from docx import Document import os os.environ['ALL_PROXY'] = 'http://127.0.0.1:10809' import docx import json import re from docx.document import Document from docx.oxml.table import CT_Tbl from docx.oxml.text.paragraph import CT_P from docx.table import _Cell, Table from docx.text.paragraph import Paragraph import requests import random fileName = "data/基于Python的电影网站设计_范文.docx" pantten_mulu= '目录(.*?)致谢' pantten_xiaobiaoti= "{}(.*?){}" pantten_yijibiaoti = '^([一二三四五六七八九])、(.*)' pantten_yijibiaoti_content = '^[一二三四五六七八九]、(.*)' pantten_erjibiaoti = '^[0-9](\.[0-9]\d*){1}\s{1,}?.*$' pantten_erjibiaoti_content = '^[0-9]\.[0-9]\d*{1}\s{1,}?(.*)$' pantten_content_tiaoshu = '[0-9]\.{1}\s{0,}?(.*)' prompt_two_title_min_max = "为论文题目“{}”生成中文目录，要求只有一级标题，二级标题，一级标题使用中文数字 例如一、xxx；二级标题使用阿拉伯数字 例如1.1 xxx；一级标题生成{}个；每个一级标题包含{}-{}个二级标题" prompt_two_title_not_min_max = "为论文题目“{}”生成中文目录，要求只有一级标题，二级标题，一级标题使用中文数字 例如一、xxx；二级标题使用阿拉伯数字 例如1.1 xxx；一级标题生成{}个；每个一级标题包含{}个二级标题" pantten_title = "为论文题目“(.*?)”生成中文目录" pantten_xiaobiaoti_geshu = "每个一级标题包含(.*?)个" pantten_dabiaoti_geshu = "一级标题生成(.*?)个" mulusuojian = "请问把以下目录缩减成只有4个一级标题作为ppt的题目，请问留下原始目录中的哪4个一级标题最合适，一级标题必须在原始目录中\n{}\n" self_api = "http://192.168.31.149:12004/predict" gpt_api = "https://api.openai.com/v1/chat/completions" def iter_block_items(parent): """ Yield each paragraph and table child within *parent*, in document order. Each returned value is an instance of either Table or Paragraph. *parent* would most commonly be a reference to a main Document object, but also works for a _Cell object, which itself can contain paragraphs and tables. """ if isinstance(parent, Document): parent_elm = parent.element.body elif isinstance(parent, _Cell): parent_elm = parent._tc else: raise ValueError("something's not right") for child in parent_elm.iterchildren(): if isinstance(child, CT_P): yield Paragraph(child, parent) elif isinstance(child, CT_Tbl): yield Table(child, parent) def read_table(table): return [[cell.text for cell in row.cells] for row in table.rows] def read_word(word_path): paper_text = [] doc = docx.Document(word_path) for block in iter_block_items(doc): if isinstance(block, Paragraph): paper_text.append(block.text) elif isinstance(block, Table): table_list = read_table(block) table_list_new = [] for row in table_list: table_list_new.append("" + "\n".join(row) + "") table_str = "\n\n" + "\n\n\n".join(table_list_new) + "\n\n" table_str = "\n" + table_str + "
\n\n" paper_text.append(table_str) paper_text = "\n".join(paper_text) return paper_text def getText(fileName): doc = docx.Document(fileName) TextList = [] for paragraph in doc.paragraphs: TextList.append(paragraph.text) return '\n'.join(TextList) def request_selfmodel_api(prompt): print(prompt) url = "http://192.168.31.149:12004/predict" data = { "model": "gpt-4-turbo-preview", "messages": [ {"role": "user", "content": prompt} ], "top_p": 0.7, "temperature": 0.95 } response = requests.post( url, json=data, timeout=100000 ) return response.json() def request_chatgpt_api(prompt): OPENAI_API_KEY = "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" url = "https://api.openai.com/v1/chat/completions" # url = "https://one.aiskt.com" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {OPENAI_API_KEY}" } data = { "model": "gpt-4-turbo-preview", "messages": [ {"role": "user", "content": prompt} ], "top_p": 0.7, "temperature": 