Build CompText Universe inside Hugging Face Space

[ProfRandom92]

Implements the approved CompText Universe + Compression Lab design while modifying only hf_space/**.

Includes:

• static Universe snapshot with provenance and maturity states
• architecture, capability, and skill explorers
• deterministic non-executable AIR previews
• simulated non-persistent Evidence previews
• existing fail-closed hybrid compression and benchmarks
• explicit no-provider/no-repository-write boundaries
• isolated HF tests and design/implementation docs

Boundary verification: every changed path begins with hf_space/.

The Space remains an experimental public demonstration; the CompText runtime, CLI, schemas, workflows, providers, and TUI are untouched.

[gemini-code-assist]

Code Review

This pull request transforms the existing Hugging Face Space into 'CompText Universe,' a public showcase for the CompText local orchestration architecture. It introduces a modular UI with new tabs for architecture, capabilities, and contract previews, while implementing static data loading, secret scanning, and comprehensive testing. The reviewer suggested improving consistency in the UI return types for secret detection and refactoring string literals into constants in the preview module for better maintainability.

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[gemini-code-assist]

The object returned for the compression_metrics UI component is inconsistent. In this 'secret detected' case, you return a payload dictionary with lowercase keys. In the successful compression case, you return a metrics dictionary with capitalized keys and a different structure. This can be confusing for the UI and any programmatic consumers.

I suggest creating a metrics dictionary here that is consistent with the one in the success path, and use that for the UI component, while keeping the payload for the raw JSON output.

Suggested change

	payload = {
	"decision": "blocked",
	"reason": "Potential secret material detected. Input was not processed.",
	"secret_matches": secret_matches,
	}
	return text, payload, pd.DataFrame(), json.dumps(payload, ensure_ascii=False, indent=2), None
	metrics = {
	"Decision": "blocked",
	"Reason": "Potential secret material detected. Input was not processed.",
	"Secret matches": secret_matches,
	}
	payload = {
	"decision": "blocked",
	"reason": "Potential secret material detected. Input was not processed.",
	"secret_matches": secret_matches,
	}
	return text, metrics, pd.DataFrame(), json.dumps(payload, ensure_ascii=False, indent=2), None

[gemini-code-assist]

For improved readability and maintainability, the tuples of string literals used for matching constraints should be extracted into module-level constants. This avoids 'magic values' within the function and makes them easier to manage and reuse.

For example:

_NO_PROVIDER_TERMS = ("no provider", "keine provider", "keine live-provider", "without provider")
_NO_WRITES_TERMS = ("do not write", "nicht ändern", "nicht veraendern", "read only", "nur lesen")
_NO_COMMITS_TERMS = ("do not commit", "nicht commit", "no commit")

def _constraints(text: str) -> list[str]:
    lower = text.lower()
    constraints: list[str] = []
    if any(term in lower for term in _NO_PROVIDER_TERMS):
        constraints.append("no_provider_calls")
    if "--dry-run" in text:
        constraints.append("dry_run_only")
    if any(term in lower for term in _NO_WRITES_TERMS):
        constraints.append("no_writes")
    if any(term in lower for term in _NO_COMMITS_TERMS):
        constraints.append("no_commits")
    return sorted(set(constraints))