# -*- coding: utf-8 -*- import argparse import base64 import re import sys import os import time from pathlib import Path from typing import Dict, List, Optional # --- ライブラリのインポートチェック --- try: import win32com.client # pywin32 (PPTX -> PDF画像化用) except ImportError: print("Import error: win32com. Please install it with: pip install pywin32") input("\nPress ENTER to terminate>>\n") sys.exit(1) try: import fitz # PyMuPDF (PDF -> PNG用) except ImportError: print("Import error: fitz. Please install it with: pip install pymupdf") input("\nPress ENTER to terminate>>\n") sys.exit(1) try: from pptx import Presentation # テキスト抽出用 from lxml import etree # 数式解析用 except ImportError: print("Import error: python-pptx or lxml. Please install: pip install python-pptx lxml") input("\nPress ENTER to terminate>>\n") sys.exit(1) from openai import OpenAI try: import google.generativeai as genai except ImportError: print("Geminiを利用する場合は 'pip install google-generativeai' が必要です。") input("\nPress ENTER to terminate>>\n") sys.exit(1) pause = 0 # ========================================================= # 1. Advanced Math Extraction Logic (Provided Code Integrated) # ========================================================= # 名前空間の定義 NAMESPACES = { 'p': 'http://schemas.openxmlformats.org/presentationml/2006/main', 'a': 'http://schemas.openxmlformats.org/drawingml/2006/main', 'm': 'http://schemas.openxmlformats.org/officeDocument/2006/math', 'r': 'http://schemas.openxmlformats.org/officeDocument/2006/relationships', 'a14': 'http://schemas.microsoft.com/office/drawing/2010/main' } # Unicode 数学記号をASCII/LaTeXに変換する辞書 MATH_UNICODE_MAP = { '𝑷': 'P', '𝑽': 'V', '𝑹': 'R', '𝑻': 'T', '𝒂': 'a', '𝒃': 'b', '𝒄': 'c', '𝒅': '\\mathrm{d}', '𝒆': 'e', '𝒇': 'f', '𝒈': 'g', '𝒉': 'h', '𝒊': 'i', '𝒋': 'j', '𝒌': 'k', '𝒍': 'l', '𝒎': 'm', '𝒏': 'n', '𝒙': 'x', '𝒚': 'y', '𝒛': 'z', '𝟐': '2', '𝟏': '1', '𝟎': '0', '−': '-', '＋': '+', '÷': '/', '×': '*', '⋅': '\\cdot', '…': '...', '∞': '\\infty', '∑': '\\sum', '∏': '\\prod', '∫': '\\int', '∬': '\\iint', '∭': '\\iiint', '∮': '\\oint', 'α': '\\alpha', 'β': '\\beta', 'γ': '\\gamma', 'δ': '\\delta', 'ε': '\\epsilon', 'ζ': '\\zeta', 'η': '\\eta', 'θ': '\\theta', 'ι': '\\iota', 'κ': '\\kappa', 'λ': '\\lambda', 'μ': '\\mu', 'ν': '\\nu', 'ξ': '\\xi', 'π': '\\pi', 'ρ': '\\rho', 'σ': '\\sigma', 'τ': '\\tau', 'υ': '\\upsilon', 'φ': '\\phi', 'χ': '\\chi', 'ψ': '\\psi', 'ω': '\\omega', 'Γ': '\\Gamma', 'Δ': '\\Delta', 'Θ': '\\Theta', 'Λ': '\\Lambda', 'Ξ': '\\Xi', 'Π': '\\Pi', 'Σ': '\\Sigma', 'Φ': '\\Phi', 'Ψ': '\\Psi', 'Ω': '\\Omega', '′': "'", '°': '\\degree', '℃': '\\degree C' } # n-ary演算子のOMML文字→LaTeXの対応 NARY_TO_LATEX = { '∑': '\\sum', '∏': '\\prod', '∫': '\\int', '∬': '\\iint', '∭': '\\iiint', '∮': '\\oint', '⋀': '\\bigwedge', '⋁': '\\bigvee', '⋂': '\\bigcap', '⋃': '\\bigcup', } OPERATOR_CHARS = set(['∑', '∏', '∫', '∬', '∭', '∮']) DEFAULT_PROMPT_TEMPLATE = """ あなたはプロフェッショナルな講師です。 スライドから抽出されたテキスト情報（Markdown形式）と添付のスライド画像を統合し、 解説を作成してください。 # スライド番号: {slide_no} # 抽出テキスト情報: {slide_text} # 出力言語: {lang} # 指示: 1. 画像内の図表やレイアウトを考慮しつつ、抽出テキスト内の数式を含めて正確に解説してください。 2. 以下のフォーマットで出力してください。 3. 出力Markdown中の数式は全て、 $$改行 LaTeX改行 $$ のブロック数式で出力してください 改行 ## 1. 解説 ## 2. 図・グラフの分析 ## 3. このスライドから読み取れる詳細な情報、データの議論・予測 改行 """ prompt_template = DEFAULT_PROMPT_TEMPLATE _VAR_RE = re.compile(r"\$([A-Za-z_][A-Za-z0-9_]*)") def terminate(): if pause: input("\nPress ENTER to terminate>>\n") exit() def parse_args(): parser = argparse.ArgumentParser(description="PPTX -> Text/Math -> AI Explanation") parser.add_argument("infile", type=str, help="Path to input PPTX") parser.add_argument("-o", "--output", default = None, help="出力するMarkdownファイル名") parser.add_argument("--txt", type=str, default=None, help="Optional: Path to existing text file") parser.add_argument("--ini", "-i", type=str, default=Path(__file__).stem + ".ini", help="prompt_templateを読み込むINIファイル。指定がなければスクリプト名.iniを探索。") openai_model = os.getenv("OPENAI_MODEL", "gpt-4o") openai_model5 = os.getenv("OPENAI_MODEL5", "gpt-5.2") google_model = os.getenv("GEMINI_MODEL") or os.getenv("GOOGLE_MODEL", "gemini-2.5-flash") parser.add_argument("--api", "-a", type=str, default="gemini", choices=["gemini", "google", "openai5", "openai"]) parser.add_argument("--model", default=None) parser.add_argument("--google_model", default=google_model) parser.add_argument("--openai_model", default=openai_model) parser.add_argument("--openai_model5", default=openai_model5) parser.add_argument("--visible", action="store_true") parser.add_argument("--language", type=str, default="Japanese") parser.add_argument("--pause", type=int, default=0) args = parser.parse_args() args.openai_key = os.getenv("OPENAI_API_KEY") args.gemini_key = os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY") if args.model: if args.api == 'openai5': args.openai_model5 = args.model elif args.api == 'openai': args.openai_model = args.model elif args.api in ('gemini', 'google'): args.google_model = args.model return parser, args def search_file(infile=None): script_path = os.path.abspath(sys.argv[0]) script_dir = os.path.dirname(script_path) script_name = os.path.splitext(os.path.basename(script_path))[0] default_ini = f"{script_name}.ini" if infile is None: for path in [os.getcwd(), script_dir]: candidate = os.path.join(path, default_ini) if os.path.isfile(candidate): return candidate return None if not os.path.isfile(infile): candidate = os.path.join(script_dir, infile) if os.path.isfile(candidate): return candidate return None return infile def read_ini(inifile=None): path = search_file(inifile) if path is None: raise FileNotFoundError("INIファイルが見つかりませんでした") result = {} variables = {} current_key = None multiline_val = [] multiline_delim = None with open(path, 'r', encoding='utf-8') as f: for line in f: line = line.rstrip() if not line or line.startswith('#') or line.startswith(';'): continue # 複数行値の終了判定（stripで判定） if multiline_delim: if line.strip() == multiline_delim: val = '\n'.join(multiline_val) result[current_key] = val variables[current_key] = val current_key = None multiline_val = [] multiline_delim = None else: multiline_val.append(line) continue # key=val の解析 if '=' in line: key, val = map(str.strip, line.split('=', 1)) val = val.strip() # 複数行値の開始判定（空文字でも対応） if (val == '"""' or val == "'''" or (val.startswith('"""') and not val.endswith('"""')) or \ (val.startswith("'''") and not val.endswith("'''")) ): multiline_delim = val[:3] content = val[3:] multiline_val = [content] if content else [] current_key = key continue # 単一行の複数行値 if (val.startswith('"""') and val.endswith('"""')) or \ (val.startswith("'''") and val.endswith("'''")): val = val[3:-3] result[key] = val variables[key] = val # 変数展開（あとから一括処理） for key, val in result.items(): def expand_var(match): var_name = match.group(1) return variables.get(var_name, match.group(0)) result[key] = re.sub(r"\$(\w+)\b", expand_var, val) return result def find_ini_path(ini_name: str) -> Optional[Path]: """cwd→スクリプトdirの順にiniを探索""" if not ini_name: return None p = Path(ini_name) if p.is_absolute() and p.is_file(): return p cwd_path = Path.cwd() / p.name