feat: add Gemini image fallback circuit breaker
This commit is contained in:
202
api/main.py
202
api/main.py
@@ -97,14 +97,24 @@ AI_HTTP_PROXY = (
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or os.getenv("http_proxy")
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or ""
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).strip()
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# Product decision: every image-generation/editing path is locked to gpt-image-2.
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# Environment variables may still choose the gateway URL/key, but not the model.
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# Product decision: gpt-image-2 remains the primary image model. Gemini is only
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# allowed as an outage fallback when the primary gateway times out or returns
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# transient upstream failures.
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GPT_IMAGE_MODEL = "gpt-image-2"
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IMAGE_FALLBACK_MODEL = os.getenv("IMAGE_FALLBACK_MODEL", "gemini-3-pro-image-preview").strip() or ""
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IMAGE_FALLBACK_ENABLED = os.getenv("IMAGE_FALLBACK_ENABLED", "true").strip().lower() not in {"0", "false", "no", "off"}
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IMAGE_MODEL = GPT_IMAGE_MODEL
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PRODUCT_VIEW_MODEL = GPT_IMAGE_MODEL
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SUBJECT_ASSET_IMAGE_MODEL = GPT_IMAGE_MODEL
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SUBJECT_ASSET_IMAGE_MODELS = [GPT_IMAGE_MODEL]
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SUBJECT_ASSET_IMAGE_MODELS = [GPT_IMAGE_MODEL] + (
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[IMAGE_FALLBACK_MODEL] if IMAGE_FALLBACK_ENABLED and IMAGE_FALLBACK_MODEL and IMAGE_FALLBACK_MODEL != GPT_IMAGE_MODEL else []
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)
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IMAGE_REQUEST_TIMEOUT_SECONDS = max(15, min(180, int(os.getenv("IMAGE_REQUEST_TIMEOUT_SECONDS", "60"))))
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IMAGE_CIRCUIT_FAILURE_THRESHOLD = max(1, int(os.getenv("IMAGE_CIRCUIT_FAILURE_THRESHOLD", "2")))
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IMAGE_CIRCUIT_COOLDOWN_SECONDS = max(60, int(os.getenv("IMAGE_CIRCUIT_COOLDOWN_SECONDS", "600")))
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_IMAGE_CIRCUIT_LOCK = threading.Lock()
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_IMAGE_PRIMARY_FAILURES = 0
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_IMAGE_PRIMARY_OPEN_UNTIL = 0.0
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PRODUCT_ASSET_MAX_SIDE = max(1024, int(os.getenv("PRODUCT_ASSET_MAX_SIDE", "1600")))
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PRODUCT_ASSET_MIN_LONG_SIDE = max(512, int(os.getenv("PRODUCT_ASSET_MIN_LONG_SIDE", "900")))
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PRODUCT_ASSET_MIN_SHORT_SIDE = max(320, int(os.getenv("PRODUCT_ASSET_MIN_SHORT_SIDE", "600")))
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@@ -3511,6 +3521,83 @@ def _image_is_transport_error(message: str) -> bool:
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)
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def _image_fallback_models() -> list[str]:
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if not IMAGE_FALLBACK_ENABLED or not IMAGE_FALLBACK_MODEL or IMAGE_FALLBACK_MODEL == GPT_IMAGE_MODEL:
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return []
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return [IMAGE_FALLBACK_MODEL]
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def _image_circuit_snapshot() -> dict:
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now = time.time()
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with _IMAGE_CIRCUIT_LOCK:
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open_until = _IMAGE_PRIMARY_OPEN_UNTIL
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return {
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"primary": GPT_IMAGE_MODEL,
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"fallbacks": _image_fallback_models(),
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"failure_threshold": IMAGE_CIRCUIT_FAILURE_THRESHOLD,
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"cooldown_seconds": IMAGE_CIRCUIT_COOLDOWN_SECONDS,
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"primary_failures": _IMAGE_PRIMARY_FAILURES,
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"primary_open": open_until > now,
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"primary_open_until": open_until if open_until > now else 0,
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"primary_open_remaining_seconds": max(0, int(open_until - now)),
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}
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def _image_primary_circuit_open() -> bool:
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return _image_circuit_snapshot()["primary_open"]
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def _image_model_candidates(force_fallback: bool = False) -> list[str]:
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fallbacks = _image_fallback_models()
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if not fallbacks:
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return [GPT_IMAGE_MODEL]
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if force_fallback or _image_primary_circuit_open():
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return fallbacks
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return [GPT_IMAGE_MODEL, *fallbacks]
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def _image_failure_can_fallback(status_code: int, body: str, last_err: str) -> bool:
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if status_code in (400, 401, 403, 404):
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return False
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return (
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status_code == 429
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or status_code >= 500
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or _image_is_capacity_error(status_code, body)
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or _image_is_transport_error(last_err)
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or "timeout" in (body or "").lower()
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)
