feat: 错误提示中文化、GPT Image 余额查询、Nano Banana 自动重试
- gemini_client: 更新 429/504 中文错误文案，与产品文案对齐 - gpt_image_client: 新增 query_balance_sync / format_balance_info 余额查询方法 - gpt_image: 每次执行后打印余额日志（finally 块保证触发） - nano_banana_pro: 单张生成支持自动重试，遇 429/503/504 最多重试 4 次，指数退避 2-32s，终端显示显眼重试状态 - nano_banana_pro: 关闭调试日志（DEBUG_LOG_ENABLED = False） Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
This commit is contained in:
o1key
co-authored by
Claude Sonnet 4.5
Claude Sonnet 4.5
parent
949f7bb180
commit
fe3cc65b71
@@ -115,13 +115,13 @@ class GeminiAPIClient(BaseAPIClient):
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return "/v1beta/models/gemini-3-pro-image-preview:generateContent"
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def get_http_error_message(self, status_code: int, error_message: str) -> Optional[str]:
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"""Gemini 请求 429/503 时返回图中约定的多行错误框文案。"""
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"""Gemini 请求 429/503/504 时返回中文错误文案。"""
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if status_code == 429:
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return "该型号资源已耗尽，但只是暂时的，请稍后重试。"
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return "此型号资源暂时耗尽，继续重试即可"
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if status_code == 503:
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return "此型号目前需求量较大。需求高峰通常是暂时的。请稍后再试。"
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if status_code == 504:
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return "服务无法在截止期限内完成处理。请稍后重试。"
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return "服务无法在截止期限内完成处理。可能原因是：您的提示词过大，无法及时处理。"
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return None
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def build_request_body(
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@@ -417,3 +417,35 @@ class GptImageClient:
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raise RuntimeError(
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f"o1key GPT Image 请求超时（>{_REQUEST_TIMEOUT}s），请检查网络或稍后重试"
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)
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# ── 余额查询 ──────────────────────────────────────────────────────────────
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async def _query_balance_async(self) -> dict:
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url = f"{self.base_url}/api/usage/token"
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connector = aiohttp.TCPConnector(ssl=False, force_close=True)
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timeout = aiohttp.ClientTimeout(total=10)
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async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
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async with session.get(url, headers=self._auth_headers()) as resp:
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if resp.status != 200:
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raise RuntimeError(f"余额查询失败 HTTP {resp.status}")
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return await resp.json()
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def query_balance_sync(self) -> dict:
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def _run():
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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return loop.run_until_complete(self._query_balance_async())
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finally:
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loop.close()
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with ThreadPoolExecutor(max_workers=1) as executor:
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return executor.submit(_run).result(timeout=15)
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@staticmethod
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def format_balance_info(balance_data: dict) -> str:
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data = balance_data.get("data", {})
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api_name = data.get("name", "未知")
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total_available = data.get("total_available", 0)
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balance_in_dollars = total_available / 500000
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return f"当前余额：{balance_in_dollars:.2f} | API：{api_name}"
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+17
-5
@@ -141,10 +141,11 @@ class O1keyGPTImage:
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raise ValueError("未授权！") from None
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raise
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# ── 4. 解析批量提示词 ─────────────────────────────────────────────────
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try:
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# ── 4. 解析批量提示词 ─────────────────────────────────────────────
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batch_prompts = parse_batch_prompts(prompt)
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# ── 5. 调用 API ───────────────────────────────────────────────────────
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# ── 5. 调用 API ───────────────────────────────────────────────────
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all_pil_images = []
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if batch_prompts:
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@@ -193,14 +194,14 @@ class O1keyGPTImage:
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print(f"[o1key GPT Image] ❌ {error_msg}")
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raise RuntimeError(error_msg) from None
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# ── 6. 检查是否有可用图像 ─────────────────────────────────────────────
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# ── 6. 检查是否有可用图像 ─────────────────────────────────────────
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if not all_pil_images:
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raise RuntimeError("所有提示词均生成失败，无可用图像输出")
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# ── 7. PIL → tensor ───────────────────────────────────────────────────
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# ── 7. PIL → tensor ───────────────────────────────────────────────
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output_tensor = GptImageClient._pil_list_to_tensor(all_pil_images)
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# ── 8. 完成日志 ───────────────────────────────────────────────────────
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# ── 8. 完成日志 ───────────────────────────────────────────────────
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elapsed = time.time() - start_time
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print(
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f"[o1key GPT Image] 完成！耗时 {elapsed:.1f}s，"
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@@ -209,3 +210,14 @@ class O1keyGPTImage:
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)
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return (output_tensor,)
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finally:
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self._print_balance(client)
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def _print_balance(self, client):
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try:
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balance_data = client.query_balance_sync()
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balance_info = client.format_balance_info(balance_data)
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print(f"[o1key GPT Image] {balance_info}")
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except Exception:
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pass
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@@ -52,7 +52,7 @@ except ImportError:
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# ============================================================================
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# 是否启用调试日志（打印完整的 API 响应内容）
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# 设置为 True 以启用调试日志，False 以禁用
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DEBUG_LOG_ENABLED = True
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DEBUG_LOG_ENABLED = False
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# 是否启用请求体日志（打印发送给 API 的请求体，base64 图片数据将自动截断）
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# 设置为 True 以启用请求体日志，False 以禁用
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REQUEST_LOG_ENABLED = False
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@@ -665,7 +665,11 @@ class NanoBananaPro:
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else:
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# 单提示词模式
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if 生图数量 == 1:
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# 单张：同步生成，输出 tensor
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# 单张：同步生成，自动重试（429/503/504）
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_RETRY_CODES = ("429", "503", "504")
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_MAX_RETRIES = 5
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for _attempt in range(1, _MAX_RETRIES + 1):
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try:
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generated_images = self.client.generate_sync(
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prompt=prompt,
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model=模型,
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@@ -679,6 +683,19 @@ class NanoBananaPro:
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enable_grounding=enable_grounding,
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enable_image_search=enable_image_search,
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)
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break
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except RuntimeError as e:
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error_msg = str(e)
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if any(code in error_msg for code in _RETRY_CODES) and _attempt < _MAX_RETRIES:
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_wait = 2 ** _attempt
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print(f"{'=' * 60}")
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print(f"⚠️ Nano Banana Pro 自动重试 [{_attempt}/{_MAX_RETRIES - 1}]")
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print(f" 原因：{error_msg}")
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print(f" 等待 {_wait}s 后重试...")
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print(f"{'=' * 60}")
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time.sleep(_wait)
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else:
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raise
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else:
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# 多张：异步并发，内存输出
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