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feat(links): support ModelScope and TensorArt as model sources
A model file could only ever be linked to huggingface.co: `set_hf_url` validated the URL with a huggingface-only regex, the agent fetched the card from a hardcoded HF URL, and the readme processor built every relative image path off `https://huggingface.co/{repo}/resolve/main`. ModelScope publishes the same model-card convention (README.md + YAML frontmatter, often carrying `base_model:` and `trigger_words:`) behind a public, key-less API, so the enrichment pipeline could already serve it - it was the plumbing that was HF-shaped, not the idea. Make the external source a first-class, provider-driven concept: - New `py/services/model_sources/` registry. A `ModelSource` owns URL recognition (lenient for stored values, strict for user input), the canonical page URL, model-card fetching, the asset base URL and the capability flags. `HuggingFaceSource` is the previous logic relocated; `ModelScopeSource` reads `/models/{o}/{n}/resolve/{master|main}/README.md` and falls back to `/api/v1/models/{o}/{n}/repo`. `TensorArtSource` is link-only on purpose: tensor.art answers plain HTTP clients with a Cloudflare challenge and its internal API (ap-east-1.tensorart.cloud / cn.tensorart.net) rejects every /v1/model/* route with "invalid authorization header", so it declares supports_enrichment=False rather than failing silently later. - Metadata gains `source_platform` + `source_url`; `hf_url` stays as a read/write alias, written only for Hugging Face, so existing sidecars, cached rows and third-party consumers keep working. Normalisation runs at the scanner, the persistent cache (both directions, plus two new columns behind an ALTER migration) and the linking handler - which is what stops a user who switches sources from leaving a stale `hf_url` on a ModelScope model. - The agent pipeline keys off the provider instead of `hf_url`: the fast-fail gate now explains *why* a model is skipped (no source / unknown source / source without a reachable card), the prompt context exposes source_url/source_id/source_label/asset_base_url while still filling the legacy hf_url/repo aliases, and the four README image extractors take a base_url (defaulting to HF) so relative paths resolve against the right site. Version grouping generalises to hf: / ms: / ta: keys. - `POST /api/lm/set-hf-url` keeps its path and its legacy payload keys but accepts `source_url`, validates against every provider and returns the platform. `GET /api/lm/model-sources` lets the UI render the supported-site list from the server. - Frontend: a `modelSourceHelpers` mirror of the registry drives the link dialog, the card/modal globe (branded "View on ModelScope/TensorArt"), the version-group key and the enrichment gate; the versions tab no longer sends ms:/ta: keys to the CivitAI API. TensorArt stays in the list because provenance is worth keeping even when the card is unreadable - the dialog says so plainly ("Sites that don't expose one (currently TensorArt) can only be linked") and the context menu disables enrichment with a matching tooltip, instead of the user getting "Unsupported URL". Verified against the real ModelScope API: jj3550945163/Krea-2-LORA returns a 1882-byte card whose frontmatter carries base_model/tags/trigger_words, and relative images resolve to .../resolve/master/.... Tests: backend 2815 passed; frontend 1130 JS + 91 Vue passed; pytest tests/i18n and a Jinja compile pass over templates/. The nine locales carry [TODO: Translate] for the new strings, completed in the next commit.
This commit is contained in:
Will Miao
@@ -154,3 +154,155 @@ async def test_set_hf_url_rejects_non_repo_url(tmp_path, hf_env):
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payload = _json_payload(response)
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assert payload["success"] is False
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hf_env["cache_write"].assert_not_awaited()
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# ---------------------------------------------------------------------------
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# Multi-source linking (ModelScope / TensorArt)
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# ---------------------------------------------------------------------------
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async def _write_plain_model(model_path, sha: str = "d" * 64) -> None:
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await _write_model(
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model_path,
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{
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"file_name": "model",
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"model_name": "model",
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"file_path": str(model_path),
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"size": 32,
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"modified": 1.0,
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"sha256": sha,
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"base_model": "Unknown",
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"preview_url": "",
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},
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)
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@pytest.mark.asyncio
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async def test_set_hf_url_accepts_modelscope_and_stores_source_fields(tmp_path, hf_env):
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model_path = tmp_path / "ms_model.safetensors"
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await _write_plain_model(model_path)
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response = await HfHandler().set_hf_url(
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FakeRequest(
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json_data={
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"file_path": str(model_path),
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"source_url": "https://modelscope.cn/models/jj3550945163/Krea-2-LORA",
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}
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)
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)
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assert response.status == 200
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payload = _json_payload(response)
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assert payload["source_platform"] == "modelscope"
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assert payload["source_url"] == "https://modelscope.cn/models/jj3550945163/Krea-2-LORA"
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saved = json.loads(open(_sidecar_path(model_path), encoding="utf-8").read())
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assert saved["source_platform"] == "modelscope"
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assert saved["source_url"] == "https://modelscope.cn/models/jj3550945163/Krea-2-LORA"
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# No stale Hugging Face alias for a ModelScope model.
