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_routes.py

@@ -2419,7 +2419,7 @@ def setup_email_routes():
         """Generate a quick AI summary of an email body."""
         try:
             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
             import requests as _req
 
             body = data.get("body", "")
@@ -2476,6 +2476,9 @@ def setup_email_routes():
                 "temperature": 0.3,
                 "stream": False,
             }
+            # Reasoning models (o1/o3/o4/gpt-5) reject an explicit temperature.
+            if _restricts_temperature(model):
+                payload.pop("temperature", None)
             resp = await asyncio.to_thread(
                 _req.post, url, json=payload, headers=req_headers, timeout=180
             )