6a2632da70
将 Clover 从上层产品包旧仓库中独立出来,建立专属版本控制。 当前状态=纵切片端到端已打通(登录→选品→出文出图→审核→下载包), M1文案质量去套路化已验收。此提交作为后续按核销清单逐条修复的基线。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
226 lines
11 KiB
Python
226 lines
11 KiB
Python
"""
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gemini_factory.py — 每任务构建独立的 AI client 实例
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解决全局单例问题（扒 banana gemini_service.py __init__，改造为每任务局部实例）
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铁律（基石B）：
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- 调用方只传 task_id，不传 key
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- 本模块在 worker 内部查库 → Fernet 解密 → 构建 client
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- 解密结果只活在局部变量，函数返回后即销毁
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- 绝不打印 / 记录 / 传递明文 key
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"""
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from __future__ import annotations
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import asyncio
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import logging
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import os
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from dataclasses import dataclass, field
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from typing import Any
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import httpx
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logger = logging.getLogger(__name__)
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@dataclass
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class AIClients:
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"""
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一个任务专用的 AI client 集合。
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worker 在任务开始时构建，任务结束后释放（局部变量，不存 Redis/DB）。
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"""
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# httpx AsyncClient 懒加载 + 按事件循环缓存：Celery 每任务多次 asyncio.run，
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# 持久 client 会绑死到首个已关闭的 loop → "Event loop is closed"。
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# 故只存 token/base，按当前运行 loop 缓存 client，loop 变了就重建。
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_gpt_token: str | None = field(default=None, repr=False)
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_gpt_base: str | None = field(default=None, repr=False)
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_gpt_client: httpx.AsyncClient | None = field(default=None, repr=False)
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_gpt_client_loop_id: int | None = field(default=None, repr=False)
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# 备用 OpenAI 兼容中转站（codeproxy）：apiports 503 时真正切过去（独立 base+key）
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_alt_token: str | None = field(default=None, repr=False)
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_alt_base: str | None = field(default=None, repr=False)
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# 多 base/token 的 client 池：key=(base,token的id)，按 loop 失效重建
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_client_pool: dict = field(default_factory=dict, repr=False)
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_pool_loop_id: int | None = field(default=None, repr=False)
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_gemini_key: str | None = field(default=None, repr=False) # 局部变量不打印
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_model_image: str = "gpt-image-2"
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_model_text: str = "claude-sonnet-4-5" # apiports无gpt-4o-mini,文案用claude中文质量好
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def _client(self) -> httpx.AsyncClient:
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"""主通道(apiports) client，按当前事件循环缓存"""
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return self._client_for(self._gpt_base, self._gpt_token)
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def _client_for(self, base: str | None, token: str | None) -> httpx.AsyncClient:
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"""按 (base, token) 返回可用 client；loop 变化则整池重建（避免跨 loop 复用）"""
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if not token:
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raise RuntimeError("GPT client 未初始化（缺 token）")
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loop_id = id(asyncio.get_running_loop())
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if self._pool_loop_id != loop_id:
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self._client_pool = {}
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self._pool_loop_id = loop_id
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ck = (base or "", token)
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if ck not in self._client_pool:
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self._client_pool[ck] = httpx.AsyncClient(
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headers={"Authorization": f"Bearer {token}"},
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base_url=base or None,
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timeout=120.0,
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)
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return self._client_pool[ck]
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# ── ImageClient 协议实现（供 image_gen.py 使用）────────
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def _gpt_target(self, provider: str | None) -> tuple[str, str | None, httpx.AsyncClient]:
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"""按 provider 选 (base, token, client)；codeproxy 走备用站独立 base+key"""
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if provider == "codeproxy" and self._alt_token:
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base = (self._alt_base or os.environ.get("CODEPROXY_BASE_URL") or "").rstrip("/")
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return base, self._alt_token, self._client_for(base, self._alt_token)
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base = (os.environ.get("IMAGE_API_BASE") or os.environ.get("APIPORTS_BASE_URL") or "").rstrip("/")
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return base, self._gpt_token, self._client_for(self._gpt_base, self._gpt_token)
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async def gpt_edits(self, prompt: str, reference_images: list[bytes], size: str, provider: str | None = None) -> bytes:
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"""GPT edits endpoint（带产品参考图，禁纯文生图）"""
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import io
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files: list[tuple] = [("prompt", (None, prompt))]
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for i, img in enumerate(reference_images):
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files.append(("image[]", (f"ref_{i}.png", io.BytesIO(img), "image/png")))
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files.append(("size", (None, size)))
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files.append(("model", (None, self._model_image)))
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base, _, client = self._gpt_target(provider)
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resp = await client.post(f"{base}/images/edits", files=files, timeout=120.0)
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resp.raise_for_status()
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return _extract_image_bytes(resp.json())
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async def gpt_generate(self, prompt: str, size: str, provider: str | None = None) -> bytes:
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"""GPT 纯文生图（仅 ALLOW_TEXT_ONLY_IMAGE=true 时用）"""
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base, _, client = self._gpt_target(provider)
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payload = {"model": self._model_image, "prompt": prompt, "n": 1, "size": size}
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resp = await client.post(f"{base}/images/generations", json=payload, timeout=120.0)
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resp.raise_for_status()
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return _extract_image_bytes(resp.json())
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async def gemini_generate(self, prompt: str, reference_images: list[bytes], model: str) -> bytes:
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"""Gemini 生图（备用通道）"""
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if not self._gemini_key:
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raise RuntimeError("Gemini key 未初始化")
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gemini_base = os.environ.get("GEMINI_API_URL", "https://generativelanguage.googleapis.com/v1beta")
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url = f"{gemini_base}/models/{model}:generateContent?key={self._gemini_key}"
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parts: list[dict] = [{"text": prompt}]
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for img in reference_images:
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import base64
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parts.append({"inline_data": {"mime_type": "image/png", "data": base64.b64encode(img).decode()}})
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payload = {"contents": [{"role": "user", "parts": parts}], "generationConfig": {"responseModalities": ["IMAGE", "TEXT"]}}
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async with httpx.AsyncClient() as client:
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resp = await client.post(url, json=payload, timeout=120.0)
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resp.raise_for_status()
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return _extract_gemini_image(resp.json())
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async def chat_complete(self, messages: list[dict], model: str | None = None, max_tokens: int = 4096, temperature: float = 0.75) -> str:
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"""文字生成（文案生成用）"""
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base = (os.environ.get("IMAGE_API_BASE") or os.environ.get("APIPORTS_BASE_URL") or "").rstrip("/")
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payload = {"model": model or self._model_text, "messages": messages, "max_tokens": max_tokens, "temperature": temperature}
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# 单批≤4条文案正常 40-55s 返回；apiports 网关 ~60s 上限。客户端超时设 75s：
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# 略高于网关上限即可，过长（如180s）会在 apiports 卡顿时干等，拖慢整体。
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timeout = float(os.environ.get("TEXT_LLM_TIMEOUT", "75"))
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resp = await self._client().post(f"{base}/chat/completions", json=payload, timeout=timeout)
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resp.raise_for_status()
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return resp.json()["choices"][0]["message"]["content"] or ""
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async def gpt_vision_analyze(self, prompt: str, images: list[bytes], model: str | None = None) -> str:
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"""
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GPT/Claude vision 读产品图，返回 JSON 字符串。
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messages content 混合 text + image_url(base64)，OpenAI vision 格式。
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model 默认最强档（claude-opus-4-8），绝不偷降级。
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最多传 4 张图，避免超 token。
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"""
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import base64
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content: list[dict] = [{"type": "text", "text": prompt}]
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for img in images[:4]:
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b64 = base64.b64encode(img).decode()
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content.append({
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{b64}"},
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})
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used_model = model or os.environ.get("MODEL_TEXT", "claude-opus-4-8")
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messages = [{"role": "user", "content": content}]
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base = (os.environ.get("IMAGE_API_BASE") or os.environ.get("APIPORTS_BASE_URL") or "").rstrip("/")
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payload = {
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"model": used_model,
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"messages": messages,
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"max_tokens": 2048,
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"temperature": 0.2,
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}
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resp = await self._client().post(f"{base}/chat/completions", json=payload, timeout=90.0)
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resp.raise_for_status()
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return resp.json()["choices"][0]["message"]["content"] or ""
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# duck-type: text_variants._call_llm 用的属性
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@property
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def _model(self) -> str:
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return self._model_text
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async def aclose(self) -> None:
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# client 可能绑在已关闭的 loop（Celery 多次 asyncio.run），aclose 也可能报
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# "Event loop is closed"，吞掉即可——进程级连接随 loop 关闭自然释放。
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for c in list(self._client_pool.values()):
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try:
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await c.aclose()
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except Exception:
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pass
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self._client_pool = {}
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self._pool_loop_id = None
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self._gpt_client = None
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self._gpt_client_loop_id = None
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def build_ai_clients(plain_key: str, gemini_key: str | None = None) -> AIClients:
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"""
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用解密后的明文 key 构建 AIClients。
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只在 Celery worker 函数体内调用，plain_key 是局部变量。
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httpx client 不在此预创建（避免绑死到调用方 loop），首次 await 时按 loop 懒建。
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调用完成后 caller 负责 await clients.aclose()。
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"""
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gpt_base = (
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os.environ.get("IMAGE_API_BASE") # 旧变量名
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or os.environ.get("APIPORTS_BASE_URL") # .env 实际变量名
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or ""
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).rstrip("/")
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# 备用站 codeproxy：系统级 key（非用户录入），apiports 503 时切过去保生图成功
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alt_base = (os.environ.get("CODEPROXY_BASE_URL") or "").rstrip("/")
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alt_token = os.environ.get("CODEPROXY_KEY") or None
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return AIClients(
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_gpt_token=plain_key,
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_gpt_base=gpt_base or None,
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_alt_base=alt_base or None,
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_alt_token=alt_token,
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_gemini_key=gemini_key,
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_model_image=os.environ.get("IMAGE_MODEL") or os.environ.get("MODEL_IMAGE", "gpt-image-2"),
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_model_text=os.environ.get("MODEL_TEXT", "claude-opus-4-8"),
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)
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# ── 图片响应解析工具 ─────────────────────────────────────
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def _extract_image_bytes(resp_json: dict) -> bytes:
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"""从 OpenAI images API 响应提取图片 bytes（b64 或 url）"""
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import base64
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data = resp_json.get("data", [{}])
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if not data:
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raise ValueError("图片 API 返回空 data")
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item = data[0]
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if "b64_json" in item:
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return base64.b64decode(item["b64_json"])
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if "url" in item:
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resp = httpx.get(item["url"], timeout=30.0)
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resp.raise_for_status()
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return resp.content
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raise ValueError(f"无法解析图片响应：{list(item.keys())}")
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def _extract_gemini_image(resp_json: dict) -> bytes:
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"""从 Gemini generateContent 响应提取图片 bytes"""
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import base64
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candidates = resp_json.get("candidates", [])
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for cand in candidates:
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parts = cand.get("content", {}).get("parts", [])
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for part in parts:
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if "inlineData" in part:
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return base64.b64decode(part["inlineData"]["data"])
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raise ValueError("Gemini 响应中未找到图片数据")
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