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def _image_record_primary_success() -> None:
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global _IMAGE_PRIMARY_FAILURES, _IMAGE_PRIMARY_OPEN_UNTIL
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with _IMAGE_CIRCUIT_LOCK:
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if _IMAGE_PRIMARY_FAILURES or _IMAGE_PRIMARY_OPEN_UNTIL:
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print(f"[image circuit] primary {GPT_IMAGE_MODEL} recovered", flush=True)
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_IMAGE_PRIMARY_FAILURES = 0
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_IMAGE_PRIMARY_OPEN_UNTIL = 0.0
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def _image_record_primary_failure(reason: str) -> None:
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global _IMAGE_PRIMARY_FAILURES, _IMAGE_PRIMARY_OPEN_UNTIL
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if not _image_fallback_models():
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return
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with _IMAGE_CIRCUIT_LOCK:
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_IMAGE_PRIMARY_FAILURES += 1
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if _IMAGE_PRIMARY_FAILURES >= IMAGE_CIRCUIT_FAILURE_THRESHOLD:
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_IMAGE_PRIMARY_OPEN_UNTIL = time.time() + IMAGE_CIRCUIT_COOLDOWN_SECONDS
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print(
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f"[image circuit] primary {GPT_IMAGE_MODEL} opened for {IMAGE_CIRCUIT_COOLDOWN_SECONDS}s "
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f"after {_IMAGE_PRIMARY_FAILURES} failures; fallback={IMAGE_FALLBACK_MODEL}; reason={reason[:220]}",
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flush=True,
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)
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else:
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print(
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f"[image circuit] primary {GPT_IMAGE_MODEL} failure {_IMAGE_PRIMARY_FAILURES}/{IMAGE_CIRCUIT_FAILURE_THRESHOLD}; "
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f"fallback={IMAGE_FALLBACK_MODEL}; reason={reason[:220]}",
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flush=True,
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)
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def _image_failure_message(kind: str, attempts: int, last_err: str, capacity_seen: bool) -> str:
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if capacity_seen:
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return (
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@@ -3604,36 +3691,37 @@ def _image_edit_call(
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fallback_text: bool = False,
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max_attempts: int = 3,
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max_side: int = 1024,
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force_fallback_model: bool = False,
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) -> tuple[bytes, str]:
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"""通用 image edit 调用 · 失败重试 + 可选 text fallback。
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返回 (image_bytes, effective_mode) where effective_mode in {"edit","text"}。
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失败 raise RuntimeError。
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输入图自动 resize 到 max_side（默认 1024）边长后再用 multipart 上传；多参考图使用 image[]。
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生图模型按产品规则强制使用 gpt-image-2；model/models 参数只保留兼容旧调用。"""
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生图模型主路径使用 gpt-image-2；Gemini 只在主模型上游异常时兜底。model/models 参数只保留兼容旧调用。"""
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import base64 as b64lib
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import time as _time
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import httpx
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if not IMAGE_API_KEY:
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raise RuntimeError("IMAGE_API_KEY 或 LLM_API_KEY 未配置")
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models_cycle = [GPT_IMAGE_MODEL]
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model = GPT_IMAGE_MODEL
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image_paths = image_path if isinstance(image_path, list) else [image_path]
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image_paths = [path for path in image_paths if path and path.exists()][:10]
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if not image_paths:
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raise RuntimeError("image edit reference image missing")
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img_bytes_list = [_prepare_image_edit_bytes(path, max_side) for path in image_paths]
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plan: list[str] = ["edit"] * max_attempts
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model_candidates = _image_model_candidates(force_fallback=force_fallback_model)
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mode_plan: list[str] = ["edit"] if model_candidates != [GPT_IMAGE_MODEL] else ["edit"] * max_attempts
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if fallback_text:
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plan.append("text")
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mode_plan.append("text")
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attempt_steps = [(current_mode, current_model) for current_mode in mode_plan for current_model in model_candidates]
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last_err = ""
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resp_data: dict = {}
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effective_mode = "edit"
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capacity_seen = False
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attempts_done = 0
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for attempt, current_mode in enumerate(plan):
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for attempt, (current_mode, current_model) in enumerate(attempt_steps):
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attempts_done = attempt + 1
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current_model = models_cycle[min(attempt, len(models_cycle) - 1)]
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status_code = 0
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body = ""
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retry_after: str | None = None
