4bed7425a8
A8 多套交付包(packaging_task.py): - 修复交付包只打第1条混乱note的bug,按ImageCandidate.strategy分A/B/C组 - 每组生独立note_0N夹(6图+文案.txt),同seq留最新去重,老数据兼容 - task74端到端验:3套各6图,独立agent7项交叉验证全过 M4 归档(tasks.py/exports.py/前端): - list_tasks加date_from/date_to/product_id筛选+product_name批量填(防N+1) - 新增exports.py:产品JSON导出+标杆CSV导出(UTF-8 BOM) - 前端HistoryFilters日期/产品筛选+产品列+打回原因红banner - response.py加raise_param_error;独立agent验A1/A2/A9通过 R5 产品多图(product_images.py/020迁移/前端): - product_images表+5端点(上传/列/改场景/设主图/删图) - 生图按ROLE_SCENE_PREFERENCE选对应场景图,回落primary - 前端ProductImageManager多图画廊 R6 账号config拆页(settings/): - 配置页按角色拆/settings(运营+组长+admin)+/config(仅admin) - Key只显末4位不显余额(守红线) 核销表对齐真实代码状态:D1改稿框/M7裂变/E12评图分纠偏为已完成(曾漏回写) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
344 lines
14 KiB
Python
344 lines
14 KiB
Python
"""
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app/workers/pipeline_io.py — 生产链 Step5-8
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Step5: 文案生成（generate_text_variants）
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Step6: 图片生成（generate_storyboard_images，asyncio.gather）
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Step7: 图片后处理（image_postprocessor）
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Step8: 存 text_candidates / image_candidates → 更新状态 → 推 task_done
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"""
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import asyncio
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import json
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import logging
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import os
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logger = logging.getLogger(__name__)
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def _resolve_image_path(img_path: str) -> str:
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"""
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解析产品参考图路径，兼容绝对路径(新)与历史相对路径(旧)。
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新数据存绝对路径(/app/uploads/...)直接返回；
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旧数据存相对路径(uploads/packages/...)锚定到 UPLOAD_ABS_ROOT 的父级，
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避免 worker(cwd=/) 解析失败。
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"""
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if not img_path:
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return ""
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if os.path.isabs(img_path):
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return img_path
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from app.core.config import get_settings
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# UPLOAD_ABS_ROOT=/app/uploads，其父级 /app 是相对路径(uploads/...)的锚点
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root_parent = os.path.dirname(get_settings().UPLOAD_ABS_ROOT.rstrip("/"))
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return os.path.join(root_parent, img_path)
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def run_text_generation(db, clients, task, product_dict: dict, flywheel_fragment: str,
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push_fn, workspace_id: int, seq_start: int) -> tuple[list, int, bool]:
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"""
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Step5: 调 generate_text_variants → 存 TextCandidate → 推 SSE → 写 ai_call_logs。
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S1: 存库前过滤——只存 passed且score>=QUALITY_PASS_SCORE(80,红线)且banned_word_status!='hard_block' 的文案。
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合格数 < task.text_count 时 needs_replenish=True（由主任务发起后台补充子任务）。
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返回 (candidates_raw, next_seq, needs_replenish)。
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"""
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import time
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from app.services.ai_engine.text_variants import generate_text_variants
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from app.models.product import BannedWord
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from app.models.task import TextCandidate
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from app.models.flywheel import AiCallLog
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from app.models.workspace import UserApiKey
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from app.constants.enums import CandidateSource, BannedWordStatus
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from app.services.ai_engine.constants import QUALITY_PASS_SCORE
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banned_rows = db.query(BannedWord).filter(
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BannedWord.workspace_id == workspace_id
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).all()
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banned_dicts = [{"word": b.word, "level": b.level, "replacement": b.replacement}
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for b in banned_rows]
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# 查 key_id（只取 id，不解密，不违反基石B）
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key_row = db.query(UserApiKey).filter(
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UserApiKey.user_id == task.operator_id,
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UserApiKey.workspace_id == workspace_id,
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).first()
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key_id = key_row.id if key_row else None
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t0 = time.monotonic()
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llm_success = True
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try:
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candidates_raw = asyncio.run(generate_text_variants(
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llm_client=clients,
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product=product_dict,
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count=task.text_count,
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banned_word_rows=banned_dicts,
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flywheel_context=flywheel_fragment,
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))
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except Exception as exc:
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llm_success = False
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logger.error("generate_text_variants 失败: %s", exc)
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candidates_raw = []
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latency_ms = int((time.monotonic() - t0) * 1000)
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# 写 ai_call_logs（留痕，不含明文key）
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try:
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log = AiCallLog(
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workspace_id=workspace_id,
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user_id=task.operator_id,
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key_id=key_id,
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task_id=task.id,
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provider="apiports",
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model=clients._model,
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call_type="text",
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success=llm_success,
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latency_ms=latency_ms,
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)
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db.add(log)
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db.flush()
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except Exception as log_exc:
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logger.warning("ai_call_logs 写入失败（非阻断）: %s", log_exc)
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# S1: 存库前过滤——只存合格文案（passed + score>=QUALITY_PASS_SCORE(80) + 非hard_block）
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seq = seq_start
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saved_count = 0
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for i, c in enumerate(candidates_raw):
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score = c.get("score", 0)
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passed = c.get("passed", False)
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bw_status = c.get("banned_word_status", "pass")
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if not (passed and score >= QUALITY_PASS_SCORE and bw_status != "hard_block"):
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logger.info(
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"文案[%d] 过滤丢弃: passed=%s score=%s banned=%s",
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i, passed, score, bw_status,
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)
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continue
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tc = TextCandidate(
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workspace_id=workspace_id,
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task_id=task.id,
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source=CandidateSource.AI,
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angle_label=c.get("angle_label") or c.get("angle", ""),
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content=json.dumps(c, ensure_ascii=False),
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score_json=json.dumps(c.get("score_detail", []), ensure_ascii=False),
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banned_word_status=BannedWordStatus(bw_status),
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)
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db.add(tc)
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db.flush()
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saved_count += 1
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seq += 1
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push_fn(task.id, workspace_id, "text_candidate", {
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"candidate_id": tc.id, "angle_label": tc.angle_label,
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"content": c.get("content", ""), "score": score,
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}, seq)
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seq += 1
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push_fn(task.id, workspace_id, "text_progress", {
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"done": saved_count, "total": task.text_count
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}, seq)
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db.commit()
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# S1: 合格数不足时标记需要后台补充
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needs_replenish = saved_count < task.text_count
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if needs_replenish:
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logger.warning(
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"文案合格数不足: task_id=%s 目标=%s 实得=%s，将后台异步补充",
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task.id, task.text_count, saved_count,
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)
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return candidates_raw, seq, needs_replenish
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def run_image_generation(db, clients, task, product_dict: dict,
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push_fn, workspace_id: int, seq_start: int,
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first_copy: dict, upload_base_path: str,
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regen_strategy: str | None = None,
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regen_role: str | None = None,
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custom_prompt: str | None = None,
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flywheel_fragment: str | None = None) -> int:
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"""
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Step6+7+8(image): 调 generate_storyboard_images → 后处理 → 存 ImageCandidate → 推 SSE。
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返回 next_seq。
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R2 局部重生(均 None=全量A/B/C)：regen_strategy 限定只跑该套；regen_role 配合限定该套该张；
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custom_prompt 人工追加提示词。重生产出 is_regen=True 新增不删旧。
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"""
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import time
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from app.services.ai_engine.image_gen import generate_storyboard_images
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from app.services.ai_engine.image_postprocessor import process_image
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from app.models.task import ImageCandidate
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from app.models.flywheel import AiCallLog
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from app.models.workspace import UserApiKey
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from app.constants.enums import ImageRole as IR
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# 取 key_id（不解密，不记录明文 key）
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key_row = db.query(UserApiKey).filter(
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UserApiKey.user_id == task.operator_id,
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UserApiKey.workspace_id == workspace_id,
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).first()
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key_id = key_row.id if key_row else None
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# TODO: 尺寸字段后续加产品级配置（products 表现无 aspect_ratio 字段）
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# 本轮固定 '3:4'=1024×1536，与 gpt-image-2 原生尺寸一致，免后处理二次拉伸
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aspect_ratio = "3:4"
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# image_count=0 直接跳过(纯文案任务/测试)，不空跑生图通道触发无谓失败日志。
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if not task.image_count or task.image_count <= 0:
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logger.info("image_count=0，跳过生图: task_id=%s", task.id)
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return seq_start
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reference_images: list[bytes] = []
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_img_path = _resolve_image_path(product_dict.get("image_path", ""))
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if _img_path and os.path.isfile(_img_path):
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try:
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with open(_img_path, "rb") as _f:
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reference_images = [_f.read()]
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logger.info("产品参考图已加载：%s (%d bytes)", _img_path, len(reference_images[0]))
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except Exception as _e:
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logger.warning("产品参考图读取失败，退化为空列表：%s %s", _img_path, _e)
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else:
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logger.warning(
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"product.image_path 未设置或文件不存在（%r），生图将以无参考图模式运行，"
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"可能导致产品包装跑偏。", _img_path
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)
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# 禁降级兜底：本次产品入镜但无参考图 → 硬失败，绝不降级纯文生图（建任务已拦一道，这是防绕过）
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if getattr(task, "need_product_image", True) and not reference_images:
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raise ValueError(
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"本次产品入镜(need_product_image=True)但未获取到产品参考图，"
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"拒绝降级纯文生图。请确认产品已上传参考图。"
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)
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# R5多图：按场景分组加载产品图，生图按分镜 role 选对应场景图
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images_by_scene: dict[str, list[bytes]] = {}
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for _im in (product_dict.get("images") or []):
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_p = _resolve_image_path(_im.get("path", ""))
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_scene = _im.get("scene") or "primary"
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if _p and os.path.isfile(_p):
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try:
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with open(_p, "rb") as _f:
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images_by_scene.setdefault(_scene, []).append(_f.read())
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except Exception as _e:
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logger.warning("产品图(scene=%s)读取失败，跳过：%s %s", _scene, _p, _e)
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# 主图始终保底进 primary（多图表为空或主图未入表时仍可用）
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if reference_images and not images_by_scene.get("primary"):
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images_by_scene.setdefault("primary", []).extend(reference_images)
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if images_by_scene:
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logger.info("R5多图已加载：%s", {k: len(v) for k, v in images_by_scene.items()})
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seq = seq_start
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# R2: 限定重生套别(regen_strategy)则只跑该套，否则全量 A/B/C 三套正交叙事
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_strategies = (regen_strategy,) if regen_strategy else ("A", "B", "C")
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_is_regen = bool(regen_strategy or regen_role)
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# 进度总数：单张重生=1，单套=image_count，全量=image_count×3
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if regen_role:
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_img_total = 1
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elif regen_strategy:
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_img_total = task.image_count
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else:
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_img_total = task.image_count * 3
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for si, strategy in enumerate(_strategies):
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t0 = time.monotonic()
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img_success = True
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img_error_code = None
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try:
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image_results = asyncio.run(generate_storyboard_images(
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client=clients,
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note=first_copy,
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product=product_dict,
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image_count=task.image_count,
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reference_images=reference_images or None,
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strategy=strategy,
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target_role=regen_role,
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custom_prompt=custom_prompt,
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images_by_scene=images_by_scene or None,
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flywheel_fragment=flywheel_fragment,
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))
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except Exception as exc:
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img_success = False
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img_error_code = type(exc).__name__
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logger.error("generate_storyboard_images 套%s 失败: %s", strategy, exc)
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image_results = []
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latency_ms = int((time.monotonic() - t0) * 1000)
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fail_count = 0
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first_img_error: str | None = None
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for i, img_result in enumerate(image_results):
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if img_result.get("error"):
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fail_count += 1
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if first_img_error is None:
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first_img_error = str(img_result["error"])[:32]
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seq += 1
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push_fn(task.id, workspace_id, "batch_failed", {
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"batch": img_result["role"], "reason": img_result["error"],
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"strategy": strategy, "retryable": True,
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}, seq)
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continue
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raw_bytes = img_result["image_bytes"]
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try:
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processed = process_image(raw_bytes, aspect_ratio=aspect_ratio, resample_strength=1)
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except Exception as e:
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logger.warning("图片后处理失败，使用原图: %s", e)
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processed = raw_bytes
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img_dir = os.path.join(upload_base_path, str(workspace_id), str(task.id))
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os.makedirs(img_dir, exist_ok=True)
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filename = f"{strategy}_{i+1:02d}_{img_result['role']}.jpg"
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img_path = os.path.join(img_dir, filename)
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with open(img_path, "wb") as f:
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f.write(processed)
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img_url = f"/uploads/{workspace_id}/{task.id}/{filename}"
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role_enum = IR.MAIN
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try:
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role_enum = IR(img_result["role"])
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except ValueError:
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pass
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ic = ImageCandidate(
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workspace_id=workspace_id,
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task_id=task.id,
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role=role_enum,
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url=img_url,
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seq=i + 1,
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strategy=strategy, # 写入 A/B/C（非 hardcode）
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is_regen=_is_regen, # R2 重生标记：新增不删旧，前端同strategy+role默认展示最新
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# E12 AI评图分：只落展示分,绝不碰 eval_score(留 NULL)；AI 分不进飞轮权重
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ai_visual_score=img_result.get("ai_visual_score"),
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ai_visual_note=img_result.get("ai_visual_note"),
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)
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db.add(ic)
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db.flush()
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seq += 1
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push_fn(task.id, workspace_id, "image_candidate", {
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"candidate_id": ic.id, "strategy": strategy, "seq": i + 1,
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"url": img_url, "role": img_result["role"],
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"is_regen": _is_regen,
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"ai_visual_score": img_result.get("ai_visual_score"),
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"ai_visual_note": img_result.get("ai_visual_note"),
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}, seq)
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seq += 1
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push_fn(task.id, workspace_id, "image_progress", {
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"done": si * task.image_count + (i + 1),
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"total": _img_total, "strategy": strategy,
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}, seq)
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# 写 ai_call_logs（每套一条，失败不阻断）
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actual_provider = os.environ.get("IMAGE_PROVIDER_PRIMARY", "gpt")
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final_error_code = first_img_error or img_error_code
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try:
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img_log = AiCallLog(
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workspace_id=workspace_id,
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user_id=task.operator_id,
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key_id=key_id,
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task_id=task.id,
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provider=actual_provider,
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call_type="image",
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success=(img_success and fail_count == 0),
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latency_ms=latency_ms,
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error_code=final_error_code,
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)
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db.add(img_log)
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db.flush()
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except Exception as log_exc:
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logger.warning("ai_call_logs(image) 套%s 写入失败（非阻断）: %s", strategy, log_exc)
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db.commit()
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return seq
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