gpt-academic / crazy_functions /总结word文档.py
hieroglyphs's picture
Duplicate from qingxu98/gpt-academic
f81fd9e
from toolbox import update_ui
from toolbox import CatchException, report_execption, write_results_to_file
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
fast_debug = False
def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
import time, os
# pip install python-docx 用于docx格式,跨平台
# pip install pywin32 用于doc格式,仅支持Win平台
for index, fp in enumerate(file_manifest):
if fp.split(".")[-1] == "docx":
from docx import Document
doc = Document(fp)
file_content = "\n".join([para.text for para in doc.paragraphs])
else:
try:
import win32com.client
word = win32com.client.Dispatch("Word.Application")
word.visible = False
# 打开文件
doc = word.Documents.Open(os.getcwd() + '/' + fp)
# file_content = doc.Content.Text
doc = word.ActiveDocument
file_content = doc.Range().Text
doc.Close()
word.Quit()
except:
raise RuntimeError('请先将.doc文档转换为.docx文档。')
print(file_content)
# private_upload里面的文件名在解压zip后容易出现乱码(rar和7z格式正常),故可以只分析文章内容,不输入文件名
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
from request_llm.bridge_all import model_info
max_token = model_info[llm_kwargs['llm_model']]['max_token']
TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
txt=file_content,
get_token_fn=model_info[llm_kwargs['llm_model']]['token_cnt'],
limit=TOKEN_LIMIT_PER_FRAGMENT
)
this_paper_history = []
for i, paper_frag in enumerate(paper_fragments):
i_say = f'请对下面的文章片段用中文做概述,文件名是{os.path.relpath(fp, project_folder)},文章内容是 ```{paper_frag}```'
i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=i_say,
inputs_show_user=i_say_show_user,
llm_kwargs=llm_kwargs,
chatbot=chatbot,
history=[],
sys_prompt="总结文章。"
)
chatbot[-1] = (i_say_show_user, gpt_say)
history.extend([i_say_show_user,gpt_say])
this_paper_history.extend([i_say_show_user,gpt_say])
# 已经对该文章的所有片段总结完毕,如果文章被切分了,
if len(paper_fragments) > 1:
i_say = f"根据以上的对话,总结文章{os.path.abspath(fp)}的主要内容。"
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=i_say,
inputs_show_user=i_say,
llm_kwargs=llm_kwargs,
chatbot=chatbot,
history=this_paper_history,
sys_prompt="总结文章。"
)
history.extend([i_say,gpt_say])
this_paper_history.extend([i_say,gpt_say])
res = write_results_to_file(history)
chatbot.append(("完成了吗?", res))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
res = write_results_to_file(history)
chatbot.append(("所有文件都总结完成了吗?", res))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
@CatchException
def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
import glob, os
# 基本信息:功能、贡献者
chatbot.append([
"函数插件功能?",
"批量总结Word文档。函数插件贡献者: JasonGuo1。注意, 如果是.doc文件, 请先转化为.docx格式。"])
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
try:
from docx import Document
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 python-docx pywin32```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 清空历史,以免输入溢出
history = []
# 检测输入参数,如没有给定输入参数,直接退出
if os.path.exists(txt):
project_folder = txt
else:
if txt == "": txt = '空空如也的输入栏'
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 搜索需要处理的文件清单
if txt.endswith('.docx') or txt.endswith('.doc'):
file_manifest = [txt]
else:
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \
[f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]
# 如果没找到任何文件
if len(file_manifest) == 0:
report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 开始正式执行任务
yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)