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@@ -3660,8 +3748,10 @@ def _image_edit_call(
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else:
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resp_data = _image_generation_response(prompt, current_model)
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if resp_data.get("data"):
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effective_mode = current_mode
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effective_mode = f"{current_mode}:{current_model}"
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model = current_model # 记录实际成功的 model
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if current_model == GPT_IMAGE_MODEL:
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_image_record_primary_success()
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break
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err_obj = resp_data.get("error") or {}
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last_err = f"empty data · {err_obj.get('code', '')} · {str(err_obj.get('message', ''))[:200]} · model={current_model}"
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@@ -3677,9 +3767,15 @@ def _image_edit_call(
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except Exception as e:
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last_err = f"{type(e).__name__}: {e} · model={current_model}"
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next_mode_changed = attempt < len(plan) - 1 and plan[attempt + 1] != current_mode
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if _image_should_retry(attempt, len(plan), status_code, body, last_err, next_mode_changed):
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tag = f"retry {attempt + 1}/{len(plan)} → {GPT_IMAGE_MODEL}"
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fallbackable = current_model == GPT_IMAGE_MODEL and _image_failure_can_fallback(status_code, body, last_err)
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if fallbackable:
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_image_record_primary_failure(last_err)
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if any(next_model != GPT_IMAGE_MODEL for _next_mode, next_model in attempt_steps[attempt + 1:]):
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print(f"[image edit fallback → {IMAGE_FALLBACK_MODEL}] {last_err}", flush=True)
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continue
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next_mode_changed = attempt < len(attempt_steps) - 1 and attempt_steps[attempt + 1][0] != current_mode
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if _image_should_retry(attempt, len(attempt_steps), status_code, body, last_err, next_mode_changed):
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tag = f"retry {attempt + 1}/{len(attempt_steps)} → {current_model}"
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delay = _image_retry_delay(attempt, status_code, body, retry_after)
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print(f"[image edit {tag}, sleep {delay:.0f}s] {last_err}", flush=True)
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_time.sleep(delay)
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@@ -3706,20 +3802,21 @@ def _image_text_call(
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model: str | None = None,
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models: list[str] | None = None,
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max_attempts: int = 3,
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force_fallback_model: bool = False,
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) -> tuple[bytes, str]:
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"""Text-only image generation. 生图模型强制使用 gpt-image-2。"""
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"""Text-only image generation. gpt-image-2 primary, Gemini only as outage fallback."""
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import base64 as b64lib
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import time as _time
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import httpx
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if not IMAGE_API_KEY:
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raise RuntimeError("IMAGE_API_KEY 或 LLM_API_KEY 未配置")
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models_cycle = [GPT_IMAGE_MODEL]
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candidates = _image_model_candidates(force_fallback=force_fallback_model)
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attempt_models = candidates if candidates != [GPT_IMAGE_MODEL] else [GPT_IMAGE_MODEL] * max_attempts
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last_err = ""
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capacity_seen = False
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attempts_done = 0
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for attempt in range(max_attempts):
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for attempt, current_model in enumerate(attempt_models):
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attempts_done = attempt + 1
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current_model = models_cycle[min(attempt, len(models_cycle) - 1)]
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status_code = 0
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body = ""
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retry_after: str | None = None
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@@ -3729,12 +3826,16 @@ def _image_text_call(
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item = resp_data["data"][0]
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b64 = item.get("b64_json")
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if b64:
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return b64lib.b64decode(b64), "text"
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if current_model == GPT_IMAGE_MODEL:
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_image_record_primary_success()
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return b64lib.b64decode(b64), f"text:{current_model}"
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if item.get("url"):
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with ai_http_client(timeout=IMAGE_REQUEST_TIMEOUT_SECONDS) as client:
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image_resp = client.get(item["url"])
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image_resp.raise_for_status()
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return image_resp.content, "text"
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if current_model == GPT_IMAGE_MODEL:
