wehub-resource-sync
181 lines
8.0 KiB
Python
181 lines
8.0 KiB
Python
#!/usr/bin/env python3
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"""Create an editable source-file evidence list before code extraction."""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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from typing import Any
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from common import COPYRIGHT_CODE_EXTS, FRONTEND_EXTS, ensure_dir, is_known_config_file, iter_project_files, read_json, rel, write_json
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from extract_code_material import LINES_PER_PAGE, SPLIT_THRESHOLD_PAGES, category_weight, material_code_lines, should_skip_file
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DEFAULT_MAX_FILES = 0
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def evidence_for(path: Path, project: Path) -> str:
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priority, _ = category_weight(path, project)
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if priority == 0:
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return "入口文件证据"
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if priority == 10:
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return "路由文件证据"
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if priority == 20:
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return "页面文件证据"
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if priority == 30:
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return "数据交互文件证据"
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if priority == 40:
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return "状态或数据文件证据"
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if priority == 50:
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return "页面组成文件证据"
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if priority == 60:
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return "通用能力文件证据"
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if priority == 90:
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return "样式文件证据"
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if path.suffix.lower() not in FRONTEND_EXTS:
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return "补充源码证据"
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return "普通源码文件"
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def build_candidates(project: Path) -> list[dict[str, Any]]:
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files = [p for p in iter_project_files(project, COPYRIGHT_CODE_EXTS) if not should_skip_file(p) and not is_known_config_file(p)]
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files.sort(key=lambda p: category_weight(p, project))
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candidates: list[dict[str, Any]] = []
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for path in files:
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try:
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text = path.read_text(encoding="utf-8", errors="replace")
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line_count = len(text.splitlines())
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material_line_count = len(material_code_lines(text))
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except Exception:
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line_count = 0
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material_line_count = 0
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priority, _ = category_weight(path, project)
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candidates.append(
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{
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"path": rel(path, project),
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"selected": False,
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"line_count": line_count,
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"material_line_count": material_line_count,
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"priority": priority,
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"selection_tier": "frontend" if path.suffix.lower() in FRONTEND_EXTS else "supplement",
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"evidence": evidence_for(path, project),
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"model_reason": "",
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}
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)
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return candidates
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def selected_line_estimate(item: dict[str, Any]) -> int:
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total = int(item.get("material_line_count") or item.get("line_count") or 0)
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return total + 1 if total > 0 else 0
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def selection_stats(candidates: list[dict[str, Any]]) -> dict[str, int]:
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selected_items = [item for item in candidates if item.get("selected")]
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return {
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"selected_count": len(selected_items),
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"selected_lines": sum(selected_line_estimate(item) for item in selected_items),
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}
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def all_candidate_lines(candidates: list[dict[str, Any]]) -> int:
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return sum(selected_line_estimate(item) for item in candidates)
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def write_selection_md(path: Path, data: dict[str, Any]) -> None:
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lines = [
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"# 代码文件候选清单",
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"",
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"请先确认要抽取哪些源码文件，再运行代码材料抽取。",
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"",
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"本清单只列出候选源码证据，不默认决定抽取文件。",
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"模型需要先理解项目业务、页面入口和源码职责，再填写 `selected/model_reason`。",
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f"当前已选约 {data['estimated_selected_pages']} 页，全部候选源码约 {data['estimated_all_candidate_pages']} 页。",
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"",
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"```text",
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"STOP_FOR_USER",
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"NEXT_ACTION: 请由模型先填写 草稿/代码文件选择.json 的抽取选择和选择理由，再让用户确认；确认后运行 confirm_stage.py --stage code-selection。",
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"```",
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"",
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"确认方式：",
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"",