if cwd_path.is_file(): return cwd_path script_path = Path(__file__).resolve().parent / p.name if script_path.is_file(): return script_path return None def _safe_text_replace_math_unicode(text: str) -> str: if not text: return "" for u, ltx in MATH_UNICODE_MAP.items(): text = text.replace(u, ltx) return text def _find_first(element, candidates): """候補タグ名（'m:sub'や'm:low'など）のうち最初に見つかった要素を返す""" for cand in candidates: found = element.find(cand, NAMESPACES) if found is not None: return found return None def _detect_nary_op_char(element): op_tag = element.find('m:naryPr/m:chr', NAMESPACES) if op_tag is not None: val = op_tag.get(f"{{{NAMESPACES['m']}}}val", "") if val: return val ts = element.xpath('.//m:t[not(ancestor::m:e) and not(ancestor::m:sub) and not(ancestor::m:sup)]', namespaces=NAMESPACES) for t in ts: s = t.text or "" for ch in s: if ch in OPERATOR_CHARS: return ch return "" def omml_to_latex(element): """ OMMLのXML要素を再帰的に解析し、LaTeXに変換します。 """ tag = etree.QName(element).localname if tag in ('oMath', 'oMathPara'): return "".join(omml_to_latex(child) for child in element) elif tag == 'f': # Fraction num = element.find('m:num', NAMESPACES) den = element.find('m:den', NAMESPACES) return f"\\frac{{{omml_to_latex(num)}}}{{{omml_to_latex(den)}}}" elif tag == 'rad': # Root deg = element.find('m:deg', NAMESPACES) e = element.find('m:e', NAMESPACES) if deg is not None: return f"\\sqrt[{omml_to_latex(deg)}]{{{omml_to_latex(e)}}}" return f"\\sqrt{{{omml_to_latex(e)}}}" elif tag == 'sSup': # Superscript e = element.find('m:e', NAMESPACES) sup = element.find('m:sup', NAMESPACES) return f"{omml_to_latex(e)}^{{{omml_to_latex(sup)}}}" elif tag == 'sSub': # Subscript e = element.find('m:e', NAMESPACES) sub = element.find('m:sub', NAMESPACES) return f"{omml_to_latex(e)}_{{{omml_to_latex(sub)}}}" elif tag == 'sSubSup': # Subscript + Superscript e = element.find('m:e', NAMESPACES) sub = element.find('m:sub', NAMESPACES) sup = element.find('m:sup', NAMESPACES) base = omml_to_latex(e) sub_l = omml_to_latex(sub) if sub is not None else "" sup_l = omml_to_latex(sup) if sup is not None else "" return f"{base}_{{{sub_l}}}^{{{sup_l}}}" elif tag == 'd': # Delimiter beg_chr = element.find('m:dPr/m:begChr', NAMESPACES) end_chr = element.find('m:dPr/m:endChr', NAMESPACES) beg = beg_chr.get(f"{{{NAMESPACES['m']}}}val") if beg_chr is not None else "(" end = end_chr.get(f"{{{NAMESPACES['m']}}}val") if end_chr is not None else ")" content = "".join(omml_to_latex(child) for child in element if etree.QName(child).localname != 'dPr') return f"{beg}{content}{end}" elif tag == 'r': # Run return "".join(omml_to_latex(child) for child in element) elif tag == 't': # Text return _safe_text_replace_math_unicode(element.text or "") elif tag == 'limLow': base = omml_to_latex(element.find('m:e', NAMESPACES)) low = omml_to_latex(element.find('m:lim', NAMESPACES)) return f"{base}_{{{low}}}" elif tag == 'limUpp': base = omml_to_latex(element.find('m:e', NAMESPACES)) upp = omml_to_latex(element.find('m:lim', NAMESPACES)) return f"{base}^{{{upp}}}" elif tag == 'int': lower = _find_first(element, ['m:sub', 'm:low']) upper = _find_first(element, ['m:sup', 'm:up']) e = element.find('m:e', NAMESPACES) lower_ltx = omml_to_latex(lower) if lower is not None else "" upper_ltx = omml_to_latex(upper) if upper is not None else "" e_ltx = omml_to_latex(e) if e is not None else "" if lower_ltx or upper_ltx: return f"\\int_{{{lower_ltx}}}^{{{upper_ltx}}}{e_ltx}" else: return f"\\int {e_ltx}" elif tag == 'nary': op_char = _detect_nary_op_char(element) lower_tag = _find_first(element, ['m:sub', 'm:low']) upper_tag = _find_first(element, ['m:sup', 'm:up']) content_tag = element.find('m:e', NAMESPACES) lower_ltx = omml_to_latex(lower_tag) if lower_tag is not None else '' upper_ltx = omml_to_latex(upper_tag) if upper_tag is not None else '' content_ltx = omml_to_latex(content_tag) if content_tag is not None else '' op_ltx = NARY_TO_LATEX.get(op_char, _safe_text_replace_math_unicode(op_char)) if not op_ltx and (lower_ltx or upper_ltx): op_ltx = '\\int' if lower_ltx or upper_ltx: return f"{op_ltx}_{{{lower_ltx}}}^{{{upper_ltx}}}{content_ltx}" else: return f"{op_ltx} {content_ltx}".rstrip() return "".join(omml_to_latex(child) for child in element) def split_latex_blocks(s: str): if not s: return [] parts = [p.strip() for p in re.split(r'\\\\', s) if p.strip()] return parts if parts else [s.strip()] def get_slide_title(slide): """スライドからタイトルを抽出します。""" for shape in slide.shapes: if getattr(shape, "has_text_frame", False) and shape.is_placeholder and shape.placeholder_format.type == 1: title_text = shape.text if title_text: return title_text.strip() for shape in slide.shapes: if getattr(shape, "has_text_frame", False): first_text = (shape.text or "").strip() if first_text: return first_text.split('\n')[0] return "無題のスライド" def extract_content_to_markdown(pptx_path, output_md = None, image_dir = None, include_xml = False): """ PPTXファイルからコンテンツを抽出し、Markdownファイルに保存します。 - 数式: oMathPara を優先、oMathPara 配下の oMath は除外して重複回避 - 複数行の式は 1 行 = 1 ブロック $$ ... $$ に分割（Pandoc対策） """ try: presentation = Presentation(pptx_path) except Exception as e: print(f"エラー: ファイル '{pptx_path}' を開けませんでした。{e}") return if image_dir: os.makedirs(image_dir, exist_ok=True) print(f"Extracting text & math from PPTX: {pptx_path.name}...") slides_data = {} markdown_output = "" for i, slide in enumerate(presentation.slides): title = get_slide_title(slide) page_output = f"# スライド {i + 1}　{title}\n\n" # スライドの生XML slide_xml = slide.part.blob root = etree.fromstring(slide_xml) # テキスト text_elements = root.xpath('//a:t', namespaces=NAMESPACES) if text_elements: slide_text = " ".join([elem.text for elem in text_elements if elem.text]) if slide_text.strip(): page_output += f"## テキスト\n\n{slide_text.strip()}\n\n" # 数式（ブロック優先: oMathPara と、oMathPara外の単独oMath） math_elements = root.xpath( '//m:oMathPara | //m:oMath[not(ancestor::m:oMathPara)]', namespaces=NAMESPACES ) if math_elements: page_output += "## 数式\n\n" seen_omml = set() for math_elem in math_elements: # 重複ガード（OMML文字列単位） omml_string = etree.tostring(math_elem, encoding='unicode') if omml_string in seen_omml: continue seen_omml.add(omml_string) tag = etree.QName(math_elem).localname # oMathPara は直下の oMath を1行ごとに出力 if tag == 'oMathPara': inner_oms = math_elem.findall('m:oMath', NAMESPACES) if inner_oms: for om in inner_oms: latex_code = omml_to_latex(om) for one_line in split_latex_blocks(latex_code): one_line = one_line.replace('\\mathrm{d}', '\\,\\mathrm{d}') page_output += f"$$ {one_line} $$\n\n" else: # フォールバック latex_code = omml_to_latex(math_elem) for one_line in split_latex_blocks(latex_code): one_line = one_line.replace('\\mathrm{d}', '\\,\\mathrm{d}') page_output += f"$$ {one_line} $$\n\n" if include_xml: page_output += f"**元のOMML XML:**\n```xml\n{omml_string.strip()}\n```\n\n" # 単独 oMath はそのまま（ただし \\ があれば分割） else: latex_code = omml_to_latex(math_elem) for one_line in split_latex_blocks(latex_code): one_line = one_line.replace('\\mathrm{d}', '\\,\\mathrm{d}') page_output += f"$$ {one_line} $$\n\n" if include_xml: page_output += f"**元のOMML XML:**\n```xml\n{omml_string.strip()}\n```\n\n" # 画像 image_elements = root.xpath('//a:blip[@r:embed]', namespaces=NAMESPACES) if image_dir and image_elements: page_output += "## 図\n\n" for j, image_elem in enumerate(image_elements): r_id = image_elem.get('{http://schemas.openxmlformats.org/officeDocument/2006/relationships}embed') try: image_part = slide.part.rels[r_id].target_part image_data = image_part.blob image_ext = image_part.content_type.split('/')[-1] image_filename = f"slide{i+1}_image{j+1}.{image_ext}" image_path = os.path.join(image_dir, image_filename) with open(image_path, 'wb') as f: f.write(image_data) page_output += f"![スライド{i+1}の図{j+1}]({os.path.join(os.path.basename(image_dir), image_filename)})\n\n" except KeyError: page_output += f"図のリンクが見つかりませんでした (rId: {r_id})\n\n" page_output += "\n" page_output += "\n---\n\n" slides_data[i] = page_output markdown_output += page_output # print("page_output:", page_output) if output_md: with open(output_md, "w", encoding="utf-8") as f: f.write(markdown_output) print(f"抽出が完了しました。結果は '{output_md}' に保存されました。") else: # for key, val in slides_data.items(): # print(f"{key}: {val}") return slides_data # ========================================================= # 2. Text Parsing (Existing Markdown File Support) # ========================================================= SLIDE_HEADER_RE = re.compile(r"^\s*#\s*Slide\s+(\d+)", re.IGNORECASE) def load_slide_texts(txt_path: Path) -> Dict[int, str]: slides: Dict[int, str] = {} if not txt_path or not txt_path.exists(): return {} print(f"Loading text from file: {txt_path.name}") current_no: Optional[int] = None buf: List[str] = [] with txt_path.open("r", encoding="utf-8") as f: for line in f: m = SLIDE_HEADER_RE.match(line) if m: if current_no is not None: slides[current_no] = "".join(buf).strip() current_no = int(m.group(1)); buf = [] else: buf.append(line) if current_no is not None: slides[current_no] = "".join(buf).strip() return slides # ========================================================= # 3. PDF/Image Conversion # ========================================================= def pptx_to_pdf(pptx_path: Path, pdf_path: Path, visible: bool = False) -> None: abs_pptx = str(pptx_path.resolve()) abs_pdf = str(pdf_path.resolve()) if pdf_path.exists(): return print(f"Converting PPTX to PDF...") try: app = win32com.client.Dispatch("PowerPoint.Application") if not visible: app.DisplayAlerts = 0 presentation = app.Presentations.Open(abs_pptx, ReadOnly=True, Untitled=False, WithWindow=visible) try: presentation.SaveAs(abs_pdf, 32) finally: presentation.Close() if not visible: app.Quit() except Exception as e: print(f"PPTX->PDF Error: {e}"); raise e def convert_pdf_to_images(pdf_path: Path, out_dir: Path) -> List[Path]: if not out_dir.exists(): out_dir.mkdir(parents=True, exist_ok=True) doc = fitz.open(str(pdf_path.resolve())) paths = [] print(f"Converting PDF to Images...") for i in range(len(doc)): mat = fitz.Matrix(2.0, 2.0) # 高解像度化 pix = doc.load_page(i).get_pixmap(matrix=mat) p = out_dir / f"slide_{i+1:03d}.png" pix.save(str(p)) paths.append(p) doc.close() return paths # ========================================================= # 3. AI 呼び出し部 # ========================================================= def call_gemini(model_name, slide_no, slide_text, image_path, lang): prompt = prompt_template\ .replace("{slide_no}", f"{slide_no}")\ .replace("{slide_text}", slide_text)\ .replace("{lang}", lang)\ .strip() print(f"\nprompt:", prompt) model = genai.GenerativeModel(model_name) img_data = {'mime_type': 'image/png', 'data': image_path.read_bytes()} try: res = model.generate_content([prompt, img_data]) return res.text except Exception as e: return f"Gemini Error: {e}" def call_openai(client, model, slide_no, slide_text, image_path, lang): b64 = base64.b64encode(image_path.read_bytes()).decode('utf-8') prompt = prompt_template\ .replace("{slide_no}", f"{slide_no}")\ .replace("{slide_text}", slide_text)\ .replace("{lang}", lang)\ .strip() print(f"\nprompt:", prompt) res = client.chat.completions.create( model=model, messages=[{"role": "user", "content": [ {"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}} ]}] ) return res.choices[0].message.content def main(): global prompt_template, pause print() parser, args = parse_args() if args.pause: pause = 1 if args.output: out_md = out_md = args.output else: out_md = infile_path.with_name(infile_path.stem + ".md") # out_md = infile_path.with_name(infile_path.stem + "_explanation.md") ini_path = find_ini_path(args.ini) if ini_path: try: ini_data = read_ini(ini_path) if "prompt_template" in ini_data: prompt_template = ini_data["prompt_template"] print(f"Loaded prompt_template from {ini_path}") else: print(f"prompt_template key not found in {ini_path}, using default.") except Exception as e: print(f"Warning: failed to read ini ({e}), using default prompt.") else: print(f"INI not found ({args.ini}), using default prompt.") # print("prompt_template:", prompt_template) print(f"infile: {args.infile}") infile_path = Path(args.infile) work_dir = infile_path.parent / (infile_path.stem + "_work") img_dir = work_dir / "slides_png" work_dir.mkdir(exist_ok=True) # 1. Text/Math Extraction slide_texts = {} if args.txt and Path(args.txt).exists(): slide_texts = load_slide_texts(Path(args.txt)) elif infile_path.suffix.lower() == ".pptx": # PPTXから高度な数式抽出を実行 slide_texts = extract_content_to_markdown(infile_path) # 2. PDF/Image Conversion if infile_path.suffix.lower() == ".pptx": pdf_path = work_dir / (infile_path.stem + ".pdf") pptx_to_pdf(infile_path, pdf_path, visible=args.visible) else: # PDF入力の場合 pdf_path = infile_path images = convert_pdf_to_images(pdf_path, img_dir) # 3. AI Setup if args.api == "openai5": model = args.openai_model5 elif args.api == "openai": model = args.openai_model else: model = args.google_model print(f"Using API: {args.api} / Model: {model}") client_openai = None if args.api == "openai" or args.api == "openai5": if args.openai_key is None: print("Error: OPENAI_API_KEY missing.") sys.exit(1) client_openai = OpenAI() elif args.api == "gemini": if args.gemini_key is None: print("Error: GEMINI_KEY and GOOGLE_API_KEY missing.") sys.exit(1) genai.configure(api_key=args.gemini_key) # 4. Processing with open(out_md, "w", encoding="utf-8") as f: f.write(f"# Analysis Report: {infile_path.name}\n\n---\n") for i, img_p in enumerate(images, start=1): print() print(f"Processing Slide {i}/{len(images)}...", end=" ", flush=True) txt = slide_texts.get(i-1, "(テキスト情報なし)") try: if args.api in ("openai5", "openai"): content = call_openai(client_openai, model, i, txt, img_p, args.language) else: content = call_gemini(model, i, txt, img_p, args.language) chunk = f"\n# Slide {i}\n\n{content}\n\n---\n" print("Done.") time.sleep(1) except Exception as e: print(f"Error: {e}") chunk = f"# Slide {i}\n\nError: {e}\n\n---\n" # ✅ 毎回 append モードで開いて書き込む with open(out_md, "a", encoding="utf-8") as f: f.write(chunk) print(f"\nSaved to: {out_md}") if __name__ == "__main__": main() terminate()