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assert saved.get("hf_url", "") == ""
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cached_metadata = hf_env["cache_write"].await_args.args[1]
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assert cached_metadata["source_platform"] == "modelscope"
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@pytest.mark.asyncio
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async def test_set_hf_url_accepts_tensorart_url(tmp_path, hf_env):
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model_path = tmp_path / "ta_model.safetensors"
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await _write_plain_model(model_path, sha="e" * 64)
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response = await HfHandler().set_hf_url(
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FakeRequest(
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json_data={
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"file_path": str(model_path),
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"source_url": (
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"https://tensor.art/models/827823520299086029/"
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"Vivid-Impressions-Storybook-Sstyle-V1.0"
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),
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}
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)
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)
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assert response.status == 200
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payload = _json_payload(response)
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assert payload["source_platform"] == "tensorart"
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# The canonical page URL is stored, without the slug.
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assert payload["source_url"] == "https://tensor.art/models/827823520299086029"
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@pytest.mark.asyncio
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async def test_set_hf_url_canonicalises_modelscope_subpage(tmp_path, hf_env):
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model_path = tmp_path / "ms_sub.safetensors"
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await _write_plain_model(model_path, sha="f" * 64)
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response = await HfHandler().set_hf_url(
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FakeRequest(
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json_data={
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"file_path": str(model_path),
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"source_url": "https://modelscope.cn/models/user/repo/summary",
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}
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)
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)
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assert response.status == 200
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assert _json_payload(response)["source_url"] == "https://modelscope.cn/models/user/repo"
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@pytest.mark.asyncio
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async def test_set_hf_url_is_idempotent_for_modelscope(tmp_path, hf_env):
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model_path = tmp_path / "ms_twice.safetensors"
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await _write_plain_model(model_path, sha="1" * 64)
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request = FakeRequest(
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json_data={
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"file_path": str(model_path),
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"source_url": "https://modelscope.cn/models/user/repo",
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}
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)
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await HfHandler().set_hf_url(request)
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await HfHandler().set_hf_url(request)
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# The second call short-circuits without rewriting the cache entry.
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assert hf_env["cache_write"].await_count == 1
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@pytest.mark.asyncio
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async def test_set_hf_url_switching_source_clears_hf_alias(tmp_path, hf_env):
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model_path = tmp_path / "switch.safetensors"
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await _write_plain_model(model_path, sha="2" * 64)
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await HfHandler().set_hf_url(
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FakeRequest(
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json_data={
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"file_path": str(model_path),
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"source_url": "https://huggingface.co/user/repo",
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}
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)
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)
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await HfHandler().set_hf_url(
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FakeRequest(
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json_data={
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"file_path": str(model_path),
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"source_url": "https://modelscope.cn/models/user/repo",
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}
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)
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)
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saved = json.loads(open(_sidecar_path(model_path), encoding="utf-8").read())
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assert saved["source_platform"] == "modelscope"
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assert saved.get("hf_url", "") == ""
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@pytest.mark.asyncio
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async def test_get_model_sources_lists_capabilities():
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response = await HfHandler().get_model_sources(FakeRequest())
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sources = _json_payload(response)
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by_platform = {s["platform"]: s for s in sources}
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assert set(by_platform) == {"huggingface", "modelscope", "tensorart"}
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assert by_platform["huggingface"]["supports_enrichment"] is True
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assert by_platform["modelscope"]["supports_enrichment"] is True
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# TensorArt is link-only: no accessible model card for the backend.
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assert by_platform["tensorart"]["supports_enrichment"] is False
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assert by_platform["modelscope"]["supports_download"] is False
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assert all(s["example_url"] for s in sources)
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