Implementace prevodu HTML na PDF

Sluzba prijme adresu HTML dokumentu nebo HTML v tele requestu a vrati PDF.
Navrzena pro dokumenty o stovkach az tisicich stranek.

Rendering:
- WeasyPrint jako vychozi engine, spravne CSS Paged Media, nizka pametova
  narocnost, bez JavaScriptu
- Chromium pres Playwright pro dokumenty dokreslovane skripty
- rezim auto s detekci skriptu a fallbackem pri selhani WeasyPrintu

Velke dokumenty:
- deleni na casti na strukturalnich hranicich, rez nikdy uvnitr tabulky
  nebo odstavce
- dvoupruchodovy render obsahu se skutecnymi cisly stranek, pozice nadpisu
  se ctou z kotev hlasenych u kazde stranky
- cislovani stranek bud pres CSS countery, nebo pres cislovaci vrstvu
  nastampovanou na hotove PDF, rozmer stranky se cte z vysledneho souboru
- Chromium se restartuje po N jobech, nikdy vsak behem beziciho renderu

API:
- POST /convert synchronne, POST /jobs asynchronne se sledovanim stavu,
  stahovanim vysledku, rusenim a volitelnym callbackem
- GET /health s overenim dostupnosti obou enginu a stavem fronty
- OpenAPI respektuje prefix reverse proxy pres root_path

Bezpecnost a provoz:
- SSRF kontrola po DNS resolvu, na kazdem presmerovani a u vsech pozadavku
  prohlizece
- nedostupne assety render nezastavi, ale hlasi se v odpovedi i v logu
- fronta s omezenym poctem workeru, rozpracovane joby se pri ukonceni
  oznaci jako failed, nezmizi potichu
- strukturovane JSON logovani s job_id
- vsechny limity vypnute ve vychozim stavu

Dockerfile je dvoufazovy, obsahuje zavislosti WeasyPrintu, Chromium
a fonty s ceskou diakritikou.

Autentizace zamerne neni implementovana, zpusob predavani neni domluveny.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

app/logging_setup.py

@@ -0,0 +1,58 @@
+"""Structured JSON logging.
+
+Every log record is a single JSON line. Anything related to a job carries the
+job_id so the whole conversion can be reconstructed from the log.
+"""
+
+import json
+import logging
+import sys
+from contextvars import ContextVar
+
+current_job_id: ContextVar[str | None] = ContextVar("current_job_id", default=None)
+
+_RESERVED = {
+    "args", "asctime", "created", "exc_info", "exc_text", "filename", "funcName",
+    "levelname", "levelno", "lineno", "module", "msecs", "message", "msg", "name",
+    "pathname", "process", "processName", "relativeCreated", "stack_info",
+    "thread", "threadName", "taskName",
+}
+
+
+class JsonFormatter(logging.Formatter):
+    def format(self, record: logging.LogRecord) -> str:
+        payload: dict[str, object] = {
+            "ts": self.formatTime(record, "%Y-%m-%dT%H:%M:%S%z"),
+            "level": record.levelname,
+            "logger": record.name,
+            "message": record.getMessage(),
+        }
+
+        job_id = getattr(record, "job_id", None) or current_job_id.get()
+        if job_id:
+            payload["job_id"] = job_id
+
+        for key, value in record.__dict__.items():
+            if key not in _RESERVED and not key.startswith("_") and key != "job_id":
+                payload[key] = value
+
+        if record.exc_info:
+            payload["exception"] = self.formatException(record.exc_info)
+
+        return json.dumps(payload, ensure_ascii=False, default=str)
+
+
+def setup_logging(level: str) -> None:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(JsonFormatter())
+
+    root = logging.getLogger()
+    root.handlers.clear()
+    root.addHandler(handler)
+    root.setLevel(getattr(logging, level, logging.INFO))
+
+    # uvicorn keeps its own handlers, route them through ours as well
+    for name in ("uvicorn", "uvicorn.access", "uvicorn.error"):
+        logger = logging.getLogger(name)
+        logger.handlers.clear()
+        logger.propagate = True