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"1. 模型根据项目业务和代码入口选择最能体现软件功能的文件。",
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"2. 把需要抽取的文件设为 `selected: true`，并填写 `model_reason`。",
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"3. 代码材料按完整文件抽取并去除纯空行，不支持只抽取某个文件的中间行段。",
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"4. 用户确认模型选择后，再记录 `code-selection` 门禁。",
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"",
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"## 默认选中文件",
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"",
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"| 文件 | 行数 | 模型选择理由 |",
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"| --- | ---: | --- |",
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]
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for item in data["files"]:
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if item.get("selected"):
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lines.append(f"| `{item['path']}` | {item['line_count']} | {item.get('model_reason') or '待模型填写'} |")
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lines.extend(["", "## 未选候选文件", "", "| 文件 | 行数 | 证据类型 |", "| --- | ---: | --- |"])
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for item in data["files"]:
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if not item.get("selected"):
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lines.append(f"| `{item['path']}` | {item['line_count']} | {item['evidence']} |")
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path.write_text("\n".join(lines) + "\n", encoding="utf-8")
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--project", required=True)
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parser.add_argument("--analysis", help="Optional project analysis JSON; retained for workflow traceability")
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parser.add_argument("--out-dir", default="软件著作权申请资料/草稿")
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parser.add_argument("--max-files", type=int, default=DEFAULT_MAX_FILES, help="Only limits candidate inventory size; does not auto-select files")
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parser.add_argument("--target-pages", type=int, default=SPLIT_THRESHOLD_PAGES)
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parser.add_argument("--lines-per-page", type=int, default=LINES_PER_PAGE)
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args = parser.parse_args()
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project = Path(args.project)
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if not project.exists():
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raise SystemExit(f"Project not found: {project}")
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if args.analysis and not Path(args.analysis).exists():
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raise SystemExit(f"Analysis JSON not found: {args.analysis}")
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out_dir = ensure_dir(Path(args.out_dir))
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candidates = build_candidates(project)
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target_lines = max(1, args.target_pages) * max(1, args.lines_per_page)
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if args.max_files:
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candidates = candidates[: args.max_files]
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stats = selection_stats(candidates)
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candidate_lines = all_candidate_lines(candidates)
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selected_pages = (stats["selected_lines"] + args.lines_per_page - 1) // args.lines_per_page if stats["selected_lines"] else 0
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all_pages = (candidate_lines + args.lines_per_page - 1) // args.lines_per_page if candidate_lines else 0
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data = {
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"project_root": str(project.resolve()),
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"selection_required": True,
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"model_selection_required": True,
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"confirmation_required": True,
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"user_confirmed": False,
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"target_pages": args.target_pages,
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"lines_per_page": args.lines_per_page,
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"target_lines": target_lines,
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"estimated_selected_lines": stats["selected_lines"],
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"estimated_selected_pages": selected_pages,
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"estimated_all_candidate_lines": candidate_lines,
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"estimated_all_candidate_pages": all_pages,
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"supplement_rule": "模型优先选择能体现软件核心功能和真实运行逻辑的源码；不足60页时再从其他相关源码中补充；候选源码仍不足时才生成全部代码材料。",
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"confirmation_stage": "code-selection",
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"next_action": "请由模型填写 草稿/代码文件选择.json 的抽取选择和选择理由，再让用户确认；确认后运行 confirm_stage.py --stage code-selection。",
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"instructions": "The script only inventories source files. The model must choose selected/model_reason before user confirmation. Selected files are extracted in full with blank-only lines removed.",
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"files": candidates,
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}
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write_json(out_dir / "代码文件选择.json", data)
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write_selection_md(out_dir / "代码文件候选清单.md", data)
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selected_count = sum(1 for item in candidates if item.get("selected"))
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print(f"OK code selection draft: {out_dir}")
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print(f"Candidates: {len(candidates)}")
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print(f"Model selected: {selected_count}")
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print(f"Estimated selected pages: {selected_pages}")
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print(f"Estimated all candidate pages: {all_pages}")
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print("STOP_FOR_USER")
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print(f"NEXT_ACTION: {data['next_action']}")
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if __name__ == "__main__":
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main()
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