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_image_record_primary_success()
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return image_resp.content, f"text:{current_model}"
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err_obj = resp_data.get("error") or {}
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last_err = f"empty data · {err_obj.get('code', '')} · {str(err_obj.get('message', ''))[:200]} · model={current_model}"
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except httpx.HTTPStatusError as e:
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@@ -3748,9 +3849,15 @@ def _image_text_call(
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body = str(e)
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status_code = 429 if "429" in body or "saturated" in body.lower() or "饱和" in body else 0
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capacity_seen = capacity_seen or _image_is_capacity_error(status_code, body)
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if _image_should_retry(attempt, max_attempts, status_code, body, last_err):
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fallbackable = current_model == GPT_IMAGE_MODEL and _image_failure_can_fallback(status_code, body, last_err)
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if fallbackable:
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_image_record_primary_failure(last_err)
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if any(next_model != GPT_IMAGE_MODEL for next_model in attempt_models[attempt + 1:]):
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print(f"[image text fallback → {IMAGE_FALLBACK_MODEL}] {last_err}", flush=True)
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continue
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if _image_should_retry(attempt, len(attempt_models), status_code, body, last_err):
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delay = _image_retry_delay(attempt, status_code, body, retry_after)
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print(f"[image text retry {attempt + 1}/{max_attempts} → {GPT_IMAGE_MODEL}, sleep {delay:.0f}s] {last_err}", flush=True)
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print(f"[image text retry {attempt + 1}/{len(attempt_models)} → {current_model}, sleep {delay:.0f}s] {last_err}", flush=True)
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_time.sleep(delay)
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else:
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break
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@@ -4116,7 +4223,8 @@ def health() -> dict:
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"image_base_url": IMAGE_BASE_URL or LLM_BASE_URL or "openai-default",
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"image_request_timeout_seconds": IMAGE_REQUEST_TIMEOUT_SECONDS,
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"ai_proxy_configured": bool(AI_HTTP_PROXY),
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"image_fallbacks": [GPT_IMAGE_MODEL],
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"image_fallbacks": _image_fallback_models(),
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"image_circuit": _image_circuit_snapshot(),
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"subject_image": SUBJECT_ASSET_IMAGE_MODEL,
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"subject_image_fallbacks": SUBJECT_ASSET_IMAGE_MODELS,
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"voice_provider": VOICE_PROVIDER,
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@@ -4447,16 +4555,18 @@ def generate_image(job_id: str, idx: int, req: GenerateReq) -> Job:
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if req.mode == "edit":
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img_bytes_in = reference_path.read_bytes()
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# 尝试 i2i 最多 3 次，全失败时降级 text-only 再试 1 次
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plan: list[str] = ([req.mode] * 3) if req.mode == "edit" else [req.mode]
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# 尝试 i2i；主模型上游异常时允许 Gemini 兜底。无兜底时保留旧的多次重试。
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model_candidates = _image_model_candidates()
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plan: list[str] = ([req.mode] if model_candidates != [GPT_IMAGE_MODEL] else [req.mode] * 3) if req.mode == "edit" else [req.mode]
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if req.mode == "edit":
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plan.append("text") # i2i 都失败时自动降级
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attempt_steps = [(current_mode, current_model) for current_mode in plan for current_model in model_candidates]
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resp_data: dict = {}
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last_err = ""
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effective_mode = req.mode
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capacity_seen = False
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attempts_done = 0
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for attempt, current_mode in enumerate(plan):
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for attempt, (current_mode, current_model) in enumerate(attempt_steps):
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attempts_done = attempt + 1
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status_code = 0
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body = ""
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@@ -4471,20 +4581,23 @@ def generate_image(job_id: str, idx: int, req: GenerateReq) -> Job:
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headers={
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"Authorization": f"Bearer {IMAGE_API_KEY}",
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},
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data={"model": model, "prompt": full_prompt, "n": "1"},
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data={"model": current_model, "prompt": full_prompt, "n": "1"},
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files={"image": ("reference.jpg", img_bytes_in, "image/jpeg")},
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)
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r.raise_for_status()
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resp_data = r.json()
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else:
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# text-only
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resp_data = _image_generation_response(full_prompt, model)
