Providers: omit temperature for OpenAI reasoning models

* fix: omit temperature for OpenAI reasoning models (o1/o3/o4/gpt-5)

These models only accept the default temperature; sending any explicit
value (even 0.0) returns HTTP 400 "Only the default (1) value is
supported". This broke two paths:

- Endpoint probing in _probe_single_model hardcodes temperature: 0.0, so
  a perfectly valid o3/gpt-5 endpoint is reported as failing in the
  Model Endpoints health check.
- Chat/stream payloads send temperature unconditionally, so a non-default
  temperature preset 400s on these models.

The code already special-cases the same model family for
max_completion_tokens, so this adds a sibling _restricts_temperature()
helper and omits the field for those models, letting the API use its
required default. gpt-4.5 is intentionally excluded (not a reasoning
model; accepts temperature normally).

Adds tests/test_llm_core_temperature.py covering the predicate and the
synchronous payload builder.

* fix: also omit temperature for reasoning models on the direct-POST paths

The first commit only covered llm_call/llm_call_async/stream_llm and the
endpoint probe. Email auto-summary, urgency-less spam classification, the
email reply-summary endpoint, and gallery vision tagging build their
OpenAI payloads inline and POST them directly (requests/httpx), bypassing
llm_core — so a reasoning model configured there would still 400 on the
temperature field. These sites already branch on _uses_max_completion_tokens,
so they're the same class; added the matching _restricts_temperature guard.

gallery_routes also gains the max_completion_tokens branch it was missing,
so gpt-5 vision tagging works end to end.

Note: email_pollers urgency scoring goes through llm_call_async and was
already covered.

routes/email_pollers.py

@@ -132,7 +132,7 @@ async def _auto_summarize_pass_single(days_back: int = 1, account_id: str | None
     import sqlite3 as _sql3
     import requests as _req
     from src.endpoint_resolver import resolve_endpoint
-    from src.llm_core import _uses_max_completion_tokens
+    from src.llm_core import _uses_max_completion_tokens, _restricts_temperature
 
     settings = _load_settings()
     auto_sum = settings.get("email_auto_summarize", False)
@@ -355,6 +355,9 @@ async def _auto_summarize_pass_single(days_back: int = 1, account_id: str | None
                         "temperature": 0.3,
                         "stream": False,
                     }
+                    # Reasoning models (o1/o3/o4/gpt-5) reject an explicit temperature.
+                    if _restricts_temperature(model):
+                        payload.pop("temperature", None)
                     try:
                         # Use to_thread so this sync HTTP call doesn't freeze
                         # the entire event loop while the LLM thinks (240s).
@@ -806,6 +809,9 @@ async def _auto_summarize_pass_single(days_back: int = 1, account_id: str | None
                             "temperature": 0.1,
                             "stream": False,
                         }
+                        # Reasoning models (o1/o3/o4/gpt-5) reject an explicit temperature.
+                        if _restricts_temperature(model):
+                            payload.pop("temperature", None)
                         # to_thread keeps the event loop responsive during the LLM call
                         resp = await asyncio.to_thread(
                             _req.post, url, json=payload, headers=req_headers, timeout=120