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/gallery_routes.py

@@ -1707,7 +1707,7 @@ def setup_gallery_routes() -> APIRouter:
                 return {"error": "No vision-capable endpoint configured"}
 
             # Call vision model — format differs between Anthropic and OpenAI
-            from src.llm_core import _detect_provider
+            from src.llm_core import _detect_provider, _restricts_temperature, _uses_max_completion_tokens
             provider = _detect_provider(chat_url)
             tag_prompt = (
                 "Analyze this photo. Return ONLY a comma-separated list of tags. "
@@ -1732,6 +1732,7 @@ def setup_gallery_routes() -> APIRouter:
                     }],
                 }
             else:
+                _tok_key = "max_completion_tokens" if _uses_max_completion_tokens(model_name) else "max_tokens"
                 payload = {
                     "model": model_name,
                     "messages": [{
@@ -1741,9 +1742,12 @@ def setup_gallery_routes() -> APIRouter:
                             {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}},
                         ],
                     }],
-                    "max_tokens": 200,
+                    _tok_key: 200,
                     "temperature": 0.3,
                 }
+                # Reasoning models (o1/o3/o4/gpt-5) reject an explicit temperature.
+                if _restricts_temperature(model_name):
+                    payload.pop("temperature", None)
 
             h = {"Content-Type": "application/json"}
             if headers: