results = []
|
||||
final_results.append(paper_meta)
|
||||
|
||||
############################## <第二步，迭代地历遍整个文章，提取精炼信息> ##################################
|
||||
i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
|
||||
|
||||
@CatchException
|
||||
def 理解PDF文档内容(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
import glob, os
|
||||
iteration_results = []
|
||||
last_iteration_result = paper_meta # 初始值是摘要
|
||||
MAX_WORD_TOTAL = 4096
|
||||
n_fragment = len(paper_fragments)
|
||||
if n_fragment >= 20: print('文章极长，不能达到预期效果')
|
||||
for i in range(n_fragment):
|
||||
NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
|
||||
i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
|
||||
llm_kwargs, chatbot,
|
||||
history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
|
||||
sys_prompt="Extract the main idea of this section." # 提示
|
||||
)
|
||||
iteration_results.append(gpt_say)
|
||||
last_iteration_result = gpt_say
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
import tkinter as tk
|
||||
from tkinter import filedialog
|
||||
|
||||
root = tk.Tk()
|
||||
root.withdraw()
|
||||
txt = filedialog.askopenfilename()
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import fitz
|
||||
except:
|
||||
report_execption(chatbot, history,
|
||||
a = f"解析项目: {txt}",
|
||||
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
# 开始正式执行任务
|
||||
yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
############################## <第三步，整理history> ##################################
|
||||
final_results.extend(iteration_results)
|
||||
final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')
|
||||
# 接下来两句话只显示在界面上，不起实际作用
|
||||
i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。"
|
||||
chatbot.append([i_say_show_user, gpt_say])
|
||||
|
||||
############################## <第四步，设置一个token上限，防止回答时Token溢出> ##################################
|
||||
from .crazy_utils import input_clipping
|
||||
_, final_results = input_clipping("", final_results, max_token_limit=3200)
|
||||
yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
|
||||
|
||||
|
||||
@CatchException
|
||||
@ -146,7 +70,7 @@ def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chat
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe。"])
|
||||
"理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user