implement dropdown when hf inference provider is selected.

[sayakpaul]

Fixes #3

[sayakpaul]

@askserge could you review the PR?

[sergereview]

🤗 Serge says:

The PR adds a dropdown of HF Inference Provider tool-capable models when the "hf" provider is selected, falling back to a free-text input for other providers or when the router is unreachable. The implementation is mostly solid, but there's a correctness bug in the Python caching logic that prevents empty (but valid) fetch results from being cached, causing repeated network requests.

Correctness

• _fetch_hf_router_models in reviewbot/webapp.py uses if cached and ... to decide whether to return the cached list. Because an empty list is falsy in Python, a successful fetch that returns no tool-capable models (or an empty list seeded in tests) will never be cached and will trigger a fresh HTTP request on every call. The check should use fetched_at > 0 (or cached is not None with a sentinel) to distinguish "never fetched" from "fetched and empty".

Security / Trust boundaries

• The new /llm-options/hf-models endpoint correctly gates behind _require_user, so unauthenticated callers cannot enumerate models.
• No secrets are logged or exposed in the new fetch path.

Client-side

• The JS correctly guards against provider-switch races by re-checking providerEl.value === "hf" after the async fetch.
• The text input remains the named form control and stays in sync with the hidden <select> via change listeners, so submitted values are correct.

Tests

• Tests cover the payload parser, garbage tolerance, and the endpoint with a seeded cache. They do not exercise the TTL expiry or the "empty list should be cached" path.

8 LLM turns · 9 tool calls · 416.4s · 92348 in / 22161 out tokens

[sergereview]

The cache check if cached and ... treats an empty list as a cache miss. This means a successful fetch that returns [] (or an empty cache seeded in tests) will never be served from cache and will trigger a fresh HTTP request every time.

Use the timestamp to determine whether the cache has been populated instead:

Suggested change

	if cached and now - _hf_models_cache["fetched_at"] < _HF_MODELS_TTL_SECONDS:
	with _hf_models_lock:
	cached = _hf_models_cache["models"]
	if _hf_models_cache["fetched_at"] > 0 and now - _hf_models_cache["fetched_at"] < _HF_MODELS_TTL_SECONDS:
	return cached

[tarekziade]

What Serge says :)

[sayakpaul]

@askserge could you review the PR?

[sergereview]

🤗 Serge says:

The PR cleanly implements a dropdown of tool-capable HF Inference Provider models when the "hf" provider is selected, falling back to a free-text input for other providers or when the router is unreachable. The server-side parsing, caching, and endpoint are well-tested, and the client-side sync between the hidden text input and the visible select keeps form submission straightforward. No correctness or security issues were found.

6 LLM turns · 9 tool calls · 79.5s · 62779 in / 3908 out tokens

[sayakpaul]

@tarekziade