2380d33861
- Context Compaction System with token counting and summarization - Deterministic State Machine for flow control (no LLM decisions) - Parallel Execution Engine (up to 12 concurrent sessions) - Event-Driven Coordination via Event Bus - Agent Workspace Isolation (tools, memory, identity, files) - YAML Workflow Integration (OpenClaw/Lobster compatible) - Claude Code integration layer - Complete demo UI with real-time visualization - Comprehensive documentation and README Components: - agent-system/: Context management, token counting, subagent spawning - pipeline-system/: State machine, parallel executor, event bus, workflows - skills/: AI capabilities (LLM, ASR, TTS, VLM, image generation, etc.) - src/app/: Next.js demo application Total: ~100KB of production-ready TypeScript code
110 lines
3.5 KiB
Python
Executable File
110 lines
3.5 KiB
Python
Executable File
import re
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import html
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import sys
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from typing import Dict
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# ---------- Step 0: restore literal unicode escapes/entities to real chars ----------
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_RE_UNICODE_ESC = re.compile(r"(\\u[0-9a-fA-F]{4})|(\\U[0-9a-fA-F]{8})|(\\x[0-9a-fA-F]{2})")
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def _restore_escapes(s: str) -> str:
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# HTML entities: ³ ≤ α ...
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s = html.unescape(s)
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# Literal backslash escapes: "\\u00B3" -> "³"
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def _dec(m: re.Match) -> str:
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esc = m.group(0)
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try:
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if esc.startswith("\\u") or esc.startswith("\\U"):
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return chr(int(esc[2:], 16))
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if esc.startswith("\\x"):
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return chr(int(esc[2:], 16))
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except Exception:
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return esc
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return esc
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return _RE_UNICODE_ESC.sub(_dec, s)
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# ---------- Step 1: superscripts/subscripts -> <super>/<sub> ----------
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_SUPERSCRIPT_MAP: Dict[str, str] = {
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"⁰": "0", "¹": "1", "²": "2", "³": "3", "⁴": "4",
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"⁵": "5", "⁶": "6", "⁷": "7", "⁸": "8", "⁹": "9",
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"⁺": "+", "⁻": "-", "⁼": "=", "⁽": "(", "⁾": ")",
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"ⁿ": "n", "ᶦ": "i",
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}
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_SUBSCRIPT_MAP: Dict[str, str] = {
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"₀": "0", "₁": "1", "₂": "2", "₃": "3", "₄": "4",
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"₅": "5", "₆": "6", "₇": "7", "₈": "8", "₉": "9",
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"₊": "+", "₋": "-", "₌": "=", "₍": "(", "₎": ")",
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"ₐ": "a", "ₑ": "e", "ₕ": "h", "ᵢ": "i", "ⱼ": "j",
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"ₖ": "k", "ₗ": "l", "ₘ": "m", "ₙ": "n", "ₒ": "o",
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"ₚ": "p", "ᵣ": "r", "ₛ": "s", "ₜ": "t", "ᵤ": "u",
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"ᵥ": "v", "ₓ": "x",
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}
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def _replace_super_sub(s: str) -> str:
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out = []
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for ch in s:
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if ch in _SUPERSCRIPT_MAP:
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out.append(f"<super>{_SUPERSCRIPT_MAP[ch]}</super>")
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elif ch in _SUBSCRIPT_MAP:
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out.append(f"<sub>{_SUBSCRIPT_MAP[ch]}</sub>")
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else:
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out.append(ch)
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return "".join(out)
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# ---------- Step 2: symbol fallback for SimHei (protect tags, then replace) ----------
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_SYMBOL_FALLBACK: Dict[str, str] = {
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# Currently empty - enable entries as needed for fonts missing specific glyphs
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# "±": "+/-",
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# "×": "*",
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# "÷": "/",
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# "≤": "<=",
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# "≥": ">=",
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# "≠": "!=",
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# "≈": "~=",
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# "∞": "inf",
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}
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def _fallback_symbols(s: str) -> str:
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# Protect <super>/<sub> tags from being modified
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placeholders = {}
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def _protect_tag(m: re.Match) -> str:
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key = f"@@TAG{len(placeholders)}@@"
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placeholders[key] = m.group(0)
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return key
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protected = re.sub(r"</?super>|</?sub>", _protect_tag, s)
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# Replace symbols
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protected = "".join(_SYMBOL_FALLBACK.get(ch, ch) for ch in protected)
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# Restore tags
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for k, v in placeholders.items():
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protected = protected.replace(k, v)
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return protected
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def sanitize_code(text: str) -> str:
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"""
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Full sanitization pipeline for PDF generation code.
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- Restore unicode escapes/entities to real characters
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- Replace superscript/subscript unicode with <super>/<sub>
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- Replace other risky symbols with ASCII/text fallbacks
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"""
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s = _restore_escapes(text)
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s = _replace_super_sub(s)
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s = _fallback_symbols(s)
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return s
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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print("Usage: python sanitize_code.py <target_script.py>")
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sys.exit(1)
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target = sys.argv[1]
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with open(target, "r", encoding="utf-8") as f:
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code = f.read()
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sanitized = sanitize_code(code)
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with open(target, "w", encoding="utf-8") as f:
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f.write(sanitized)
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print(f"Sanitized: {target}") |