0.95 } response = requests.post(url, headers=headers, data=json.dumps(data), timeout=1200) return response.json() def yanzhengyijibiaoti(mulu, res): ''' 判断生成的大标题是否可用 :param mulu: :param res: :return: ''' mulu_list = str(mulu).split("\n") dabiaoti_list = [] dabiaoti_res_list = [] for i in mulu_list: res_re = re.findall(pantten_yijibiaoti, i) if res_re != []: dabiaoti_list.append(re.findall(pantten_yijibiaoti, i)[0]) for i in dabiaoti_list: if i[1].strip() in res: dabiaoti_res_list.append("、".join(i)) if len(dabiaoti_res_list) == 4: return_bool = True else: return_bool = False return return_bool, dabiaoti_res_list if __name__ == '__main__': text_1 = json.dumps(read_word(fileName),ensure_ascii=False) print(text_1) mulu_str = re.findall(pantten_mulu, text_1)[0] print(mulu_str) mulu_list_xuhao = str(mulu_str).split("\\n") mulu_list = [] for i in mulu_list_xuhao: if i != "": mulu_list.append(i.split("\\t")[0]) mulu_list.append("致谢") print(mulu_list) content_list = [] yijibiaoti = "" paper_content = {} for i in range(len(mulu_list) -1): title = mulu_list[i].strip(" ").strip("\\n") content = str(re.findall(pantten_xiaobiaoti.format(mulu_list[i], mulu_list[i+1]), text_1)[1]).strip(" ").strip("\\n") # print(title) # print(content) yijibiaoti_res = re.findall(pantten_yijibiaoti, title) erjibiaoti_res = re.findall(pantten_erjibiaoti, title) if yijibiaoti_res != []: # title = "、".join([yijibiaoti_res[0][1], yijibiaoti_res[0][1].strip()]) paper_content[title] = {} yijibiaoti = title continue elif erjibiaoti_res != []: paper_content[yijibiaoti][title] = content.replace("\\n", "\n") else: paper_content[yijibiaoti][title] += "\n".join(title + content) while True: mulu_str = "\n".join(mulu_list[:-1]) prompt = f'请问把以下目录缩减成只有4个一级标题作为ppt的题目，请问留下原始目录中的哪4个一级标题最合适，一级标题必须在原始目录中\n{mulu_str}\n' # try: # res = request_chatgpt_api(prompt)['choices'][0]['message']['content'] # except: # continue res = '''根据您提供的目录内容，如果要将其缩减为只包含4个一级标题的PPT题目，建议选择以下四个一级标题，因为它们分别代表了研究的引入、理论框架、实际应用与实践，以及未来展望，从而形成了一个完整的研究过程和内容框架： 1. 一、绪论 2. 二、电影网站设计的基本概念 3. 三、Python在电影网站设计中的应用 4. 五、电影网站设计的实践与展望 这样的选择既涵盖了研究的背景、目的与意义（绪论），也包括了研究的理论基础（电影网站设计的基本概念），以及研究的实际操作和技术实现（Python在电影网站设计中的应用），最后还有对项目实践经验的总结和对未来发展的展望（电影网站设计的实践与展望）。这四个部分共同构成了一个完整的研究报告或项目介绍的框架，能够全面展示电影网站设计项目的各个方面。 ''' shaixuan_bool, dabiaoti_res_list = yanzhengyijibiaoti("\n".join(mulu_list), res.replace("\n", "\\n")) if shaixuan_bool == True: break content_1 = [] for yijibiaoti in dabiaoti_res_list: content_2 = [] for erjibiaoti in paper_content[yijibiaoti]: num = random.randint(2, 6) content = paper_content[yijibiaoti][erjibiaoti] res = request_selfmodel_api(f'任务：生成段落主要内容\n请对以下内容进行提取信息，只需要提取{str(num)}条主要内容，使用条数罗列下面这段话的主要信息，例如1. xxx\n2.xxx \n' + content)['choices'][0]['message']['content'] tiaoshu_list = str(res).split("\n") tiaoshu_list_new = [] for dantiao in tiaoshu_list: tiaoshu_list_new.append(re.findall(pantten_content_tiaoshu, dantiao)[0].strip()) content_2.append({ "title_small": erjibiaoti, "content_3": tiaoshu_list_new }) content_1.append({ "title_big": yijibiaoti, "content_2": content_2 }) data_new = { "title": fileName, "catalogue": dabiaoti_res_list, "content_1": content_1 } with open("data/ceshi.json", "w", encoding="utf-8") as f: f.write(json.dumps(data_new, ensure_ascii=False, indent=2)) # res = request_chatgpt_api(f'针对下面这篇文章，请回答，我为什么选择这个题目，做这个研究有什么意义？\n' + data)['choices'][0]['message']['content']