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resp_data = _image_generation_response(full_prompt, current_model)
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if resp_data.get("data"):
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effective_mode = current_mode
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effective_mode = f"{current_mode}:{current_model}"
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model = current_model
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if current_model == GPT_IMAGE_MODEL:
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_image_record_primary_success()
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break
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err_obj = resp_data.get("error") or {}
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last_err = f"empty data · {err_obj.get('code', '')} · {str(err_obj.get('message', ''))[:200]}"
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last_err = f"empty data · {err_obj.get('code', '')} · {str(err_obj.get('message', ''))[:200]} · model={current_model}"
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except httpx.HTTPStatusError as e:
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body = e.response.text
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status_code = e.response.status_code
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@@ -4498,16 +4611,22 @@ def generate_image(job_id: str, idx: int, req: GenerateReq) -> Job:
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or "timeout" in body.lower()
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or _image_is_capacity_error(status_code, body)
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)
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last_err = f"HTTP {status_code}: {body[:200]}"
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last_err = f"HTTP {status_code}: {body[:200]} · model={current_model}"
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if not transient:
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raise HTTPException(500, f"image gen HTTP {status_code}: {body[:300]}")
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except Exception as e:
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last_err = f"{type(e).__name__}: {e}"
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last_err = f"{type(e).__name__}: {e} · model={current_model}"
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next_mode_changed = attempt < len(plan) - 1 and plan[attempt + 1] != current_mode
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if _image_should_retry(attempt, len(plan), status_code, body, last_err, next_mode_changed):
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next_mode = plan[attempt + 1]
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tag = f"fallback → {next_mode}" if next_mode != current_mode else f"retry {attempt + 1}/{len(plan)}"
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fallbackable = current_model == GPT_IMAGE_MODEL and _image_failure_can_fallback(status_code, body, last_err)
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if fallbackable:
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_image_record_primary_failure(last_err)
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if any(next_model != GPT_IMAGE_MODEL for _next_mode, next_model in attempt_steps[attempt + 1:]):
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print(f"[image gen fallback → {IMAGE_FALLBACK_MODEL}] {last_err}", flush=True)
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continue
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next_mode_changed = attempt < len(attempt_steps) - 1 and attempt_steps[attempt + 1][0] != current_mode
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if _image_should_retry(attempt, len(attempt_steps), status_code, body, last_err, next_mode_changed):
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next_mode = attempt_steps[attempt + 1][0]
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tag = f"fallback → {next_mode}" if next_mode != current_mode else f"retry {attempt + 1}/{len(attempt_steps)}"
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print(f"[image gen {tag}] {last_err}", flush=True)
|
||||
_time.sleep(_image_retry_delay(attempt, status_code, body, retry_after))
|
||||
else:
|
||||
@@ -5677,10 +5796,11 @@ def _generate_subject_assets_sync(job_id: str, idx: int, element_id: str, req: G
|
||||
"Avoid bulky collars, scarves, hair, hoods, props, or poses that hide the neck/shoulder placement area. "
|
||||
"For back and close-up views, prioritize the cervical spine, shoulder blades, upper trapezius, and clean wearable-device contact area. "
|
||||
)
|
||||
models = [GPT_IMAGE_MODEL]
|
||||
models = SUBJECT_ASSET_IMAGE_MODELS
|
||||
generated: list[SubjectAsset] = []
|
||||
generation_errors: list[str] = []
|
||||
first_generation_error: RuntimeError | None = None
|
||||
pack_force_fallback_model = _image_primary_circuit_open()
|
||||
try:
|
||||
for view, view_label in _subject_view_labels(req.subject_kind, req.views):
|
||||
closeup_view = view in {"bust", "back_detail", "bust_front", "bust_left_45", "bust_right_45", "back_neck_detail"} or "detail" in view
|
||||
@@ -5741,14 +5861,18 @@ def _generate_subject_assets_sync(job_id: str, idx: int, element_id: str, req: G
|
||||
try:
|
||||
if similar_mode:
|
||||
print(
|
||||
f"[subject assets] reconstruction_mode=similar endpoint=/images/generations view={view} image_refs=0 model={GPT_IMAGE_MODEL}",
|
||||
f"[subject assets] reconstruction_mode=similar endpoint=/images/generations view={view} image_refs=0 model={'fallback' if pack_force_fallback_model else GPT_IMAGE_MODEL}",
|
||||
flush=True,
|
||||
)
|
||||
img_bytes, _mode = _image_text_call(prompt, models=models, max_attempts=3)
|
||||
img_bytes, _mode = _image_text_call(prompt, models=models, max_attempts=3, force_fallback_model=pack_force_fallback_model)
|
||||
if _mode.endswith(f":{IMAGE_FALLBACK_MODEL}"):
|
||||
pack_force_fallback_model = True
|
||||
else:
|
||||
if model_src is None:
|
||||
raise RuntimeError("subject asset edit reference image missing")
|
||||
img_bytes, _mode = _image_edit_call(model_src, prompt, models=models, fallback_text=False, max_attempts=3, max_side=1280)
|
||||
img_bytes, _mode = _image_edit_call(model_src, prompt, models=models, fallback_text=False, max_attempts=3, max_side=1280, force_fallback_model=pack_force_fallback_model)
|
||||
if _mode.endswith(f":{IMAGE_FALLBACK_MODEL}"):
|
||||
pack_force_fallback_model = True
|
||||
except RuntimeError as e:
|
||||
if first_generation_error is None:
|
||||
first_generation_error = e
|
||||
|
||||
Reference in New Issue
Block a user