refactor(ocr): extract _record_training for shared metric bookkeeping

The /train, /train-sender, and /segtrain endpoints each duplicated the
same eight-line try/except + counter + gauge block around the
asyncio.to_thread call. Lift it into _record_training(runner, kind),
which accepts a sync- or async-returning callable for flexibility.
Each endpoint now ends with a single return line. Behaviour preserved —
status codes, error propagation, and metric labels stay identical.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ocr-service/main.py

@@ -2,6 +2,7 @@
 
 import asyncio
 import glob
+import inspect
 import io
 import json
 import logging
@@ -14,6 +15,7 @@ import time
 import zipfile
 from contextlib import asynccontextmanager
 from datetime import datetime, timezone
+from typing import Awaitable, Callable
 from urllib.parse import urlparse
 
 import httpx
@@ -50,6 +52,30 @@ ALLOWED_PDF_HOSTS = set(
 _SPELL_CHECK_SCRIPT_TYPES = {"HANDWRITING_KURRENT", "HANDWRITING_LATIN"}
 
 
+async def _record_training(
+    runner: Callable[[], Awaitable[dict] | dict],
+    kind: str,
+) -> dict:
+    """Run a training callable and record outcome + accuracy metrics.
+
+    Wraps the per-endpoint try/except + outcome counter + accuracy gauge
+    block that used to be repeated at /train, /train-sender, and /segtrain.
+    The runner returns a dict with at least an `accuracy` key; if its value
+    is None, the gauge is left at its default.
+    """
+    try:
+        result = runner()
+        if inspect.isawaitable(result):
+            result = await result
+    except Exception:
+        metrics.ocr_training_runs_total.labels(kind=kind, outcome="error").inc()
+        raise
+    metrics.ocr_training_runs_total.labels(kind=kind, outcome="success").inc()
+    if result.get("accuracy") is not None:
+        metrics.ocr_model_accuracy.labels(kind=kind).set(result["accuracy"])
+    return result
+
+
 def _observe_block_words(words: list[dict], threshold: float) -> None:
     """Record per-block word counts and below-threshold word counts.
 
@@ -504,15 +530,7 @@ async def train_model(
 
             return {"loss": None, "accuracy": accuracy, "cer": cer, "epochs": epochs}
 
-    try:
+    return await _record_training(lambda: asyncio.to_thread(_run_training), kind="recognition")
-        result = await asyncio.to_thread(_run_training)
-    except Exception:
-        metrics.ocr_training_runs_total.labels(kind="recognition", outcome="error").inc()
-        raise
-    metrics.ocr_training_runs_total.labels(kind="recognition", outcome="success").inc()
-    if result.get("accuracy") is not None:
-        metrics.ocr_model_accuracy.labels(kind="recognition").set(result["accuracy"])
-    return result
 
 
 @app.post("/train-sender")
@@ -591,15 +609,9 @@ async def train_sender_model(
 
             return {"loss": None, "accuracy": accuracy, "cer": cer, "epochs": epochs}
 
-    try:
+    return await _record_training(
-        result = await asyncio.to_thread(_run_sender_training)
+        lambda: asyncio.to_thread(_run_sender_training), kind="recognition"
-    except Exception:
+    )
-        metrics.ocr_training_runs_total.labels(kind="recognition", outcome="error").inc()
-        raise
-    metrics.ocr_training_runs_total.labels(kind="recognition", outcome="success").inc()
-    if result.get("accuracy") is not None:
-        metrics.ocr_model_accuracy.labels(kind="recognition").set(result["accuracy"])
-    return result
 
 
 @app.post("/segtrain")
@@ -708,15 +720,7 @@ async def segtrain_model(
 
             return {"loss": None, "accuracy": accuracy, "cer": cer, "epochs": epochs}
 
-    try:
+    return await _record_training(lambda: asyncio.to_thread(_run_segtrain), kind="segmentation")
-        result = await asyncio.to_thread(_run_segtrain)
-    except Exception:
-        metrics.ocr_training_runs_total.labels(kind="segmentation", outcome="error").inc()
-        raise
-    metrics.ocr_training_runs_total.labels(kind="segmentation", outcome="success").inc()
-    if result.get("accuracy") is not None:
-        metrics.ocr_model_accuracy.labels(kind="segmentation").set(result["accuracy"])
-    return result
 
 
 async def _download_and_convert_pdf(url: str) -> list[Image.Image]: