Compare commits
1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Coze Agent
|
4ccab8ebc1 |
+1
-15
@@ -211,24 +211,10 @@ COSYVOICE_CLONE_MODEL=voice-enrollment
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# 用于 AI 文案生成、智能剪辑等需要大模型能力的场景
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DOUBAO_API_KEY=your-doubao-api-key
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DOUBAO_MODEL=doubao-seed-2-1-pro-260915
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DOUBAO_FAST_MODEL=doubao-seed-2-1-lite-260915
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DOUBAO_MODEL=doubao-seed-1-6-250615
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DOUBAO_BASE_URL=https://ark.cn-beijing.volces.com/api/v3
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DOUBAO_TIMEOUT=30
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DOUBAO_MAX_RETRIES=2
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# 视觉模型：pro 精度高，lite 速度快（viral-video 商品识别默认用 lite 提速）
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DOUBAO_VISION_MODEL=doubao-seed-2-1-pro-260915
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DOUBAO_VISION_LITE_MODEL=doubao-seed-2-1-lite-260915
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DOUBAO_VISION_USE_LITE=true
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# Embedding 向量化模型
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DOUBAO_EMBEDDING_MODEL=doubao-embedding-vision-251215
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# 视频模型（Seedance 2.5，统一走方舟；真人参考图通过信任链自动 AI 化）
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DOUBAO_VIDEO_MODEL=doubao-seedance-2-5-260628
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DOUBAO_VIDEO_TIMEOUT=480
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DOUBAO_VIDEO_POLL_INTERVAL=10
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# 图片模型（Seedream 5.0 Pro，用于信任链真人 AI 化 + 文生图）
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DOUBAO_IMAGE_MODEL=doubao-seedream-5-0-pro-260628
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DOUBAO_IMAGE_TIMEOUT=120
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# ==================== 积分/会员系统 (#1895) ====================
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# 积分系统总开关：默认 false（暂停积分系统）。
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@@ -1187,14 +1187,6 @@ jobs:
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DOUBAO_MODEL: "${{ secrets.DOUBAO_MODEL }}"
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DOUBAO_BASE_URL: "${{ secrets.DOUBAO_BASE_URL }}"
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DOUBAO_VISION_MODEL: "${{ secrets.DOUBAO_VISION_MODEL }}"
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DOUBAO_VISION_LITE_MODEL: "${{ secrets.DOUBAO_VISION_LITE_MODEL }}"
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DOUBAO_VISION_USE_LITE: "${{ secrets.DOUBAO_VISION_USE_LITE }}"
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DOUBAO_IMAGE_MODEL: "${{ secrets.DOUBAO_IMAGE_MODEL }}"
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DOUBAO_IMAGE_SIZE: "${{ secrets.DOUBAO_IMAGE_SIZE }}"
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DOUBAO_IMAGE_TIMEOUT: "${{ secrets.DOUBAO_IMAGE_TIMEOUT }}"
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DOUBAO_FAST_MODEL: "${{ secrets.DOUBAO_FAST_MODEL }}"
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DOUBAO_TIMEOUT: "${{ secrets.DOUBAO_TIMEOUT }}"
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DOUBAO_MAX_RETRIES: "${{ secrets.DOUBAO_MAX_RETRIES }}"
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WECHAT_APP_ID: "${{ secrets.WECHAT_APP_ID }}"
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WECHAT_APP_SECRET: "${{ secrets.WECHAT_APP_SECRET }}"
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TIKHUB_API_KEY: "${{ secrets.TIKHUB_API_KEY }}"
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@@ -1,2 +0,0 @@
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Mon Oct 5 04:09:11 PM CST 2026
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2198 lite/pro并行竞速 (commit 9699a1f) — CI rebuild trigger Mon Oct 5 08:09:11 AM UTC 2026
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@@ -1,35 +0,0 @@
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"""viral video add phase_message column (#2134)
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Revision ID: 090_viral_video_phase_msg
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Revises: 089_viral_video_cols
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Create Date: 2026-10-02
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#2134 阶段细粒度提示：viral_video 表新增 phase_message 列（中文阶段提示文案）。
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current_stage 列已在之前版本存在，本迁移只补 phase_message。
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幂等 ADD COLUMN IF NOT EXISTS。
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "090_viral_video_phase_msg"
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down_revision = "089_viral_video_cols"
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branch_labels = None
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depends_on = None
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def upgrade() -> None:
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# SQLite/PostgreSQL 兼容的幂等添加列
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conn = op.get_bind()
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inspector = sa.inspect(conn)
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cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
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if "phase_message" not in cols:
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op.add_column(
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"viral_video_jobs",
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sa.Column("phase_message", sa.String(length=500), nullable=False, server_default=""),
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)
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def downgrade() -> None:
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op.drop_column("viral_video_jobs", "phase_message")
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@@ -1,49 +0,0 @@
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"""viral video add current_stage column (#2137 follow-up)
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Revision ID: 091_viral_video_stage
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Revises: 090_viral_video_phase_msg
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Create Date: 2026-10-02
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#2137 follow-up fix: 090 migration missed current_stage column on viral_video_jobs,
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causing UndefinedColumn errors and 500s on all authenticated viral-video endpoints.
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Idempotently add current_stage and double-check phase_message.
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "091_viral_video_stage"
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down_revision = "090_viral_video_phase_msg"
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branch_labels = None
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depends_on = None
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def upgrade() -> None:
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conn = op.get_bind()
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inspector = sa.inspect(conn)
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cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
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if "current_stage" not in cols:
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op.add_column(
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"viral_video_jobs",
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sa.Column(
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"current_stage",
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sa.String(length=200),
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nullable=False,
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server_default="",
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),
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)
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if "phase_message" not in cols:
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op.add_column(
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"viral_video_jobs",
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sa.Column(
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"phase_message",
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sa.String(length=500),
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nullable=False,
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server_default="",
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),
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)
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def downgrade() -> None:
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op.drop_column("viral_video_jobs", "current_stage")
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@@ -1,42 +0,0 @@
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"""viral_video_jobs 增加 heartbeat_at 列（worker 心跳，用于僵尸任务超时回收）
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Revision ID: 092_viral_video_heartbeat
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Revises: 091_viral_video_stage
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Create Date: 2026-10-02
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "092_viral_video_heartbeat"
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down_revision = "091_viral_video_stage"
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branch_labels = None
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depends_on = None
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def upgrade() -> None:
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conn = op.get_bind()
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inspector = sa.inspect(conn)
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cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
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if "heartbeat_at" not in cols:
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op.add_column("viral_video_jobs", sa.Column("heartbeat_at", sa.DateTime(), nullable=True))
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op.execute(
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"UPDATE viral_video_jobs SET heartbeat_at = updated_at " "WHERE status = 'running' AND heartbeat_at IS NULL"
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)
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try:
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op.create_index("ix_viral_video_jobs_heartbeat_at", "viral_video_jobs", ["heartbeat_at"])
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except Exception:
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pass
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def downgrade() -> None:
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conn = op.get_bind()
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inspector = sa.inspect(conn)
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cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
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if "heartbeat_at" in cols:
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try:
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op.drop_index("ix_viral_video_jobs_heartbeat_at", table_name="viral_video_jobs")
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except Exception:
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pass
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op.drop_column("viral_video_jobs", "heartbeat_at")
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@@ -1,87 +0,0 @@
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"""viral_video 动态积分定价 + 积分字段从 Integer 改为 Float (#2151)
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Revision ID: 093
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Revises: 092_viral_video_heartbeat
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Create Date: 2026-10-02
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "093"
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down_revision = "092_viral_video_heartbeat"
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branch_labels = None
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depends_on = None
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def upgrade() -> None:
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conn = op.get_bind()
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inspector = sa.inspect(conn)
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# 1) points_accounts 三列 Integer -> Float
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pa_cols = {c["name"]: c for c in inspector.get_columns("points_accounts")}
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for col in ("balance", "total_earned", "total_spent"):
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if col in pa_cols:
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op.alter_column(
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"points_accounts",
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col,
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existing_type=sa.Integer(),
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type_=sa.Float(),
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existing_nullable=False,
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)
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# 2) points_transactions amount/balance_after Integer -> Float
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pt_cols = {c["name"]: c for c in inspector.get_columns("points_transactions")}
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for col in ("amount", "balance_after"):
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if col in pt_cols:
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op.alter_column(
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"points_transactions",
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col,
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existing_type=sa.Integer(),
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type_=sa.Float(),
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existing_nullable=False,
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)
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# 3) users.points_balance Integer -> Float
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user_cols = {c["name"]: c for c in inspector.get_columns("users")}
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if "points_balance" in user_cols:
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op.alter_column(
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"users",
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"points_balance",
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existing_type=sa.Integer(),
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type_=sa.Float(),
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existing_nullable=False,
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)
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# 4) viral_video_jobs.credits_cost Integer -> Float
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vv_cols = {c["name"]: c for c in inspector.get_columns("viral_video_jobs")}
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if "credits_cost" in vv_cols:
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op.alter_column(
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"viral_video_jobs",
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"credits_cost",
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existing_type=sa.Integer(),
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type_=sa.Float(),
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existing_nullable=False,
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)
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# 5) viral_video_jobs 新增列
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if "video_resolution" not in vv_cols:
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op.add_column(
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"viral_video_jobs",
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sa.Column("video_resolution", sa.String(20), nullable=False, server_default="720p"),
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)
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if "credits_prepaid" not in vv_cols:
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op.add_column(
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"viral_video_jobs",
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sa.Column("credits_prepaid", sa.Float(), nullable=False, server_default="0"),
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)
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if "credits_transaction_id" not in vv_cols:
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op.add_column(
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"viral_video_jobs",
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sa.Column("credits_transaction_id", sa.String(36), nullable=False, server_default=""),
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)
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def downgrade() -> None:
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pass
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@@ -1,31 +0,0 @@
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"""viral_video_jobs 增加 pre_trusted_images 列（信任链Seedream预热结果）
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Revision ID: 094_viral_video_pre_trusted
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Revises: 093_viral_video_pricing_points_float
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Create Date: 2026-10-04
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "094_viral_video_pre_trusted"
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down_revision = "093"
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branch_labels = None
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depends_on = None
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|
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|
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def upgrade() -> None:
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conn = op.get_bind()
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inspector = sa.inspect(conn)
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cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
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if "pre_trusted_images" not in cols:
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op.add_column("viral_video_jobs", sa.Column("pre_trusted_images", sa.Text(), nullable=True))
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def downgrade() -> None:
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conn = op.get_bind()
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inspector = sa.inspect(conn)
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cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
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if "pre_trusted_images" in cols:
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op.drop_column("viral_video_jobs", "pre_trusted_images")
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@@ -1,102 +0,0 @@
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"""爆款视频 Prompt 模板配置表（#2040）。
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086 曾预留同名旧表（id varchar / content / variables json），从未被业务使用；
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本迁移将其替换为 #2040 新结构。
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Revision ID: 095_viral_video_prompt_templates
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Revises: 094_viral_video_pre_trusted
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Create Date: 2026-10-04
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "095_viral_video_prompt_templates"
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down_revision = "094_viral_video_pre_trusted"
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branch_labels = None
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depends_on = None
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|
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|
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def _table_exists(conn, name: str) -> bool:
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return name in sa.inspect(conn).get_table_names()
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def upgrade() -> None:
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conn = op.get_bind()
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# 086 预留的旧结构表：先删除（无业务数据、无任何引用）
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if _table_exists(conn, "viral_video_prompt_templates"):
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op.drop_table("viral_video_prompt_templates")
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op.create_table(
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"viral_video_prompt_templates",
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sa.Column("id", sa.Integer, primary_key=True, autoincrement=True),
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sa.Column("name", sa.String(128), nullable=False),
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sa.Column("prompt_type", sa.String(32), nullable=False),
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sa.Column("version", sa.Integer, nullable=False, server_default="1"),
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sa.Column("system_prompt", sa.Text, nullable=False),
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sa.Column("user_prompt_template", sa.Text, nullable=False),
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sa.Column("example_output", sa.Text, nullable=True),
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sa.Column("is_active", sa.Boolean, nullable=False, server_default=sa.text("true")),
|
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sa.Column(
|
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"created_at",
|
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sa.DateTime(timezone=True),
|
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server_default=sa.func.now(),
|
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nullable=False,
|
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),
|
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sa.Column(
|
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"updated_at",
|
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sa.DateTime(timezone=True),
|
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server_default=sa.func.now(),
|
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nullable=False,
|
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),
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)
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op.create_index(
|
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"ix_vvpt_type_active",
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"viral_video_prompt_templates",
|
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["prompt_type", "is_active"],
|
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)
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op.create_index(
|
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"uq_vvpt_type_version",
|
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"viral_video_prompt_templates",
|
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["prompt_type", "version"],
|
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unique=True,
|
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)
|
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|
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|
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def downgrade() -> None:
|
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conn = op.get_bind()
|
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if _table_exists(conn, "viral_video_prompt_templates"):
|
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op.drop_index("uq_vvpt_type_version", table_name="viral_video_prompt_templates")
|
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op.drop_index("ix_vvpt_type_active", table_name="viral_video_prompt_templates")
|
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op.drop_table("viral_video_prompt_templates")
|
||||
|
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# 恢复 086 的旧预留结构
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op.create_table(
|
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"viral_video_prompt_templates",
|
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sa.Column("id", sa.String(36), primary_key=True),
|
||||
sa.Column("prompt_type", sa.String(50), nullable=False, index=True),
|
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sa.Column("name", sa.String(200), nullable=False),
|
||||
sa.Column("content", sa.Text, nullable=False, server_default=""),
|
||||
sa.Column("variables", sa.JSON, nullable=False, server_default="[]"),
|
||||
sa.Column("version", sa.Integer, nullable=False, server_default="1"),
|
||||
sa.Column(
|
||||
"is_active",
|
||||
sa.Boolean,
|
||||
nullable=False,
|
||||
server_default=sa.text("true"),
|
||||
index=True,
|
||||
),
|
||||
sa.Column(
|
||||
"created_at",
|
||||
sa.DateTime(timezone=True),
|
||||
nullable=False,
|
||||
server_default=sa.func.now(),
|
||||
),
|
||||
sa.Column(
|
||||
"updated_at",
|
||||
sa.DateTime(timezone=True),
|
||||
nullable=False,
|
||||
server_default=sa.func.now(),
|
||||
),
|
||||
)
|
||||
@@ -1,61 +0,0 @@
|
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"""功能计费积分字段（爆款/对口型/智能剪辑 DB 化计费）。
|
||||
|
||||
给 gpu_lipsync_tasks / generation_tasks / lipsync_jobs 三张表加积分字段：
|
||||
- credits_prepaid: 提交任务时预扣积分
|
||||
- credits_cost: 最终结算积分
|
||||
- credits_transaction_id: 预扣流水 ID
|
||||
|
||||
注意：feature_pricing_configs 配置表由 xiaoxia-admin 侧 migration 建立，
|
||||
本仓库只读，不在此创建。
|
||||
|
||||
Revision ID: 096_feature_billing_fields
|
||||
Revises: 095_viral_video_prompt_templates
|
||||
Create Date: 2026-10-05
|
||||
"""
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "096_feature_billing_fields"
|
||||
down_revision = "095_viral_video_prompt_templates"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
_TABLES = ("gpu_lipsync_tasks", "generation_tasks", "lipsync_jobs")
|
||||
_COLUMNS = (
|
||||
("credits_prepaid", sa.Float(), "0"),
|
||||
("credits_cost", sa.Float(), "0"),
|
||||
("credits_transaction_id", sa.String(36), ""),
|
||||
)
|
||||
|
||||
|
||||
def _table_exists(conn, name: str) -> bool:
|
||||
return name in sa.inspect(conn).get_table_names()
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
for table in _TABLES:
|
||||
if not _table_exists(conn, table):
|
||||
continue
|
||||
existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
|
||||
for col_name, col_type, default in _COLUMNS:
|
||||
if col_name in existing:
|
||||
continue
|
||||
op.add_column(
|
||||
table,
|
||||
sa.Column(col_name, col_type, nullable=False, server_default=default),
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
for table in _TABLES:
|
||||
if not _table_exists(conn, table):
|
||||
continue
|
||||
existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
|
||||
for col_name, _col_type, _default in _COLUMNS:
|
||||
if col_name not in existing:
|
||||
continue
|
||||
op.drop_column(table, col_name)
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1,222 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""099: AI 模型路由层 seed — 补齐缺失模型和能力配置.
|
||||
|
||||
幂等：所有 INSERT 先检查存在性。
|
||||
- ai_models: 补齐 qwen3.7-plus, seedream, seedance, embedding, wan3.0 等
|
||||
- ai_capability_configs: 补齐 image_generation, video_generation, embedding
|
||||
- 更新已有 capability 的 lite_model_id
|
||||
"""
|
||||
|
||||
import json
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "099_ai_model_router_seed"
|
||||
down_revision = "098_viral_video_image_analysis_v5"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
|
||||
# CI 环境下 ai_models 表可能尚未创建（由 ORM 自动建表，非 migration）
|
||||
# 如果表不存在则跳过 seed，由应用启动时 ORM 建表后首次访问时生效
|
||||
table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
|
||||
if not table_check:
|
||||
# ai_models 表不存在，跳过所有 seed（CI 环境）
|
||||
return
|
||||
|
||||
# ── 1. 补齐 ai_models 缺失记录 ────────────────────────────────────────────
|
||||
existing_models = {
|
||||
row[0]
|
||||
for row in conn.execute(
|
||||
sa.text("SELECT model_key FROM ai_models WHERE deleted_at IS NULL")
|
||||
).fetchall()
|
||||
}
|
||||
|
||||
# 从已有 active 记录获取 API key（复用，不硬编码）
|
||||
dashscope_key_row = conn.execute(
|
||||
sa.text(
|
||||
"SELECT api_key FROM ai_models WHERE provider='dashscope' AND deleted_at IS NULL AND api_key IS NOT NULL AND api_key != '' LIMIT 1"
|
||||
)
|
||||
).first()
|
||||
dashscope_key = dashscope_key_row[0] if dashscope_key_row else ""
|
||||
|
||||
volcengine_key_row = conn.execute(
|
||||
sa.text(
|
||||
"SELECT api_key FROM ai_models WHERE provider='volcengine' AND deleted_at IS NULL AND api_key IS NOT NULL AND api_key != '' LIMIT 1"
|
||||
)
|
||||
).first()
|
||||
volcengine_key = volcengine_key_row[0] if volcengine_key_row else ""
|
||||
|
||||
new_models = [
|
||||
{
|
||||
"model_key": "qwen3.7-plus",
|
||||
"name": "通义千问3.7 Plus（VLM 兜底）",
|
||||
"provider": "dashscope",
|
||||
"api_key": dashscope_key,
|
||||
"api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
"description": "阿里云百炼 Qwen3.7 Plus 多模态模型，用于 VLM 兜底分析",
|
||||
},
|
||||
{
|
||||
"model_key": "doubao-seedream-5-0-flash-260915",
|
||||
"name": "Seedream 5.0 Flash（图片生成）",
|
||||
"provider": "volcengine",
|
||||
"api_key": volcengine_key,
|
||||
"api_base": "https://ark.cn-beijing.volces.com/api/v3",
|
||||
"description": "火山引擎 Seedream 5.0 Flash 文生图模型",
|
||||
},
|
||||
{
|
||||
"model_key": "doubao-seedance-2-5-260628",
|
||||
"name": "Seedance 2.5（视频生成）",
|
||||
"provider": "volcengine",
|
||||
"api_key": volcengine_key,
|
||||
"api_base": "https://ark.cn-beijing.volces.com/api/v3",
|
||||
"description": "火山引擎 Seedance 2.5 图/文生视频模型",
|
||||
},
|
||||
{
|
||||
"model_key": "doubao-embedding-vision-251215",
|
||||
"name": "豆包多模态向量嵌入",
|
||||
"provider": "volcengine",
|
||||
"api_key": volcengine_key,
|
||||
"api_base": "https://ark.cn-beijing.volces.com/api/v3",
|
||||
"description": "火山引擎豆包多模态向量嵌入模型",
|
||||
},
|
||||
{
|
||||
"model_key": "wan3.0-video",
|
||||
"name": "Wan 3.0 视频生成",
|
||||
"provider": "dashscope",
|
||||
"api_key": dashscope_key,
|
||||
"api_base": "https://dashscope.aliyuncs.com/api/v1",
|
||||
"description": "阿里云百炼 Wan 3.0 视频生成模型",
|
||||
},
|
||||
{
|
||||
"model_key": "doubao-seed-2-1-pro-260915",
|
||||
"name": "豆包 Seed 2.1 Pro（高精度推理）",
|
||||
"provider": "volcengine",
|
||||
"api_key": volcengine_key,
|
||||
"api_base": "https://ark.cn-beijing.volces.com/api/v3",
|
||||
"description": "火山引擎豆包 Seed 2.1 Pro 深度思考+多模态",
|
||||
},
|
||||
]
|
||||
|
||||
for m in new_models:
|
||||
if m["model_key"] not in existing_models:
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"""
|
||||
INSERT INTO ai_models (id, name, provider, model_key, api_key, api_base, description, status, is_default, usage_today, created_at, updated_at)
|
||||
VALUES (gen_random_uuid()::text, :name, :provider, :model_key, :api_key, :api_base, :description, 'active', false, 0, now(), now())
|
||||
"""
|
||||
),
|
||||
m,
|
||||
)
|
||||
|
||||
# ── 2. 补齐 ai_capability_configs 缺失项 ──────────────────────────────────
|
||||
cap_table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
|
||||
if not cap_table_check:
|
||||
return
|
||||
|
||||
existing_caps = {
|
||||
row[0]
|
||||
for row in conn.execute(
|
||||
sa.text("SELECT capability_key FROM ai_capability_configs")
|
||||
).fetchall()
|
||||
}
|
||||
|
||||
def _get_model_id(model_key: str) -> str | None:
|
||||
row = conn.execute(
|
||||
sa.text(
|
||||
"SELECT id FROM ai_models WHERE model_key = :key AND deleted_at IS NULL AND status = 'active' LIMIT 1"
|
||||
),
|
||||
{"key": model_key},
|
||||
).first()
|
||||
return row[0] if row else None
|
||||
|
||||
# image_generation
|
||||
if "image_generation" not in existing_caps:
|
||||
mid = _get_model_id("doubao-seedream-5-0-flash-260915")
|
||||
if mid:
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"""
|
||||
INSERT INTO ai_capability_configs (id, capability_key, capability_name, primary_model_id, timeout_seconds, max_retries, concurrency, extra_params, is_enabled, created_at, updated_at)
|
||||
VALUES (gen_random_uuid()::text, :ck, :cn, :pm, 60, 1, 2, :ep, true, now(), now())
|
||||
"""
|
||||
),
|
||||
{
|
||||
"ck": "image_generation",
|
||||
"cn": "图片生成（Seedream）",
|
||||
"pm": mid,
|
||||
"ep": json.dumps({"size": "1K"}),
|
||||
},
|
||||
)
|
||||
|
||||
# video_generation
|
||||
if "video_generation" not in existing_caps:
|
||||
mid = _get_model_id("doubao-seedance-2-5-260628")
|
||||
fb_mid = _get_model_id("wan3.0-video")
|
||||
if mid:
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"""
|
||||
INSERT INTO ai_capability_configs (id, capability_key, capability_name, primary_model_id, fallback_model_id, timeout_seconds, max_retries, concurrency, extra_params, is_enabled, created_at, updated_at)
|
||||
VALUES (gen_random_uuid()::text, :ck, :cn, :pm, :fm, 600, 1, 1, :ep, true, now(), now())
|
||||
"""
|
||||
),
|
||||
{
|
||||
"ck": "video_generation",
|
||||
"cn": "视频生成（Seedance/Wan）",
|
||||
"pm": mid,
|
||||
"fm": fb_mid,
|
||||
"ep": json.dumps({}),
|
||||
},
|
||||
)
|
||||
|
||||
# embedding
|
||||
if "embedding" not in existing_caps:
|
||||
mid = _get_model_id("doubao-embedding-vision-251215")
|
||||
if mid:
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"""
|
||||
INSERT INTO ai_capability_configs (id, capability_key, capability_name, primary_model_id, timeout_seconds, max_retries, concurrency, extra_params, is_enabled, created_at, updated_at)
|
||||
VALUES (gen_random_uuid()::text, :ck, :cn, :pm, 30, 2, 5, :ep, true, now(), now())
|
||||
"""
|
||||
),
|
||||
{
|
||||
"ck": "embedding",
|
||||
"cn": "向量嵌入",
|
||||
"pm": mid,
|
||||
"ep": json.dumps({}),
|
||||
},
|
||||
)
|
||||
|
||||
# ── 3. 更新 image_analysis 的 lite_model_id ─────────────────────────────
|
||||
lite_model_id = _get_model_id("qwen3.8-flash")
|
||||
if lite_model_id:
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"UPDATE ai_capability_configs SET lite_model_id = :lite WHERE capability_key = 'image_analysis' AND lite_model_id IS NULL"
|
||||
),
|
||||
{"lite": lite_model_id},
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
# 安全检查表是否存在
|
||||
table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
|
||||
if not table_check:
|
||||
return
|
||||
conn.execute(
|
||||
sa.text("DELETE FROM ai_capability_configs WHERE capability_key IN ('image_generation', 'video_generation', 'embedding')")
|
||||
)
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"DELETE FROM ai_models WHERE model_key IN ('qwen3.7-plus', 'doubao-seedream-5-0-flash-260915', 'doubao-seedance-2-5-260628', 'doubao-embedding-vision-251215', 'wan3.0-video', 'doubao-seed-2-1-pro-260915') AND deleted_at IS NULL"
|
||||
)
|
||||
)
|
||||
@@ -1,107 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""100: 修正已有 capability 的模型绑定.
|
||||
|
||||
幂等：仅当 primary_model_id 当前绑定到旧模型 (doubao-seed-1-6) 时才更新，
|
||||
避免覆盖用户在后台的自定义配置。
|
||||
|
||||
- 更新 5 个 LLM capability (intent_parsing, copy_fusion, storyboard, copy_review, asset_classify)
|
||||
的 primary_model_id 从 doubao-seed-1-6 改为 doubao-seed-2-1-pro-260915
|
||||
- 更新 image_analysis 的 primary/lite/fallback 模型绑定
|
||||
"""
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "100_fix_capability_model_bindings"
|
||||
down_revision = "099_ai_model_router_seed"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
|
||||
# Check tables exist
|
||||
table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
|
||||
if not table_check:
|
||||
return
|
||||
|
||||
config_table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
|
||||
if not config_table_check:
|
||||
return
|
||||
|
||||
# Look up model IDs by model_key (not hardcoded UUIDs)
|
||||
pro_model_row = conn.execute(
|
||||
sa.text(
|
||||
"SELECT id FROM ai_models WHERE model_key = 'doubao-seed-2-1-pro-260915' AND deleted_at IS NULL LIMIT 1"
|
||||
)
|
||||
).first()
|
||||
if not pro_model_row:
|
||||
return
|
||||
pro_model_id = pro_model_row[0]
|
||||
|
||||
old_model_row = conn.execute(
|
||||
sa.text("SELECT id FROM ai_models WHERE model_key = 'doubao-seed-1-6-250615' LIMIT 1")
|
||||
).first()
|
||||
old_model_id = old_model_row[0] if old_model_row else None
|
||||
|
||||
llm_capabilities = [
|
||||
"intent_parsing",
|
||||
"copy_fusion",
|
||||
"storyboard",
|
||||
"copy_review",
|
||||
"asset_classify",
|
||||
]
|
||||
|
||||
for cap_key in llm_capabilities:
|
||||
if old_model_id:
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"UPDATE ai_capability_configs SET primary_model_id = :new_id, updated_at = NOW() "
|
||||
"WHERE capability_key = :cap_key AND primary_model_id = :old_id"
|
||||
),
|
||||
{"new_id": pro_model_id, "old_id": old_model_id, "cap_key": cap_key},
|
||||
)
|
||||
|
||||
# Update image_analysis
|
||||
qwen38_row = conn.execute(
|
||||
sa.text("SELECT id FROM ai_models WHERE model_key = 'qwen3.8-flash' AND deleted_at IS NULL LIMIT 1")
|
||||
).first()
|
||||
qwen37_row = conn.execute(
|
||||
sa.text("SELECT id FROM ai_models WHERE model_key = 'qwen3.7-plus' AND deleted_at IS NULL LIMIT 1")
|
||||
).first()
|
||||
|
||||
if qwen38_row and qwen37_row:
|
||||
qwen38_id = qwen38_row[0]
|
||||
qwen37_id = qwen37_row[0]
|
||||
|
||||
current_ia = conn.execute(
|
||||
sa.text(
|
||||
"SELECT primary_model_id, lite_model_id, fallback_model_id "
|
||||
"FROM ai_capability_configs WHERE capability_key = 'image_analysis'"
|
||||
)
|
||||
).first()
|
||||
|
||||
if current_ia:
|
||||
current_primary, current_lite, current_fallback = current_ia
|
||||
updates = {}
|
||||
if current_primary != qwen38_id:
|
||||
updates["primary_model_id"] = qwen38_id
|
||||
if current_lite != qwen38_id:
|
||||
updates["lite_model_id"] = qwen38_id
|
||||
if current_fallback != qwen37_id:
|
||||
updates["fallback_model_id"] = qwen37_id
|
||||
|
||||
if updates:
|
||||
set_clause = ", ".join([f"{k} = :{k}" for k in updates.keys()])
|
||||
set_clause += ", updated_at = NOW()"
|
||||
updates["cap_key"] = "image_analysis"
|
||||
conn.execute(
|
||||
sa.text(f"UPDATE ai_capability_configs SET {set_clause} WHERE capability_key = :cap_key"),
|
||||
updates,
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
pass
|
||||
@@ -1,153 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""101: 补齐 qwen-vl-plus 视觉模型并修正 image_analysis 绑定与 max_tokens.
|
||||
|
||||
背景：
|
||||
- qwen-vl-plus 做图片识别时返回 JSON 约 500-600 tokens，旧硬编码
|
||||
max_tokens=350 导致 JSON 被截断、解析失败返回"未识别"。
|
||||
- 代码侧已移除硬编码，改由 capability 的 DB 配置决定 max_tokens。
|
||||
|
||||
幂等：
|
||||
- qwen-vl-plus 已存在则不插入；
|
||||
- 仅当 image_analysis 当前 primary_model 不是 qwen-vl-plus 时才更新绑定，
|
||||
避免覆盖后台手动配置。
|
||||
"""
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "101_qwen_vl_plus_and_max_tokens"
|
||||
down_revision = "100_fix_capability_model_bindings"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
|
||||
models_table = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
|
||||
if not models_table:
|
||||
return
|
||||
|
||||
caps_table = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
|
||||
if not caps_table:
|
||||
return
|
||||
|
||||
# ── c. 补全其他 capability 的 max_tokens 默认值（幂等）──────────────────
|
||||
# 放在 image_analysis 特定逻辑之前，确保任何分支 return 都不会跳过本段。
|
||||
# 仅在当前值为 NULL 或过小 (<100) 时更新，不覆盖已有合理配置。
|
||||
# embedding / tts / voice_clone 不走 chat 接口，无需设置。
|
||||
default_max_tokens = {
|
||||
"intent_parsing": 500,
|
||||
"copy_fusion": 2500,
|
||||
"storyboard": 4000,
|
||||
"copy_review": 1000,
|
||||
"asset_classify": 500,
|
||||
"image_generation": 500,
|
||||
"video_generation": 500,
|
||||
}
|
||||
for cap_key, mt in default_max_tokens.items():
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"UPDATE ai_capability_configs "
|
||||
"SET max_tokens = :mt, updated_at = now() "
|
||||
"WHERE capability_key = :key "
|
||||
"AND (max_tokens IS NULL OR max_tokens < 100)"
|
||||
),
|
||||
{"mt": mt, "key": cap_key},
|
||||
)
|
||||
|
||||
# ── a. 确保 qwen-vl-plus 模型存在 ────────────────────────────────────────
|
||||
conn.execute(sa.text("""
|
||||
INSERT INTO ai_models (id, name, provider, model_key, api_key, api_base,
|
||||
description, status, is_default, usage_today,
|
||||
created_at, updated_at)
|
||||
SELECT gen_random_uuid()::text,
|
||||
'通义千问VL Plus',
|
||||
'dashscope',
|
||||
'qwen-vl-plus',
|
||||
COALESCE(
|
||||
(SELECT api_key FROM ai_models
|
||||
WHERE provider = 'dashscope' AND deleted_at IS NULL
|
||||
AND api_key IS NOT NULL AND api_key != ''
|
||||
LIMIT 1),
|
||||
''
|
||||
),
|
||||
'https://dashscope.aliyuncs.com/compatible-mode/v1',
|
||||
'阿里云视觉理解模型（图片识别/分析）',
|
||||
'active', false, 0, now(), now()
|
||||
WHERE NOT EXISTS (
|
||||
SELECT 1 FROM ai_models
|
||||
WHERE model_key = 'qwen-vl-plus' AND deleted_at IS NULL
|
||||
)
|
||||
"""))
|
||||
|
||||
qwen_vl_row = conn.execute(
|
||||
sa.text(
|
||||
"SELECT id FROM ai_models WHERE model_key = 'qwen-vl-plus' "
|
||||
"AND deleted_at IS NULL AND status = 'active' LIMIT 1"
|
||||
)
|
||||
).first()
|
||||
if not qwen_vl_row:
|
||||
return
|
||||
qwen_vl_id = qwen_vl_row[0]
|
||||
|
||||
qwen37_row = conn.execute(
|
||||
sa.text(
|
||||
"SELECT id FROM ai_models WHERE model_key = 'qwen3.7-plus' "
|
||||
"AND deleted_at IS NULL AND status = 'active' LIMIT 1"
|
||||
)
|
||||
).first()
|
||||
qwen37_id = qwen37_row[0] if qwen37_row else None
|
||||
|
||||
# ── b. 仅当当前 primary 不是 qwen-vl-plus 时修正绑定与 max_tokens ───────
|
||||
current = conn.execute(
|
||||
sa.text(
|
||||
"SELECT primary_model_id, lite_model_id, fallback_model_id, max_tokens "
|
||||
"FROM ai_capability_configs WHERE capability_key = 'image_analysis'"
|
||||
)
|
||||
).first()
|
||||
|
||||
if current is None:
|
||||
# capability 不存在则创建
|
||||
conn.execute(
|
||||
sa.text("""
|
||||
INSERT INTO ai_capability_configs
|
||||
(id, capability_key, capability_name, primary_model_id,
|
||||
lite_model_id, fallback_model_id, timeout_seconds,
|
||||
max_retries, max_tokens, concurrency, extra_params,
|
||||
is_enabled, created_at, updated_at)
|
||||
VALUES (gen_random_uuid()::text, 'image_analysis', '图片分析',
|
||||
:primary, :primary, :fallback, 30, 1, 1000, 2,
|
||||
'{}'::jsonb, true, now(), now())
|
||||
"""),
|
||||
{"primary": qwen_vl_id, "fallback": qwen37_id},
|
||||
)
|
||||
return
|
||||
|
||||
current_primary = current[0]
|
||||
if current_primary == qwen_vl_id:
|
||||
# 已经绑定 qwen-vl-plus：视为后台/数据迁移已处理，不覆盖任何配置
|
||||
return
|
||||
|
||||
set_parts = [
|
||||
"primary_model_id = :vl_id",
|
||||
"lite_model_id = :vl_id",
|
||||
"max_tokens = 1000",
|
||||
"updated_at = now()",
|
||||
]
|
||||
params: dict = {"vl_id": qwen_vl_id}
|
||||
if qwen37_id is not None:
|
||||
set_parts.insert(2, "fallback_model_id = :qwen37_id")
|
||||
params["qwen37_id"] = qwen37_id
|
||||
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"UPDATE ai_capability_configs SET " + ", ".join(set_parts) + " WHERE capability_key = 'image_analysis'"
|
||||
),
|
||||
params,
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
pass
|
||||
@@ -1,36 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""102: image_analysis max_tokens 1200 -> 1500.
|
||||
|
||||
v6 prompt 更长、字段更多，旧 max_tokens 容易截断 JSON。
|
||||
仅在 image_analysis 当前 max_tokens < 1500 时更新（幂等，不覆盖后台已调到 >=1500 的配置）。
|
||||
"""
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "102_image_analysis_max_tokens_1500"
|
||||
down_revision = "101_qwen_vl_plus_and_max_tokens"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
|
||||
caps_table = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
|
||||
if not caps_table:
|
||||
return
|
||||
|
||||
conn.execute(
|
||||
sa.text(
|
||||
"UPDATE ai_capability_configs "
|
||||
"SET max_tokens = 1500, updated_at = now() "
|
||||
"WHERE capability_key = 'image_analysis' "
|
||||
"AND (max_tokens IS NULL OR max_tokens < 1500)"
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
pass
|
||||
@@ -1,194 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""image_analysis v7 prompt + max_tokens 3000 + max_retries 3
|
||||
|
||||
Revision ID: 103_v7_prompt_and_tokens_3000
|
||||
Revises: 102_image_analysis_max_tokens_1500
|
||||
Create Date: 2026-10-07
|
||||
|
||||
变更：
|
||||
1. 插入v7精简prompt（~1KB，v6 ~4.5KB，删除few-shot/冗长规则，减少输出token占用），设为active
|
||||
2. v6停用（is_active=False），保留历史
|
||||
3. image_analysis capability: max_tokens 1500→3000，max_retries 1→3
|
||||
|
||||
ai_capability_configs 由应用 create_all 创建，全新 alembic-only 库可能不存在，
|
||||
故第3步做 to_regclass 守卫（同 102）。
|
||||
"""
|
||||
|
||||
from sqlalchemy import text
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "103_v7_prompt_and_tokens_3000"
|
||||
down_revision = "102_image_analysis_max_tokens_1500"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
V7_SYSTEM = """# 角色
|
||||
你是一位专业的图片分析师，擅长准确识别图片中的场景、人物、物体、文字、氛围。
|
||||
|
||||
# 任务
|
||||
对用户上传的图片逐张分析，描述你看到的内容，输出JSON格式。
|
||||
|
||||
## 技能
|
||||
|
||||
### 技能1：判断图片类型
|
||||
判断图片属于哪种类型，type字段填对应的英文值：
|
||||
- 商品图（product）：单个或多个商品、产品包装
|
||||
- 门店场景图（store）：店铺内部、门头招牌、货架陈列
|
||||
- 人物图（person）：人物形象、穿搭造型、肖像照片
|
||||
- 风景图（scene）：风景、动物、美食、街景
|
||||
- 其他（other）：以上都不是
|
||||
|
||||
### 技能2：描述通用信息
|
||||
不管什么图都要描述：
|
||||
- type：图片类型，填product/store/person/scene/other其中一个
|
||||
- scene：一句话描述场景，例如"理疗养生店内部，摆着多张理疗床和产品货架"
|
||||
- mood：整体氛围，2-4个词，例如"整洁专业"、"热闹温馨"
|
||||
- colors：主要颜色，最多5个，写具体颜色名（亮红色/米白色/深蓝色，不写笼统的红色蓝色）
|
||||
- visible_text：图片里看到的文字，说明什么字、在什么位置，最多5条；没看到就空数组
|
||||
- lighting：光线情况，例如"明亮柔光"、"自然光"、"室内暖黄灯"
|
||||
- composition：怎么拍的，例如"居中特写"、"中景平视"、"俯拍"
|
||||
- has_person：有没有人，true或false
|
||||
|
||||
### 技能3：描述门店场景
|
||||
如果是门店场景图（type="store"），还要描述：
|
||||
- store_type：什么类型的店，例如"养生馆"、"便利店"、"餐饮店"、"母婴店"
|
||||
- brand_signage：招牌上写了什么字、有什么品牌标识
|
||||
- visual_elements：看到哪些显眼的东西（招牌样式、灯光、货架、商品陈列、海报、收银台等），最多8个
|
||||
- product_categories：看到哪些品类的商品，例如"饮料零食"、"养生产品"
|
||||
- promotion_elements：有没有促销活动（打折海报、满减吊旗等），没有就空数组
|
||||
- atmosphere：店内什么氛围，例如"亲民生活化"、"老字号专业感"
|
||||
- cleanliness：店内干净程度，例如"干净整洁"、"货架整齐"
|
||||
- 看到顾客或店员要描述他们在做什么，has_person填true
|
||||
|
||||
### 技能4：描述商品
|
||||
如果是商品图（type="product"），逐个商品描述：
|
||||
- product_name：商品名称，尽量具体，例如"OMO奥妙除菌除螨洗衣液"；看不出来填null
|
||||
- brand：什么牌子，看不出来填null
|
||||
- category：类目，从以下选一个：服饰鞋包/美妆/数码/食品/家居清洁/母婴/配饰/其他
|
||||
- package_type：什么包装，例如"瓶装"、"盒装"、"罐装"、"袋装"、"多瓶装"
|
||||
- package_color：包装主要颜色，写具体色（亮红色不写红色）
|
||||
- body_shape：瓶身或包装形状，例如"圆润胖瓶"、"竖款带把手瓶身"
|
||||
- label_design：标签设计，例如"红色标签印白色品牌logo"
|
||||
- key_text_on_package：包装上最显眼的文字（品牌名、功能词、卖点词），最多5个
|
||||
- product_features：包装特征，3-6个短语，包含颜色、瓶盖、形状、标签图案
|
||||
- key_selling_points：核心卖点，1-3个短语
|
||||
|
||||
### 技能5：描述人物
|
||||
如果是人物图（type="person"），描述：
|
||||
- person_count：几个人
|
||||
- gender：性别（男/女/无法判断）
|
||||
- age_range：年龄段（儿童/青少年/青年/中年/老年/无法判断）
|
||||
- outfit_style：穿搭风格，例如"休闲日常"、"通勤商务"、"街头潮流"
|
||||
- upper_wear：上装（颜色+款式+材质），穿裙装不填
|
||||
- lower_wear：下装（颜色+款式+版型），穿裙装不填
|
||||
- dress_wear：裙装描述，穿上下装不填
|
||||
- outerwear：外套
|
||||
- shoes：鞋子
|
||||
- bag：包袋，没有填null
|
||||
- accessories：配饰（眼镜/帽子/项链/耳环/手表/手链/围巾/腰带等），没有填空数组
|
||||
- hairstyle：发型
|
||||
- makeup：妆容，男生或看不出填null
|
||||
- expression：表情，例如"微笑看镜头"、"冷酷无表情"
|
||||
- pose：姿势动作，例如"身直立正对镜头"、"单手撩发"
|
||||
- body_type：身材，例如"纤细苗条"、"高挑身材"、"丰满匀称"
|
||||
- portrait_prompt：80-150字详细描述人物形象（后面用来AI生成肖像图），要写清年龄段、穿搭完整细节、发型发色、妆容、表情、姿势、场景、光线、风格感觉，语言要有画面感
|
||||
|
||||
### 技能6：描述风景
|
||||
如果是风景图（type="scene"），描述：
|
||||
- scene_type：什么场景，例如"自然风景"、"城市街景"、"动物"、"美食"
|
||||
- main_subject：画面主体是什么
|
||||
- key_elements：关键元素，最多8个
|
||||
- environment_objects：周围环境物体，最多8个
|
||||
- atmosphere：整体氛围，例如"秋日慵懒氛围感"、"清新自然氧气感"
|
||||
- 有人物就描述人物特征
|
||||
|
||||
## 限制
|
||||
- 只输出JSON，不要任何解释文字，不要markdown代码块包裹，不要写"好的""以下是分析结果"这种废话
|
||||
- 颜色写具体色调（亮红色/米白色/深蓝色/翠绿色），不写笼统词汇
|
||||
- 瓶身、包装、招牌上的文字尽量识别出来（品牌名、功能词、卖点词）
|
||||
- 多个商品、多个人物分开描述，不要合并
|
||||
- 看不出来、不确定的字段填null或空数组，布尔值填true/false，绝对不要瞎编
|
||||
- 确保JSON格式合法，所有大括号、中括号、引号正确闭合
|
||||
- 数组字段控制数量：colors最多5个，visible_text最多5条，visual_elements最多8个，accessories最多10个"""
|
||||
V7_USER = "请分析这张图片，按系统消息的JSON结构输出。"
|
||||
|
||||
|
||||
def _capability_table_exists(bind) -> bool:
|
||||
return bool(bind.execute(text("SELECT to_regclass('public.ai_capability_configs')")).scalar())
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
bind = op.get_bind()
|
||||
# 1. 停用旧的active image_analysis prompt（含v6）
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = FALSE "
|
||||
"WHERE prompt_type = 'image_analysis' AND is_active = TRUE"
|
||||
)
|
||||
)
|
||||
# 2. 幂等插入v7（存在则更新并重新激活）
|
||||
existing = bind.execute(
|
||||
text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 7")
|
||||
).fetchone()
|
||||
if existing:
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = TRUE, "
|
||||
"system_prompt = :sys, user_prompt_template = :usr, "
|
||||
"name = 'v7 精简结构化分析', updated_at = NOW() "
|
||||
"WHERE prompt_type = 'image_analysis' AND version = 7"
|
||||
),
|
||||
{"sys": V7_SYSTEM, "usr": V7_USER},
|
||||
)
|
||||
else:
|
||||
bind.execute(
|
||||
text(
|
||||
"INSERT INTO viral_video_prompt_templates "
|
||||
"(prompt_type, version, name, system_prompt, user_prompt_template, "
|
||||
"is_active, created_at, updated_at) "
|
||||
"VALUES ('image_analysis', 7, 'v7 精简结构化分析', "
|
||||
":sys, :usr, TRUE, NOW(), NOW())"
|
||||
),
|
||||
{"sys": V7_SYSTEM, "usr": V7_USER},
|
||||
)
|
||||
# 3. capability max_tokens=3000、max_retries=3（表不存在则跳过）
|
||||
if _capability_table_exists(bind):
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE ai_capability_configs SET max_tokens = 3000, "
|
||||
"updated_at = NOW() "
|
||||
"WHERE capability_key = 'image_analysis' AND "
|
||||
"(max_tokens IS NULL OR max_tokens < 3000)"
|
||||
)
|
||||
)
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE ai_capability_configs SET max_retries = 3, updated_at = NOW() "
|
||||
"WHERE capability_key = 'image_analysis' AND "
|
||||
"(max_retries IS NULL OR max_retries < 3)"
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
bind = op.get_bind()
|
||||
# 删除v7
|
||||
bind.execute(
|
||||
text("DELETE FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 7")
|
||||
)
|
||||
# 恢复v6为active
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = TRUE "
|
||||
"WHERE prompt_type = 'image_analysis' AND version = 6"
|
||||
)
|
||||
)
|
||||
# tokens/retries回退
|
||||
if _capability_table_exists(bind):
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE ai_capability_configs SET max_tokens = 1500, max_retries = 1, "
|
||||
"updated_at = NOW() WHERE capability_key = 'image_analysis'"
|
||||
)
|
||||
)
|
||||
@@ -1,242 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""image_analysis v8 prompt + storyboard v3 prompt - 用户端展示格式 markdown 控制
|
||||
|
||||
Revision ID: 104_v8_display_markdown
|
||||
Revises: 103_v7_prompt_and_tokens_3000
|
||||
Create Date: 2026-10-07
|
||||
|
||||
变更：
|
||||
1. image_analysis v8: 在 v7 基础上 system_prompt 末尾追加「## 用户端展示格式」章节，
|
||||
要求 VLM 在每张图的 JSON 里输出 summary_markdown 字段（markdown 格式的图片描述），
|
||||
v8 设 is_active=true，v7 设 is_active=false。
|
||||
2. storyboard v3: 在 v2 基础上 system_prompt 追加要求 LLM 在 copy_result 中
|
||||
输出 copy_display_markdown 字段（markdown 格式的完整文案展示），
|
||||
v3 设 is_active=true，v2 设 is_active=false。
|
||||
"""
|
||||
|
||||
from sqlalchemy import text
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "104_v8_display_markdown"
|
||||
down_revision = "103_v7_prompt_and_tokens_3000"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
# ── v8 追加的 system prompt 内容 ──────────────────────────────────────
|
||||
V8_SYSTEM_APPEND = """
|
||||
|
||||
## 用户端展示格式
|
||||
|
||||
对于每张分析的图片，在 JSON 中额外输出一个 **summary_markdown** 字段，用 markdown 格式写出给用户看的图片描述。
|
||||
|
||||
格式要求（根据图片类型自适应）：
|
||||
|
||||
**商品图（type=product）**示例：
|
||||
### 商品名称
|
||||
**品牌**：品牌名 | **类目**：服饰鞋包/美妆/数码/...
|
||||
**核心特征**
|
||||
- 特征1：描述
|
||||
- 特征2：描述
|
||||
**外观**：颜色+材质+设计描述
|
||||
**包装**：包装类型描述
|
||||
**文字信息**：包装上看到的文字
|
||||
|
||||
**门店场景图（type=store）**示例：
|
||||
### 门店名称/类型
|
||||
**类型**：奶茶店/便利店/养生馆/...
|
||||
**品牌标识**：招牌文字描述
|
||||
**环境氛围**：店内整体感觉
|
||||
**陈列亮点**
|
||||
- 亮点1
|
||||
- 亮点2
|
||||
**氛围**：亲民/专业/时尚/...
|
||||
|
||||
**人物图（type=person）**示例：
|
||||
### 人物描述
|
||||
**形象**：年龄段 + 风格
|
||||
**穿搭**
|
||||
- 上装：颜色+款式
|
||||
- 下装：颜色+款式
|
||||
- 配饰：...
|
||||
**气质**：表情+姿势+整体感觉
|
||||
|
||||
**风景/场景图（type=scene）**示例：
|
||||
### 场景名称
|
||||
**类型**：自然风景/城市街景/动物/美食
|
||||
**主体**：画面主要元素
|
||||
**氛围**：整体感觉描述
|
||||
|
||||
要求：
|
||||
- 内容真实具体，从实际图片分析得出
|
||||
- 用 markdown 语法：**加粗**、列表、标题
|
||||
- 控制在 100-200 字
|
||||
- 不要编造图片中没有的信息
|
||||
"""
|
||||
|
||||
# ── storyboard v3 追加的 system prompt 内容 ──────────────────────────
|
||||
V3_STORYBOARD_APPEND = """
|
||||
|
||||
## 用户端展示格式
|
||||
|
||||
在输出分镜脚本的同时，在顶层输出一个 **copy_display_markdown** 字段（用 XML 标签 <copy_display_markdown> 包裹），用 markdown 格式写出完整文案展示。
|
||||
|
||||
格式示例：
|
||||
# 标题/主题
|
||||
|
||||
## 整体概要
|
||||
一句话描述视频内容
|
||||
|
||||
## 分镜预览
|
||||
|
||||
### 镜头1（0-3秒）
|
||||
**景别**：近景俯拍，缓慢推镜
|
||||
**画面**：场景描述
|
||||
**台词**：口播文本
|
||||
**动作**：人物动作描述
|
||||
|
||||
### 镜头2（3-9秒）
|
||||
...
|
||||
|
||||
## 完整口播
|
||||
完整口播文案文本
|
||||
|
||||
要求：
|
||||
- 把所有分镜按时间顺序整理成易读的格式
|
||||
- 用 markdown 语法组织，**加粗**标签、##二级标题、列表等
|
||||
- 控制在 300-500 字
|
||||
- 让用户一眼看懂视频会拍成什么样
|
||||
"""
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
bind = op.get_bind()
|
||||
|
||||
# ── 1. image_analysis v8 ──────────────────────────────────────────
|
||||
# 停用所有 active image_analysis prompt
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = FALSE "
|
||||
"WHERE prompt_type = 'image_analysis' AND is_active = TRUE"
|
||||
)
|
||||
)
|
||||
|
||||
# 读取 v7 的 prompt 内容作为基础
|
||||
v7_row = bind.execute(
|
||||
text(
|
||||
"SELECT system_prompt, user_prompt_template, COALESCE(example_output, '') "
|
||||
"FROM viral_video_prompt_templates "
|
||||
"WHERE prompt_type = 'image_analysis' "
|
||||
"ORDER BY version DESC LIMIT 1"
|
||||
)
|
||||
).fetchone()
|
||||
|
||||
if v7_row:
|
||||
v7_system = v7_row[0] or ""
|
||||
v8_system = v7_system + V8_SYSTEM_APPEND
|
||||
v8_user = v7_row[1] or "{image_url}"
|
||||
v8_example = v7_row[2] or ""
|
||||
|
||||
# 幂等：已有 v8 则更新，否则插入
|
||||
existing_v8 = bind.execute(
|
||||
text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 8")
|
||||
).fetchone()
|
||||
if existing_v8:
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = TRUE, "
|
||||
"system_prompt = :sys, user_prompt_template = :usr, "
|
||||
"example_output = :ex, name = 'v8 用户端展示格式', "
|
||||
"updated_at = NOW() "
|
||||
"WHERE prompt_type = 'image_analysis' AND version = 8"
|
||||
),
|
||||
{"sys": v8_system, "usr": v8_user, "ex": v8_example},
|
||||
)
|
||||
else:
|
||||
bind.execute(
|
||||
text(
|
||||
"INSERT INTO viral_video_prompt_templates "
|
||||
"(prompt_type, version, name, system_prompt, user_prompt_template, "
|
||||
"example_output, is_active, created_at, updated_at) "
|
||||
"VALUES ('image_analysis', 8, 'v8 用户端展示格式', "
|
||||
":sys, :usr, :ex, TRUE, NOW(), NOW())"
|
||||
),
|
||||
{"sys": v8_system, "usr": v8_user, "ex": v8_example},
|
||||
)
|
||||
|
||||
# ── 2. storyboard v3 ─────────────────────────────────────────────
|
||||
# 停用所有 active storyboard prompt
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = FALSE "
|
||||
"WHERE prompt_type = 'storyboard' AND is_active = TRUE"
|
||||
)
|
||||
)
|
||||
|
||||
# 读取当前 storyboard prompt
|
||||
sb_row = bind.execute(
|
||||
text(
|
||||
"SELECT system_prompt, user_prompt_template, COALESCE(example_output, '') "
|
||||
"FROM viral_video_prompt_templates "
|
||||
"WHERE prompt_type = 'storyboard' "
|
||||
"ORDER BY version DESC LIMIT 1"
|
||||
)
|
||||
).fetchone()
|
||||
|
||||
if sb_row:
|
||||
sb_system = sb_row[0] or ""
|
||||
v3_system = sb_system + V3_STORYBOARD_APPEND
|
||||
v3_user = sb_row[1] or ""
|
||||
v3_example = sb_row[2] or ""
|
||||
|
||||
existing_v3 = bind.execute(
|
||||
text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = 'storyboard' AND version = 3")
|
||||
).fetchone()
|
||||
if existing_v3:
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = TRUE, "
|
||||
"system_prompt = :sys, user_prompt_template = :usr, "
|
||||
"example_output = :ex, name = 'v3 用户端展示格式', "
|
||||
"updated_at = NOW() "
|
||||
"WHERE prompt_type = 'storyboard' AND version = 3"
|
||||
),
|
||||
{"sys": v3_system, "usr": v3_user, "ex": v3_example},
|
||||
)
|
||||
else:
|
||||
bind.execute(
|
||||
text(
|
||||
"INSERT INTO viral_video_prompt_templates "
|
||||
"(prompt_type, version, name, system_prompt, user_prompt_template, "
|
||||
"example_output, is_active, created_at, updated_at) "
|
||||
"VALUES ('storyboard', 3, 'v3 用户端展示格式', "
|
||||
":sys, :usr, :ex, TRUE, NOW(), NOW())"
|
||||
),
|
||||
{"sys": v3_system, "usr": v3_user, "ex": v3_example},
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
bind = op.get_bind()
|
||||
|
||||
# 删除 v8
|
||||
bind.execute(
|
||||
text("DELETE FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 8")
|
||||
)
|
||||
# 恢复 v7 active
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = TRUE, updated_at = NOW() "
|
||||
"WHERE prompt_type = 'image_analysis' AND version = 7"
|
||||
)
|
||||
)
|
||||
|
||||
# 删除 v3
|
||||
bind.execute(text("DELETE FROM viral_video_prompt_templates " "WHERE prompt_type = 'storyboard' AND version = 3"))
|
||||
# 恢复 storyboard v2 active
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = TRUE, updated_at = NOW() "
|
||||
"WHERE prompt_type = 'storyboard' AND version = 2"
|
||||
)
|
||||
)
|
||||
@@ -29,6 +29,8 @@ from app.services.ai_avatar_render_service import (
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from packages.middleware.points_gate import points_gate
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter()
|
||||
@@ -42,6 +44,7 @@ def _get_service(db: Session = Depends(get_db_session)) -> AiAvatarRenderService
|
||||
|
||||
|
||||
@router.post("", response_model=AiAvatarRenderJobResponse, status_code=201)
|
||||
@points_gate("ai_digital_human", per_unit=15)
|
||||
def create_render_job(
|
||||
body: CreateAiAvatarRenderRequest,
|
||||
current_user: AuthenticatedUser = Depends(get_current_user),
|
||||
|
||||
@@ -27,6 +27,7 @@ from packages.adapters.sqlalchemy_impl.generation_task_repository import (
|
||||
)
|
||||
from packages.application import ListGeneratedVideosByTaskUseCase
|
||||
from packages.domain.config_schemas import normalize_plan_config
|
||||
from packages.middleware.points_gate import points_gate
|
||||
from packages.shared.storage import get_shared_storage_service
|
||||
|
||||
from .templates_editor.dependencies import get_draft_plan_id, get_editor_services
|
||||
@@ -345,6 +346,7 @@ def _is_trusted_media_url(url: str) -> bool:
|
||||
|
||||
|
||||
@router.post("/generate-cover", response_model=GenerateCoverResponse)
|
||||
@points_gate("ai_cover")
|
||||
def generate_cover(
|
||||
body: GenerateCoverRequest,
|
||||
template_id: str = Query(..., description="模板 ID"),
|
||||
|
||||
@@ -41,6 +41,7 @@ from packages.application import (
|
||||
GetGenerationTaskUseCase,
|
||||
ListGeneratedVideosByTaskUseCase,
|
||||
)
|
||||
from packages.middleware.points_gate import points_gate
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -269,6 +270,7 @@ def _variant_value(values: list[str], index: int, fallback: str = "") -> str:
|
||||
|
||||
|
||||
@router.post("/preview", response_model=BatchPreviewGenerationTaskResponse, status_code=201)
|
||||
@points_gate("ai_video", quantity_field="preview_count")
|
||||
def create_preview_generation_task(
|
||||
request: CreatePreviewGenerationTaskRequest,
|
||||
authenticated_user: AuthenticatedUser = Depends(get_current_user),
|
||||
|
||||
@@ -44,7 +44,6 @@ from packages.application import (
|
||||
GetGenerationTaskUseCase,
|
||||
ListGeneratedVideosByTaskUseCase,
|
||||
)
|
||||
from packages.domain import feature_pricing_service
|
||||
from packages.domain.smart_match import smart_select_assets
|
||||
|
||||
# #2035：文案关键词 → 素材分类 映射表（用于 smart_match category_match 维度）
|
||||
@@ -164,6 +163,8 @@ def _infer_expected_categories(script_tags: set[str] | None) -> set[str] | None:
|
||||
return matched or None
|
||||
|
||||
|
||||
from packages.middleware.points_gate import points_gate
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter()
|
||||
@@ -464,6 +465,7 @@ def _resolve_project_and_library(
|
||||
|
||||
|
||||
@router.post("/tasks", response_model=BatchGenerationTaskResponse)
|
||||
@points_gate("ai_video", quantity_field="count")
|
||||
def create_generation_task(
|
||||
request: CreateGenerationTaskRequest,
|
||||
authenticated_user: AuthenticatedUser = Depends(get_current_user),
|
||||
@@ -700,17 +702,6 @@ def create_generation_task(
|
||||
logger.info("画中画已下线，strategy_id %s → one_take", effective_strategy_id)
|
||||
effective_strategy_id = "one_take"
|
||||
|
||||
# ── smart_edit 计费预扣（全局 points 开关 + 功能开关均开才扣） ──
|
||||
# 首期固定价：dynamic_cost=0，price=(0+fixed_cost)×multiplier，price_cap 封顶。
|
||||
# 预览任务不扣费；按任务条数扣费，任一任务预扣失败（余额不足）整体拒绝。
|
||||
smart_edit_charge = 0.0
|
||||
charged_task_count = 0
|
||||
if not request.is_preview and feature_pricing_service.is_feature_enabled("smart_edit"):
|
||||
unit_credits, _bd = feature_pricing_service.calculate_price("smart_edit", 0.0)
|
||||
if unit_credits > 0:
|
||||
smart_edit_charge = round(unit_credits * count, 2)
|
||||
charged_task_count = count
|
||||
|
||||
# 批量生成（count>1）：每个变体必须走与单视频完全相同的独立选片流程（#1743/#1749）。
|
||||
# - 变体 0：clone 源 plan（不污染源 plan），变体 1..N-1 用 reselect_plan_for_variant
|
||||
# 完整重跑选片（素材级去重：fresh 优先 → 受控复用 overlap≤20% → 短素材禁复用）；
|
||||
@@ -941,42 +932,6 @@ def create_generation_task(
|
||||
)
|
||||
# 变体序号写入 extra_meta（响应/排查时可辨识）
|
||||
task.extra_meta["variant_index"] = task_index
|
||||
|
||||
# smart_edit 逐条预扣（首期固定价，credits_cost=prepaid，不做结算）
|
||||
task_txn_id = ""
|
||||
if charged_task_count > 0:
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
unit_credits = round(smart_edit_charge / count, 2)
|
||||
res = PointsService().deduct_points(
|
||||
user_id=user_id,
|
||||
amount=unit_credits,
|
||||
source="smart_edit",
|
||||
db=db,
|
||||
description="智能剪辑生成预扣",
|
||||
ref_id=task.id,
|
||||
)
|
||||
if not res.get("success"):
|
||||
# 余额不足：退还本次请求已扣积分后整体拒绝
|
||||
already_charged = round(unit_credits * task_index, 2)
|
||||
if already_charged > 0:
|
||||
PointsService().refund_points(
|
||||
user_id=user_id,
|
||||
amount=already_charged,
|
||||
source="smart_edit",
|
||||
db=db,
|
||||
ref_id=task.id,
|
||||
description="智能剪辑批量提交失败退回",
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=402,
|
||||
detail=(f"积分不足：智能剪辑每条需 {unit_credits:.2f} 积分，当前余额 {res.get('balance', 0)}"),
|
||||
)
|
||||
task_txn_id = str(res.get("transaction_id") or "")
|
||||
task.credits_prepaid = unit_credits
|
||||
task.credits_cost = unit_credits
|
||||
task.credits_transaction_id = task_txn_id
|
||||
generation_task_repository.update(task)
|
||||
try:
|
||||
# 兜底关联编辑计划：前端未传 source_edit_plan_id 时，
|
||||
# 通过 template_id + user_id 在 DB 层直接查找最新的 plan。
|
||||
|
||||
@@ -12,9 +12,11 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import math
|
||||
from datetime import UTC
|
||||
|
||||
from app.auth import AuthenticatedUser, get_current_user
|
||||
from app.config import settings
|
||||
from app.dependencies import (
|
||||
get_db_session,
|
||||
get_voice_clone_profile_repository,
|
||||
@@ -30,6 +32,9 @@ from app.services.mediakit_client import MediaKitError
|
||||
from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, Query
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from packages.domain.points_rules import calculate_points_cost
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter()
|
||||
@@ -56,6 +61,37 @@ def create_lipsync_job(
|
||||
db: Session = Depends(get_db_session),
|
||||
svc: LipsyncService = Depends(_get_service),
|
||||
):
|
||||
user_id = current_user.user.id
|
||||
|
||||
# ── 积分扣点（#1895 P2） ──
|
||||
_points_deducted = 0
|
||||
_points_scene = "ai_digital_human"
|
||||
_points_svc = PointsService() if settings.points_enabled else None
|
||||
if _points_svc is not None:
|
||||
# 口型同步：TTS 模式按 script_text 估时长（240字/分钟）；音频直传按 audio_duration（秒→分钟）
|
||||
if body.audio_url and body.audio_duration and body.audio_duration > 0:
|
||||
est_minutes = max(1.0, math.ceil(body.audio_duration / 60.0))
|
||||
elif body.script_text:
|
||||
est_minutes = max(1.0, math.ceil(len(body.script_text) / 240))
|
||||
else:
|
||||
est_minutes = 1.0
|
||||
_points_deducted = calculate_points_cost(
|
||||
_points_scene,
|
||||
is_member=getattr(current_user.user, "is_member", False),
|
||||
duration_minutes=est_minutes,
|
||||
member_type=getattr(current_user.user, "member_type", None),
|
||||
)
|
||||
_deduct_res = _points_svc.deduct_points(user_id, _points_deducted, _points_scene, db)
|
||||
if not _deduct_res["success"]:
|
||||
raise HTTPException(
|
||||
status_code=402,
|
||||
detail={
|
||||
"code": "INSUFFICIENT_POINTS",
|
||||
"message": f"积分不足，需要 {_points_deducted} 积分，当前余额 {_deduct_res['balance']}",
|
||||
"required": _points_deducted,
|
||||
"balance": _deduct_res["balance"],
|
||||
},
|
||||
)
|
||||
"""提交对口型任务.
|
||||
|
||||
三种模式：
|
||||
@@ -65,8 +101,6 @@ def create_lipsync_job(
|
||||
- 预合成音频（#1845 新主路径）：传 {video_url, audio_url, audio_duration, sentence_timings}，
|
||||
后端同步ffprobe+写入timings+直接提交MediaKit（~2-3s）。
|
||||
"""
|
||||
user_id = current_user.user.id
|
||||
|
||||
try:
|
||||
job = svc.create_job(
|
||||
user_id=user_id,
|
||||
@@ -84,8 +118,18 @@ def create_lipsync_job(
|
||||
project_id=body.project_id,
|
||||
)
|
||||
except ValueError as exc:
|
||||
if _points_deducted > 0 and _points_svc is not None:
|
||||
try:
|
||||
_points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
|
||||
except Exception as refund_err:
|
||||
logger.warning(f"对口型 ValueError 退积分异常: err={refund_err}")
|
||||
raise HTTPException(status_code=400, detail=str(exc)) from exc
|
||||
except MediaKitError as exc:
|
||||
if _points_deducted > 0 and _points_svc is not None:
|
||||
try:
|
||||
_points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
|
||||
except Exception as refund_err:
|
||||
logger.warning(f"对口型 MediaKitError 退积分异常: err={refund_err}")
|
||||
status_code = 502
|
||||
if exc.code in ("VoiceForbidden",):
|
||||
status_code = 403
|
||||
@@ -101,11 +145,24 @@ def create_lipsync_job(
|
||||
) from exc
|
||||
except Exception as exc:
|
||||
logger.error("创建对口型任务异常: %s", exc, exc_info=True)
|
||||
if _points_deducted > 0 and _points_svc is not None:
|
||||
try:
|
||||
_points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
|
||||
except Exception as refund_err:
|
||||
logger.warning(f"对口型异常退积分异常: err={refund_err}")
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"创建对口型任务失败: {exc}",
|
||||
) from exc
|
||||
|
||||
# 创建成功但状态为 failed（同步路径失败已抛异常到上面 except；此处处理 Celery 调度失败等）
|
||||
# 若任务已创建且状态为 failed，退费
|
||||
if _points_deducted > 0 and _points_svc is not None and getattr(job, "status", None) == "failed":
|
||||
try:
|
||||
_points_svc.refund_points(user_id, _points_deducted, _points_scene, db, ref_id=job.id)
|
||||
except Exception as refund_err:
|
||||
logger.warning(f"对口型任务失败退积分异常: job_id={job.id}, err={refund_err}")
|
||||
|
||||
return job
|
||||
|
||||
|
||||
@@ -119,14 +176,37 @@ def preview_tts(
|
||||
db: Session = Depends(get_db_session),
|
||||
svc: LipsyncService = Depends(_get_service),
|
||||
):
|
||||
user_id = current_user.user.id
|
||||
|
||||
# ── 积分扣点（#1895 P2） ──
|
||||
_points_deducted = 0
|
||||
_points_scene = "ai_digital_human"
|
||||
_points_svc = PointsService() if settings.points_enabled else None
|
||||
if _points_svc is not None:
|
||||
est_minutes = max(1.0, math.ceil(len(body.script_text or "") / 240)) if body.script_text else 1.0
|
||||
_points_deducted = calculate_points_cost(
|
||||
_points_scene,
|
||||
is_member=getattr(current_user.user, "is_member", False),
|
||||
duration_minutes=est_minutes,
|
||||
member_type=getattr(current_user.user, "member_type", None),
|
||||
)
|
||||
_deduct_res = _points_svc.deduct_points(user_id, _points_deducted, _points_scene, db)
|
||||
if not _deduct_res["success"]:
|
||||
raise HTTPException(
|
||||
status_code=402,
|
||||
detail={
|
||||
"code": "INSUFFICIENT_POINTS",
|
||||
"message": f"积分不足，需要 {_points_deducted} 积分，当前余额 {_deduct_res['balance']}",
|
||||
"required": _points_deducted,
|
||||
"balance": _deduct_res["balance"],
|
||||
},
|
||||
)
|
||||
"""步骤1「生成配音」同步 TTS 预合成.
|
||||
|
||||
同步执行 TTS 合成 → 下载音频 → ffprobe 时长 → 句子时间戳计算，
|
||||
不创建 LipsyncJob、不转存 OSS，直接返回 CosyVoice 临时 URL（~24h 有效）。
|
||||
耗时约 2-3 秒。
|
||||
"""
|
||||
user_id = current_user.user.id
|
||||
|
||||
try:
|
||||
result = svc.preview_tts(
|
||||
user_id=user_id,
|
||||
@@ -138,6 +218,11 @@ def preview_tts(
|
||||
emotion=body.emotion,
|
||||
)
|
||||
except MediaKitError as exc:
|
||||
if _points_deducted > 0 and _points_svc is not None:
|
||||
try:
|
||||
_points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
|
||||
except Exception as refund_err:
|
||||
logger.warning(f"TTS 预合成 MediaKitError 退积分异常: err={refund_err}")
|
||||
status_code = 400
|
||||
if exc.code in ("VoiceForbidden",):
|
||||
status_code = 403
|
||||
@@ -152,6 +237,11 @@ def preview_tts(
|
||||
) from exc
|
||||
except Exception as exc:
|
||||
logger.error("TTS 预合成异常: %s", exc, exc_info=True)
|
||||
if _points_deducted > 0 and _points_svc is not None:
|
||||
try:
|
||||
_points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
|
||||
except Exception as refund_err:
|
||||
logger.warning(f"TTS 预合成异常退积分异常: err={refund_err}")
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"TTS 合成失败: {exc}",
|
||||
|
||||
@@ -145,22 +145,19 @@ def get_rules(
|
||||
def get_packages(
|
||||
current_user: AuthenticatedUser = Depends(get_current_user),
|
||||
):
|
||||
"""查询可购买的积分包列表（读管理后台 credit_packages 表真实数据）。
|
||||
|
||||
仅返回 is_active=true；后台改价/启停后最多 30 秒生效。
|
||||
"""
|
||||
from packages.application.catalog.admin_catalog import get_points_packages
|
||||
|
||||
packages = [
|
||||
PointsPackageItem(
|
||||
code=row["code"],
|
||||
name=row["name"],
|
||||
points=row["points"],
|
||||
price_cents=row["price_cents"],
|
||||
unit_price=row["unit_price"],
|
||||
"""查询可购买的积分包列表。"""
|
||||
packages = []
|
||||
for code, pkg in POINTS_PACKAGES.items():
|
||||
unit_price = f"¥{pkg['price_cents'] / 100 / pkg['points']:.3f}/积分"
|
||||
packages.append(
|
||||
PointsPackageItem(
|
||||
code=code,
|
||||
name=pkg["name"],
|
||||
points=pkg["points"],
|
||||
price_cents=pkg["price_cents"],
|
||||
unit_price=unit_price,
|
||||
)
|
||||
)
|
||||
for row in get_points_packages()
|
||||
]
|
||||
mt = _member_type(current_user)
|
||||
discount = MEMBER_DISCOUNT.get(mt) if mt else None
|
||||
return PointsPackagesResponse(packages=packages, user_discount=discount)
|
||||
@@ -172,7 +169,17 @@ def check_points(
|
||||
current_user: AuthenticatedUser = Depends(get_current_user),
|
||||
db: Session = Depends(get_db_session),
|
||||
):
|
||||
"""消费前检查余额是否足够。已下线/未知场景返回 cost=0（免费）。"""
|
||||
"""消费前检查余额是否足够。未知 scene_key 返回 400（而非 500）。"""
|
||||
if body.scene_key not in POINTS_SCENES:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail={
|
||||
"code": "UNKNOWN_SCENE",
|
||||
"message": f"未知场景: {body.scene_key}",
|
||||
"valid_scenes": sorted(POINTS_SCENES.keys()),
|
||||
},
|
||||
)
|
||||
|
||||
# 积分系统暂停（ENABLE_CREDIT_SYSTEM=false）：所有场景直接放行，需 0 积分
|
||||
if not _credits_enabled():
|
||||
svc = _get_service()
|
||||
@@ -188,6 +195,13 @@ def check_points(
|
||||
is_mem = _is_member(current_user)
|
||||
mt = _member_type(current_user)
|
||||
|
||||
# 混剪场景先检查免费额度
|
||||
is_free_quota = False
|
||||
if body.scene_key == "ai_video" and not is_mem:
|
||||
svc = _get_service()
|
||||
if svc.check_daily_free_clip(current_user.user.id, db):
|
||||
is_free_quota = True
|
||||
|
||||
required = calculate_points_cost(
|
||||
body.scene_key,
|
||||
is_mem,
|
||||
@@ -201,11 +215,11 @@ def check_points(
|
||||
balance = account["balance"]
|
||||
|
||||
return PointsCheckResponse(
|
||||
allowed=balance >= required,
|
||||
allowed=is_free_quota or balance >= required,
|
||||
required_points=required,
|
||||
current_balance=balance,
|
||||
remaining_after=balance - required,
|
||||
is_free_quota=False,
|
||||
is_free_quota=is_free_quota,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -44,6 +44,7 @@ from app.services.script_asr_service import (
|
||||
from fastapi import APIRouter, Depends, HTTPException, status
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from packages.middleware.points_gate import points_gate
|
||||
from packages.shared.ai_client import get_doubao_client
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -372,6 +373,7 @@ def douyin_diag():
|
||||
|
||||
|
||||
@router.post("/extract-from-douyin", response_model=ExtractFromDouyinResponse)
|
||||
@points_gate("douyin_extract")
|
||||
def extract_from_douyin(
|
||||
request: ExtractFromDouyinRequest,
|
||||
current_user: AuthenticatedUser = Depends(get_current_user),
|
||||
@@ -495,6 +497,7 @@ def extract_from_douyin(
|
||||
|
||||
|
||||
@router.post("/ai-rewrite", response_model=AiRewriteResponse)
|
||||
@points_gate("ai_rewrite")
|
||||
def ai_rewrite(
|
||||
request: AiRewriteRequest,
|
||||
current_user: AuthenticatedUser = Depends(get_current_user),
|
||||
@@ -534,6 +537,7 @@ def ai_rewrite(
|
||||
|
||||
|
||||
@router.post("/ai-generate-titles", response_model=AiGenerateTitlesResponse)
|
||||
@points_gate("ai_title")
|
||||
def ai_generate_titles(
|
||||
request: AiGenerateTitlesRequest,
|
||||
current_user: AuthenticatedUser = Depends(get_current_user),
|
||||
|
||||
@@ -86,13 +86,33 @@ async def get_current_subscription(
|
||||
def list_membership_plans(
|
||||
current_user: AuthenticatedUser = Depends(get_current_user),
|
||||
) -> dict[str, list[dict[str, Any]]]:
|
||||
"""查询可购买的会员套餐（读管理后台 plans 表真实数据）。
|
||||
"""查询所有会员档位（供前端会员购买页展示）。
|
||||
|
||||
仅返回 is_enabled=true 的套餐；后台启停/改价后最多 30 秒生效。
|
||||
返回 points 积分体系下的会员档位（月卡/季卡/年卡），含价格、时长、积分折扣等信息。
|
||||
"""
|
||||
from packages.application.catalog.admin_catalog import get_membership_plans
|
||||
from packages.domain.points_rules import MEMBER_DISCOUNT, MEMBERSHIP_PRICES
|
||||
|
||||
return {"plans": get_membership_plans()}
|
||||
plans: list[dict[str, Any]] = []
|
||||
for plan_id, info in MEMBERSHIP_PRICES.items():
|
||||
days = info["duration_days"]
|
||||
monthly_cents = round(info["price_cents"] * 30 / days)
|
||||
features: dict[str, Any] = {"max_resolution": "1080p"}
|
||||
if plan_id == MembershipType.MONTHLY:
|
||||
features.update({"free_clips_daily": 2})
|
||||
elif plan_id == MembershipType.QUARTERLY:
|
||||
features.update({"free_clips_daily": 5})
|
||||
elif plan_id == MembershipType.YEARLY:
|
||||
features.update({"free_clips_daily": "unlimited"})
|
||||
plans.append({
|
||||
"plan_id": plan_id,
|
||||
"name": info["name"],
|
||||
"price_cents": info["price_cents"],
|
||||
"monthly_price_cents": monthly_cents,
|
||||
"duration_days": days,
|
||||
"points_discount": MEMBER_DISCOUNT.get(plan_id, 1.0),
|
||||
"features": features,
|
||||
})
|
||||
return {"plans": plans}
|
||||
|
||||
|
||||
@router.get("/billing-records", response_model=list[BillingRecord])
|
||||
|
||||
@@ -4,12 +4,14 @@ from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import math
|
||||
import subprocess
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
from typing import Any, Optional
|
||||
|
||||
from app.auth import AuthenticatedUser, get_current_user
|
||||
from app.config import settings
|
||||
from app.core.celery_app import celery_app
|
||||
from app.core.storage import get_storage_service
|
||||
from app.dependencies import (
|
||||
@@ -51,6 +53,8 @@ from packages.application.tts_job.use_cases import (
|
||||
)
|
||||
from packages.application.tts_job.workflow import TTSWorkflowService
|
||||
from packages.domain import Asset, AssetLibrary, AssetLibraryKind, AssetStatus, ClassificationStatus
|
||||
from packages.domain.points_rules import calculate_points_cost
|
||||
from packages.domain.points_service import PointsService
|
||||
from packages.domain.voice_presets import list_voices
|
||||
from packages.ports.asset_library_repository import AssetLibraryRepository
|
||||
from packages.ports.asset_repository import AssetRepository
|
||||
@@ -140,6 +144,31 @@ def synthesize(
|
||||
"""
|
||||
user_id = authenticated_user.user.id
|
||||
|
||||
# ── 积分扣点（#1895 P2） ──
|
||||
_points_deducted = 0
|
||||
_points_scene = "ai_voice"
|
||||
_points_svc = PointsService() if settings.points_enabled else None
|
||||
if _points_svc is not None:
|
||||
# 中文按 ~240 字/分钟粗估时长，至少按 1 分钟扣 1 分
|
||||
est_minutes = max(1.0, math.ceil(len(request.text) / 240))
|
||||
_points_deducted = calculate_points_cost(
|
||||
_points_scene,
|
||||
is_member=getattr(authenticated_user.user, "is_member", False),
|
||||
duration_minutes=est_minutes,
|
||||
member_type=getattr(authenticated_user.user, "member_type", None),
|
||||
)
|
||||
_deduct_res = _points_svc.deduct_points(user_id, _points_deducted, _points_scene, db)
|
||||
if not _deduct_res["success"]:
|
||||
raise HTTPException(
|
||||
status_code=402,
|
||||
detail={
|
||||
"code": "INSUFFICIENT_POINTS",
|
||||
"message": f"积分不足，需要 {_points_deducted} 积分，当前余额 {_deduct_res['balance']}",
|
||||
"required": _points_deducted,
|
||||
"balance": _deduct_res["balance"],
|
||||
},
|
||||
)
|
||||
|
||||
# 解析 voice_id：前端可能传克隆音色 profile UUID（而非 CosyVoice voice_id），
|
||||
# 与 /tts/preview 保持一致：命中 profile → 校验归属 → 取 CosyVoice voice_id
|
||||
actual_voice_id = request.voice_id
|
||||
@@ -202,6 +231,7 @@ def synthesize(
|
||||
cosyvoice_service=cosyvoice_service,
|
||||
)
|
||||
|
||||
synthesis_error: Exception | None = None
|
||||
try:
|
||||
job = workflow.start_synthesis(job.id)
|
||||
except Exception as e:
|
||||
@@ -209,10 +239,18 @@ def synthesize(
|
||||
# 但 DB 异常、网络异常等意外错误可能逃逸。
|
||||
# 与音色克隆接口保持一致：标记 failed，返回 201，不抛 500。
|
||||
logger.error(f"TTS 合成异常: job_id={job.id}, error={e}", exc_info=True)
|
||||
synthesis_error = e
|
||||
try:
|
||||
job = workflow.process_synthesis_failure(job.id, str(e))
|
||||
except Exception as inner_e:
|
||||
logger.error(f"标记 TTS job 失败时出错: job_id={job.id}, error={inner_e}")
|
||||
# 合成失败且已扣积分 → 退费
|
||||
if synthesis_error is not None and _points_deducted > 0 and _points_svc is not None:
|
||||
try:
|
||||
_points_svc.refund_points(user_id, _points_deducted, _points_scene, db, ref_id=job.id)
|
||||
except Exception as refund_err:
|
||||
logger.warning(f"TTS 合失败退积分异常: job_id={job.id}, err={refund_err}")
|
||||
|
||||
# 若任务处于 processing 状态（异步模式），触发 Celery 后台轮询
|
||||
if job.status.value == "processing":
|
||||
# 分段合成任务 vs 普通单段任务
|
||||
@@ -231,6 +269,13 @@ def synthesize(
|
||||
workflow.process_synthesis_failure(job.id, f"Celery 任务调度失败: {e}")
|
||||
except Exception as inner_e:
|
||||
logger.error(f"Celery 调度后标记失败时出错: job_id={job.id}, error={inner_e}")
|
||||
# 调度失败退费
|
||||
if _points_deducted > 0 and _points_svc is not None:
|
||||
try:
|
||||
_points_svc.refund_points(user_id, _points_deducted, _points_scene, db, ref_id=job.id)
|
||||
except Exception as refund_err:
|
||||
logger.warning(f"Celery 调度失败退积分异常: job_id={job.id}, err={refund_err}")
|
||||
|
||||
return TTSSynthesizeResponse(
|
||||
job_id=job.id,
|
||||
status=job.status,
|
||||
@@ -565,6 +610,31 @@ def preview_tts(
|
||||
用于前端预览配音效果，限制文本长度 200 字以内。
|
||||
支持预设音色和克隆音色：克隆音色传的是 profile UUID，需解析为 CosyVoice voice_id。
|
||||
"""
|
||||
user_id = authenticated_user.user.id
|
||||
# ── 积分扣点（#1895 P2） ──
|
||||
_points_deducted = 0
|
||||
_points_scene = "ai_voice"
|
||||
_points_svc = PointsService() if settings.points_enabled else None
|
||||
if _points_svc is not None:
|
||||
est_minutes = max(1.0, math.ceil(len(request.text) / 240))
|
||||
_points_deducted = calculate_points_cost(
|
||||
_points_scene,
|
||||
is_member=getattr(authenticated_user.user, "is_member", False),
|
||||
duration_minutes=est_minutes,
|
||||
member_type=getattr(authenticated_user.user, "member_type", None),
|
||||
)
|
||||
_deduct_res = _points_svc.deduct_points(user_id, _points_deducted, _points_scene, db)
|
||||
if not _deduct_res["success"]:
|
||||
raise HTTPException(
|
||||
status_code=402,
|
||||
detail={
|
||||
"code": "INSUFFICIENT_POINTS",
|
||||
"message": f"积分不足，需要 {_points_deducted} 积分，当前余额 {_deduct_res['balance']}",
|
||||
"required": _points_deducted,
|
||||
"balance": _deduct_res["balance"],
|
||||
},
|
||||
)
|
||||
|
||||
# 解析 voice_id：前端可能传 VoiceCloneProfile UUID 或预设音色 ID
|
||||
actual_voice_id = request.voice_id
|
||||
profile = voice_clone_repo.get(request.voice_id)
|
||||
@@ -594,6 +664,12 @@ def preview_tts(
|
||||
language=getattr(request, "language", "zh-CN"),
|
||||
)
|
||||
except (CosyVoiceError, ValueError) as e:
|
||||
# 合成失败退费
|
||||
if _points_deducted > 0 and _points_svc is not None:
|
||||
try:
|
||||
_points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
|
||||
except Exception as refund_err:
|
||||
logger.warning(f"TTS 预览失败退积分异常: {refund_err}")
|
||||
if isinstance(e, CosyVoiceError):
|
||||
raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail=f"TTS 合成失败: {e}") from e
|
||||
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e)) from e
|
||||
|
||||
@@ -32,11 +32,7 @@ from app.schemas.viral_video import (
|
||||
ConfirmCopyRequest,
|
||||
ConfirmIntentRequest,
|
||||
CreateViralVideoRequest,
|
||||
CreditsFormulaBreakdown,
|
||||
EstimateCreditsRequest,
|
||||
EstimateCreditsResponse,
|
||||
GenerateCopyRequest,
|
||||
RetryViralVideoRequest,
|
||||
StyleTemplateListResponse,
|
||||
StyleTemplateResponse,
|
||||
ViralVideoHistoryResponse,
|
||||
@@ -49,9 +45,7 @@ from packages.adapters.sqlalchemy_impl.viral_video_repository import (
|
||||
SQLAlchemyViralVideoJobRepository,
|
||||
SQLAlchemyViralVideoStyleTemplateRepository,
|
||||
)
|
||||
from packages.domain.points_rules import list_viral_video_models
|
||||
from packages.domain.viral_video import ViralVideoStatus
|
||||
from packages.shared.dashscope_client import get_dashscope_client
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -134,8 +128,6 @@ def _to_response(job) -> ViralVideoJobResponse:
|
||||
style_guide=job.style_guide,
|
||||
style_template_id=job.style_template_id,
|
||||
status=job.status,
|
||||
current_stage=getattr(job, "current_stage", "") or "",
|
||||
phase_message=getattr(job, "phase_message", "") or "",
|
||||
image_analysis=getattr(job, "image_analysis", None),
|
||||
storyboard=getattr(job, "storyboard", None),
|
||||
generated_copy_text=getattr(job, "generated_copy_text", "") or "",
|
||||
@@ -146,10 +138,7 @@ def _to_response(job) -> ViralVideoJobResponse:
|
||||
video_model=getattr(job, "video_model", "") or "",
|
||||
intent_result=job.intent_result,
|
||||
result_video_url=job.result_video_url,
|
||||
pre_trusted_images=getattr(job, "pre_trusted_images", None) or None,
|
||||
video_resolution=getattr(job, "video_resolution", "720p") or "720p",
|
||||
credits_prepaid=float(getattr(job, "credits_prepaid", 0) or 0),
|
||||
credits_cost=float(getattr(job, "credits_cost", 0) or 0),
|
||||
credits_cost=job.credits_cost,
|
||||
error_msg=job.error_msg,
|
||||
retry_count=job.retry_count,
|
||||
started_at=job.started_at,
|
||||
@@ -202,7 +191,6 @@ def create_viral_video(
|
||||
voice_source=getattr(request, "voice_source", "") or "",
|
||||
video_ratio=getattr(request, "video_ratio", "9:16") or "9:16",
|
||||
video_model=getattr(request, "video_model", "") or "",
|
||||
video_resolution=getattr(request, "video_resolution", "720p") or "720p",
|
||||
copy_result=None,
|
||||
)
|
||||
|
||||
@@ -245,7 +233,6 @@ def analyze_images(
|
||||
voice_source=request.voice_source or "",
|
||||
video_ratio=request.video_ratio or "9:16",
|
||||
video_model=request.video_model or "",
|
||||
video_resolution=getattr(request, "video_resolution", "720p") or "720p",
|
||||
duration=request.duration or 15,
|
||||
)
|
||||
repo.save(job)
|
||||
@@ -280,18 +267,11 @@ def generate_copy(
|
||||
raise HTTPException(status_code=404, detail="任务不存在")
|
||||
if job.user_id != authenticated_user.user.id:
|
||||
raise HTTPException(status_code=403, detail="无权操作此任务")
|
||||
# 允许首次进入（IMAGE_ANALYZED/PENDING）、失败重试（FAILED）、文案重新生成（COPY_GENERATED/COMPLETED）
|
||||
if job.status not in (
|
||||
ViralVideoStatus.IMAGE_ANALYZED,
|
||||
ViralVideoStatus.PENDING,
|
||||
ViralVideoStatus.FAILED,
|
||||
ViralVideoStatus.COPY_GENERATED,
|
||||
ViralVideoStatus.COMPLETED,
|
||||
):
|
||||
if job.status not in (ViralVideoStatus.IMAGE_ANALYZED, ViralVideoStatus.PENDING, ViralVideoStatus.FAILED):
|
||||
raise HTTPException(status_code=409, detail=f"任务当前状态 {job.status} 不能生成文案")
|
||||
|
||||
# 失败重试 / 重新生成：retry_count 自增
|
||||
if job.status in (ViralVideoStatus.FAILED, ViralVideoStatus.COPY_GENERATED, ViralVideoStatus.COMPLETED):
|
||||
# 允许失败任务重试：重置
|
||||
if job.status == ViralVideoStatus.FAILED:
|
||||
job.retry_count += 1
|
||||
job.error_msg = ""
|
||||
|
||||
@@ -316,7 +296,6 @@ def generate_copy(
|
||||
job.voice_source = request.voice_source or job.voice_source
|
||||
job.video_ratio = request.video_ratio or job.video_ratio or "9:16"
|
||||
job.video_model = request.video_model or job.video_model or ""
|
||||
job.video_resolution = getattr(request, "video_resolution", "") or job.video_resolution or "720p"
|
||||
|
||||
job.resume_from_image_analyzed()
|
||||
repo.update(job)
|
||||
@@ -348,44 +327,6 @@ def confirm_copy(
|
||||
raise HTTPException(status_code=403, detail="无权操作此任务")
|
||||
if job.status != ViralVideoStatus.COPY_GENERATED:
|
||||
raise HTTPException(status_code=409, detail=f"任务当前状态 {job.status} 不能确认文案（需 copy_generated）")
|
||||
# #2218: 额外校验 copy_result 完整性，防止孤儿/脏数据进入渲染
|
||||
if not isinstance(job.copy_result, dict) or not job.copy_result:
|
||||
raise HTTPException(status_code=409, detail="文案数据缺失，请先点击「生成文案」")
|
||||
|
||||
# 积分预扣（已扣过/重试任务跳过）
|
||||
from app.config import settings as _settings
|
||||
|
||||
if _settings.points_enabled:
|
||||
already_paid = (float(getattr(job, "credits_prepaid", 0) or 0) > 0) or (
|
||||
float(getattr(job, "credits_cost", 0) or 0) > 0
|
||||
)
|
||||
if not already_paid:
|
||||
from packages.domain.points_rules import calculate_viral_video_credits, resolve_video_dimensions
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
w, h = resolve_video_dimensions(
|
||||
getattr(job, "video_resolution", "720p") or "720p",
|
||||
job.video_ratio or "9:16",
|
||||
)
|
||||
est_credits = calculate_viral_video_credits(
|
||||
int(job.duration or 15), w, h, job.video_model or "seedance-2.5"
|
||||
)
|
||||
svc = PointsService()
|
||||
res = svc.deduct_viral_video(authenticated_user.user.id, est_credits, job.id, session)
|
||||
if not res.get("success"):
|
||||
balance = res.get("balance", 0)
|
||||
raise HTTPException(
|
||||
status_code=402,
|
||||
detail={
|
||||
"code": "INSUFFICIENT_POINTS",
|
||||
"message": f"积分不足，需要 {est_credits} 积分，当前余额 {balance}",
|
||||
"required": est_credits,
|
||||
"balance": balance,
|
||||
},
|
||||
)
|
||||
job.credits_prepaid = est_credits
|
||||
job.credits_transaction_id = res.get("transaction_id", "") or ""
|
||||
repo.update(job)
|
||||
|
||||
job.resume_from_copy_generated(edited_copy=request.edited_copy or None)
|
||||
repo.update(job)
|
||||
@@ -401,38 +342,6 @@ def confirm_copy(
|
||||
return _to_response(job)
|
||||
|
||||
|
||||
@router.post("/estimate-credits", response_model=EstimateCreditsResponse)
|
||||
def estimate_credits(
|
||||
request: EstimateCreditsRequest,
|
||||
authenticated_user: AuthenticatedUser = Depends(get_current_user),
|
||||
) -> EstimateCreditsResponse:
|
||||
"""爆款视频积分预估（纯计算，不扣费、不创建任务）。
|
||||
|
||||
返回 estimated_credits 与 formula_breakdown（tokens / video_cost / fixed_cost /
|
||||
profit_multiplier / model_price / width / height / fps），便于前端展示计费明细。
|
||||
同时兼容前端传 model 或 video_model、resolution 或 video_resolution、ratio 或 video_ratio。
|
||||
"""
|
||||
from packages.domain.points_rules import (
|
||||
calculate_viral_video_credits_with_breakdown,
|
||||
resolve_video_dimensions,
|
||||
)
|
||||
|
||||
model = (request.model or "").strip() or "seedance-2.5"
|
||||
resolution = (request.resolution or "").strip() or "720p"
|
||||
ratio = (request.ratio or "").strip() or "9:16"
|
||||
duration = int(request.duration or 15)
|
||||
|
||||
w, h = resolve_video_dimensions(resolution, ratio)
|
||||
credits, bd = calculate_viral_video_credits_with_breakdown(
|
||||
duration,
|
||||
w,
|
||||
h,
|
||||
model,
|
||||
)
|
||||
breakdown = CreditsFormulaBreakdown(**bd)
|
||||
return EstimateCreditsResponse(estimated_credits=credits, formula_breakdown=breakdown)
|
||||
|
||||
|
||||
@router.get("/history", response_model=ViralVideoHistoryResponse)
|
||||
def list_viral_video_history(
|
||||
limit: int = 50,
|
||||
@@ -467,17 +376,6 @@ def list_style_templates(
|
||||
return StyleTemplateListResponse(items=items)
|
||||
|
||||
|
||||
@router.get("/models")
|
||||
def list_available_models() -> dict:
|
||||
"""返回爆款视频可用模型列表（供前端模型选择器使用）。"""
|
||||
dashscope_available = get_dashscope_client() is not None
|
||||
models = list_viral_video_models(
|
||||
include_placeholder=False,
|
||||
dashscope_available=dashscope_available,
|
||||
)
|
||||
return {"models": models}
|
||||
|
||||
|
||||
@router.get("/{job_id}", response_model=ViralVideoJobResponse)
|
||||
def get_viral_video_job(
|
||||
job_id: str,
|
||||
@@ -497,156 +395,31 @@ def get_viral_video_job(
|
||||
@router.post("/{job_id}/retry", response_model=ViralVideoJobResponse)
|
||||
def retry_viral_video_job(
|
||||
job_id: str,
|
||||
request: RetryViralVideoRequest | None = None,
|
||||
authenticated_user: AuthenticatedUser = Depends(get_current_user),
|
||||
session: Session = Depends(get_db_session),
|
||||
) -> ViralVideoJobResponse:
|
||||
"""重试失败的爆款视频任务（也支持对僵尸/超时 running 任务强制重置后重试）。
|
||||
|
||||
可选 body (RetryViralVideoRequest)：若传入新的 duration/video_resolution/video_ratio/
|
||||
video_model，会重新预估积分并与原 credits_prepaid 做差额多退少补（不足抛 402 阻止重试）；
|
||||
不传 body 或参数无变化时，保持原参数、原预扣金额不变，仅重置状态并入队。
|
||||
credits_prepaid 为 0 的老任务首次重试会走预扣流程（与 confirm-copy 一致）。
|
||||
"""
|
||||
from datetime import datetime, timezone
|
||||
|
||||
"""重试失败的爆款视频任务。"""
|
||||
repo = _get_job_repo(session)
|
||||
job = repo.get(job_id)
|
||||
if job is None:
|
||||
raise HTTPException(status_code=404, detail="任务不存在")
|
||||
if job.user_id != authenticated_user.user.id:
|
||||
raise HTTPException(status_code=403, detail="无权操作此任务")
|
||||
|
||||
# 判定是否为僵尸 running 任务：running 超过 10 分钟且心跳停止超过 2 分钟
|
||||
now = datetime.now(timezone.utc)
|
||||
is_stale_running = False
|
||||
if job.status == ViralVideoStatus.RUNNING and job.started_at is not None:
|
||||
hb = getattr(job, "heartbeat_at", None) or job.updated_at
|
||||
if (now - job.started_at).total_seconds() > 10 * 60 and hb is not None and (now - hb).total_seconds() > 2 * 60:
|
||||
is_stale_running = True
|
||||
|
||||
if job.status != ViralVideoStatus.FAILED and not is_stale_running:
|
||||
raise HTTPException(status_code=409, detail="只有失败或超时的任务可以重试")
|
||||
|
||||
# ── 参数变更检测 + 积分多退少补 ──────────────────────────────────────
|
||||
req = request or RetryViralVideoRequest()
|
||||
new_duration = req.duration
|
||||
new_resolution = (req.video_resolution or "").strip() or None
|
||||
new_ratio = (req.video_ratio or "").strip() or None
|
||||
new_model = (req.video_model or "").strip() or None
|
||||
|
||||
old_duration = int(getattr(job, "duration", 15) or 15)
|
||||
old_resolution = (getattr(job, "video_resolution", "720p") or "720p").strip() or "720p"
|
||||
old_ratio = (getattr(job, "video_ratio", "9:16") or "9:16").strip() or "9:16"
|
||||
old_model = (getattr(job, "video_model", "") or "").strip()
|
||||
|
||||
# 仅当有任意字段传入且值不同才算"参数变更"
|
||||
param_changed = bool(
|
||||
(new_duration is not None and int(new_duration) != old_duration)
|
||||
or (new_resolution is not None and new_resolution != old_resolution)
|
||||
or (new_ratio is not None and new_ratio != old_ratio)
|
||||
or (new_model is not None and new_model != old_model)
|
||||
)
|
||||
|
||||
from app.config import settings as _settings
|
||||
|
||||
need_points_settle = False
|
||||
new_est = 0.0
|
||||
if _settings.points_enabled and param_changed:
|
||||
from packages.domain.points_rules import (
|
||||
calculate_viral_video_credits_with_breakdown,
|
||||
resolve_video_dimensions,
|
||||
)
|
||||
|
||||
eff_dur = int(new_duration if new_duration is not None else old_duration)
|
||||
eff_res = new_resolution if new_resolution is not None else old_resolution
|
||||
eff_ratio = new_ratio if new_ratio is not None else old_ratio
|
||||
eff_model = new_model if new_model is not None else (old_model or "seedance-2.5")
|
||||
w, h = resolve_video_dimensions(eff_res, eff_ratio)
|
||||
new_est, _ = calculate_viral_video_credits_with_breakdown(eff_dur, w, h, eff_model or "seedance-2.5")
|
||||
need_points_settle = True
|
||||
|
||||
# 写入新参数（即使不开 points 也要允许用户重试时改参数）
|
||||
if new_duration is not None:
|
||||
job.duration = max(5, min(30, int(new_duration)))
|
||||
if new_resolution is not None:
|
||||
job.video_resolution = new_resolution
|
||||
if new_ratio is not None:
|
||||
job.video_ratio = new_ratio
|
||||
if new_model is not None:
|
||||
job.video_model = new_model
|
||||
|
||||
if need_points_settle:
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
old_prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
|
||||
svc = PointsService()
|
||||
diff = round(new_est - old_prepaid, 2)
|
||||
if abs(diff) >= 0.01:
|
||||
if diff > 0:
|
||||
# 新预扣更多：补扣差额
|
||||
res = svc.deduct_viral_video(authenticated_user.user.id, diff, job.id, session)
|
||||
if not res.get("success"):
|
||||
balance = res.get("balance", 0)
|
||||
raise HTTPException(
|
||||
status_code=402,
|
||||
detail={
|
||||
"code": "INSUFFICIENT_POINTS",
|
||||
"message": f"重试参数变更后需补扣 {diff} 积分，余额不足（当前 {balance}，需 {new_est}）",
|
||||
"required": new_est,
|
||||
"balance": balance,
|
||||
"delta": diff,
|
||||
},
|
||||
)
|
||||
job.credits_prepaid = round(old_prepaid + diff, 2)
|
||||
logger.info(
|
||||
"[爆款视频][retry] 补扣差额 job_id=%s diff=%.2f new_prepaid=%.2f",
|
||||
job.id,
|
||||
diff,
|
||||
job.credits_prepaid,
|
||||
)
|
||||
else:
|
||||
# 新预扣更少：退还差额
|
||||
refund = round(-diff, 2)
|
||||
txn_id = getattr(job, "credits_transaction_id", "") or ""
|
||||
svc.refund_points(
|
||||
user_id=authenticated_user.user.id,
|
||||
amount=refund,
|
||||
source="viral_video",
|
||||
db=session,
|
||||
ref_id=txn_id or job.id,
|
||||
description="爆款视频重试参数变更退费",
|
||||
)
|
||||
job.credits_prepaid = round(old_prepaid - refund, 2)
|
||||
logger.info(
|
||||
"[爆款视频][retry] 退还差额 job_id=%s refund=%.2f new_prepaid=%.2f",
|
||||
job.id,
|
||||
refund,
|
||||
job.credits_prepaid,
|
||||
)
|
||||
# 差额为 0 则不调整
|
||||
if job.status != ViralVideoStatus.FAILED:
|
||||
raise HTTPException(status_code=409, detail="只有失败的任务可以重试")
|
||||
|
||||
# 重置状态
|
||||
job.retry_count += 1
|
||||
job.status = ViralVideoStatus.PENDING
|
||||
job.error_msg = "" if not is_stale_running else "任务执行超时，已重置重试"
|
||||
job.error_msg = ""
|
||||
job.started_at = None
|
||||
job.completed_at = None
|
||||
job.current_stage = ""
|
||||
job.phase_message = ""
|
||||
job.heartbeat_at = None
|
||||
repo.update(job)
|
||||
|
||||
# 重新入队
|
||||
try:
|
||||
celery_app.send_task("worker.run_viral_video_pipeline", args=[job.id])
|
||||
logger.info(
|
||||
"[爆款视频] 重试入队: job_id=%s retry_count=%d stale=%s params_changed=%s",
|
||||
job.id,
|
||||
job.retry_count,
|
||||
is_stale_running,
|
||||
param_changed,
|
||||
)
|
||||
logger.info("[爆款视频] 重试入队: job_id=%s retry_count=%d", job.id, job.retry_count)
|
||||
except Exception as e:
|
||||
logger.error("[爆款视频] 重试入队失败: %s", e, exc_info=True)
|
||||
job.mark_failed(f"重试入队失败: {e}")
|
||||
|
||||
@@ -13,9 +13,9 @@ from pydantic import BaseModel, Field
|
||||
class PointsBalanceResponse(BaseModel):
|
||||
"""积分余额 + 会员状态"""
|
||||
|
||||
balance: float = Field(..., description="当前积分余额")
|
||||
total_earned: float = Field(..., description="累计获得积分")
|
||||
total_spent: float = Field(..., description="累计消耗积分")
|
||||
balance: int = Field(..., description="当前积分余额")
|
||||
total_earned: int = Field(..., description="累计获得积分")
|
||||
total_spent: int = Field(..., description="累计消耗积分")
|
||||
is_member: bool = Field(default=False, description="是否付费会员")
|
||||
member_type: Optional[str] = Field(None, description="会员类型: monthly/quarterly/yearly")
|
||||
member_expires_at: Optional[datetime] = Field(None, description="会员到期时间")
|
||||
@@ -30,8 +30,8 @@ class PointsTransactionItem(BaseModel):
|
||||
id: str
|
||||
type: str = Field(..., description="类型: add/deduct")
|
||||
source: str = Field(..., description="来源场景")
|
||||
amount: float
|
||||
balance_after: float
|
||||
amount: int
|
||||
balance_after: int
|
||||
description: str = ""
|
||||
ref_id: str = ""
|
||||
created_at: Optional[str] = None
|
||||
@@ -99,9 +99,9 @@ class PointsCheckResponse(BaseModel):
|
||||
"""消费前余额检查响应"""
|
||||
|
||||
allowed: bool
|
||||
required_points: float
|
||||
current_balance: float
|
||||
remaining_after: float
|
||||
required_points: int
|
||||
current_balance: int
|
||||
remaining_after: int
|
||||
is_free_quota: bool = False
|
||||
|
||||
|
||||
@@ -112,7 +112,7 @@ class PointsDeductRequest(BaseModel):
|
||||
"""积分扣减请求"""
|
||||
|
||||
scene_key: str
|
||||
amount: float
|
||||
amount: int
|
||||
description: Optional[str] = ""
|
||||
ref_id: Optional[str] = ""
|
||||
|
||||
@@ -170,7 +170,7 @@ class MembershipStatusResponse(BaseModel):
|
||||
is_member: bool
|
||||
member_type: Optional[str] = None
|
||||
member_expires_at: Optional[datetime] = None
|
||||
points_balance: float
|
||||
points_balance: int
|
||||
max_resolution: str = Field(
|
||||
default="1080p",
|
||||
description="可用最高分辨率: 720p(free) / 1080p(paid)",
|
||||
|
||||
@@ -22,7 +22,6 @@ VALID_STAGES = (
|
||||
)
|
||||
VALID_VIDEO_RATIOS = ("9:16", "16:9", "1:1", "4:3", "3:4", "21:9")
|
||||
VALID_DURATIONS = (5, 10, 15, 20, 25, 30)
|
||||
VALID_VIDEO_RESOLUTIONS = ("480p", "720p", "1080p", "普清", "高清", "超清")
|
||||
|
||||
|
||||
# -- 编导脚本结构（v1.6） --
|
||||
@@ -89,7 +88,6 @@ class CreateViralVideoRequest(BaseModel):
|
||||
voice_source: str = ""
|
||||
video_ratio: str = "9:16"
|
||||
video_model: str = ""
|
||||
video_resolution: str = "720p"
|
||||
|
||||
@field_validator("fusion_level")
|
||||
@classmethod
|
||||
@@ -119,7 +117,6 @@ class AnalyzeImagesRequest(BaseModel):
|
||||
voice_source: str = ""
|
||||
video_ratio: str = "9:16"
|
||||
video_model: str = ""
|
||||
video_resolution: str = "720p"
|
||||
duration: int = Field(default=15, ge=5, le=30)
|
||||
|
||||
|
||||
@@ -144,7 +141,6 @@ class GenerateCopyRequest(BaseModel):
|
||||
voice_source: str = ""
|
||||
video_ratio: str = "9:16"
|
||||
video_model: str = ""
|
||||
video_resolution: str = "720p"
|
||||
|
||||
@field_validator("fusion_level")
|
||||
@classmethod
|
||||
@@ -205,10 +201,6 @@ class ViralVideoJobResponse(BaseModel):
|
||||
style_guide: dict | None = None
|
||||
style_template_id: str = ""
|
||||
status: str
|
||||
current_stage: str = (
|
||||
"" # 细粒度阶段 snake_case（analyzing_images/parsing_intent/generating_script/reviewing/tts_synthesizing/rendering_video/uploading）
|
||||
)
|
||||
phase_message: str = "" # 中文阶段提示文案（前端轮询/SSE 直接展示）
|
||||
image_analysis: dict | None = None
|
||||
# v1.6 编导脚本（推荐前端使用）
|
||||
copy_result: dict | None = None
|
||||
@@ -222,10 +214,7 @@ class ViralVideoJobResponse(BaseModel):
|
||||
video_model: str = ""
|
||||
intent_result: dict | None = None
|
||||
result_video_url: str = ""
|
||||
pre_trusted_images: list[str] | None = None
|
||||
video_resolution: str = "720p"
|
||||
credits_prepaid: float = 0.0
|
||||
credits_cost: float = 0.0
|
||||
credits_cost: int = 0
|
||||
error_msg: str = ""
|
||||
retry_count: int = 0
|
||||
started_at: datetime | None = None
|
||||
@@ -257,57 +246,6 @@ class AnalyzeStyleResponse(BaseModel):
|
||||
style_guide: dict | None = None
|
||||
|
||||
|
||||
# -- 积分预估 --
|
||||
|
||||
|
||||
class EstimateCreditsRequest(BaseModel):
|
||||
"""爆款视频积分预估请求。
|
||||
|
||||
前端可传 model 或 video_model（兼容老字段）；resolution/ratio/duration 为预估所需参数。
|
||||
"""
|
||||
|
||||
model: str = Field(default="", alias="video_model")
|
||||
resolution: str = Field(default="720p", alias="video_resolution")
|
||||
ratio: str = Field(default="9:16", alias="video_ratio")
|
||||
duration: int = Field(default=15, ge=5, le=30)
|
||||
|
||||
model_config = {"populate_by_name": True}
|
||||
|
||||
|
||||
class CreditsFormulaBreakdown(BaseModel):
|
||||
"""爆款视频积分计费公式明细（前端展示用）。"""
|
||||
|
||||
tokens: float = Field(..., description="估算视频 tokens 数 (duration*width*height*fps/1024)")
|
||||
video_cost: float = Field(..., description="视频生成成本（元）= tokens/1e6 * model_price")
|
||||
fixed_cost: float = Field(..., description="固定成本（元），含 VLM/LLM/TTS/OSS/服务器")
|
||||
profit_multiplier: float = Field(..., description="利润系数（默认 1.3）")
|
||||
model_price: float = Field(..., description="模型单价（元/百万 tokens）")
|
||||
width: int = Field(..., description="视频宽度像素")
|
||||
height: int = Field(..., description="视频高度像素")
|
||||
fps: int = Field(..., description="视频帧率")
|
||||
|
||||
|
||||
class EstimateCreditsResponse(BaseModel):
|
||||
"""爆款视频积分预估响应。"""
|
||||
|
||||
estimated_credits: float
|
||||
formula_breakdown: CreditsFormulaBreakdown = Field(..., description="计费公式明细")
|
||||
|
||||
|
||||
class RetryViralVideoRequest(BaseModel):
|
||||
"""重试爆款视频任务的请求体（可选，允许改参数重新预估积分多退少补）。
|
||||
|
||||
不传 body 或字段全缺省：保持原参数、不重新扣点，走默认重置+入队逻辑。
|
||||
传入新的 duration/video_resolution/video_ratio/video_model：重新预估积分，
|
||||
与原 credits_prepaid 比较后多退少补（差额补扣不足抛 402）。
|
||||
"""
|
||||
|
||||
duration: int | None = Field(default=None, ge=5, le=30, description="重试时新的视频时长（秒）")
|
||||
video_resolution: str | None = Field(default=None, description="重试时新的分辨率，如 720p/1080p")
|
||||
video_ratio: str | None = Field(default=None, description="重试时新的画幅比，如 9:16/16:9")
|
||||
video_model: str | None = Field(default=None, description="重试时新的视频模型，如 seedance-2.5")
|
||||
|
||||
|
||||
# -- WebSocket 事件 Schema --
|
||||
|
||||
|
||||
|
||||
@@ -228,8 +228,6 @@ class GpuLipsyncService:
|
||||
lipsync_job_id: str = "",
|
||||
user_id: str = "",
|
||||
project_id: str = "",
|
||||
credits_prepaid: float = 0.0,
|
||||
credits_transaction_id: str = "",
|
||||
) -> GpuLipsyncTaskModel:
|
||||
task_id = str(uuid.uuid4())
|
||||
now = datetime.now(UTC)
|
||||
@@ -242,8 +240,6 @@ class GpuLipsyncService:
|
||||
audio_url=audio_url,
|
||||
status="pending",
|
||||
attempt=0,
|
||||
credits_prepaid=float(credits_prepaid or 0.0),
|
||||
credits_transaction_id=str(credits_transaction_id or ""),
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
)
|
||||
|
||||
@@ -38,7 +38,6 @@ from sqlalchemy.orm import Session
|
||||
from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
|
||||
from packages.application.cosyvoice_service import CosyVoiceError
|
||||
from packages.config import get_api_settings
|
||||
from packages.domain import feature_pricing_service
|
||||
from packages.domain.sentence_timings import (
|
||||
compute_sentence_timings,
|
||||
probe_audio_duration,
|
||||
@@ -223,47 +222,7 @@ class LipsyncService:
|
||||
if timings:
|
||||
job.sentence_timings = timings
|
||||
|
||||
# 4. 检查是否走 Ditto（蚂蚁数字人，#2076）：开关 + 配置完整
|
||||
use_ditto = False
|
||||
if self.settings.use_ditto_lipsync:
|
||||
try:
|
||||
from packages.application.ditto_service import get_ditto_client
|
||||
|
||||
ditto = get_ditto_client()
|
||||
if ditto.is_configured:
|
||||
use_ditto = True
|
||||
logger.info("[lipsync] 优先走 Ditto 蚂蚁数字人: job_id=%s", job.id)
|
||||
else:
|
||||
logger.info(
|
||||
"[lipsync] Ditto 开关已开但配置不完整（base_url=%s, template=%s），继续判断 GPU: job_id=%s",
|
||||
bool(ditto.base_url),
|
||||
bool(ditto.default_video_url),
|
||||
job.id,
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.warning("[lipsync] Ditto 初始化失败，继续判断 GPU: job_id=%s err=%s", job.id, exc)
|
||||
|
||||
if use_ditto:
|
||||
try:
|
||||
# Ditto 使用预置人物模板视频，不用用户上传的 video_url；
|
||||
# 但保留用户 video_url 以便失败回退到 GPU/MediaKit。
|
||||
job.status = "processing"
|
||||
job.mediakit_task_id = "ditto:submitted"
|
||||
job.updated_at = datetime.now(UTC)
|
||||
self.db.commit()
|
||||
from app.tasks.lipsync_ditto import lipsync_ditto_process_async
|
||||
|
||||
lipsync_ditto_process_async.apply_async(args=(job.id, job.user_id))
|
||||
logger.info("[lipsync] Ditto 任务已异步派发: job_id=%s", job.id)
|
||||
return
|
||||
except Exception as exc:
|
||||
logger.warning("[lipsync] Ditto 派发失败，回退 GPU/MediaKit: job_id=%s err=%s", job.id, exc)
|
||||
try:
|
||||
self.db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 5. 检查是否走 GPU 路径：开关打开 + 有可用 Worker
|
||||
# 4. 检查是否走 GPU 路径：开关打开 + 有可用 Worker
|
||||
use_gpu = False
|
||||
if self.settings.use_gpu_lipsync:
|
||||
try:
|
||||
@@ -409,8 +368,6 @@ class LipsyncService:
|
||||
lipsync_job_id=job.id,
|
||||
user_id=job.user_id,
|
||||
project_id=job.project_id,
|
||||
credits_prepaid=float(getattr(job, "credits_prepaid", 0) or 0),
|
||||
credits_transaction_id=str(getattr(job, "credits_transaction_id", "") or ""),
|
||||
)
|
||||
logger.info(
|
||||
"[lipsync] 已创建 GPU 任务（异步）: job_id=%s gpu_task=%s",
|
||||
@@ -458,121 +415,6 @@ class LipsyncService:
|
||||
job.output_duration,
|
||||
)
|
||||
|
||||
# ── lip_sync 计费辅助 ────────────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _estimate_duration(
|
||||
*,
|
||||
audio_duration: Optional[float] = None,
|
||||
sentence_timings: Optional[list] = None,
|
||||
script_text: str = "",
|
||||
) -> float:
|
||||
"""预估音频/成片秒数。
|
||||
|
||||
优先级：audio_duration（预合成前端已 ffprobe）> timings 末句 end_time >
|
||||
脚本字数 / 5 字每秒 > 默认 10 秒。
|
||||
"""
|
||||
if audio_duration and float(audio_duration) > 0:
|
||||
return float(audio_duration)
|
||||
if sentence_timings:
|
||||
max_end = 0.0
|
||||
for item in sentence_timings:
|
||||
if isinstance(item, dict):
|
||||
end = item.get("end_time") or item.get("end") or 0.0
|
||||
else:
|
||||
end = 0.0
|
||||
try:
|
||||
max_end = max(max_end, float(end))
|
||||
except (TypeError, ValueError):
|
||||
continue
|
||||
if max_end > 0:
|
||||
return max_end
|
||||
text = (script_text or "").strip()
|
||||
if text:
|
||||
return max(1.0, len(text) / 5.0)
|
||||
return 10.0
|
||||
|
||||
def _settle_lip_sync(self, job: LipsyncJobModel, actual_duration: float) -> None:
|
||||
"""按实际时长结算（首期只退不补：final < prepaid 退差额，> 不补）。
|
||||
|
||||
幂等：credits_cost 已 > 0 说明结算过，直接跳过。
|
||||
结算失败不阻塞业务（结果已产出），仅记录日志。
|
||||
"""
|
||||
try:
|
||||
prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
|
||||
if prepaid <= 0:
|
||||
return
|
||||
if float(getattr(job, "credits_cost", 0) or 0) > 0:
|
||||
return
|
||||
feature_cfg = feature_pricing_service.get_feature_config("lip_sync")
|
||||
unit_cost = float(feature_cfg.dynamic_unit_cost) if feature_cfg is not None else 0.0
|
||||
duration = float(actual_duration or 0.0)
|
||||
if duration <= 0:
|
||||
duration = self._estimate_duration(
|
||||
sentence_timings=job.sentence_timings,
|
||||
script_text=job.script_text,
|
||||
)
|
||||
final_price, _bd = feature_pricing_service.calculate_price("lip_sync", duration * unit_cost)
|
||||
final_price = round(float(final_price), 2)
|
||||
job.credits_cost = final_price
|
||||
if final_price < prepaid - 0.009:
|
||||
refund = round(prepaid - final_price, 2)
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
res = PointsService().refund_points(
|
||||
user_id=job.user_id,
|
||||
amount=refund,
|
||||
source="lip_sync",
|
||||
db=self.db,
|
||||
ref_id=str(job.credits_transaction_id or job.id),
|
||||
description="对口型结算退费",
|
||||
)
|
||||
if not res.get("success"):
|
||||
logger.warning(
|
||||
"[lip_sync] 结算退费失败 job_id=%s refund=%.2f（不阻塞）",
|
||||
job.id,
|
||||
refund,
|
||||
)
|
||||
# final > prepaid：首期只退不补，不补扣
|
||||
self.db.commit()
|
||||
except Exception: # noqa: BLE001
|
||||
logger.exception("[lip_sync] 结算异常 job_id=%s（不阻塞结果）", job.id)
|
||||
try:
|
||||
self.db.rollback()
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
|
||||
def _refund_lip_sync(self, job: LipsyncJobModel) -> None:
|
||||
"""任务失败/取消时全额退还预扣积分（credits_cost 已结算则退实际未消耗部分）。"""
|
||||
try:
|
||||
prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
|
||||
if prepaid <= 0:
|
||||
return
|
||||
txn_id = str(getattr(job, "credits_transaction_id", "") or "")
|
||||
cost = float(getattr(job, "credits_cost", 0) or 0)
|
||||
refund = round(prepaid - cost, 2) if cost > 0 else round(prepaid, 2)
|
||||
if refund <= 0:
|
||||
return
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
res = PointsService().refund_points(
|
||||
user_id=job.user_id,
|
||||
amount=refund,
|
||||
source="lip_sync",
|
||||
db=self.db,
|
||||
ref_id=txn_id or job.id,
|
||||
description="对口型失败/取消退款",
|
||||
)
|
||||
if res.get("success"):
|
||||
job.credits_cost = prepaid # 标记已全额退回，防重复退
|
||||
self.db.commit()
|
||||
except Exception: # noqa: BLE001
|
||||
logger.exception("[lip_sync] 退款异常 job_id=%s", job.id)
|
||||
try:
|
||||
self.db.rollback()
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
|
||||
# ── 创建任务 ──────────────────────────────────────────────────────────
|
||||
|
||||
def create_job(
|
||||
@@ -624,35 +466,6 @@ class LipsyncService:
|
||||
if not isinstance(sentence_timings, list) or len(sentence_timings) == 0:
|
||||
raise MediaKitError("预合成模式 sentence_timings 不能为空", code="InvalidInput")
|
||||
|
||||
# 0.5 lip_sync 计费预扣（全局 points 开关 + 功能开关均开才扣）
|
||||
prepaid_credits = 0.0
|
||||
prepaid_txn_id = ""
|
||||
if feature_pricing_service.is_feature_enabled("lip_sync"):
|
||||
est_duration = self._estimate_duration(
|
||||
audio_duration=audio_duration,
|
||||
sentence_timings=sentence_timings,
|
||||
script_text=script_text,
|
||||
)
|
||||
feature_cfg = feature_pricing_service.get_feature_config("lip_sync")
|
||||
unit_cost = float(feature_cfg.dynamic_unit_cost) if feature_cfg is not None else 0.0
|
||||
dynamic_cost = est_duration * unit_cost
|
||||
prepaid_credits, _bd = feature_pricing_service.calculate_price("lip_sync", dynamic_cost)
|
||||
if prepaid_credits > 0:
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
res = PointsService().deduct_points(
|
||||
user_id=user_id,
|
||||
amount=prepaid_credits,
|
||||
source="lip_sync",
|
||||
db=self.db,
|
||||
description="对口型生成预扣",
|
||||
)
|
||||
if not res.get("success"):
|
||||
raise ValueError(
|
||||
f"积分不足：本次对口型需 {prepaid_credits:.2f} 积分，当前余额 {res.get('balance', 0)}"
|
||||
)
|
||||
prepaid_txn_id = str(res.get("transaction_id") or "")
|
||||
|
||||
# 1. 创建数据库记录
|
||||
job_id = str(uuid.uuid4())
|
||||
job = LipsyncJobModel(
|
||||
@@ -669,8 +482,6 @@ class LipsyncService:
|
||||
emotion=emotion or "",
|
||||
# 音频直传（含预合成）直接进入 pending（后续同步改为 submitted）；TTS 模式进入 tts_processing
|
||||
status="tts_processing" if is_tts_mode else "pending",
|
||||
credits_prepaid=prepaid_credits,
|
||||
credits_transaction_id=prepaid_txn_id,
|
||||
)
|
||||
self.db.add(job)
|
||||
self.db.flush()
|
||||
@@ -866,8 +677,6 @@ class LipsyncService:
|
||||
job.completed_at = _now
|
||||
job.updated_at = _now
|
||||
self.db.commit()
|
||||
# lip_sync 超时全额退款
|
||||
self._refund_lip_sync(job)
|
||||
return job
|
||||
|
||||
# 未提交的任务不轮询
|
||||
@@ -893,8 +702,6 @@ class LipsyncService:
|
||||
job.completed_at = datetime.now(UTC)
|
||||
job.updated_at = datetime.now(UTC)
|
||||
self.db.commit()
|
||||
# lip_sync 结算（只退不补）
|
||||
self._settle_lip_sync(job, float(job.output_duration or 0.0))
|
||||
# 异步转存自家 OSS
|
||||
try:
|
||||
from app.tasks.lipsync_tts import persist_output_video_task
|
||||
@@ -912,8 +719,6 @@ class LipsyncService:
|
||||
job.error_message = error.get("message", "任务执行失败")
|
||||
job.error_code = error.get("code", "TaskFailed")
|
||||
job.completed_at = datetime.now(UTC)
|
||||
# lip_sync 失败全额退款（先退款再统一 commit）
|
||||
self._refund_lip_sync(job)
|
||||
else:
|
||||
# 中间状态（running/processing/queued 等）同步到 DB，避免前端永远卡在 submitted
|
||||
if isinstance(mk_status, str) and mk_status:
|
||||
@@ -1007,8 +812,6 @@ class LipsyncService:
|
||||
job.status = "cancelled"
|
||||
job.updated_at = datetime.now(UTC)
|
||||
self.db.commit()
|
||||
# lip_sync 取消全额退款
|
||||
self._refund_lip_sync(job)
|
||||
self.db.refresh(job)
|
||||
|
||||
return job
|
||||
|
||||
@@ -11,12 +11,14 @@
|
||||
存储路径与元信息约定），返回 asset_id —— 下游仍以 voice_library_id（实为
|
||||
audio asset id）消费，渲染链路零改动。
|
||||
|
||||
积分扣点与 /tts 合成端点保持一致（ai_voice 场景），失败退费。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import math
|
||||
import subprocess
|
||||
import tempfile
|
||||
from dataclasses import dataclass
|
||||
@@ -30,10 +32,13 @@ from packages.application.cosyvoice_service import CosyVoiceService
|
||||
from packages.application.tts_job.use_cases import CreateTTSJobUseCase
|
||||
from packages.application.tts_job.workflow import TTSWorkflowService
|
||||
from packages.domain import Asset, AssetLibrary, AssetLibraryKind, AssetStatus, ClassificationStatus
|
||||
from packages.domain.points_rules import calculate_points_cost
|
||||
from packages.domain.points_service import PointsService
|
||||
from packages.shared.storage import SharedStorageService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_POINTS_SCENE = "ai_voice"
|
||||
_SYNTH_TIMEOUT = 180.0 # 叙事配音在 HTTP 请求内同步等待，长文案分段合成时留出余量
|
||||
_CONTENT_TYPE_MAP = {"mp3": "audio/mpeg", "wav": "audio/wav", "pcm": "audio/pcm", "opus": "audio/opus"}
|
||||
|
||||
@@ -268,6 +273,24 @@ def prepare_narrative_voice(
|
||||
voice_clone_repository=voice_clone_repository,
|
||||
)
|
||||
|
||||
# 积分扣点（与 /tts 合成端点同口径），失败时在合成失败分支退费
|
||||
points_svc = PointsService() if points_enabled else None
|
||||
points_deducted = 0
|
||||
if points_svc is not None:
|
||||
est_minutes = max(1.0, math.ceil(len(content) / 240))
|
||||
points_deducted = calculate_points_cost(
|
||||
_POINTS_SCENE,
|
||||
is_member=is_member,
|
||||
duration_minutes=est_minutes,
|
||||
member_type=member_type,
|
||||
)
|
||||
deduct_res = points_svc.deduct_points(user_id, points_deducted, _POINTS_SCENE, db)
|
||||
if not deduct_res["success"]:
|
||||
raise NarrativeError(
|
||||
f"积分不足，需要 {points_deducted} 积分，当前余额 {deduct_res['balance']}",
|
||||
status_code=402,
|
||||
)
|
||||
|
||||
use_case = CreateTTSJobUseCase(tts_repository)
|
||||
job = use_case.execute(
|
||||
user_id=user_id,
|
||||
@@ -288,9 +311,19 @@ def prepare_narrative_voice(
|
||||
workflow.process_synthesis_failure(job.id, str(e))
|
||||
except Exception: # noqa: BLE001
|
||||
logger.warning("标记叙事 TTS job 失败出错: job_id=%s", job.id, exc_info=True)
|
||||
if points_deducted and points_svc is not None:
|
||||
try:
|
||||
points_svc.refund_points(user_id, points_deducted, _POINTS_SCENE, db, ref_id=job.id)
|
||||
except Exception: # noqa: BLE001
|
||||
logger.warning("叙事 TTS 失败退积分异常: job_id=%s", job.id, exc_info=True)
|
||||
raise NarrativeError(f"配音合成失败：{e}", status_code=502) from e
|
||||
|
||||
if not job.is_completed:
|
||||
if points_deducted and points_svc is not None:
|
||||
try:
|
||||
points_svc.refund_points(user_id, points_deducted, _POINTS_SCENE, db, ref_id=job.id)
|
||||
except Exception: # noqa: BLE001
|
||||
logger.warning("叙事 TTS 未完成退积分异常: job_id=%s", job.id, exc_info=True)
|
||||
raise NarrativeError("配音合成未完成，请稍后重试", status_code=504)
|
||||
|
||||
asset = _save_tts_job_as_voice_asset(
|
||||
|
||||
@@ -1,318 +0,0 @@
|
||||
"""Ditto 蚂蚁数字人口型异步任务 — #2076.
|
||||
|
||||
把 Ditto 同步 HTTP 调用（30-120s）从 API 请求移到 Celery 后台执行：
|
||||
1. 加载 LipsyncJob
|
||||
2. 调 DittoClient.generate_and_persist(video_url=默认模板, audio_url=job.audio_url, script=job.script_text)
|
||||
3. 成功：标记 completed，写入 output_video_url（Ditto 输出自带音频，无需二次混流/超分）
|
||||
4. 失败：回退 GPU MuseTalk → 再失败回退 MediaKit
|
||||
|
||||
注意：
|
||||
- 保留 MuseTalk 代码不动；Ditto 优先，失败按原链路兜底
|
||||
- Ditto 使用预置的人物模板视频（settings.ditto_default_video_url），不用用户上传的 video_url
|
||||
- 不传 GFPGAN 超分，不需要 ffmpeg 音视频混流
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from datetime import UTC, datetime
|
||||
from typing import TYPE_CHECKING, Optional
|
||||
|
||||
from celery import shared_task
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DITTO_URL_TTL_SECONDS = 7 * 24 * 3600 # Ditto 结果 OSS URL 7 天有效
|
||||
|
||||
|
||||
def _get_db_session() -> Session:
|
||||
try:
|
||||
from worker_app.db import SessionLocal # type: ignore
|
||||
except ImportError:
|
||||
from app.db import SessionLocal # type: ignore
|
||||
return SessionLocal()
|
||||
|
||||
|
||||
def _sign_media_url(url: str) -> str:
|
||||
"""对自家 OSS URL 签 7 天预签名。"""
|
||||
if not url:
|
||||
return url
|
||||
try:
|
||||
from urllib.parse import urlparse
|
||||
|
||||
from packages.shared.storage import get_shared_storage_service
|
||||
|
||||
storage = get_shared_storage_service()
|
||||
public_base = getattr(storage, "public_url", "")
|
||||
if not isinstance(public_base, str) or not public_base:
|
||||
return url
|
||||
own_host = urlparse(public_base).netloc.lower()
|
||||
host = urlparse(url).netloc.lower()
|
||||
if not own_host or host != own_host:
|
||||
return url
|
||||
return storage.get_download_url(url, expires_seconds=_DITTO_URL_TTL_SECONDS)
|
||||
except Exception:
|
||||
return url
|
||||
|
||||
|
||||
def _probe_video_duration(video_bytes: bytes) -> float:
|
||||
"""用 ffprobe 探测视频时长（秒）；失败返回 0。"""
|
||||
try:
|
||||
import os
|
||||
import subprocess
|
||||
import tempfile
|
||||
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp:
|
||||
tmp.write(video_bytes)
|
||||
tmp_path = tmp.name
|
||||
try:
|
||||
out = subprocess.check_output(
|
||||
[
|
||||
"ffprobe",
|
||||
"-v",
|
||||
"error",
|
||||
"-show_entries",
|
||||
"format=duration",
|
||||
"-of",
|
||||
"default=noprint_wrappers=1:nokey=1",
|
||||
tmp_path,
|
||||
],
|
||||
stderr=subprocess.STDOUT,
|
||||
timeout=10,
|
||||
)
|
||||
return float(out.decode().strip() or 0)
|
||||
finally:
|
||||
os.unlink(tmp_path)
|
||||
except Exception as exc:
|
||||
logger.warning("[ditto_task] ffprobe 失败: %s", exc)
|
||||
return 0.0
|
||||
|
||||
|
||||
def _refund_lip_sync(db: Session, job: "LipsyncJobModel") -> None:
|
||||
"""Ditto 失败/取消时全额退款（复用 lipsync_service 的退款逻辑）。"""
|
||||
try:
|
||||
from app.services.lipsync_service import LipsyncService
|
||||
|
||||
LipsyncService(db)._refund_lip_sync(job)
|
||||
except Exception:
|
||||
logger.exception("[ditto_task] lip_sync 退款异常 job_id=%s", job.id)
|
||||
|
||||
|
||||
def _settle_lip_sync(db: Session, job: "LipsyncJobModel", duration: float) -> None:
|
||||
"""Ditto 成功后按实际时长结算。"""
|
||||
try:
|
||||
from app.services.lipsync_service import LipsyncService
|
||||
|
||||
LipsyncService(db)._settle_lip_sync(job, duration)
|
||||
except Exception:
|
||||
logger.exception("[ditto_task] lip_sync 结算异常 job_id=%s（不阻塞）", job.id)
|
||||
|
||||
|
||||
def _fallback_to_gpu_then_mediakit(db: Session, job: "LipsyncJobModel") -> None:
|
||||
"""Ditto 失败后：优先回退 GPU MuseTalk，再回退 MediaKit 云端。
|
||||
|
||||
复用 lipsync_service 现有路径逻辑以保证兜底一致性。
|
||||
"""
|
||||
# 先尝试走 GPU MuseTalk（若可用）
|
||||
try:
|
||||
from app.services.gpu_lipsync_service import GpuLipsyncService
|
||||
from app.tasks.lipsync_gpu import lipsync_gpu_process_async
|
||||
|
||||
gpu_svc = GpuLipsyncService(db)
|
||||
if gpu_svc.has_available_worker():
|
||||
logger.info("[ditto_task] 回退 GPU MuseTalk: job_id=%s", job.id)
|
||||
# 复用 lipsync_service._submit_to_gpu_create 逻辑
|
||||
from app.services.lipsync_service import LipsyncService
|
||||
|
||||
svc = LipsyncService(db)
|
||||
storage = _shared_storage()
|
||||
persisted_audio = None
|
||||
try:
|
||||
persisted_audio = svc._persist_external_audio_for_gpu(job=job, storage=storage)
|
||||
except Exception as exc:
|
||||
logger.warning("[ditto_task] GPU 外部音频转存失败: %s", exc)
|
||||
audio_url_for_task = persisted_audio or job.audio_url
|
||||
gpu_task = gpu_svc.create_task(
|
||||
video_url=job.video_url,
|
||||
audio_url=audio_url_for_task,
|
||||
lipsync_job_id=job.id,
|
||||
user_id=job.user_id,
|
||||
)
|
||||
if gpu_task is not None:
|
||||
job.mediakit_task_id = f"gpu:{gpu_task.id}"
|
||||
job.status = "processing"
|
||||
job.updated_at = datetime.now(UTC)
|
||||
db.commit()
|
||||
lipsync_gpu_process_async.apply_async(args=(job.id, job.user_id, gpu_task.id))
|
||||
return
|
||||
db.rollback()
|
||||
except Exception as exc:
|
||||
logger.warning("[ditto_task] GPU MuseTalk 回退失败，转 MediaKit: %s", exc)
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 最后兜底：MediaKit 云端
|
||||
try:
|
||||
from app.services.mediakit_client import get_mediakit_client
|
||||
|
||||
client = get_mediakit_client()
|
||||
video_url = _sign_media_url(job.video_url)
|
||||
signed_audio_url = _sign_media_url(job.audio_url)
|
||||
result = client.submit_lipsync(
|
||||
video_url=video_url,
|
||||
audio_url=signed_audio_url,
|
||||
enable_video_loop=job.enable_video_loop,
|
||||
client_token=job.id,
|
||||
)
|
||||
job.mediakit_task_id = result["task_id"]
|
||||
job.status = "submitted"
|
||||
job.submitted_at = datetime.now(UTC)
|
||||
job.updated_at = datetime.now(UTC)
|
||||
db.commit()
|
||||
logger.info("[ditto_task] 已回退 MediaKit: job_id=%s task_id=%s", job.id, result["task_id"])
|
||||
except Exception as exc:
|
||||
job.status = "failed"
|
||||
job.error_message = f"Ditto/GPU/MediaKit 均失败: {exc}"
|
||||
job.error_code = "AllBackendsFailed"
|
||||
job.updated_at = datetime.now(UTC)
|
||||
db.commit()
|
||||
logger.error("[ditto_task] 所有兜底均失败: job_id=%s err=%s", job.id, exc)
|
||||
|
||||
|
||||
def _shared_storage():
|
||||
from packages.shared.storage import get_shared_storage_service
|
||||
|
||||
return get_shared_storage_service()
|
||||
|
||||
|
||||
@shared_task(
|
||||
name="lipsync_ditto_process_async",
|
||||
bind=True,
|
||||
max_retries=0,
|
||||
acks_late=True,
|
||||
time_limit=600,
|
||||
soft_time_limit=540,
|
||||
)
|
||||
def lipsync_ditto_process_async(self, job_id: str, user_id: str) -> None:
|
||||
"""异步调用 Ditto 生成口型视频。
|
||||
|
||||
Args:
|
||||
job_id: LipsyncJob ID
|
||||
user_id: 用户 ID
|
||||
"""
|
||||
from packages.application.ditto_service import DittoError, get_ditto_client
|
||||
|
||||
db: Session = _get_db_session()
|
||||
job: Optional[LipsyncJobModel] = None
|
||||
try:
|
||||
from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
|
||||
|
||||
job = db.query(LipsyncJobModel).filter_by(id=job_id, user_id=user_id).first()
|
||||
if job is None:
|
||||
logger.error("[ditto_task] job 不存在: job_id=%s", job_id)
|
||||
return
|
||||
|
||||
if job.status != "processing":
|
||||
logger.warning(
|
||||
"[ditto_task] job 状态异常（非 processing），跳过: job_id=%s status=%s",
|
||||
job_id,
|
||||
job.status,
|
||||
)
|
||||
return
|
||||
|
||||
audio_url = job.audio_url or ""
|
||||
script = job.script_text or ""
|
||||
if not audio_url:
|
||||
raise DittoError("job.audio_url 为空，无法调用 Ditto", code="InvalidParam")
|
||||
|
||||
logger.info(
|
||||
"[ditto_task] 开始 Ditto 生成: job_id=%s audio=%s script_len=%d",
|
||||
job_id,
|
||||
audio_url[:100],
|
||||
len(script),
|
||||
)
|
||||
client = get_ditto_client()
|
||||
result = client.generate_and_persist(
|
||||
job_id=job_id,
|
||||
user_id=user_id,
|
||||
audio_url=audio_url,
|
||||
script=script,
|
||||
# video_url 不传则用默认模板
|
||||
)
|
||||
|
||||
# Ditto 返回的 MP4 自带音频，直接标记完成
|
||||
job.output_video_url = result.video_url
|
||||
# 探测时长（用于计费）
|
||||
duration = _probe_video_duration(result.video_bytes)
|
||||
if duration <= 0:
|
||||
# 兜底：按音频时长估算（1秒≈1秒）
|
||||
try:
|
||||
from packages.domain.sentence_timings import probe_audio_duration
|
||||
from packages.shared.url_security import safe_download_bytes
|
||||
|
||||
audio_data = safe_download_bytes(
|
||||
audio_url, allowed_mime_types=("audio/mpeg", "audio/wav", "audio/x-wav"), timeout=30
|
||||
)
|
||||
duration = probe_audio_duration(audio_data)
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
job.output_duration = duration
|
||||
job.status = "completed"
|
||||
job.completed_at = datetime.now(UTC)
|
||||
job.updated_at = datetime.now(UTC)
|
||||
db.commit()
|
||||
logger.info(
|
||||
"[ditto_task] Ditto 完成: job_id=%s url=%s duration=%.2fs rtf=%.2f frames=%d",
|
||||
job_id,
|
||||
result.video_url[:100],
|
||||
duration,
|
||||
result.rtf,
|
||||
result.frames,
|
||||
)
|
||||
_settle_lip_sync(db, job, duration)
|
||||
|
||||
except DittoError as exc:
|
||||
logger.error("[ditto_task] Ditto 失败，回退: job_id=%s code=%s err=%s", job_id, exc.code, exc)
|
||||
if job is not None:
|
||||
try:
|
||||
db.rollback()
|
||||
job = db.query(type(job)).filter_by(id=job_id).first() if hasattr(job, "id") else job
|
||||
# 回退 GPU/MediaKit
|
||||
_fallback_to_gpu_then_mediakit(db, job)
|
||||
except Exception as fallback_exc:
|
||||
logger.exception("[ditto_task] 回退也失败 job_id=%s err=%s", job_id, fallback_exc)
|
||||
try:
|
||||
if job:
|
||||
job.status = "failed"
|
||||
job.error_message = f"Ditto 失败且回退异常: {exc}; fallback: {fallback_exc}"
|
||||
job.error_code = "FallbackError"
|
||||
job.updated_at = datetime.now(UTC)
|
||||
db.commit()
|
||||
except Exception:
|
||||
pass
|
||||
except Exception as exc:
|
||||
logger.exception("[ditto_task] 未预期异常: job_id=%s err=%s", job_id, exc)
|
||||
if job is not None:
|
||||
try:
|
||||
db.rollback()
|
||||
job = db.query(type(job)).filter_by(id=job_id).first()
|
||||
_fallback_to_gpu_then_mediakit(db, job)
|
||||
except Exception as fallback_exc:
|
||||
logger.exception("[ditto_task] 回退也失败 job_id=%s err=%s", job_id, fallback_exc)
|
||||
try:
|
||||
if job:
|
||||
job.status = "failed"
|
||||
job.error_message = f"Ditto 异常: {exc}"
|
||||
job.error_code = "DittoAsyncError"
|
||||
job.updated_at = datetime.now(UTC)
|
||||
db.commit()
|
||||
except Exception:
|
||||
pass
|
||||
finally:
|
||||
db.close()
|
||||
@@ -104,7 +104,6 @@ def lipsync_gpu_process_async(self, job_id: str, user_id: str, gpu_task_id: str)
|
||||
job.updated_at = datetime.now(UTC)
|
||||
db.commit()
|
||||
logger.info("[lipsync_gpu_async] GPU 任务已被用户取消: job_id=%s", job_id)
|
||||
_refund_lip_sync(db, job)
|
||||
return
|
||||
|
||||
if final_task.status != "done":
|
||||
@@ -142,7 +141,6 @@ def lipsync_gpu_process_async(self, job_id: str, user_id: str, gpu_task_id: str)
|
||||
job_id,
|
||||
job.output_duration,
|
||||
)
|
||||
_settle_lip_sync(db, job, final_task)
|
||||
except Exception as exc:
|
||||
logger.exception("[lipsync_gpu_async] 异常: job_id=%s err=%s", job_id, exc)
|
||||
try:
|
||||
@@ -159,33 +157,6 @@ def lipsync_gpu_process_async(self, job_id: str, user_id: str, gpu_task_id: str)
|
||||
db.close()
|
||||
|
||||
|
||||
def _settle_lip_sync(db: Session, job: LipsyncJobModel, gpu_task) -> None:
|
||||
"""GPU 成功后结算：同步 credits_cost 到 gpu 任务并按实际时长多退少不补。"""
|
||||
try:
|
||||
from app.services.lipsync_service import LipsyncService
|
||||
|
||||
# GPU 任务表先同步结算结果（标记用）
|
||||
LipsyncService._settle_lip_sync(job, float(getattr(gpu_task, "result_duration", 0) or 0.0))
|
||||
gpu_task.credits_cost = float(job.credits_cost or 0.0)
|
||||
db.commit()
|
||||
except Exception: # noqa: BLE001
|
||||
logger.exception("[lipsync_gpu_async] lip_sync 结算异常 job_id=%s（不阻塞）", job.id)
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
|
||||
|
||||
def _refund_lip_sync(db: Session, job: LipsyncJobModel) -> None:
|
||||
"""GPU 取消/失败路径全额退款。"""
|
||||
try:
|
||||
from app.services.lipsync_service import LipsyncService
|
||||
|
||||
LipsyncService(db)._refund_lip_sync(job)
|
||||
except Exception: # noqa: BLE001
|
||||
logger.exception("[lipsync_gpu_async] lip_sync 退款异常 job_id=%s", job.id)
|
||||
|
||||
|
||||
def _fallback_to_mediakit(db: Session, job: LipsyncJobModel) -> None:
|
||||
"""GPU 失败时回退到 MediaKit 云端渲染。"""
|
||||
try:
|
||||
|
||||
@@ -261,33 +261,7 @@ def tts_synthesize_and_submit(
|
||||
"[lipsync_tts] 句子时间戳计算失败（不影响主流程）: job_id=%s err=%s", job_id, _st_err, exc_info=True
|
||||
)
|
||||
|
||||
# 3. 优先走 Ditto（#2076）：开关打开且配置完整时，派发 Ditto 异步任务，不再走 MediaKit
|
||||
ditto_dispatched = False
|
||||
try:
|
||||
from packages.config import get_api_settings as _get_settings
|
||||
|
||||
_settings = _get_settings()
|
||||
if _settings.use_ditto_lipsync and _settings.ditto_api_base_url and _settings.ditto_default_video_url:
|
||||
from app.tasks.lipsync_ditto import lipsync_ditto_process_async
|
||||
|
||||
job.status = "processing"
|
||||
job.mediakit_task_id = "ditto:tts-submitted"
|
||||
job.updated_at = datetime.now(UTC)
|
||||
db.commit()
|
||||
lipsync_ditto_process_async.apply_async(args=(job_id, user_id))
|
||||
logger.info("[lipsync_tts] TTS 完成，已派发 Ditto 任务: job_id=%s", job_id)
|
||||
ditto_dispatched = True
|
||||
except Exception as _ditto_err:
|
||||
logger.warning("[lipsync_tts] Ditto 派发失败，回退 MediaKit: job_id=%s err=%s", job_id, _ditto_err)
|
||||
try:
|
||||
db.rollback()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if ditto_dispatched:
|
||||
return
|
||||
|
||||
# 4. 签名 URL 并提交到 MediaKit（复用模块内 _sign_media_url，避免对 LipsyncService 的耦合）
|
||||
# 3. 签名 URL 并提交到 MediaKit（复用模块内 _sign_media_url，避免对 LipsyncService 的耦合）
|
||||
audio_url = _sign_media_url(job.audio_url)
|
||||
video_url = _sign_media_url(job.video_url)
|
||||
|
||||
|
||||
Generated
-12
@@ -14,7 +14,6 @@
|
||||
"axios": "^1.7.2",
|
||||
"classnames": "^2.5.1",
|
||||
"dayjs": "^1.11.23",
|
||||
"marked": "^12.0.2",
|
||||
"mp4box": "^2.4.1",
|
||||
"react": "^18.3.1",
|
||||
"react-dom": "^18.3.1",
|
||||
@@ -4503,17 +4502,6 @@
|
||||
"url": "https://github.com/sponsors/sindresorhus"
|
||||
}
|
||||
},
|
||||
"node_modules/marked": {
|
||||
"version": "12.0.2",
|
||||
"resolved": "https://registry.npmmirror.com/marked/-/marked-12.0.2.tgz",
|
||||
"integrity": "sha512-qXUm7e/YKFoqFPYPa3Ukg9xlI5cyAtGmyEIzMfW//m6kXwCy2Ps9DYf5ioijFKQ8qyuscrHoY04iJGctu2Kg0Q==",
|
||||
"bin": {
|
||||
"marked": "bin/marked.js"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">= 18"
|
||||
}
|
||||
},
|
||||
"node_modules/math-intrinsics": {
|
||||
"version": "1.1.0",
|
||||
"resolved": "https://registry.npmjs.org/math-intrinsics/-/math-intrinsics-1.1.0.tgz",
|
||||
|
||||
@@ -25,7 +25,6 @@
|
||||
"axios": "^1.7.2",
|
||||
"classnames": "^2.5.1",
|
||||
"dayjs": "^1.11.23",
|
||||
"marked": "^12.0.2",
|
||||
"mp4box": "^2.4.1",
|
||||
"react": "^18.3.1",
|
||||
"react-dom": "^18.3.1",
|
||||
|
||||
@@ -9,8 +9,6 @@ import type {
|
||||
AnalyzeImagesRequest,
|
||||
GenerateCopyRequest,
|
||||
ConfirmCopyRequest,
|
||||
ViralVideoModel,
|
||||
ViralVideoModelsResponse,
|
||||
} from "./types"
|
||||
|
||||
/** 创建爆款视频任务 */
|
||||
@@ -53,29 +51,6 @@ export function analyzeViralStyle(id: string) {
|
||||
return apiClient.post<ViralVideoJob>(`/viral-video/${id}/analyze-style`).then((r) => r.data)
|
||||
}
|
||||
|
||||
/** 动态预估积分消耗（STEP3 参数变化时调用） */
|
||||
export function estimateViralVideoCredits(params: {
|
||||
video_model: string
|
||||
resolution: string
|
||||
video_ratio: string
|
||||
duration: number
|
||||
}) {
|
||||
return apiClient
|
||||
.post<{ estimated_credits: number }>("/viral-video/estimate-credits", params)
|
||||
.then((r) => r.data)
|
||||
}
|
||||
|
||||
/** 获取支持的视频模型列表（GET /viral-video/models）。后端返回 {models: [...]} 包装 */
|
||||
export function getViralVideoModels() {
|
||||
return apiClient.get<ViralVideoModelsResponse>("/viral-video/models").then((r) => {
|
||||
const data = r.data as ViralVideoModelsResponse | ViralVideoModel[] | null | undefined
|
||||
if (Array.isArray(data)) return data
|
||||
if (data && Array.isArray((data as ViralVideoModelsResponse).models)) {
|
||||
return (data as ViralVideoModelsResponse).models
|
||||
}
|
||||
return []
|
||||
})
|
||||
}
|
||||
/** ── 三步拆分：前端 mock 辅助函数（后端新接口上线后可替换） ── */
|
||||
|
||||
/**
|
||||
|
||||
@@ -83,8 +83,6 @@ export interface ImageProductAnalysis {
|
||||
label_text?: string
|
||||
selling_points?: string
|
||||
image_index?: number
|
||||
/** v8: 用户端展示用的 markdown 描述（由提示词控制排版） */
|
||||
summary_markdown?: string
|
||||
}
|
||||
|
||||
export interface ImageAnalysisResult {
|
||||
@@ -134,8 +132,6 @@ export interface CopyResult {
|
||||
/** 向后兼容：= voiceover_script */
|
||||
suggested_copy?: string
|
||||
title?: string
|
||||
/** v3 storyboard: 用户端展示用的 markdown 文案（由提示词控制排版） */
|
||||
copy_display_markdown?: string
|
||||
/** v1.5 旧字段兼容（老数据降级时可能出现） */
|
||||
scenes?: Array<{ shot: string; narration: string; duration?: number }>
|
||||
}
|
||||
@@ -180,7 +176,7 @@ export interface ViralVideoJob {
|
||||
fusion_level?: FusionLevel
|
||||
voice_id?: string
|
||||
voice_mode?: "global" | "per_video"
|
||||
voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
|
||||
voice_source?: "preset" | "library" | "clone" | "upload"
|
||||
bgm_preference?: string
|
||||
intent_result?: IntentResult
|
||||
intent_text?: string
|
||||
@@ -216,7 +212,7 @@ export interface GenerateViralVideoRequest {
|
||||
user_copy_text?: string
|
||||
fusion_level?: FusionLevel
|
||||
voice_id?: string
|
||||
voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
|
||||
voice_source?: "preset" | "library" | "clone" | "upload"
|
||||
bgm_preference?: string
|
||||
industry?: string
|
||||
target_customer?: string
|
||||
@@ -248,7 +244,7 @@ export interface AnalyzeImagesRequest {
|
||||
/** TTS 音色 ID（STEP1 已选音色时传） */
|
||||
voice_id?: string
|
||||
/** 音色来源：preset | library | clone | upload */
|
||||
voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
|
||||
voice_source?: "preset" | "library" | "clone" | "upload"
|
||||
/** Seedance 视频比例：9:16 | 16:9 | 1:1 */
|
||||
video_ratio?: string
|
||||
/** Seedance 模型 ID（空则使用服务端默认） */
|
||||
@@ -277,36 +273,17 @@ export interface GenerateCopyRequest {
|
||||
/** TTS 音色 ID（优先级高于 persona_id） */
|
||||
voice_id?: string
|
||||
/** 音色来源：preset | library | clone | upload */
|
||||
voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
|
||||
voice_source?: "preset" | "library" | "clone" | "upload"
|
||||
/** Seedance 视频比例（9:16/16:9/1:1 等） */
|
||||
video_ratio?: string
|
||||
/** Seedance 模型 ID（空则使用服务端默认） */
|
||||
video_model?: string
|
||||
}
|
||||
|
||||
/** 视频模型描述（GET /viral-video/models） */
|
||||
export interface ViralVideoModel {
|
||||
key: string
|
||||
display_name: string
|
||||
supports_audio: boolean
|
||||
supported_resolutions: string[]
|
||||
max_duration: number
|
||||
/** 计费模式（可选）：per_second / per_video / token 等 */
|
||||
billing_mode?: string
|
||||
is_default?: boolean
|
||||
}
|
||||
|
||||
/** GET /viral-video/models 响应包装 */
|
||||
export interface ViralVideoModelsResponse {
|
||||
models: ViralVideoModel[]
|
||||
}
|
||||
|
||||
/** v1.6 阶段3请求：用户确认/编辑口播文案后开始单次 Seedance 出片（POST /viral-video/{id}/confirm-copy） */
|
||||
export interface ConfirmCopyRequest {
|
||||
/** 用户编辑后的口播文案；为空则使用 AI 生成的 voiceover_script */
|
||||
edited_copy?: string
|
||||
/** 视频模型 key，覆盖默认 */
|
||||
video_model?: string
|
||||
}
|
||||
|
||||
/** 旧分镜片段结构（保留兼容；新代码请使用 ShotScript） */
|
||||
|
||||
@@ -18,8 +18,6 @@ export interface VoiceClone {
|
||||
language: string
|
||||
gender: string
|
||||
error_message: string | null
|
||||
/** CosyVoice 实际使用的音色 ID（status=ready 时由后端填充，用于 TTS 调用） */
|
||||
voice_id?: string | null
|
||||
created_at: string
|
||||
updated_at: string
|
||||
}
|
||||
|
||||
@@ -18,7 +18,6 @@ export const toVoiceClone = (profile: VoiceCloneProfile): VoiceClone => ({
|
||||
language: profile.language || "",
|
||||
gender: profile.gender || "",
|
||||
error_message: profile.error_message || null,
|
||||
voice_id: profile.voice_id,
|
||||
created_at: profile.created_at,
|
||||
updated_at: profile.updated_at,
|
||||
})
|
||||
|
||||
@@ -1,182 +0,0 @@
|
||||
/* DurationWheelPicker —— 弹层式滚轮选择器（样式与表单一致） */
|
||||
|
||||
/* 触发按钮：外观复用 .vv-select 风格 */
|
||||
.dw-trigger {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
width: 100%;
|
||||
height: 36px;
|
||||
padding: 0 12px;
|
||||
background: #fff;
|
||||
border: 1px solid #e0e0e8;
|
||||
border-radius: 8px;
|
||||
font-size: 13px;
|
||||
color: #1f2937;
|
||||
cursor: pointer;
|
||||
box-sizing: border-box;
|
||||
transition: all 0.15s;
|
||||
user-select: none;
|
||||
}
|
||||
.dw-trigger:hover {
|
||||
border-color: #c0c0d0;
|
||||
}
|
||||
.dw-trigger-open,
|
||||
.dw-trigger:focus-within {
|
||||
border-color: #7c3aed !important;
|
||||
box-shadow: 0 0 0 2px rgba(124, 58, 237, 0.12);
|
||||
}
|
||||
.dw-trigger-disabled {
|
||||
opacity: 0.5;
|
||||
pointer-events: none;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
.dw-trigger-val {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
.dw-trigger-placeholder {
|
||||
color: #9ca3af;
|
||||
}
|
||||
.dw-trigger-arrow {
|
||||
font-size: 10px;
|
||||
color: #9ca3af;
|
||||
margin-left: 8px;
|
||||
transition: transform 0.2s;
|
||||
}
|
||||
.dw-trigger-arrow-up {
|
||||
transform: rotate(180deg);
|
||||
}
|
||||
|
||||
/* 弹层容器 */
|
||||
.dw-popup {
|
||||
padding: 8px;
|
||||
min-width: 140px;
|
||||
}
|
||||
|
||||
/* 滚轮 */
|
||||
.dw-picker {
|
||||
position: relative;
|
||||
width: 100%;
|
||||
overflow: hidden;
|
||||
border-radius: 8px;
|
||||
background: #fafafe;
|
||||
border: 1px solid #e5e7eb;
|
||||
}
|
||||
.dw-picker-list {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
list-style: none;
|
||||
height: 100%;
|
||||
overflow-y: scroll;
|
||||
scroll-snap-type: y mandatory;
|
||||
-webkit-overflow-scrolling: touch;
|
||||
scrollbar-width: none;
|
||||
}
|
||||
.dw-picker-list::-webkit-scrollbar {
|
||||
display: none;
|
||||
}
|
||||
.dw-picker-item {
|
||||
display: flex;
|
||||
align-items: baseline;
|
||||
justify-content: center;
|
||||
gap: 3px;
|
||||
scroll-snap-align: center;
|
||||
cursor: pointer;
|
||||
font-size: 15px;
|
||||
color: #9ca3af;
|
||||
font-weight: 400;
|
||||
transition:
|
||||
color 0.15s,
|
||||
transform 0.15s,
|
||||
font-weight 0.15s;
|
||||
}
|
||||
.dw-picker-item-val {
|
||||
font-variant-numeric: tabular-nums;
|
||||
}
|
||||
.dw-picker-item-unit {
|
||||
font-size: 13px;
|
||||
color: inherit;
|
||||
}
|
||||
.dw-picker-item-active {
|
||||
color: #7c3aed;
|
||||
font-weight: 600;
|
||||
}
|
||||
.dw-picker-item-active .dw-picker-item-val {
|
||||
font-size: 18px;
|
||||
}
|
||||
.dw-picker-item-active .dw-picker-item-unit {
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
/* 中心选中条 */
|
||||
.dw-picker-mask {
|
||||
position: absolute;
|
||||
left: 6px;
|
||||
right: 6px;
|
||||
pointer-events: none;
|
||||
background: #f5f0ff;
|
||||
border-radius: 6px;
|
||||
z-index: 1;
|
||||
}
|
||||
.dw-picker-mask::before,
|
||||
.dw-picker-mask::after {
|
||||
content: "";
|
||||
position: absolute;
|
||||
left: 0;
|
||||
right: 0;
|
||||
height: 1px;
|
||||
background: #d8c4ff;
|
||||
}
|
||||
.dw-picker-mask::before {
|
||||
top: 0;
|
||||
}
|
||||
.dw-picker-mask::after {
|
||||
bottom: 0;
|
||||
}
|
||||
|
||||
/* 上下渐变 */
|
||||
.dw-picker-fade {
|
||||
position: absolute;
|
||||
left: 0;
|
||||
right: 0;
|
||||
height: 40%;
|
||||
pointer-events: none;
|
||||
z-index: 2;
|
||||
}
|
||||
.dw-picker-fade-top {
|
||||
top: 0;
|
||||
background: linear-gradient(to bottom, #fafafe 25%, rgba(250, 250, 254, 0));
|
||||
}
|
||||
.dw-picker-fade-bottom {
|
||||
bottom: 0;
|
||||
background: linear-gradient(to top, #fafafe 25%, rgba(250, 250, 254, 0));
|
||||
}
|
||||
|
||||
/* 弹层按钮区 */
|
||||
.dw-popup-actions {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
justify-content: flex-end;
|
||||
margin-top: 8px;
|
||||
}
|
||||
.dw-popup-actions .ant-btn {
|
||||
border-radius: 6px;
|
||||
}
|
||||
.dw-popup-actions .ant-btn-primary {
|
||||
background: #7c3aed;
|
||||
}
|
||||
.dw-popup-actions .ant-btn-primary:hover {
|
||||
background: #6d28d9 !important;
|
||||
}
|
||||
|
||||
/* 覆盖 antd Popover 默认内边距 */
|
||||
.dw-popover .ant-popover-inner {
|
||||
padding: 0 !important;
|
||||
overflow: hidden;
|
||||
}
|
||||
.dw-popover .ant-popover-arrow {
|
||||
display: none;
|
||||
}
|
||||
@@ -1,180 +0,0 @@
|
||||
/**
|
||||
* DurationWheelPicker —— 竖屏滚轮式时长选择器（弹层版）
|
||||
*
|
||||
* 设计：
|
||||
* - 外观是和其他表单 Select 一致的输入框（白色底+1px灰边+紫色focus ring）
|
||||
* - 点击输入框弹出 Popover，内部是滚轮 picker（原生 scroll-snap，零依赖）
|
||||
* - 滚轮样式：白底容器，选中行 #7c3aed 紫字加粗+浅紫背景条
|
||||
* - 支持触摸/鼠标滚轮/点击；松手吸附；底部"确认/取消"按钮
|
||||
* - 默认范围 15–30 秒，步长 1 秒
|
||||
*/
|
||||
import React, { useEffect, useMemo, useRef, useState, useCallback } from "react"
|
||||
import { Popover, Button } from "antd"
|
||||
import { DownOutlined } from "@ant-design/icons"
|
||||
import "./DurationWheelPicker.css"
|
||||
|
||||
export interface DurationWheelPickerProps {
|
||||
value?: number
|
||||
min?: number
|
||||
max?: number
|
||||
step?: number
|
||||
unit?: string
|
||||
onChange?: (value: number) => void
|
||||
placeholder?: string
|
||||
disabled?: boolean
|
||||
/** 弹层宽度，默认 160px */
|
||||
popupWidth?: number
|
||||
/** 弹层内滚轮高度，默认 180px */
|
||||
wheelHeight?: number
|
||||
}
|
||||
|
||||
const ITEM_HEIGHT = 36
|
||||
|
||||
const DurationWheelPicker: React.FC<DurationWheelPickerProps> = ({
|
||||
value = 20,
|
||||
min = 15,
|
||||
max = 30,
|
||||
step = 1,
|
||||
unit = "秒",
|
||||
onChange,
|
||||
placeholder = "请选择时长",
|
||||
disabled = false,
|
||||
popupWidth = 160,
|
||||
wheelHeight = 180,
|
||||
}) => {
|
||||
const options = useMemo(() => {
|
||||
const arr: number[] = []
|
||||
for (let v = min; v <= max; v += step) arr.push(v)
|
||||
return arr
|
||||
}, [min, max, step])
|
||||
|
||||
const [open, setOpen] = useState(false)
|
||||
// 弹层内暂存值，点确认才提交
|
||||
const [draft, setDraft] = useState<number>(value)
|
||||
const listRef = useRef<HTMLUListElement>(null)
|
||||
const scrollTimerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
|
||||
|
||||
useEffect(() => {
|
||||
if (open) {
|
||||
setDraft(value)
|
||||
// 下一帧滚到当前值
|
||||
requestAnimationFrame(() => scrollToValue(value, false))
|
||||
}
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, [open])
|
||||
|
||||
const scrollToValue = useCallback(
|
||||
(v: number, smooth = true) => {
|
||||
const list = listRef.current
|
||||
if (!list) return
|
||||
const idx = options.indexOf(v)
|
||||
if (idx < 0) return
|
||||
list.scrollTo({ top: idx * ITEM_HEIGHT, behavior: smooth ? "smooth" : "auto" })
|
||||
},
|
||||
[options],
|
||||
)
|
||||
|
||||
const handleScroll = () => {
|
||||
if (scrollTimerRef.current) clearTimeout(scrollTimerRef.current)
|
||||
scrollTimerRef.current = setTimeout(() => {
|
||||
const list = listRef.current
|
||||
if (!list) return
|
||||
const idx = Math.round(list.scrollTop / ITEM_HEIGHT)
|
||||
const clamped = Math.max(0, Math.min(options.length - 1, idx))
|
||||
const targetTop = clamped * ITEM_HEIGHT
|
||||
if (Math.abs(list.scrollTop - targetTop) > 1) {
|
||||
list.scrollTo({ top: targetTop, behavior: "smooth" })
|
||||
}
|
||||
setDraft(options[clamped])
|
||||
}, 100)
|
||||
}
|
||||
|
||||
const handleConfirm = () => {
|
||||
onChange?.(draft)
|
||||
setOpen(false)
|
||||
}
|
||||
|
||||
const handleCancel = () => {
|
||||
setOpen(false)
|
||||
}
|
||||
|
||||
const handleItemClick = (v: number) => {
|
||||
setDraft(v)
|
||||
scrollToValue(v, true)
|
||||
}
|
||||
|
||||
const maskTop = wheelHeight / 2 - ITEM_HEIGHT / 2
|
||||
|
||||
const wheel = (
|
||||
<div className="dw-popup">
|
||||
<div
|
||||
className="dw-picker"
|
||||
style={{ height: wheelHeight, width: popupWidth - 24 /* padding */ }}
|
||||
>
|
||||
<div className="dw-picker-mask" style={{ top: maskTop, height: ITEM_HEIGHT }} aria-hidden />
|
||||
<div className="dw-picker-fade dw-picker-fade-top" aria-hidden />
|
||||
<div className="dw-picker-fade dw-picker-fade-bottom" aria-hidden />
|
||||
<ul
|
||||
ref={listRef}
|
||||
className="dw-picker-list"
|
||||
onScroll={handleScroll}
|
||||
style={{
|
||||
paddingTop: wheelHeight / 2 - ITEM_HEIGHT / 2,
|
||||
paddingBottom: wheelHeight / 2 - ITEM_HEIGHT / 2,
|
||||
}}
|
||||
>
|
||||
{options.map((v) => {
|
||||
const isActive = v === draft
|
||||
return (
|
||||
<li
|
||||
key={v}
|
||||
className={`dw-picker-item${isActive ? " dw-picker-item-active" : ""}`}
|
||||
style={{ height: ITEM_HEIGHT, lineHeight: `${ITEM_HEIGHT}px` }}
|
||||
onClick={() => handleItemClick(v)}
|
||||
aria-selected={isActive}
|
||||
role="option"
|
||||
>
|
||||
<span className="dw-picker-item-val">{v}</span>
|
||||
<span className="dw-picker-item-unit">{unit}</span>
|
||||
</li>
|
||||
)
|
||||
})}
|
||||
</ul>
|
||||
</div>
|
||||
<div className="dw-popup-actions">
|
||||
<Button size="small" onClick={handleCancel}>
|
||||
取消
|
||||
</Button>
|
||||
<Button size="small" type="primary" onClick={handleConfirm}>
|
||||
确认
|
||||
</Button>
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
|
||||
return (
|
||||
<Popover
|
||||
open={!disabled && open}
|
||||
onOpenChange={(v) => setOpen(v)}
|
||||
content={wheel}
|
||||
trigger="click"
|
||||
placement="bottomLeft"
|
||||
overlayClassName="dw-popover"
|
||||
overlayStyle={{ padding: 0 }}
|
||||
overlayInnerStyle={{ padding: 0, borderRadius: 10 }}
|
||||
destroyTooltipOnHide
|
||||
>
|
||||
<div
|
||||
className={`dw-trigger${disabled ? " dw-trigger-disabled" : ""}${open ? " dw-trigger-open" : ""}`}
|
||||
style={{ height: 36 }}
|
||||
>
|
||||
<span className={`dw-trigger-val${value != null ? "" : " dw-trigger-placeholder"}`}>
|
||||
{value != null ? `${value}${unit}` : placeholder}
|
||||
</span>
|
||||
<DownOutlined className={`dw-trigger-arrow${open ? " dw-trigger-arrow-up" : ""}`} />
|
||||
</div>
|
||||
</Popover>
|
||||
)
|
||||
}
|
||||
|
||||
export default DurationWheelPicker
|
||||
@@ -10,4 +10,4 @@
|
||||
* 功能流程不做积分预校验，直接走生成。
|
||||
* - true：展示完整积分系统 UI。
|
||||
*/
|
||||
export const ENABLE_CREDIT_SYSTEM = true
|
||||
export const ENABLE_CREDIT_SYSTEM = false
|
||||
|
||||
@@ -80,8 +80,6 @@ const AiAvatarPage: React.FC = () => {
|
||||
const [finalizeLoading, setFinalizeLoading] = useState(false)
|
||||
|
||||
/* ── 对口型轮询 ── */
|
||||
/** 对口型轮询总时长上限（10分钟）：超过后停止轮询并提示去历史记录查看 */
|
||||
const LIPSYNC_POLL_MAX_MS = 10 * 60 * 1000
|
||||
const lipsyncTimerRef = useRef<ReturnType<typeof setInterval> | null>(null)
|
||||
/* ── 渲染进度轮询 ── */
|
||||
const renderTimerRef = useRef<ReturnType<typeof setInterval> | null>(null)
|
||||
@@ -272,24 +270,7 @@ const AiAvatarPage: React.FC = () => {
|
||||
// 如果是预合成模式，后端会同步把状态置为 submitted（甚至可能已返回 running），
|
||||
// 但仍需轮询等 completed
|
||||
if (lipsyncTimerRef.current) clearInterval(lipsyncTimerRef.current)
|
||||
// 轮询间隔 5 秒；单请求超时 5 分钟（见 api/aiAvatar.ts）；总轮询上限 10 分钟
|
||||
// 单次请求失败/超时不中断轮询，继续下一轮；超过总上限后停止并提示用户去历史记录查看
|
||||
lipsyncTimerRef.current = setInterval(async () => {
|
||||
// 总时长保护：超过 10 分钟停止轮询
|
||||
if (Date.now() - lipsyncStartAtRef.current > LIPSYNC_POLL_MAX_MS) {
|
||||
if (lipsyncTimerRef.current) {
|
||||
clearInterval(lipsyncTimerRef.current)
|
||||
lipsyncTimerRef.current = null
|
||||
}
|
||||
if (lipsyncTickRef.current) {
|
||||
clearInterval(lipsyncTickRef.current)
|
||||
lipsyncTickRef.current = null
|
||||
}
|
||||
setLipsyncStatus("failed")
|
||||
setLipsyncErrorMessage("渲染时间较长，请稍后在历史记录中查看")
|
||||
message.warning("对口型渲染时间较长，已停止自动刷新，请稍后在历史记录中查看")
|
||||
return
|
||||
}
|
||||
try {
|
||||
const updated = await getLipsyncJob(job.id)
|
||||
state.setLipsyncJob(updated)
|
||||
@@ -315,10 +296,9 @@ const AiAvatarPage: React.FC = () => {
|
||||
setLipsyncErrorMessage(updated.error_message || "对口型生成失败")
|
||||
}
|
||||
} catch (err) {
|
||||
// 单次轮询失败（含 timeout）：不中断轮询，打印日志后等下一轮
|
||||
console.warn("[对口型] 轮询请求失败，将继续下一轮:", err)
|
||||
console.error("[对口型] 轮询错误:", err)
|
||||
}
|
||||
}, 5000)
|
||||
}, 3000)
|
||||
} catch (err) {
|
||||
console.error("[对口型] 创建失败:", {
|
||||
status: (err as { response?: { status?: number } })?.response?.status,
|
||||
|
||||
@@ -72,8 +72,7 @@ export const previewTts = async (data: {
|
||||
}
|
||||
|
||||
export const getLipsyncJob = async (id: string): Promise<LipsyncJob> => {
|
||||
// MuseTalk 渲染 8s 视频约 54s + 排队时间，给足 5 分钟超时避免单次轮询 AxiosError 中断
|
||||
const response = await apiClient.get<LipsyncJob>(`/lipsync/jobs/${id}`, { timeout: 300_000 })
|
||||
const response = await apiClient.get<LipsyncJob>(`/lipsync/jobs/${id}`, { timeout: 60000 })
|
||||
return response.data
|
||||
}
|
||||
|
||||
@@ -92,10 +91,7 @@ export const submitRender = async (data: {
|
||||
}
|
||||
|
||||
export const getRenderJob = async (jobId: string): Promise<RenderJob> => {
|
||||
// 渲染链路（对口型+B-roll+标题+合成+上传）耗时较长，给足 5 分钟超时
|
||||
const response = await apiClient.get<RenderJob>(`/ai-avatar/render/${jobId}`, {
|
||||
timeout: 300_000,
|
||||
})
|
||||
const response = await apiClient.get<RenderJob>(`/ai-avatar/render/${jobId}`, { timeout: 60000 })
|
||||
return response.data
|
||||
}
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -245,11 +245,12 @@ export default function AssetPickerModal({
|
||||
</div>
|
||||
{multiple && (
|
||||
<div className="vv-modal-foot">
|
||||
<button className="vv-btn vv-btn-ghost" onClick={onClose}>
|
||||
<button className="vv-btn vv-btn-ghost vv-btn-sm" onClick={onClose}>
|
||||
取消
|
||||
</button>
|
||||
<button
|
||||
className="vv-btn vv-btn-primary"
|
||||
style={{ width: "auto", marginTop: 0, padding: "8px 18px" }}
|
||||
onClick={handleConfirm}
|
||||
disabled={picked.size === 0}
|
||||
>
|
||||
|
||||
@@ -53,9 +53,6 @@ celery_app.conf.imports = (
|
||||
# #1998 GPU MuseTalk 异步推理：wait_for_result→签名 URL→回写 lipsync_jobs
|
||||
# 必须在 Worker 侧注册，否则 apply_async 消息无人消费，job 永远卡在 processing
|
||||
"app.tasks.lipsync_gpu",
|
||||
# #2076 Ditto 蚂蚁数字人异步推理：同步 HTTP 调用 Ditto → MP4 流转存 OSS → 回写 lipsync_jobs
|
||||
# 必须在 Worker 侧注册；失败回退 GPU MuseTalk → MediaKit
|
||||
"app.tasks.lipsync_ditto",
|
||||
)
|
||||
|
||||
# Celery Beat 定时任务调度
|
||||
|
||||
@@ -387,41 +387,6 @@ BATCH_RENDER_SIMILARITY_LIMIT = 0.20
|
||||
"""批次内成片查重相似度阈值：超过则重选独立 plan 重渲一次（20%）。"""
|
||||
|
||||
|
||||
def _refund_smart_edit_prepaid(task_id: str) -> None:
|
||||
"""智能剪辑任务最终失败时退还预扣积分（幂等）。"""
|
||||
session = SessionLocal()
|
||||
try:
|
||||
from packages.adapters.sqlalchemy_impl.generation_task_repository import (
|
||||
SQLAlchemyGenerationTaskRepository,
|
||||
)
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
repo = SQLAlchemyGenerationTaskRepository(session)
|
||||
task = repo.get(task_id)
|
||||
if not task:
|
||||
return
|
||||
prepaid = float(getattr(task, "credits_prepaid", 0) or 0)
|
||||
if prepaid <= 0:
|
||||
return
|
||||
txn_id = getattr(task, "credits_transaction_id", "") or ""
|
||||
res = PointsService().refund_points(
|
||||
user_id=task.user_id,
|
||||
amount=prepaid,
|
||||
source="smart_edit",
|
||||
db=session,
|
||||
ref_id=task.id,
|
||||
related_transaction_id=txn_id or None,
|
||||
description="智能剪辑任务失败退回",
|
||||
)
|
||||
task.credits_cost = 0.0
|
||||
task.credits_prepaid = 0.0
|
||||
repo.update(task)
|
||||
if not res.get("success"):
|
||||
logger.warning("[task_id=%s] 失败退积分未成功: %s", task_id, res)
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
|
||||
def should_rerender_for_batch_dedup(*, batch_id: str, render_attempt: int, batch_similarity) -> bool:
|
||||
"""批次内查重后判定是否需要重选 plan 重渲。
|
||||
|
||||
@@ -1202,10 +1167,6 @@ def generate_video(self, task_id: str) -> dict:
|
||||
"mark_failed",
|
||||
error_message="source_edit_plan_id is required. Please create a preview task first.",
|
||||
)
|
||||
try:
|
||||
_refund_smart_edit_prepaid(task_id)
|
||||
except Exception:
|
||||
logger.warning("[task_id=%s] 失败退积分异常", task_id, exc_info=True)
|
||||
return {
|
||||
"status": "failed",
|
||||
"task_id": task_id,
|
||||
@@ -1244,7 +1205,6 @@ def generate_video(self, task_id: str) -> dict:
|
||||
)
|
||||
|
||||
# ── 自动重试逻辑 ──────────────────────────────────────────────────
|
||||
will_retry = False
|
||||
try:
|
||||
from packages.adapters.sqlalchemy_impl.generation_task_repository import (
|
||||
SQLAlchemyGenerationTaskRepository,
|
||||
@@ -1257,7 +1217,6 @@ def generate_video(self, task_id: str) -> dict:
|
||||
if _task and _task.auto_retry_enabled and _task.auto_retry_max > 0:
|
||||
current_retry = _task.retry_count or 0
|
||||
if current_retry < _task.auto_retry_max:
|
||||
will_retry = True
|
||||
logger.info(
|
||||
"[task_id=%s] 触发自动重试: 当前重试次数=%d, 最大重试次数=%d",
|
||||
task_id,
|
||||
@@ -1291,13 +1250,6 @@ def generate_video(self, task_id: str) -> dict:
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
# 最终失败（不再重试）：退还 smart_edit 预扣积分
|
||||
if not will_retry:
|
||||
try:
|
||||
_refund_smart_edit_prepaid(task_id)
|
||||
except Exception:
|
||||
logger.warning("[task_id=%s] 失败退积分异常", task_id, exc_info=True)
|
||||
|
||||
return {
|
||||
"status": "failed",
|
||||
"task_id": task_id,
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,4 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 图片分析：火山OCR专用API + doubao-lite强约束JSON并行，单次pro VLM兜底。"""
|
||||
|
||||
from .fast_path import analyze_image_v2, analyze_images_v2 # noqa: F401
|
||||
@@ -1,208 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 prompt 解析：优先读后台 viral_video_prompt_templates 表（prompt_type='image_analysis'
|
||||
且 is_active=true），30s TTL 热加载；DB 无有效记录/异常时，fallback 到纯硬编码 JSON schema prompt。
|
||||
|
||||
规则（简单直接，不做字符串匹配判断）：
|
||||
- DB 有 is_active=true 的 image_analysis 记录（含种子版本和用户修改后的版本）：
|
||||
* system = DB.system_prompt（DB prompt 自带完整输出格式，不追加硬编码 schema，
|
||||
避免 DB 写 XML、调用强制 json_object 造成的格式冲突）
|
||||
* user = DB.user_prompt_template 渲染后使用；渲染后为空则用硬编码默认
|
||||
- DB 无记录/连接异常/返回空：system/user 全部用纯硬编码 JSON schema prompt
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ---- 纯硬编码 JSON schema（DB 无有效配置时全量使用） ----
|
||||
|
||||
_FAST_JSON_SCHEMA = (
|
||||
"你是图片结构化识别器。严格按下方 JSON schema 返回一个对象，不要任何解释、"
|
||||
"不要markdown、不要代码块、不要前后缀文字。字段值不确定时填 null 或空数组。\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "upper_wear": "上装款式，如T恤/衬衫/卫衣/毛衣/西装/夹克/连衣裙/吊带/背心/外套等",\n'
|
||||
' "upper_color": "上装主色",\n'
|
||||
' "lower_wear": "下装款式；穿连衣裙时填null",\n'
|
||||
' "lower_color": "下装主色",\n'
|
||||
' "dress_color": "连衣裙主色（穿连衣裙时填）",\n'
|
||||
' "accessories": ["眼镜"/"帽子"/"项链"/"耳环"/"背包"/"手表"等数组],\n'
|
||||
' "hairstyle": "发型，如短发/长发/马尾/卷发/丸子头/光头等",\n'
|
||||
' "expression": "表情，如微笑/严肃/酷/开心等",\n'
|
||||
' "pose": "姿势，如站立/坐姿/侧身/行走等",\n'
|
||||
' "scene": "场景，如室内/街拍/户外/办公室/家居/海边/雪景/森林等",\n'
|
||||
' "style": "风格，如休闲/商务/运动/复古/潮流/甜美/酷飒/优雅/街头/法式等",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "产品类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名称，非产品图填null",\n'
|
||||
' "brand": "品牌或文字标识，无则null",\n'
|
||||
' "material": "材质，如棉质/牛仔/皮革/真丝/针织/涤纶等",\n'
|
||||
' "pattern": "图案，如纯色/条纹/波点/格子/印花/碎花/Logo等",\n'
|
||||
' "colors": ["主色数组"],\n'
|
||||
' "mood": "整体氛围/情绪，如清新/活力/高级/温暖/冷峻/甜美/复古等"\n'
|
||||
"}\n\n"
|
||||
"你必须只返回一个合法的JSON对象，不要输出任何其他文字、解释、XML标签或markdown。"
|
||||
)
|
||||
DEFAULT_FAST_USER = "识别这张图片的人物穿搭与主体信息，只返回JSON对象。"
|
||||
|
||||
_PRO_JSON_SCHEMA = (
|
||||
"你是图片分析专家。严格按下方 JSON schema 返回一个对象，不要解释、不要markdown、不要代码块、不要XML标签。\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "outfit": "整体穿着描述（含颜色款式）",\n'
|
||||
' "hair": "发型发色",\n'
|
||||
' "pose": "姿势",\n'
|
||||
' "expression": "表情",\n'
|
||||
' "scene": "场景",\n'
|
||||
' "mood": "氛围",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名，非产品图填null",\n'
|
||||
' "brand": "品牌，无则null",\n'
|
||||
' "key_features": ["核心特征数组，3-6个短语"]\n'
|
||||
"}\n\n"
|
||||
"你必须只返回一个合法的JSON对象，不要输出任何其他文字、解释、XML标签或markdown。"
|
||||
)
|
||||
DEFAULT_PRO_USER = "分析这张图片，返回符合schema的JSON。"
|
||||
|
||||
# 保留旧 JSON schema 追加文本作为常量（DB prompt 完全控制输出格式后不再使用，
|
||||
# 保留以便排查历史行为）。
|
||||
_FAST_JSON_APPEND = (
|
||||
"\n\n【输出格式要求】无论上文如何要求，最终你必须只返回一个合法的JSON对象，"
|
||||
"严格包含以下字段（字段值不确定时填null或空数组）：\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "upper_wear": "上装款式字符串",\n'
|
||||
' "upper_color": "上装主色",\n'
|
||||
' "lower_wear": "下装款式（穿连衣裙时填null）",\n'
|
||||
' "lower_color": "下装主色",\n'
|
||||
' "dress_color": "连衣裙主色（穿连衣裙时填）",\n'
|
||||
' "accessories": ["配饰数组"],\n'
|
||||
' "hairstyle": "发型",\n'
|
||||
' "expression": "表情",\n'
|
||||
' "pose": "姿势",\n'
|
||||
' "scene": "场景",\n'
|
||||
' "style": "风格",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "产品类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名称，非产品图填null",\n'
|
||||
' "brand": "品牌或文字标识，无则null",\n'
|
||||
' "material": "材质",\n'
|
||||
' "pattern": "图案",\n'
|
||||
' "colors": ["主色数组"],\n'
|
||||
' "mood": "整体氛围"\n'
|
||||
"}\n"
|
||||
"不要输出任何其他文字、解释、XML标签或markdown。"
|
||||
)
|
||||
|
||||
_PRO_JSON_APPEND = (
|
||||
"\n\n【输出格式要求】无论上文如何要求，最终你必须只返回一个合法的JSON对象，"
|
||||
"严格包含以下字段（字段值不确定时填null或空数组）：\n"
|
||||
"{\n"
|
||||
' "has_person": true/false,\n'
|
||||
' "gender": "男"/"女"/null,\n'
|
||||
' "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
|
||||
' "outfit": "整体穿着描述（含颜色款式）",\n'
|
||||
' "hair": "发型发色",\n'
|
||||
' "pose": "姿势",\n'
|
||||
' "expression": "表情",\n'
|
||||
' "scene": "场景",\n'
|
||||
' "mood": "氛围",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
|
||||
' "product_name": "产品名，非产品图填null",\n'
|
||||
' "brand": "品牌，无则null",\n'
|
||||
' "key_features": ["核心特征3-6个短语"]\n'
|
||||
"}\n"
|
||||
"不要输出任何其他文字、解释、XML标签或markdown。"
|
||||
)
|
||||
|
||||
_cache_lock = threading.Lock()
|
||||
_cache: dict[str, tuple[float, Any]] = {}
|
||||
_CACHE_TTL = 30.0
|
||||
|
||||
|
||||
def _load_db_template() -> Any | None:
|
||||
"""直接查DB viral_video_prompt_templates 中 is_active=true 的 image_analysis 记录；
|
||||
DB不可达/无记录/异常返回None。
|
||||
复用 prompt_loader._load_from_db，它只查DB不做DEFAULT_TEMPLATES fallback，
|
||||
返回None表示DB无记录或异常。"""
|
||||
try:
|
||||
from packages.application.viral_video.prompt_loader import _load_from_db
|
||||
|
||||
return _load_from_db("image_analysis")
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 查询DB prompt配置失败: %s", e)
|
||||
return None
|
||||
|
||||
|
||||
def _render_user(tpl: Any | None, default_user: str) -> str:
|
||||
if not tpl:
|
||||
return default_user
|
||||
tpl_str = getattr(tpl, "user_prompt_template", "") or ""
|
||||
if not tpl_str.strip():
|
||||
return default_user
|
||||
rendered = tpl_str.replace("{image_count}", "1").replace("{industry}", "通用").replace("{image_urls}", "").strip()
|
||||
return rendered or default_user
|
||||
|
||||
|
||||
def resolve_fast_prompt() -> tuple[str, str]:
|
||||
return _resolve("fast")
|
||||
|
||||
|
||||
def resolve_pro_prompt() -> tuple[str, str]:
|
||||
return _resolve("pro")
|
||||
|
||||
|
||||
def _resolve(kind: str) -> tuple[str, str]:
|
||||
now = time.time()
|
||||
cache_key = f"prompt_{kind}"
|
||||
with _cache_lock:
|
||||
hit = _cache.get(cache_key)
|
||||
if hit and now - hit[0] < _CACHE_TTL:
|
||||
return hit[1]
|
||||
|
||||
default_sys = _FAST_JSON_SCHEMA if kind == "fast" else _PRO_JSON_SCHEMA
|
||||
default_user = DEFAULT_FAST_USER if kind == "fast" else DEFAULT_PRO_USER
|
||||
|
||||
sys_prompt = default_sys
|
||||
usr_prompt = default_user
|
||||
try:
|
||||
tpl = _load_db_template()
|
||||
if tpl is not None:
|
||||
db_sys = (getattr(tpl, "system_prompt", "") or "").strip()
|
||||
if db_sys:
|
||||
sys_prompt = db_sys # DB prompt自带完整输出格式，不追加硬编码schema避免冲突
|
||||
usr_prompt = _render_user(tpl, default_user)
|
||||
logger.info(
|
||||
"[vision.v2] 使用DB image_analysis prompt (kind=%s version=%s sys_len=%d)",
|
||||
kind,
|
||||
getattr(tpl, "version", "?"),
|
||||
len(db_sys),
|
||||
)
|
||||
else:
|
||||
logger.debug("[vision.v2] DB image_analysis system_prompt为空，使用默认JSON (kind=%s)", kind)
|
||||
else:
|
||||
logger.debug("[vision.v2] DB无image_analysis记录/不可达，使用默认JSON prompt (kind=%s)", kind)
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 解析DB prompt异常，使用默认: %s", e)
|
||||
|
||||
with _cache_lock:
|
||||
_cache[cache_key] = (now, (sys_prompt, usr_prompt))
|
||||
return sys_prompt, usr_prompt
|
||||
|
||||
|
||||
def invalidate_cache() -> None:
|
||||
with _cache_lock:
|
||||
_cache.clear()
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,146 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 图片分析主路径：每图并行 OCR（火山MediaKit，未配置时自动跳过）+ qwen3.8-flash JSON VLM，
|
||||
失败时单次 qwen3.7-plus 兜底。
|
||||
|
||||
架构（灵应10-05确认）：
|
||||
- 唯一后端：阿里云百炼 DashScope，qwen3.8-flash 做快速路径、qwen3.7-plus 做兜底
|
||||
- 主力：单图2路并行（OCR + fast VLM），外层N图全并发（workers=8）
|
||||
- 兜底：单次 pro VLM 调用，无竞速/重试/复杂超时
|
||||
- 输出 dict 格式与旧版完全一致，下游零改动
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from typing import Any
|
||||
|
||||
from . import assembler, ocr_volc, vlm_fallback, vlm_fast_json
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 超时（可通过环境变量覆盖）
|
||||
_IMG_WORKERS = int(os.environ.get("VISION_V2_IMG_WORKERS", "8"))
|
||||
_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "20"))
|
||||
_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "20"))
|
||||
_OCR_TIMEOUT = float(os.environ.get("VISION_V2_OCR_TIMEOUT", "6"))
|
||||
_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "45"))
|
||||
|
||||
_FALLBACK_RESULT = {
|
||||
"name": "未识别",
|
||||
"brand": "无法判断",
|
||||
"category": "非产品图",
|
||||
"appearance": "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": [],
|
||||
"key_features": ["无法判断"],
|
||||
"scene": "通用",
|
||||
"mood": "",
|
||||
"portrait_prompt": "无法判断",
|
||||
"summary": "未识别",
|
||||
}
|
||||
|
||||
|
||||
def _is_usable(r: dict[str, Any]) -> bool:
|
||||
pp = (r.get("portrait_prompt") or "").strip()
|
||||
if pp and pp not in ("无人像", "无法判断", "未识别"):
|
||||
return True
|
||||
name = (r.get("name") or "").strip()
|
||||
if name and name not in ("未识别", "无法判断", "未知"):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
|
||||
t0 = time.time()
|
||||
|
||||
fj_result: dict[str, Any] | None = None
|
||||
ocr_result: list[str] = []
|
||||
fast_elapsed = 0.0
|
||||
pool = ThreadPoolExecutor(max_workers=2)
|
||||
f_fj = pool.submit(vlm_fast_json.call_fast_json, img_url, timeout=_FAST_JSON_TIMEOUT)
|
||||
f_ocr = pool.submit(ocr_volc.call_ocr, img_url, timeout=_OCR_TIMEOUT)
|
||||
try:
|
||||
for fut in as_completed([f_fj, f_ocr], timeout=_FAST_TIMEOUT):
|
||||
try:
|
||||
res = fut.result(timeout=1)
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 图片 #%d 子任务异常: %s", idx, e)
|
||||
continue
|
||||
if fut is f_fj and isinstance(res, dict):
|
||||
fj_result = res
|
||||
elif fut is f_ocr and isinstance(res, list):
|
||||
ocr_result = res
|
||||
except TimeoutError:
|
||||
for f in (f_fj, f_ocr):
|
||||
if not f.done():
|
||||
f.cancel()
|
||||
logger.warning("[vision.v2] 图片 #%d fast路径超时(%.0fs)，走pro兜底", idx, _FAST_TIMEOUT)
|
||||
finally:
|
||||
fast_elapsed = time.time() - t0
|
||||
pool.shutdown(wait=False) # 不等待未完成的线程，避免计时膨胀
|
||||
|
||||
if fj_result:
|
||||
assembled = assembler.assemble_result(idx, fj_result, ocr_result)
|
||||
if _is_usable(assembled):
|
||||
assembled["_fast_elapsed"] = round(fast_elapsed, 2)
|
||||
logger.info(
|
||||
"[vision.v2] 图片 #%d fast命中 elapsed=%.2fs pp=%s",
|
||||
idx,
|
||||
fast_elapsed,
|
||||
(assembled.get("portrait_prompt") or "")[:40],
|
||||
)
|
||||
return assembled
|
||||
|
||||
pro_t0 = time.time()
|
||||
pro_result = vlm_fallback.call_pro_vlm(img_url, idx, timeout=_PRO_TIMEOUT)
|
||||
if pro_result and _is_usable(pro_result):
|
||||
pro_result["_fallback_used"] = True
|
||||
pro_result["_fast_elapsed"] = round(fast_elapsed, 2)
|
||||
pro_result["_pro_elapsed"] = round(time.time() - pro_t0, 2)
|
||||
if ocr_result and not pro_result.get("text_on_package"):
|
||||
pro_result["text_on_package"] = ocr_result[:8]
|
||||
logger.info("[vision.v2] 图片 #%d pro兜底命中 total=%.2fs", idx, time.time() - t0)
|
||||
return pro_result
|
||||
|
||||
logger.warning("[vision.v2] 图片 #%d 全路径失败 elapsed=%.2fs", idx, time.time() - t0)
|
||||
out = dict(_FALLBACK_RESULT)
|
||||
out["_source"] = "v2_all_failed"
|
||||
out["text_on_package"] = ocr_result[:8]
|
||||
out["_fast_elapsed"] = round(fast_elapsed, 2)
|
||||
return out
|
||||
|
||||
|
||||
def analyze_images_v2(img_urls: list[str]) -> list[dict[str, Any]]:
|
||||
if not img_urls:
|
||||
return []
|
||||
workers = min(_IMG_WORKERS, len(img_urls), 16)
|
||||
results: list[dict[str, Any] | None] = [None] * len(img_urls)
|
||||
|
||||
logger.info(
|
||||
"[vision.v2] 开始图片分析 n=%d workers=%d fast_timeout=%.0fs pro_timeout=%.0fs",
|
||||
len(img_urls),
|
||||
workers,
|
||||
_FAST_TIMEOUT,
|
||||
_PRO_TIMEOUT,
|
||||
)
|
||||
t0 = time.time()
|
||||
with ThreadPoolExecutor(max_workers=workers) as pool:
|
||||
future_to_idx = {pool.submit(analyze_image_v2, idx, url): idx for idx, url in enumerate(img_urls)}
|
||||
for fut in as_completed(future_to_idx):
|
||||
idx = future_to_idx[fut]
|
||||
try:
|
||||
results[idx] = fut.result()
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 图片 #%d future异常: %s", idx, e, exc_info=True)
|
||||
r = dict(_FALLBACK_RESULT)
|
||||
r["_source"] = "v2_future_exception"
|
||||
results[idx] = r
|
||||
|
||||
elapsed = time.time() - t0
|
||||
succ = sum(1 for r in results if r and _is_usable(r))
|
||||
fb = sum(1 for r in results if r and r.get("_fallback_used"))
|
||||
logger.info("[vision.v2] 完成 n=%d usable=%d pro_fallback=%d elapsed=%.2fs", len(img_urls), succ, fb, elapsed)
|
||||
return [r for r in results if r is not None]
|
||||
@@ -1,138 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""VLM 返回文本的稳健 JSON 提取工具。
|
||||
|
||||
背景：复杂门店图 VLM 输出经常被 max_tokens 截断（finish_reason=length），
|
||||
json.loads 失败后整个结果被丢弃，导致"未识别"。本工具提供：
|
||||
1. markdown 代码块剥离（含只开不闭的截断场景）
|
||||
2. 最外层 { } 切片
|
||||
3. 非法控制字符清理
|
||||
4. 直接 json.loads
|
||||
5. 截断 JSON 括号/引号栈补全修复
|
||||
6. 尾部逐字符截断重试（去除最后一个不完整 token 后修复）
|
||||
|
||||
成功返回 dict；截断修复产物带 _partial=True 标记；彻底失败返回 None。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_CODE_FENCE_RE = re.compile(r"^```(?:json)?\s*\n?(.*?)\n?```\s*$", re.DOTALL)
|
||||
|
||||
|
||||
def _strip_code_fence(s: str) -> str:
|
||||
s = s.strip()
|
||||
m = _CODE_FENCE_RE.match(s)
|
||||
if m:
|
||||
return m.group(1).strip()
|
||||
# 兼容开头 ```json 但结尾无 ```（截断场景）
|
||||
if s.startswith("```"):
|
||||
lines = s.split("\n")
|
||||
if lines and lines[0].startswith("```"):
|
||||
lines = lines[1:]
|
||||
s = "\n".join(lines).strip()
|
||||
return s
|
||||
|
||||
|
||||
def _repair_truncated_json(text: str) -> str:
|
||||
"""尝试补全被截断的JSON：维护 bracket/quote 栈，在末尾补闭合符。"""
|
||||
stack: list[str] = []
|
||||
in_string = False
|
||||
escape = False
|
||||
for ch in text:
|
||||
if escape:
|
||||
escape = False
|
||||
continue
|
||||
if ch == "\\" and in_string:
|
||||
escape = True
|
||||
continue
|
||||
if ch == '"':
|
||||
in_string = not in_string
|
||||
continue
|
||||
if in_string:
|
||||
continue
|
||||
if ch in "{[":
|
||||
stack.append(ch)
|
||||
elif ch == "}":
|
||||
if stack and stack[-1] == "{":
|
||||
stack.pop()
|
||||
elif ch == "]":
|
||||
if stack and stack[-1] == "[":
|
||||
stack.pop()
|
||||
repair = ""
|
||||
if in_string:
|
||||
repair += '"'
|
||||
for opener in reversed(stack):
|
||||
repair += "}" if opener == "{" else "]"
|
||||
if repair:
|
||||
logger.info(
|
||||
"[json_utils] 截断JSON修复: 补全%d个闭合符 in_string=%s",
|
||||
len(repair),
|
||||
in_string,
|
||||
)
|
||||
return text + repair
|
||||
|
||||
|
||||
def _clean_invalid_chars(text: str) -> str:
|
||||
"""清理JSON中非法的控制字符（tab/newline 之外的 0x00-0x1f 段）。"""
|
||||
return re.sub(r"[\x00-\x08\x0b\x0c\x0e-\x1f]", "", text)
|
||||
|
||||
|
||||
def extract_json_object(text: str) -> dict | None:
|
||||
"""从VLM返回文本中稳健提取JSON对象。
|
||||
|
||||
返回 dict 或 None。成功的 dict 可能带 _partial=True 标记，
|
||||
表示原始文本被截断、经括号补全后得到的产物。
|
||||
"""
|
||||
if not text or not isinstance(text, str):
|
||||
return None
|
||||
# 1. 剥离 markdown
|
||||
text = _strip_code_fence(text)
|
||||
# 2. 找最外层 { }
|
||||
lpos = text.find("{")
|
||||
if lpos < 0:
|
||||
return None
|
||||
rpos = text.rfind("}")
|
||||
if rpos > lpos:
|
||||
text = text[lpos : rpos + 1]
|
||||
else:
|
||||
# 截断场景：无任何闭合 }，取到末尾交给修复器
|
||||
text = text[lpos:]
|
||||
# 3. 清理非法控制字符
|
||||
text = _clean_invalid_chars(text)
|
||||
# 4. 直接 loads
|
||||
try:
|
||||
obj = json.loads(text)
|
||||
return obj if isinstance(obj, dict) else None
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
# 5. 尝试截断修复
|
||||
repaired = _repair_truncated_json(text)
|
||||
try:
|
||||
obj = json.loads(repaired)
|
||||
if isinstance(obj, dict):
|
||||
obj["_partial"] = True
|
||||
return obj
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
# 6. 尾部逐字符截断重试（去除最后一个不完整 token）
|
||||
for _ in range(50):
|
||||
last_comma = repaired.rfind(",")
|
||||
last_brace = max(repaired.rfind("}"), repaired.rfind("]"))
|
||||
cut = max(last_comma, last_brace)
|
||||
if cut < 10:
|
||||
break
|
||||
repaired = repaired[: cut + 1]
|
||||
repaired = _repair_truncated_json(repaired)
|
||||
try:
|
||||
obj = json.loads(repaired)
|
||||
if isinstance(obj, dict):
|
||||
obj["_partial"] = True
|
||||
return obj
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
return None
|
||||
@@ -1,109 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""火山引擎 AI MediaKit OCR（同步）调用封装。
|
||||
|
||||
接口：POST {mediakit_base_url}/tools-sync/ocr
|
||||
鉴权：Bearer {mediakit_api_key}
|
||||
请求体：{"image_url": "<公网可访问URL>"} （部分版本也支持 image_base64）
|
||||
响应：{"code":0,"data":{"texts":[{"text":"...","bbox":[x,y,w,h],...},...],...}}
|
||||
|
||||
目标：识别商品包装/Logo/水印上的文字，作为 fast_json VLM 的补充。
|
||||
返回值：识别到的文本字符串列表（失败返回 []）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
DEFAULT_TIMEOUT = 8 # OCR 秒级返回，8s 绰绰有余
|
||||
|
||||
|
||||
def call_ocr(img_url: str, *, timeout: int = DEFAULT_TIMEOUT) -> list[str]:
|
||||
"""调用 MediaKit 同步 OCR，返回去重后的纯文本列表。
|
||||
|
||||
不做重试（外层降级逻辑负责）。失败/未配置返回空列表，不抛异常。
|
||||
"""
|
||||
t0 = time.time()
|
||||
try:
|
||||
import httpx
|
||||
|
||||
from packages.shared.mediakit_client import get_mediakit_client
|
||||
|
||||
client = get_mediakit_client()
|
||||
if not client.is_available:
|
||||
logger.info("[vision.v2] mediakit 未配置，跳过 OCR")
|
||||
return []
|
||||
|
||||
url = f"{client.base_url}/tools-sync/ocr"
|
||||
headers = {
|
||||
"Authorization": f"Bearer {client.api_key}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
payload: dict[str, Any] = {"image_url": img_url}
|
||||
# 部分文档版本用 image_base64，但公网 URL 场景下 image_url 最简
|
||||
resp = httpx.post(url, headers=headers, json=payload, timeout=timeout)
|
||||
elapsed = time.time() - t0
|
||||
if resp.status_code != 200:
|
||||
logger.warning(
|
||||
"[vision.v2] OCR HTTP %d elapsed=%.1fs body=%s",
|
||||
resp.status_code,
|
||||
elapsed,
|
||||
resp.text[:200],
|
||||
)
|
||||
return []
|
||||
data = resp.json()
|
||||
# 兼容几种可能的响应结构
|
||||
code = data.get("code", data.get("status", 0))
|
||||
if code not in (0, "OK", "success", 200):
|
||||
logger.warning("[vision.v2] OCR 业务错误 code=%s elapsed=%.1fs resp=%s", code, elapsed, str(data)[:200])
|
||||
return []
|
||||
texts = _extract_texts(data)
|
||||
# 去重 + 过滤空
|
||||
seen: set[str] = set()
|
||||
out: list[str] = []
|
||||
for t in texts:
|
||||
t = (t or "").strip()
|
||||
if t and t not in seen and len(t) <= 100: # 过滤过长的误识别
|
||||
seen.add(t)
|
||||
out.append(t)
|
||||
logger.info("[vision.v2] OCR 完成 elapsed=%.1fs n=%d texts=%s", elapsed, len(out), out[:5])
|
||||
return out
|
||||
except Exception as e:
|
||||
elapsed = time.time() - t0
|
||||
logger.warning("[vision.v2] OCR 异常 elapsed=%.1fs err=%s", elapsed, e, exc_info=True)
|
||||
return []
|
||||
|
||||
|
||||
def _extract_texts(data: dict) -> list[str]:
|
||||
"""从 OCR 响应中抽取文本，兼容多种结构。"""
|
||||
out: list[str] = []
|
||||
# 常见结构1: data.texts = [{"text": "..."}, ...]
|
||||
d = data.get("data") or data
|
||||
if isinstance(d, dict):
|
||||
for key in ("texts", "lines", "words", "items", "result"):
|
||||
items = d.get(key)
|
||||
if isinstance(items, list):
|
||||
for it in items:
|
||||
if isinstance(it, dict):
|
||||
txt = it.get("text") or it.get("content") or it.get("word")
|
||||
if txt:
|
||||
out.append(str(txt))
|
||||
elif isinstance(it, str):
|
||||
out.append(it)
|
||||
break
|
||||
# 结构2: data.text = "..."
|
||||
if not out:
|
||||
t = d.get("text")
|
||||
if isinstance(t, str):
|
||||
out.append(t)
|
||||
# 结构3: data.ocr_text / data.content
|
||||
if not out:
|
||||
for key in ("ocr_text", "content", "raw_text"):
|
||||
v = d.get(key)
|
||||
if isinstance(v, str) and v.strip():
|
||||
out.append(v)
|
||||
break
|
||||
return out
|
||||
@@ -1,116 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 兜底路径：image_analysis（默认 qwen-vl-plus 视觉模型，fallback qwen3.7-plus / DashScope）单图调用。
|
||||
|
||||
fast_json 超时/返回非 JSON/识别为空时，本路径单次调用兜底。
|
||||
设计要点：
|
||||
- 通过 ai_router.get_vision_client() 获取 DoubaoClient 实例，不再自己拼 httpx 请求
|
||||
- enable_thinking=False + response_format=json_object
|
||||
- system prompt 优先读后台 viral_video_prompt_templates 配置，DB不可用时fallback到硬编码JSON schema
|
||||
- max_tokens 不传，使用 client 中 capability 的 DB 配置（避免硬编码截断 JSON）
|
||||
- timeout=30s
|
||||
- 返回 dict 统一走 assembler.assemble_result 组装，与 fast 路径输出格式完全一致
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
from . import _prompt, assembler
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DEFAULT_TIMEOUT = 45
|
||||
|
||||
|
||||
def call_pro_vlm(
|
||||
img_url: str,
|
||||
idx: int,
|
||||
*,
|
||||
timeout: int = _DEFAULT_TIMEOUT,
|
||||
max_tokens: int | None = None,
|
||||
) -> dict[str, Any] | None:
|
||||
"""max_tokens 默认 None：不显式传参，使用 client 内 capability 的 DB 配置。"""
|
||||
t0 = time.time()
|
||||
|
||||
try:
|
||||
from packages.shared.ai_router import ai_router
|
||||
|
||||
client = ai_router.get_vision_client("image_analysis", variant="fallback")
|
||||
if not client or not client.is_available:
|
||||
logger.warning("[vision.v2] pro vision client 不可用，跳过")
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] ai_router 获取失败: %s", e)
|
||||
return None
|
||||
|
||||
system_prompt, user_prompt = _prompt.resolve_pro_prompt()
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": user_prompt},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
try:
|
||||
call_kwargs: dict[str, Any] = {
|
||||
"messages": messages,
|
||||
"images": None, # 图片已在 messages 中
|
||||
"temperature": 0.3,
|
||||
"timeout": timeout,
|
||||
"enable_thinking": False,
|
||||
"response_format": {"type": "json_object"},
|
||||
}
|
||||
# pro fallback：显式4000 tokens给复杂门店图留足空间
|
||||
call_kwargs["max_tokens"] = max_tokens if max_tokens is not None else 4000
|
||||
|
||||
from .json_utils import extract_json_object
|
||||
|
||||
raw = None
|
||||
obj = None
|
||||
for _outer in range(2):
|
||||
kw = dict(call_kwargs)
|
||||
if _outer == 1:
|
||||
kw.pop("response_format", None)
|
||||
msgs2 = [dict(messages[0]), dict(messages[1])]
|
||||
cont = [dict(c) for c in list(msgs2[1]["content"])]
|
||||
cont[-1] = {"type": "text", "text": user_prompt + "\n严格只输出JSON对象，不要解释或markdown。"}
|
||||
msgs2[1] = {"role": "user", "content": cont}
|
||||
kw["messages"] = msgs2
|
||||
raw = client.vision_completion(**kw)
|
||||
if not raw:
|
||||
logger.warning("[vision.v2] pro 返回空 outer=%s", _outer)
|
||||
continue
|
||||
obj = extract_json_object(raw)
|
||||
if obj is not None:
|
||||
break
|
||||
logger.warning("[vision.v2] pro 非JSON(100字) outer=%s: %s", _outer, raw[:100])
|
||||
|
||||
elapsed = time.time() - t0
|
||||
if obj is None:
|
||||
logger.warning("[vision.v2] pro 两次均未得到JSON elapsed=%.1fs", elapsed)
|
||||
return None
|
||||
if obj.get("_partial"):
|
||||
logger.warning("[vision.v2] pro 返回截断JSON(partial) elapsed=%.1fs", elapsed)
|
||||
logger.info(
|
||||
"[vision.v2] pro 完成 model=%s elapsed=%.1fs type=%s",
|
||||
client.model,
|
||||
elapsed,
|
||||
obj.get("type"),
|
||||
)
|
||||
|
||||
# 通过assembler统一组装，兼容v4嵌套schema和旧扁平schema
|
||||
result = assembler.assemble_result(idx, obj, [])
|
||||
result["_source"] = "vlm_pro"
|
||||
result["_fallback_used"] = True
|
||||
return result
|
||||
except Exception as e:
|
||||
elapsed = time.time() - t0
|
||||
logger.warning("[vision.v2] pro 异常 elapsed=%.1fs err=%s", elapsed, e, exc_info=True)
|
||||
return None
|
||||
@@ -1,123 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 快速路径：image_analysis capability（默认 qwen-vl-plus 视觉模型 / DashScope）强约束 JSON-only 调用。
|
||||
|
||||
目标：替代"人体属性/商品检测/图像标签"三个火山不存在的专用云端 API。
|
||||
设计要点：
|
||||
- 通过 ai_router.get_vision_client() 获取 DoubaoClient 实例，不再自己拼 httpx 请求
|
||||
- enable_thinking=False 关闭推理链（reasoning 是延迟主因）
|
||||
- response_format=json_object 强约束JSON输出
|
||||
- system prompt 优先读后台 viral_video_prompt_templates 配置，DB不可用时fallback到硬编码JSON schema
|
||||
- max_tokens 不传，使用 client 中 capability 的 DB 配置（避免硬编码截断 JSON）
|
||||
- temperature=0.1（稳定输出 JSON）
|
||||
- timeout=15s（失败由外层走 pro 兜底）
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
from . import _prompt
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DEFAULT_TIMEOUT = 20
|
||||
|
||||
|
||||
def call_fast_json(
|
||||
img_url: str,
|
||||
*,
|
||||
timeout: int = _DEFAULT_TIMEOUT,
|
||||
max_tokens: int | None = None,
|
||||
) -> dict[str, Any] | None:
|
||||
"""调用 vision client 返回结构化 dict；失败/非 JSON 返回 None。
|
||||
|
||||
max_tokens 默认 None：不显式传参，使用 client 内 capability 的 DB 配置；
|
||||
显式传入时作为覆盖。
|
||||
"""
|
||||
t0 = time.time()
|
||||
|
||||
try:
|
||||
from packages.shared.ai_router import ai_router
|
||||
|
||||
client = ai_router.get_vision_client("image_analysis", variant="primary")
|
||||
if not client or not client.is_available:
|
||||
logger.warning("[vision.v2] vision client 不可用，跳过 fast_json")
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] ai_router 获取失败: %s", e)
|
||||
return None
|
||||
|
||||
system_prompt, user_prompt = _prompt.resolve_fast_prompt()
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": user_prompt},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
try:
|
||||
call_kwargs: dict[str, Any] = {
|
||||
"messages": messages,
|
||||
"images": None, # 图片已在 messages 中
|
||||
"temperature": 0.1,
|
||||
"timeout": timeout,
|
||||
"enable_thinking": False,
|
||||
"response_format": {"type": "json_object"},
|
||||
}
|
||||
if max_tokens is not None:
|
||||
call_kwargs["max_tokens"] = max_tokens
|
||||
|
||||
# 双重防护：第1次正常调用；第2次去掉json_object强约束（部分模型在该约束下
|
||||
# 反而幻觉），并加严格指令。解析全部走 json_utils，截断partial产物可用。
|
||||
from .json_utils import extract_json_object
|
||||
|
||||
raw = None
|
||||
obj = None
|
||||
for _outer in range(2):
|
||||
kw = dict(call_kwargs)
|
||||
if _outer == 1:
|
||||
kw.pop("response_format", None)
|
||||
msgs2 = [dict(messages[0]), dict(messages[1])]
|
||||
cont = list(msgs2[1]["content"])
|
||||
cont = [dict(c) for c in cont]
|
||||
cont[-1] = {"type": "text", "text": user_prompt + "\n严格只输出JSON对象，不要解释或markdown。"}
|
||||
msgs2[1] = {"role": "user", "content": cont}
|
||||
kw["messages"] = msgs2
|
||||
raw = client.vision_completion(**kw)
|
||||
if not raw:
|
||||
logger.warning("[vision.v2] fast_json 返回空 outer=%s", _outer)
|
||||
continue
|
||||
obj = extract_json_object(raw)
|
||||
if obj is not None:
|
||||
break
|
||||
logger.warning(
|
||||
"[vision.v2] fast_json 非JSON(100字) outer=%s: %s",
|
||||
_outer,
|
||||
raw[:100],
|
||||
)
|
||||
|
||||
elapsed = time.time() - t0
|
||||
if obj is None:
|
||||
logger.warning("[vision.v2] fast_json 两次均未得到JSON elapsed=%.1fs", elapsed)
|
||||
return None
|
||||
if obj.get("_partial"):
|
||||
logger.warning("[vision.v2] fast_json 返回截断JSON(partial) elapsed=%.1fs", elapsed)
|
||||
logger.info(
|
||||
"[vision.v2] fast_json 完成 model=%s elapsed=%.1fs has_person=%s type=%s",
|
||||
client.model,
|
||||
elapsed,
|
||||
obj.get("has_person"),
|
||||
obj.get("type"),
|
||||
)
|
||||
return obj
|
||||
except Exception as e:
|
||||
elapsed = time.time() - t0
|
||||
logger.warning("[vision.v2] fast_json 异常 elapsed=%.1fs err=%s", elapsed, e, exc_info=True)
|
||||
return None
|
||||
@@ -241,35 +241,19 @@ DOUBAO_API_KEY=${DOUBAO_API_KEY}
|
||||
|
||||
# 模型 Endpoint ID（在 ARK 控制台创建推理接入点后获得）
|
||||
DOUBAO_MODEL=${DOUBAO_MODEL}
|
||||
DOUBAO_FAST_MODEL=${DOUBAO_FAST_MODEL}
|
||||
|
||||
# API Base URL
|
||||
DOUBAO_BASE_URL=${DOUBAO_BASE_URL}
|
||||
|
||||
# 请求超时（秒）
|
||||
DOUBAO_TIMEOUT=${DOUBAO_TIMEOUT}
|
||||
DOUBAO_TIMEOUT=60
|
||||
|
||||
# 最大重试次数
|
||||
DOUBAO_MAX_RETRIES=${DOUBAO_MAX_RETRIES}
|
||||
DOUBAO_MAX_RETRIES=2
|
||||
|
||||
# 视觉模型（支持图片/视频理解的模型，model name 格式）
|
||||
# 视觉模型 Endpoint ID（支持图片/视频理解的模型）
|
||||
DOUBAO_VISION_MODEL=${DOUBAO_VISION_MODEL}
|
||||
|
||||
# 快速视觉模型（viral-video 图片分析 lite 路径）
|
||||
DOUBAO_VISION_LITE_MODEL=${DOUBAO_VISION_LITE_MODEL}
|
||||
|
||||
# 是否启用 lite 视觉路径（true/false）
|
||||
DOUBAO_VISION_USE_LITE=${DOUBAO_VISION_USE_LITE}
|
||||
|
||||
# 信任链文生图模型（Seedream）
|
||||
DOUBAO_IMAGE_MODEL=${DOUBAO_IMAGE_MODEL}
|
||||
|
||||
# 文生图尺寸
|
||||
DOUBAO_IMAGE_SIZE=${DOUBAO_IMAGE_SIZE}
|
||||
|
||||
# 文生图超时（秒）
|
||||
DOUBAO_IMAGE_TIMEOUT=${DOUBAO_IMAGE_TIMEOUT}
|
||||
|
||||
|
||||
# ==================== 微信开放平台 OAuth（网页扫码登录）====================
|
||||
# 回调域名：xiaoxiajianji.com（微信开放平台已配置）
|
||||
|
||||
@@ -56,7 +56,7 @@ class UserModel(Base):
|
||||
is_member = Column(Boolean, nullable=False, default=False)
|
||||
member_type = Column(String(20), nullable=True)
|
||||
member_expires_at = Column(DateTime, nullable=True)
|
||||
points_balance = Column(Float, nullable=False, default=0)
|
||||
points_balance = Column(Integer, nullable=False, default=0)
|
||||
created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
|
||||
|
||||
|
||||
@@ -335,10 +335,6 @@ class GenerationTaskModel(Base):
|
||||
bgm_config = Column(JSON, nullable=False, default=dict)
|
||||
extra_meta = Column("metadata", JSON, nullable=False, default=dict)
|
||||
logs = Column(Text, nullable=False, default="[]", server_default="[]")
|
||||
# 功能计费（smart_edit）：预扣积分 / 最终积分 / 预扣流水 ID
|
||||
credits_prepaid = Column(Float, nullable=False, default=0.0, server_default="0")
|
||||
credits_cost = Column(Float, nullable=False, default=0.0, server_default="0")
|
||||
credits_transaction_id = Column(String(36), nullable=False, default="", server_default="")
|
||||
created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
|
||||
updated_at = Column(
|
||||
DateTime,
|
||||
@@ -731,11 +727,6 @@ class LipsyncJobModel(Base):
|
||||
# 精确句子时间戳（TTS 合成后由 silencedetect 计算，用于 B-roll 精确定位）
|
||||
sentence_timings = Column(JSON, nullable=True) # list[{index,text,start_time,end_time}]
|
||||
|
||||
# 功能计费（lip_sync）：预扣积分 / 最终积分 / 预扣流水 ID
|
||||
credits_prepaid = Column(Float, nullable=False, default=0.0, server_default="0")
|
||||
credits_cost = Column(Float, nullable=False, default=0.0, server_default="0")
|
||||
credits_transaction_id = Column(String(36), nullable=False, default="", server_default="")
|
||||
|
||||
# 时间戳
|
||||
submitted_at = Column(DateTime, nullable=True)
|
||||
completed_at = Column(DateTime, nullable=True)
|
||||
@@ -784,9 +775,9 @@ class PointsAccountModel(Base):
|
||||
|
||||
id = Column(String(36), primary_key=True)
|
||||
user_id = Column(String(36), nullable=False, unique=True, index=True)
|
||||
balance = Column(Float, nullable=False, default=0)
|
||||
total_earned = Column(Float, nullable=False, default=0)
|
||||
total_spent = Column(Float, nullable=False, default=0)
|
||||
balance = Column(Integer, nullable=False, default=0)
|
||||
total_earned = Column(Integer, nullable=False, default=0)
|
||||
total_spent = Column(Integer, nullable=False, default=0)
|
||||
created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
|
||||
updated_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
|
||||
|
||||
@@ -801,8 +792,8 @@ class PointsTransactionModel(Base):
|
||||
account_id = Column(String(36), nullable=False, index=True)
|
||||
type = Column(String(20), nullable=False, index=True) # earn / spend / refund
|
||||
source = Column(String(50), nullable=False, index=True)
|
||||
amount = Column(Float, nullable=False)
|
||||
balance_after = Column(Float, nullable=False)
|
||||
amount = Column(Integer, nullable=False)
|
||||
balance_after = Column(Integer, nullable=False)
|
||||
description = Column(String(255), nullable=False, default="")
|
||||
ref_id = Column(String(100), nullable=False, default="")
|
||||
created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
|
||||
@@ -914,11 +905,6 @@ class GpuLipsyncTaskModel(Base):
|
||||
# 心跳：worker 最近一次 poll/result 的时间，用于判定 worker 失联
|
||||
last_heartbeat_at = Column(DateTime, nullable=True)
|
||||
|
||||
# 功能计费（lip_sync）：预扣积分 / 最终积分 / 预扣流水 ID
|
||||
credits_prepaid = Column(Float, nullable=False, default=0.0, server_default="0")
|
||||
credits_cost = Column(Float, nullable=False, default=0.0, server_default="0")
|
||||
credits_transaction_id = Column(String(36), nullable=False, default="", server_default="")
|
||||
|
||||
|
||||
class GpuWorkerModel(Base):
|
||||
"""GPU Worker 注册表 — 反向轮询模式下用于心跳与监控."""
|
||||
@@ -942,7 +928,6 @@ class ViralVideoJobModel(Base):
|
||||
id = Column(String(36), primary_key=True)
|
||||
user_id = Column(String(36), nullable=False, index=True)
|
||||
images = Column(JSON, nullable=False, default=list) # 产品图片 URL 列表
|
||||
pre_trusted_images = Column(JSON, nullable=True) # #2172 信任链预热结果（Seedream AI 化 URL 列表）
|
||||
industry = Column(String(100), nullable=False, default="")
|
||||
target_customer = Column(String(500), nullable=False, default="")
|
||||
persona_id = Column(String(36), nullable=False, default="")
|
||||
@@ -965,9 +950,6 @@ class ViralVideoJobModel(Base):
|
||||
video_model = Column(String(100), nullable=False, default="")
|
||||
# 结果与状态
|
||||
status = Column(String(30), nullable=False, default="pending", index=True)
|
||||
current_stage = Column(String(200), nullable=False, default="") # 细粒度阶段 snake_case
|
||||
phase_message = Column(String(500), nullable=False, default="") # 阶段中文提示文案
|
||||
heartbeat_at = Column(DateTime, nullable=True, index=True) # worker 心跳，用于僵尸任务超时回收
|
||||
intent_result = Column(JSON, nullable=True)
|
||||
image_analysis = Column(JSON, nullable=True)
|
||||
storyboard = Column(JSON, nullable=True)
|
||||
@@ -976,10 +958,7 @@ class ViralVideoJobModel(Base):
|
||||
JSON, nullable=True
|
||||
) # v1.6: 编导脚本结构{overview,scene_and_lighting,shots,hard_constraints,negative_prompts,voiceover_script}
|
||||
result_video_url = Column(String(1000), nullable=False, default="")
|
||||
credits_cost = Column(Float, nullable=False, default=0)
|
||||
video_resolution = Column(String(20), nullable=False, default="720p")
|
||||
credits_prepaid = Column(Float, nullable=False, default=0.0)
|
||||
credits_transaction_id = Column(String(36), nullable=False, default="")
|
||||
credits_cost = Column(Integer, nullable=False, default=0)
|
||||
error_msg = Column(Text, nullable=False, default="")
|
||||
retry_count = Column(Integer, nullable=False, default=0)
|
||||
started_at = Column(DateTime(timezone=True), nullable=True)
|
||||
@@ -1005,17 +984,16 @@ class ViralVideoStyleTemplateModel(Base):
|
||||
|
||||
|
||||
class ViralVideoPromptTemplateModel(Base):
|
||||
"""爆款视频 Prompt 模板表（#2040：纯文本 XML 标签模板，运营可直接编辑）"""
|
||||
"""爆款视频 Prompt 模板表（由 #2040 seed）"""
|
||||
|
||||
__tablename__ = "viral_video_prompt_templates"
|
||||
|
||||
id = Column(Integer, primary_key=True, autoincrement=True)
|
||||
name = Column(String(128), nullable=False)
|
||||
prompt_type = Column(String(32), nullable=False)
|
||||
id = Column(String(36), primary_key=True)
|
||||
prompt_type = Column(String(50), nullable=False, index=True)
|
||||
name = Column(String(200), nullable=False)
|
||||
content = Column(Text, nullable=False, default="")
|
||||
variables = Column(JSON, nullable=False, default=list)
|
||||
version = Column(Integer, nullable=False, default=1)
|
||||
system_prompt = Column(Text, nullable=False)
|
||||
user_prompt_template = Column(Text, nullable=False)
|
||||
example_output = Column(Text, nullable=True)
|
||||
is_active = Column(Boolean, nullable=False, default=True)
|
||||
is_active = Column(Boolean, nullable=False, default=True, index=True)
|
||||
created_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
|
||||
updated_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
|
||||
|
||||
@@ -39,9 +39,6 @@ class SQLAlchemyUserRepository(UserRepository):
|
||||
model.phone_verified = user.phone_verified
|
||||
model.binding_completed_at = user.binding_completed_at
|
||||
model.profile_completed = user.profile_completed
|
||||
model.is_member = user.is_member
|
||||
model.member_type = user.member_type
|
||||
model.member_expires_at = user.member_expires_at
|
||||
model.created_at = user.created_at
|
||||
|
||||
self.session.commit()
|
||||
@@ -118,8 +115,5 @@ class SQLAlchemyUserRepository(UserRepository):
|
||||
phone_verified=model.phone_verified or False,
|
||||
binding_completed_at=model.binding_completed_at,
|
||||
profile_completed=model.profile_completed if model.profile_completed is not None else True,
|
||||
is_member=model.is_member if model.is_member is not None else False,
|
||||
member_type=model.member_type,
|
||||
member_expires_at=model.member_expires_at,
|
||||
created_at=model.created_at,
|
||||
)
|
||||
|
||||
@@ -6,33 +6,18 @@ from sqlalchemy.orm import Session
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.models import (
|
||||
ViralVideoJobModel,
|
||||
ViralVideoPromptTemplateModel,
|
||||
ViralVideoStyleTemplateModel,
|
||||
)
|
||||
from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
|
||||
|
||||
|
||||
def _to_domain(model: ViralVideoJobModel) -> ViralVideoJob:
|
||||
"""ORM → 领域实体。pre_trusted_images 兼容脏数据：双序列化字符串/字符数组/list[str]。"""
|
||||
import json as _pti_json
|
||||
|
||||
_raw_pti = getattr(model, "pre_trusted_images", None)
|
||||
_pti: list[str] | None = None
|
||||
if _raw_pti is not None:
|
||||
if isinstance(_raw_pti, str):
|
||||
try:
|
||||
_p = _pti_json.loads(_raw_pti)
|
||||
if isinstance(_p, list):
|
||||
_pti = [u for u in _p if isinstance(u, str) and u] or None
|
||||
except Exception:
|
||||
_pti = None
|
||||
elif isinstance(_raw_pti, list):
|
||||
_f = [u for u in _raw_pti if isinstance(u, str) and len(u) > 5]
|
||||
_pti = _f if _f else None
|
||||
"""ORM → 领域实体。"""
|
||||
return ViralVideoJob(
|
||||
id=model.id,
|
||||
user_id=model.user_id,
|
||||
images=list(model.images or []),
|
||||
pre_trusted_images=_pti,
|
||||
industry=model.industry or "",
|
||||
target_customer=model.target_customer or "",
|
||||
persona_id=model.persona_id or "",
|
||||
@@ -52,19 +37,13 @@ def _to_domain(model: ViralVideoJobModel) -> ViralVideoJob:
|
||||
video_ratio=getattr(model, "video_ratio", "9:16") or "9:16",
|
||||
video_model=getattr(model, "video_model", "") or "",
|
||||
status=ViralVideoStatus(model.status) if model.status else ViralVideoStatus.PENDING,
|
||||
current_stage=getattr(model, "current_stage", "") or "",
|
||||
phase_message=getattr(model, "phase_message", "") or "",
|
||||
heartbeat_at=getattr(model, "heartbeat_at", None),
|
||||
intent_result=dict(model.intent_result) if model.intent_result else None,
|
||||
image_analysis=dict(model.image_analysis) if getattr(model, "image_analysis", None) else None,
|
||||
storyboard=list(model.storyboard) if getattr(model, "storyboard", None) else None,
|
||||
generated_copy_text=getattr(model, "generated_copy_text", "") or "",
|
||||
copy_result=dict(model.copy_result) if getattr(model, "copy_result", None) else None,
|
||||
result_video_url=model.result_video_url or "",
|
||||
video_resolution=getattr(model, "video_resolution", "720p") or "720p",
|
||||
credits_prepaid=float(getattr(model, "credits_prepaid", 0) or 0),
|
||||
credits_transaction_id=getattr(model, "credits_transaction_id", "") or "",
|
||||
credits_cost=float(model.credits_cost or 0),
|
||||
credits_cost=model.credits_cost or 0,
|
||||
error_msg=model.error_msg or "",
|
||||
retry_count=model.retry_count or 0,
|
||||
started_at=model.started_at,
|
||||
@@ -85,7 +64,6 @@ class SQLAlchemyViralVideoJobRepository:
|
||||
id=job.id,
|
||||
user_id=job.user_id,
|
||||
images=job.images,
|
||||
pre_trusted_images=job.pre_trusted_images,
|
||||
industry=job.industry,
|
||||
target_customer=job.target_customer,
|
||||
persona_id=job.persona_id,
|
||||
@@ -105,19 +83,13 @@ class SQLAlchemyViralVideoJobRepository:
|
||||
video_ratio=job.video_ratio,
|
||||
video_model=job.video_model,
|
||||
status=job.status,
|
||||
current_stage=job.current_stage or "",
|
||||
phase_message=job.phase_message or "",
|
||||
heartbeat_at=job.heartbeat_at,
|
||||
intent_result=job.intent_result,
|
||||
image_analysis=job.image_analysis,
|
||||
storyboard=job.storyboard,
|
||||
generated_copy_text=job.generated_copy_text,
|
||||
copy_result=job.copy_result,
|
||||
result_video_url=job.result_video_url,
|
||||
video_resolution=getattr(job, "video_resolution", "720p") or "720p",
|
||||
credits_prepaid=float(getattr(job, "credits_prepaid", 0) or 0),
|
||||
credits_transaction_id=getattr(job, "credits_transaction_id", "") or "",
|
||||
credits_cost=float(getattr(job, "credits_cost", 0) or 0),
|
||||
credits_cost=job.credits_cost,
|
||||
error_msg=job.error_msg,
|
||||
retry_count=job.retry_count,
|
||||
started_at=job.started_at,
|
||||
@@ -134,20 +106,13 @@ class SQLAlchemyViralVideoJobRepository:
|
||||
if model is None:
|
||||
raise ValueError(f"ViralVideoJob {job.id} not found")
|
||||
model.status = job.status
|
||||
model.current_stage = job.current_stage or ""
|
||||
model.phase_message = job.phase_message or ""
|
||||
model.heartbeat_at = job.heartbeat_at
|
||||
model.intent_result = job.intent_result
|
||||
model.image_analysis = job.image_analysis
|
||||
model.storyboard = job.storyboard
|
||||
model.generated_copy_text = job.generated_copy_text or ""
|
||||
model.copy_result = job.copy_result
|
||||
model.pre_trusted_images = job.pre_trusted_images
|
||||
model.result_video_url = job.result_video_url
|
||||
model.video_resolution = getattr(job, "video_resolution", "720p") or "720p"
|
||||
model.credits_prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
|
||||
model.credits_transaction_id = getattr(job, "credits_transaction_id", "") or ""
|
||||
model.credits_cost = float(getattr(job, "credits_cost", 0) or 0)
|
||||
model.credits_cost = job.credits_cost
|
||||
model.error_msg = job.error_msg
|
||||
model.retry_count = job.retry_count
|
||||
model.started_at = job.started_at
|
||||
@@ -244,3 +209,31 @@ class SQLAlchemyViralVideoStyleTemplateRepository:
|
||||
"style_config": dict(model.style_config) if model.style_config else {},
|
||||
"is_system": model.is_system,
|
||||
}
|
||||
|
||||
|
||||
class SQLAlchemyViralVideoPromptTemplateRepository:
|
||||
"""Prompt 模板仓储（由 #2040 seed，这里只读取）。"""
|
||||
|
||||
def __init__(self, session: Session):
|
||||
self.session = session
|
||||
|
||||
def get_active_by_type(self, prompt_type: str) -> dict | None:
|
||||
model = (
|
||||
self.session.query(ViralVideoPromptTemplateModel)
|
||||
.filter(
|
||||
ViralVideoPromptTemplateModel.prompt_type == prompt_type,
|
||||
ViralVideoPromptTemplateModel.is_active.is_(True),
|
||||
)
|
||||
.order_by(ViralVideoPromptTemplateModel.version.desc())
|
||||
.first()
|
||||
)
|
||||
if model is None:
|
||||
return None
|
||||
return {
|
||||
"id": model.id,
|
||||
"prompt_type": model.prompt_type,
|
||||
"name": model.name,
|
||||
"content": model.content,
|
||||
"variables": list(model.variables or []),
|
||||
"version": model.version,
|
||||
}
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
"""应用层：对外展示目录（套餐/积分包）。"""
|
||||
@@ -1,152 +0,0 @@
|
||||
"""读取管理后台配置的会员套餐 / 积分充值包（共享库真实数据）。
|
||||
|
||||
替代旧的硬编码 MEMBERSHIP_PRICES / POINTS_PACKAGES。
|
||||
短 TTL 缓存（30 秒），后台改价/启停后用户端最多 30 秒可见。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import threading
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
_CACHE_TTL = 30.0
|
||||
_lock = threading.Lock()
|
||||
_cache: dict[str, tuple[float, Any]] = {}
|
||||
|
||||
_QUOTA_LABELS = {
|
||||
"4k": "4K 超清分辨率",
|
||||
"batch_render": "批量渲染",
|
||||
"priority_queue": "优先处理队列",
|
||||
"ai_matting": "AI 智能抠像",
|
||||
"remove_watermark": "去水印",
|
||||
}
|
||||
|
||||
|
||||
def _cached(key: str, loader):
|
||||
now = time.time()
|
||||
hit = _cache.get(key)
|
||||
if hit and now - hit[0] < _CACHE_TTL:
|
||||
return hit[1]
|
||||
with _lock:
|
||||
hit = _cache.get(key)
|
||||
if hit and time.time() - hit[0] < _CACHE_TTL:
|
||||
return hit[1]
|
||||
value = loader()
|
||||
_cache[key] = (time.time(), value)
|
||||
return value
|
||||
|
||||
|
||||
def _quota_features(quotas: dict[str, Any] | None) -> dict[str, Any]:
|
||||
quotas = quotas or {}
|
||||
features: dict[str, Any] = {}
|
||||
for k, v in quotas.items():
|
||||
if k == "credits_per_month":
|
||||
features["credits_per_month"] = v
|
||||
elif k in _QUOTA_LABELS:
|
||||
features[_QUOTA_LABELS[k]] = v
|
||||
else:
|
||||
features[k] = v
|
||||
return features
|
||||
|
||||
|
||||
def get_membership_plans() -> list[dict[str, Any]]:
|
||||
"""读取 is_enabled=true 的套餐，按年/月周期展开为用户端档位。"""
|
||||
|
||||
def _load() -> list[dict[str, Any]]:
|
||||
from sqlalchemy import text
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.session import SessionLocal
|
||||
|
||||
if SessionLocal is None:
|
||||
return []
|
||||
|
||||
session = SessionLocal()
|
||||
try:
|
||||
rows = session.execute(text("""
|
||||
SELECT plan_key, name, description, monthly_price, yearly_price,
|
||||
quotas, display_order
|
||||
FROM plans
|
||||
WHERE is_enabled = TRUE
|
||||
ORDER BY display_order NULLS LAST, created_at
|
||||
""")).fetchall()
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
plans: list[dict[str, Any]] = []
|
||||
for r in rows:
|
||||
base_features = _quota_features(r.quotas if isinstance(r.quotas, dict) else None)
|
||||
if r.yearly_price and float(r.yearly_price) > 0:
|
||||
plans.append(
|
||||
{
|
||||
"plan_id": r.plan_key,
|
||||
"billing_cycle": "yearly",
|
||||
"name": r.name,
|
||||
"description": r.description,
|
||||
"price_cents": int(round(float(r.yearly_price) * 100)),
|
||||
"monthly_price_cents": int(round(float(r.yearly_price) * 100 / 12)),
|
||||
"duration_days": 365,
|
||||
"features": dict(base_features),
|
||||
}
|
||||
)
|
||||
if r.monthly_price and float(r.monthly_price) > 0:
|
||||
plans.append(
|
||||
{
|
||||
"plan_id": r.plan_key,
|
||||
"billing_cycle": "monthly",
|
||||
"name": r.name,
|
||||
"description": r.description,
|
||||
"price_cents": int(round(float(r.monthly_price) * 100)),
|
||||
"monthly_price_cents": int(round(float(r.monthly_price) * 100)),
|
||||
"duration_days": 30,
|
||||
"features": dict(base_features),
|
||||
}
|
||||
)
|
||||
return plans
|
||||
|
||||
return _cached("membership_plans", _load)
|
||||
|
||||
|
||||
def get_points_packages() -> list[dict[str, Any]]:
|
||||
"""读取 is_active=true 的积分充值包。"""
|
||||
|
||||
def _load() -> list[dict[str, Any]]:
|
||||
from sqlalchemy import text
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.session import SessionLocal
|
||||
|
||||
if SessionLocal is None:
|
||||
return []
|
||||
|
||||
session = SessionLocal()
|
||||
try:
|
||||
rows = session.execute(text("""
|
||||
SELECT package_key, name, price, credits, bonus_credits,
|
||||
is_recommended, description, sort_order
|
||||
FROM credit_packages
|
||||
WHERE is_active = TRUE
|
||||
ORDER BY sort_order NULLS LAST, price
|
||||
""")).fetchall()
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
packages: list[dict[str, Any]] = []
|
||||
for r in rows:
|
||||
total_points = int(r.credits or 0) + int(r.bonus_credits or 0)
|
||||
price_cents = int(round(float(r.price) * 100))
|
||||
unit = (price_cents / 100 / total_points) if total_points else 0
|
||||
packages.append(
|
||||
{
|
||||
"code": r.package_key,
|
||||
"name": r.name,
|
||||
"points": total_points,
|
||||
"bonus_credits": int(r.bonus_credits or 0),
|
||||
"price_cents": price_cents,
|
||||
"unit_price": f"¥{unit:.3f}/积分",
|
||||
"is_recommended": bool(r.is_recommended),
|
||||
"description": r.description,
|
||||
}
|
||||
)
|
||||
return packages
|
||||
|
||||
return _cached("points_packages", _load)
|
||||
@@ -351,25 +351,12 @@ class CosyVoiceService:
|
||||
用于私有 bucket 下，将裸 URL 转为预签名 URL，
|
||||
确保 CosyVoice 服务器能下载参考音频.
|
||||
"""
|
||||
# 优先从 ai_router 获取 DB 配置
|
||||
_router_key, _router_url, _router_model = "", "", ""
|
||||
try:
|
||||
from packages.shared.ai_router import ai_router
|
||||
|
||||
tts_client = ai_router.get_tts_client("tts")
|
||||
if tts_client and tts_client.is_available:
|
||||
_router_key = tts_client.api_key
|
||||
_router_url = tts_client.base_url
|
||||
_router_model = tts_client.model
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
settings = get_shared_settings()
|
||||
|
||||
self._api_key = api_key or _router_key or settings.cosyvoice_api_key
|
||||
self._base_url = base_url or _router_url or settings.cosyvoice_base_url
|
||||
self._model = model or _router_model or settings.cosyvoice_model
|
||||
self._clone_model = clone_model or getattr(settings, "cosyvoice_clone_model", "")
|
||||
self._api_key = api_key or settings.cosyvoice_api_key
|
||||
self._base_url = base_url or settings.cosyvoice_base_url
|
||||
self._model = model or settings.cosyvoice_model
|
||||
self._clone_model = clone_model or getattr(settings, "cosyvoice_clone_model", "voice-enrollment")
|
||||
self._audio_url_signer = audio_url_signer
|
||||
|
||||
# base_url 规范化：去掉末尾的路径残留（兼容旧版配置）
|
||||
|
||||
@@ -1,269 +0,0 @@
|
||||
"""蚂蚁 Ditto 数字人口型 API 客户端 — #2076.
|
||||
|
||||
封装 Ditto FastAPI（部署在 5060Ti GPU 节点，Tailscale 内网可达）：
|
||||
- GET /health 健康检查
|
||||
- POST /generate 生成口型视频（同步返回 MP4 流）
|
||||
|
||||
关键特性：
|
||||
- 入参：video_url（人物模板视频 URL） + audio_url（TTS 音频 URL） + script（文案原文）
|
||||
- 出参：直接返回 video/mp4 字节流（自带音频，无需二次混流）
|
||||
- 429 时指数退避重试（最多 ditto_max_retries 次）
|
||||
- 500/超时视为失败
|
||||
- 输出 MP4 字节流转存到自家 OSS，返回公网 URL
|
||||
|
||||
注意：
|
||||
- 保留 MuseTalk/GPU 路径不变；本服务作为更高优先级的第三条口型路径
|
||||
- 不传 emotion/表情精细控制，使用默认 emo_global=4（中性）+ use_script_emo=true（关键词驱动表情）
|
||||
- Ditto 输出自带音视频，不需要 GFPGAN 超分，不需要 ffmpeg 音视频混流
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import io
|
||||
import logging
|
||||
import time
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
|
||||
import httpx
|
||||
|
||||
from packages.config import get_api_settings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DittoError(Exception):
|
||||
"""Ditto API 调用失败."""
|
||||
|
||||
def __init__(self, message: str, code: str = "DittoError", status_code: int = 0):
|
||||
self.code = code
|
||||
self.status_code = status_code
|
||||
super().__init__(message)
|
||||
|
||||
|
||||
@dataclass
|
||||
class DittoResult:
|
||||
"""Ditto 生成结果."""
|
||||
|
||||
video_bytes: bytes
|
||||
video_url: str = "" # 转存 OSS 后填充
|
||||
elapsed_seconds: float = 0.0
|
||||
rtf: float = 0.0 # 实时率（响应头 X-RTF）
|
||||
frames: int = 0 # 帧数（响应头 X-Frames）
|
||||
|
||||
|
||||
class DittoClient:
|
||||
"""蚂蚁 Ditto 数字人口型 API 客户端."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_url: Optional[str] = None,
|
||||
default_video_url: Optional[str] = None,
|
||||
max_retries: Optional[int] = None,
|
||||
timeout: Optional[int] = None,
|
||||
):
|
||||
s = get_api_settings()
|
||||
self.base_url = (base_url or s.ditto_api_base_url or "").rstrip("/")
|
||||
self.default_video_url = default_video_url or s.ditto_default_video_url or ""
|
||||
self.max_retries = int(max_retries if max_retries is not None else s.ditto_max_retries)
|
||||
self.timeout = int(timeout if timeout is not None else s.ditto_request_timeout)
|
||||
|
||||
@property
|
||||
def is_configured(self) -> bool:
|
||||
"""配置是否完整（base_url + 默认模板视频都有值）."""
|
||||
return bool(self.base_url) and bool(self.default_video_url)
|
||||
|
||||
def health(self) -> bool:
|
||||
"""健康检查；成功返回 True，失败返回 False（不抛异常）."""
|
||||
if not self.base_url:
|
||||
return False
|
||||
url = f"{self.base_url}/health"
|
||||
try:
|
||||
with httpx.Client(timeout=5.0) as client:
|
||||
resp = client.get(url)
|
||||
ok = resp.status_code == 200
|
||||
if ok:
|
||||
logger.info("[ditto] health check OK: %s", url)
|
||||
else:
|
||||
logger.warning("[ditto] health check status=%d: %s", resp.status_code, url)
|
||||
return ok
|
||||
except Exception as exc:
|
||||
logger.warning("[ditto] health check failed: %s", exc)
|
||||
return False
|
||||
|
||||
def generate(
|
||||
self,
|
||||
*,
|
||||
audio_url: str,
|
||||
script: str,
|
||||
video_url: Optional[str] = None,
|
||||
emo_global: int = 4,
|
||||
use_script_emo: bool = True,
|
||||
blend_frames: int = 6,
|
||||
) -> DittoResult:
|
||||
"""调用 Ditto /generate 接口，返回 MP4 字节流结果.
|
||||
|
||||
Raises DittoError on failure.
|
||||
"""
|
||||
if not self.base_url:
|
||||
raise DittoError("DITTO_API_BASE_URL 未配置", code="ConfigMissing")
|
||||
driver_url = video_url or self.default_video_url
|
||||
if not driver_url:
|
||||
raise DittoError("Ditto 人物模板视频 URL 未配置", code="ConfigMissing")
|
||||
if not audio_url:
|
||||
raise DittoError("audio_url 不能为空", code="InvalidParam")
|
||||
if not script:
|
||||
script = " "
|
||||
|
||||
payload = {
|
||||
"video_url": driver_url,
|
||||
"audio_url": audio_url,
|
||||
"script": script,
|
||||
"emo_global": emo_global,
|
||||
"use_script_emo": use_script_emo,
|
||||
"blend_frames": blend_frames,
|
||||
}
|
||||
url = f"{self.base_url}/generate"
|
||||
|
||||
last_exc: Optional[Exception] = None
|
||||
for attempt in range(self.max_retries + 1):
|
||||
try:
|
||||
start = time.monotonic()
|
||||
with httpx.Client(timeout=self.timeout, follow_redirects=True) as client:
|
||||
resp = client.post(url, json=payload)
|
||||
elapsed = time.monotonic() - start
|
||||
|
||||
if resp.status_code == 429:
|
||||
wait = min(2**attempt, 30)
|
||||
logger.warning(
|
||||
"[ditto] GPU 繁忙 (429)，%ds 后重试 (%d/%d)",
|
||||
wait,
|
||||
attempt + 1,
|
||||
self.max_retries,
|
||||
)
|
||||
if attempt >= self.max_retries:
|
||||
raise DittoError(
|
||||
f"Ditto GPU 繁忙，重试 {self.max_retries} 次仍失败",
|
||||
code="BusyRetriesExhausted",
|
||||
status_code=429,
|
||||
)
|
||||
time.sleep(wait)
|
||||
continue
|
||||
|
||||
if resp.status_code != 200:
|
||||
_text = (resp.text or "")[:300]
|
||||
logger.error(
|
||||
"[ditto] generate 失败 status=%d attempt=%d body=%s",
|
||||
resp.status_code,
|
||||
attempt + 1,
|
||||
_text,
|
||||
)
|
||||
if resp.status_code >= 500 and attempt < self.max_retries:
|
||||
time.sleep(min(2**attempt, 15))
|
||||
continue
|
||||
raise DittoError(
|
||||
f"Ditto 返回 {resp.status_code}: {_text}",
|
||||
code="DittoAPIError",
|
||||
status_code=resp.status_code,
|
||||
)
|
||||
|
||||
video_bytes = resp.content
|
||||
if not video_bytes or len(video_bytes) < 1024:
|
||||
raise DittoError(
|
||||
f"Ditto 返回内容异常（size={len(video_bytes) if video_bytes else 0}）",
|
||||
code="EmptyResponse",
|
||||
)
|
||||
try:
|
||||
rtf = float(resp.headers.get("X-RTF", "0") or 0)
|
||||
except ValueError:
|
||||
rtf = 0.0
|
||||
try:
|
||||
frames = int(resp.headers.get("X-Frames", "0") or 0)
|
||||
except ValueError:
|
||||
frames = 0
|
||||
try:
|
||||
x_time = float(resp.headers.get("X-Time", "0") or 0)
|
||||
if x_time > 0:
|
||||
elapsed = x_time
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
logger.info(
|
||||
"[ditto] generate 成功 size=%d rtf=%.2f frames=%d elapsed=%.1fs attempt=%d",
|
||||
len(video_bytes),
|
||||
rtf,
|
||||
frames,
|
||||
elapsed,
|
||||
attempt + 1,
|
||||
)
|
||||
return DittoResult(
|
||||
video_bytes=video_bytes,
|
||||
elapsed_seconds=elapsed,
|
||||
rtf=rtf,
|
||||
frames=frames,
|
||||
)
|
||||
|
||||
except DittoError:
|
||||
raise
|
||||
except httpx.TimeoutException as exc:
|
||||
last_exc = exc
|
||||
logger.warning("[ditto] 请求超时 attempt=%d err=%s", attempt + 1, exc)
|
||||
if attempt < self.max_retries:
|
||||
time.sleep(min(2**attempt, 15))
|
||||
continue
|
||||
raise DittoError(
|
||||
f"Ditto 请求超时（{self.timeout}s），重试耗尽",
|
||||
code="Timeout",
|
||||
) from exc
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
logger.warning("[ditto] 请求异常 attempt=%d err=%s", attempt + 1, exc)
|
||||
if attempt < self.max_retries:
|
||||
time.sleep(min(2**attempt, 10))
|
||||
continue
|
||||
raise DittoError(f"Ditto 调用异常: {exc}", code="NetworkError") from exc
|
||||
|
||||
raise DittoError("Ditto 未知错误", code="Unknown") from last_exc
|
||||
|
||||
def generate_and_persist(
|
||||
self,
|
||||
*,
|
||||
job_id: str,
|
||||
user_id: str,
|
||||
audio_url: str,
|
||||
script: str,
|
||||
video_url: Optional[str] = None,
|
||||
) -> DittoResult:
|
||||
"""调用 generate 并把 MP4 转存到自家 OSS，返回带 video_url 的结果."""
|
||||
result = self.generate(audio_url=audio_url, script=script, video_url=video_url)
|
||||
try:
|
||||
from packages.shared.storage import get_shared_storage_service
|
||||
|
||||
storage = get_shared_storage_service()
|
||||
storage_key = f"ditto-output/{user_id}/{job_id}.mp4"
|
||||
public_url = storage.upload_file(
|
||||
io.BytesIO(result.video_bytes),
|
||||
storage_key,
|
||||
content_type="video/mp4",
|
||||
)
|
||||
result.video_url = public_url
|
||||
logger.info(
|
||||
"[ditto] 转存 OSS 完成 job=%s key=%s",
|
||||
job_id,
|
||||
storage_key,
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.error("[ditto] 转存 OSS 失败 job=%s err=%s", job_id, exc, exc_info=True)
|
||||
raise DittoError(f"Ditto 结果转存 OSS 失败: {exc}", code="StorageError") from exc
|
||||
return result
|
||||
|
||||
|
||||
_ditto_client_singleton: Optional[DittoClient] = None
|
||||
|
||||
|
||||
def get_ditto_client() -> DittoClient:
|
||||
"""获取 DittoClient 单例（简易工厂，便于单测 mock）."""
|
||||
global _ditto_client_singleton
|
||||
if _ditto_client_singleton is None:
|
||||
_ditto_client_singleton = DittoClient()
|
||||
return _ditto_client_singleton
|
||||
@@ -1 +0,0 @@
|
||||
"""应用层：爆款视频 Prompt 模板系统（#2040）。"""
|
||||
@@ -1,427 +0,0 @@
|
||||
"""爆款视频 5 步编排：图片分析 → 意图解析 → 文案融合 → 分镜 → 审核重写。
|
||||
|
||||
所有 LLM 调用走 DoubaoClient，单测通过 client 参数注入 mock，不真调 API。
|
||||
任何一步解析失败都走规则 fallback，不抛异常阻断。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import Optional
|
||||
|
||||
from packages.application.viral_video import xml_parser as xp
|
||||
from packages.application.viral_video.prompt_loader import (
|
||||
PromptTemplate,
|
||||
get_template,
|
||||
render_system_prompt,
|
||||
render_user_prompt,
|
||||
)
|
||||
from packages.application.viral_video.prompts import (
|
||||
FUSION_INSTRUCTIONS,
|
||||
GLOBAL_CONSTRAINTS,
|
||||
NEGATIVE_RULES,
|
||||
)
|
||||
from packages.application.viral_video.reviewer import Reviewer
|
||||
from packages.application.viral_video.schemas import (
|
||||
BodyPoint,
|
||||
Clip,
|
||||
ColorItem,
|
||||
CoreMessage,
|
||||
FusionResult,
|
||||
ImageAnalysis,
|
||||
IntentResult,
|
||||
KenBurns,
|
||||
PersonalBrand,
|
||||
ProductItem,
|
||||
ReviewResult,
|
||||
ScriptSegment,
|
||||
Storyboard,
|
||||
TextItem,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CopyGenerator:
|
||||
"""5 步 Prompt 编排器。"""
|
||||
|
||||
def __init__(self, client=None, reviewer: Optional[Reviewer] = None):
|
||||
if client is None:
|
||||
from packages.shared.ai_client import get_doubao_client
|
||||
|
||||
client = get_doubao_client()
|
||||
self.client = client
|
||||
self.reviewer = reviewer or Reviewer(client)
|
||||
|
||||
# ── 底层调用 ────────────────────────────────────────────────────────
|
||||
def _chat(self, template: PromptTemplate, system_kwargs: dict | None, **user_kwargs) -> str:
|
||||
system = render_system_prompt(template, **(system_kwargs or {}))
|
||||
user = render_user_prompt(template, **user_kwargs)
|
||||
result = self.client.chat_completion(
|
||||
[
|
||||
{"role": "system", "content": system},
|
||||
{"role": "user", "content": user},
|
||||
],
|
||||
temperature=0.7,
|
||||
max_tokens=2048,
|
||||
)
|
||||
return result or ""
|
||||
|
||||
# ── 步骤1：图片多模态分析 ───────────────────────────────────────────
|
||||
def analyze_images(self, images: list[str], industry: str = "") -> ImageAnalysis:
|
||||
template = get_template("image_analysis")
|
||||
image_urls = "\n".join(f"第{i + 1}张：{url}" for i, url in enumerate(images))
|
||||
system = render_system_prompt(template)
|
||||
user = render_user_prompt(template, image_count=len(images), industry=industry or "通用", image_urls=image_urls)
|
||||
raw = self.client.vision_completion(
|
||||
[
|
||||
{"role": "system", "content": system},
|
||||
{"role": "user", "content": user},
|
||||
],
|
||||
images=images,
|
||||
max_tokens=2048,
|
||||
temperature=0.3,
|
||||
)
|
||||
analysis = self._parse_image_analysis(raw or "")
|
||||
if not analysis.products and not analysis.key_selling_points:
|
||||
logger.warning("图片分析标签解析失败，走规则 fallback")
|
||||
return self._fallback_image_analysis(images, raw or "")
|
||||
return analysis
|
||||
|
||||
def _parse_image_analysis(self, raw: str) -> ImageAnalysis:
|
||||
products = [
|
||||
ProductItem(
|
||||
name=n["attrs"].get("name", "无法判断"),
|
||||
features=n["attrs"].get("features", "无法判断"),
|
||||
position=n["attrs"].get("position", "secondary"),
|
||||
image_index=xp.attr_int(n["attrs"].get("image_index"), 0),
|
||||
)
|
||||
for n in xp.find_all(raw, "product")
|
||||
]
|
||||
colors = [
|
||||
ColorItem(
|
||||
hex=c["attrs"].get("hex", "#000000"),
|
||||
name=c["attrs"].get("name", "无法判断"),
|
||||
coverage=xp.attr_float(c["attrs"].get("coverage"), 0.0),
|
||||
)
|
||||
for c in xp.find_all(raw, "color")
|
||||
]
|
||||
people = xp.find_first(raw, "people")
|
||||
visible_text = [
|
||||
TextItem(text=t["attrs"].get("text", ""), position=t["attrs"].get("position", ""))
|
||||
for t in xp.find_all(raw, "text_item")
|
||||
]
|
||||
quality_node = xp.find_first(raw, "quality")
|
||||
selling_points = [n["text"] or n["attrs"].get("text", "") for n in xp.find_all(raw, "point")]
|
||||
return ImageAnalysis(
|
||||
products=products,
|
||||
colors=colors,
|
||||
has_person=xp.attr_bool(people["attrs"].get("has_person")) if people else False,
|
||||
person_count=xp.attr_int(people["attrs"].get("count"), 0) if people else 0,
|
||||
people=people["attrs"] if people else {},
|
||||
mood=xp.text_of(raw, "mood"),
|
||||
visible_text=visible_text,
|
||||
scene=xp.text_of(raw, "scene"),
|
||||
quality=quality_node["attrs"] if quality_node else {},
|
||||
key_selling_points=[p for p in selling_points if p],
|
||||
raw=raw,
|
||||
)
|
||||
|
||||
def _fallback_image_analysis(self, images: list[str], raw: str) -> ImageAnalysis:
|
||||
return ImageAnalysis(
|
||||
products=[ProductItem(name="无法判断（视觉分析不可用）", image_index=0)],
|
||||
scene="无法判断",
|
||||
raw=raw,
|
||||
)
|
||||
|
||||
# ── 步骤2：意图解析 ─────────────────────────────────────────────────
|
||||
def parse_intent(self, user_copy_text: str, image_analysis: ImageAnalysis, industry: str = "") -> IntentResult:
|
||||
template = get_template("intent_parsing")
|
||||
raw = self._chat(
|
||||
template,
|
||||
None,
|
||||
user_copy_text=user_copy_text or "（用户没有提供文案）",
|
||||
industry=industry or "通用",
|
||||
image_analysis=self._image_brief(image_analysis),
|
||||
)
|
||||
intent = self._parse_intent(raw)
|
||||
if not intent.intent_summary and not intent.core_messages:
|
||||
logger.warning("意图解析标签解析失败，走规则 fallback")
|
||||
return self._fallback_intent(user_copy_text, raw)
|
||||
return intent
|
||||
|
||||
def _parse_intent(self, raw: str) -> IntentResult:
|
||||
messages = [
|
||||
CoreMessage(
|
||||
text=n["text"],
|
||||
must_keep=xp.attr_bool(n["attrs"].get("must_keep"), default=False),
|
||||
confidence=xp.attr_float(n["attrs"].get("confidence"), 0.0),
|
||||
)
|
||||
for n in xp.find_all(raw, "message")
|
||||
if n["text"]
|
||||
]
|
||||
brands = [
|
||||
PersonalBrand(text=n["text"], category=n["attrs"].get("category", "brand"))
|
||||
for n in xp.find_all(raw, "brand")
|
||||
if n["text"]
|
||||
]
|
||||
missing = [n["text"] for n in xp.find_all(raw, "info") if n["text"]]
|
||||
return IntentResult(
|
||||
intent_summary=xp.text_of(raw, "intent_summary"),
|
||||
core_messages=messages,
|
||||
personal_brands=brands,
|
||||
emotion_tone=xp.text_of(raw, "emotion_tone"),
|
||||
missing_info=missing,
|
||||
raw=raw,
|
||||
)
|
||||
|
||||
def _fallback_intent(self, user_copy_text: str, raw: str) -> IntentResult:
|
||||
text = (user_copy_text or "").strip()
|
||||
messages = [CoreMessage(text=text[:80], must_keep=True, confidence=1.0)] if text else []
|
||||
return IntentResult(
|
||||
intent_summary=text[:30] or "未提供文案，按产品图片自由创作",
|
||||
core_messages=messages,
|
||||
personal_brands=[],
|
||||
raw=raw,
|
||||
)
|
||||
|
||||
# ── 步骤3：文案融合生成（三档）──────────────────────────────────────
|
||||
def fuse(
|
||||
self,
|
||||
fusion_level: str,
|
||||
image_analysis: ImageAnalysis,
|
||||
intent: IntentResult,
|
||||
industry: str = "",
|
||||
target_customer: str = "",
|
||||
marketing_purpose: str = "",
|
||||
duration: int = 15,
|
||||
) -> FusionResult:
|
||||
template = get_template("copy_fusion")
|
||||
system_kwargs = {
|
||||
"fusion_instruction": FUSION_INSTRUCTIONS.get(fusion_level, FUSION_INSTRUCTIONS["ai_polish"]),
|
||||
"global_constraints": GLOBAL_CONSTRAINTS,
|
||||
"negative_rules": NEGATIVE_RULES,
|
||||
}
|
||||
raw = self._chat(
|
||||
template,
|
||||
system_kwargs,
|
||||
industry=industry or "通用",
|
||||
target_customer=target_customer or "通用消费者",
|
||||
marketing_purpose=marketing_purpose or "产品种草",
|
||||
duration=duration,
|
||||
image_analysis=self._image_brief(image_analysis),
|
||||
intent_result=self._intent_brief(intent),
|
||||
)
|
||||
result = self._parse_fusion(raw)
|
||||
if not result.title and not result.script_segments:
|
||||
logger.warning("文案融合标签解析失败（fusion=%s），走规则 fallback", fusion_level)
|
||||
return self._fallback_fusion(fusion_level, image_analysis, intent, duration, raw)
|
||||
return result
|
||||
|
||||
def _parse_fusion(self, raw: str) -> FusionResult:
|
||||
body_points = [
|
||||
BodyPoint(
|
||||
text=n["text"],
|
||||
elaboration=n["attrs"].get("elaboration", ""),
|
||||
image_index=xp.attr_int(n["attrs"].get("image_index"), 0),
|
||||
)
|
||||
for n in xp.find_all(raw, "point")
|
||||
if n["text"]
|
||||
]
|
||||
segments = [
|
||||
ScriptSegment(
|
||||
text=n["text"],
|
||||
duration_sec=xp.attr_float(n["attrs"].get("duration_sec"), 0.0),
|
||||
image_index=xp.attr_int(n["attrs"].get("image_index"), 0),
|
||||
)
|
||||
for n in xp.find_all(raw, "segment")
|
||||
if n["text"]
|
||||
]
|
||||
return FusionResult(
|
||||
title=xp.text_of(raw, "title"),
|
||||
hook=xp.text_of(raw, "hook"),
|
||||
body_points=body_points,
|
||||
cta=xp.text_of(raw, "cta"),
|
||||
script_segments=segments,
|
||||
word_count=xp.attr_int(xp.text_of(raw, "word_count"), 0),
|
||||
estimated_duration=xp.attr_int(xp.text_of(raw, "estimated_duration"), 0),
|
||||
raw=raw,
|
||||
)
|
||||
|
||||
def _fallback_fusion(
|
||||
self,
|
||||
fusion_level: str,
|
||||
image_analysis: ImageAnalysis,
|
||||
intent: IntentResult,
|
||||
duration: int,
|
||||
raw: str,
|
||||
) -> FusionResult:
|
||||
product_name = image_analysis.products[0].name if image_analysis.products else "这款产品"
|
||||
selling = image_analysis.key_selling_points[:2]
|
||||
if fusion_level == "ai_full":
|
||||
title = f"{product_name}，很多人用完都回购了"
|
||||
hook = f"这个{product_name}，我想认真说说"
|
||||
body = selling or ["图片可见的产品卖点"]
|
||||
cta = "感兴趣的可以了解一下"
|
||||
elif fusion_level == "user_primary":
|
||||
user_text = intent.intent_summary or product_name
|
||||
title = user_text[:20]
|
||||
hook = user_text[:15]
|
||||
body = [m.text for m in intent.core_messages] or [user_text]
|
||||
cta = "想了解的可以看看"
|
||||
else:
|
||||
title = intent.intent_summary[:20] or product_name
|
||||
hook = intent.core_messages[0].text[:15] if intent.core_messages else product_name
|
||||
body = [m.text for m in intent.core_messages] or selling or [product_name]
|
||||
cta = "有需要的可以了解一下"
|
||||
|
||||
brand_texts = [b.text for b in intent.personal_brands]
|
||||
points = [BodyPoint(text=b) for b in body]
|
||||
lines = [hook] + body + brand_texts[:2] + [cta]
|
||||
joined = "，".join(lines)
|
||||
per = max(3, duration // max(1, len(lines)))
|
||||
segments = [ScriptSegment(text=line, duration_sec=per, image_index=0) for line in lines]
|
||||
return FusionResult(
|
||||
title=title,
|
||||
hook=hook,
|
||||
body_points=points,
|
||||
cta=cta,
|
||||
script_segments=segments,
|
||||
word_count=len(joined),
|
||||
estimated_duration=duration,
|
||||
raw=raw,
|
||||
)
|
||||
|
||||
# ── 步骤4：编导级分镜 ───────────────────────────────────────────────
|
||||
def storyboard(
|
||||
self, fusion: FusionResult, image_analysis: ImageAnalysis, images: list[str], duration: int
|
||||
) -> Storyboard:
|
||||
template = get_template("storyboard")
|
||||
raw = self._chat(
|
||||
template,
|
||||
None,
|
||||
duration=duration,
|
||||
image_count=len(images),
|
||||
fusion_result=self._fusion_brief(fusion),
|
||||
image_analysis=self._image_brief(image_analysis),
|
||||
)
|
||||
board = self._parse_storyboard(raw)
|
||||
if not board.clips:
|
||||
logger.warning("分镜标签解析失败，走规则 fallback")
|
||||
return self._fallback_storyboard(fusion, duration, raw)
|
||||
return board
|
||||
|
||||
def _parse_storyboard(self, raw: str) -> Storyboard:
|
||||
clips: list[Clip] = []
|
||||
for node in xp.find_all(raw, "clip"):
|
||||
attrs = node["attrs"]
|
||||
body = node["text"]
|
||||
kb = xp.find_first(node["text"] and f"<root>{node['text']}</root>", "ken_burns")
|
||||
clips.append(
|
||||
Clip(
|
||||
image_index=xp.attr_int(attrs.get("image_index"), 0),
|
||||
transition=attrs.get("transition", "cut"),
|
||||
zoom=(None if attrs.get("zoom") in (None, "null", "None", "") else attrs.get("zoom")),
|
||||
duration_sec=xp.attr_float(attrs.get("duration_sec"), 0.0),
|
||||
bgm_note=attrs.get("bgm_note", ""),
|
||||
voice_text=xp.text_of(body and f"<root>{body}</root>", "voice_text"),
|
||||
subtitle_text=xp.text_of(body and f"<root>{body}</root>", "subtitle_text"),
|
||||
ken_burns=KenBurns(
|
||||
start=kb["attrs"].get("start", "0,0") if kb else "0,0",
|
||||
end=kb["attrs"].get("end", "0,0") if kb else "0,0",
|
||||
ease=kb["attrs"].get("ease", "linear") if kb else "linear",
|
||||
),
|
||||
)
|
||||
)
|
||||
return Storyboard(clips=clips, raw=raw)
|
||||
|
||||
def _fallback_storyboard(self, fusion: FusionResult, duration: int, raw: str) -> Storyboard:
|
||||
segments = fusion.script_segments or [ScriptSegment(text=fusion.hook or fusion.title, duration_sec=duration)]
|
||||
total = sum(s.duration_sec for s in segments) or duration
|
||||
clips = [
|
||||
Clip(
|
||||
image_index=min(s.image_index, 0),
|
||||
transition="cut",
|
||||
duration_sec=max(2.0, s.duration_sec * duration / total if total else duration / len(segments)),
|
||||
voice_text=s.text,
|
||||
subtitle_text=s.text[:20],
|
||||
)
|
||||
for s in segments
|
||||
]
|
||||
return Storyboard(clips=clips, raw=raw)
|
||||
|
||||
# ── 步骤5：审核（不通过自动重写1次）─────────────────────────────────
|
||||
def review_and_rewrite(
|
||||
self, fusion: FusionResult, intent: IntentResult, fusion_level: str
|
||||
) -> tuple[FusionResult, ReviewResult, int]:
|
||||
"""返回最终文案、最后一次审核结果、重写次数（0或1）。"""
|
||||
review = self.reviewer.review(fusion, intent, fusion_level)
|
||||
if review.passed:
|
||||
return fusion, review, 0
|
||||
|
||||
logger.info("文案审核不通过，自动重写 1 次：%s", [i.text for i in review.issues])
|
||||
rewritten = self.reviewer.rewrite(fusion, review, intent, fusion_level)
|
||||
second = self.reviewer.review(rewritten, intent, fusion_level)
|
||||
if second.passed:
|
||||
return rewritten, second, 1
|
||||
# 二次仍不通过：带上重写结果和问题返回，由上游决定是否交给前端
|
||||
return rewritten, second, 1
|
||||
|
||||
# ── 全流程编排 ──────────────────────────────────────────────────────
|
||||
def generate(
|
||||
self,
|
||||
images: list[str],
|
||||
*,
|
||||
industry: str = "",
|
||||
target_customer: str = "",
|
||||
marketing_purpose: str = "",
|
||||
duration: int = 15,
|
||||
user_copy_text: str = "",
|
||||
fusion_level: str = "ai_polish",
|
||||
) -> dict:
|
||||
image_analysis = self.analyze_images(images, industry)
|
||||
intent = self.parse_intent(user_copy_text, image_analysis, industry)
|
||||
fusion = self.fuse(
|
||||
fusion_level,
|
||||
image_analysis,
|
||||
intent,
|
||||
industry=industry,
|
||||
target_customer=target_customer,
|
||||
marketing_purpose=marketing_purpose,
|
||||
duration=duration,
|
||||
)
|
||||
fusion, review, rewrites = self.review_and_rewrite(fusion, intent, fusion_level)
|
||||
board = self.storyboard(fusion, image_analysis, images, duration)
|
||||
return {
|
||||
"image_analysis": image_analysis,
|
||||
"intent_result": intent,
|
||||
"fusion_result": fusion,
|
||||
"review_result": review,
|
||||
"storyboard": board,
|
||||
"rewrite_count": rewrites,
|
||||
}
|
||||
|
||||
# ── 简报工具 ────────────────────────────────────────────────────────
|
||||
@staticmethod
|
||||
def _image_brief(a) -> str:
|
||||
if a is None:
|
||||
return "无图片分析信息"
|
||||
lines = [f"产品：{p.name}（{p.features}）" for p in a.products]
|
||||
lines += [f"卖点：{s}" for s in a.key_selling_points]
|
||||
lines.append(f"场景：{a.scene}")
|
||||
return "\n".join(lines) or "无图片分析信息"
|
||||
|
||||
@staticmethod
|
||||
def _intent_brief(i: IntentResult) -> str:
|
||||
lines = [f"意图：{i.intent_summary}"]
|
||||
lines += [f"核心信息[must_keep={m.must_keep}]：{m.text}" for m in i.core_messages]
|
||||
lines += [f"事实（{b.category}）：{b.text}" for b in i.personal_brands]
|
||||
return "\n".join(lines)
|
||||
|
||||
@staticmethod
|
||||
def _fusion_brief(f: FusionResult) -> str:
|
||||
lines = [f"标题：{f.title}", f"钩子：{f.hook}"]
|
||||
lines += [f"要点：{p.text}" for p in f.body_points]
|
||||
lines += [f"配音：{s.text}" for s in f.script_segments]
|
||||
lines.append(f"行动号召：{f.cta}")
|
||||
return "\n".join(lines)
|
||||
@@ -1,160 +0,0 @@
|
||||
"""Prompt 模板加载器：从 viral_video_prompt_templates 读模板，30 秒 TTL 热加载。
|
||||
|
||||
DB 不可用或没有数据时自动回落到 prompts.DEFAULT_TEMPLATES，保证流程不阻断。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import threading
|
||||
import time
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from packages.adapters.sqlalchemy_impl import session as _session_mod
|
||||
from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
|
||||
|
||||
CACHE_TTL_SECONDS = 30.0
|
||||
|
||||
_VALID_TYPES = {"image_analysis", "intent_parsing", "copy_fusion", "storyboard", "review"}
|
||||
|
||||
|
||||
@dataclass
|
||||
class PromptTemplate:
|
||||
name: str
|
||||
prompt_type: str
|
||||
version: int
|
||||
system_prompt: str
|
||||
user_prompt_template: str
|
||||
example_output: str = ""
|
||||
is_active: bool = True
|
||||
|
||||
|
||||
_lock = threading.Lock()
|
||||
_cache: dict[str, tuple[float, PromptTemplate]] = {}
|
||||
|
||||
|
||||
def _fallback(prompt_type: str) -> Optional[PromptTemplate]:
|
||||
for item in DEFAULT_TEMPLATES:
|
||||
if item["prompt_type"] == prompt_type:
|
||||
return PromptTemplate(
|
||||
name=item["name"],
|
||||
prompt_type=item["prompt_type"],
|
||||
version=item["version"],
|
||||
system_prompt=item["system_prompt"],
|
||||
user_prompt_template=item["user_prompt_template"],
|
||||
example_output=item["example_output"] or "",
|
||||
is_active=bool(item["is_active"]),
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
_lazy_session = None
|
||||
|
||||
|
||||
def _get_session():
|
||||
"""优先用全局 SessionLocal（worker）；否则按应用配置懒建同步引擎（api）。"""
|
||||
global _lazy_session
|
||||
if _session_mod.SessionLocal is not None:
|
||||
return _session_mod.SessionLocal()
|
||||
if _lazy_session is not None:
|
||||
return _lazy_session()
|
||||
try:
|
||||
from packages.config import get_shared_settings
|
||||
|
||||
url = str(get_shared_settings().database_url)
|
||||
except Exception: # noqa: BLE001
|
||||
return None
|
||||
if not url:
|
||||
return None
|
||||
url = url.replace("postgresql+asyncpg://", "postgresql+psycopg://")
|
||||
url = url.replace("postgresql://", "postgresql+psycopg://") if url.startswith("postgresql://") else url
|
||||
engine = sa.create_engine(url, pool_pre_ping=True, pool_size=2, max_overflow=2)
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
|
||||
_lazy_session = sessionmaker(bind=engine)
|
||||
return _lazy_session()
|
||||
|
||||
|
||||
def _load_from_db(prompt_type: str) -> Optional[PromptTemplate]:
|
||||
session = None
|
||||
try:
|
||||
session = _get_session()
|
||||
if session is None:
|
||||
return None
|
||||
sql = sa.text("""
|
||||
SELECT name, prompt_type, version, system_prompt,
|
||||
user_prompt_template, COALESCE(example_output, '') AS example_output,
|
||||
is_active
|
||||
FROM viral_video_prompt_templates
|
||||
WHERE prompt_type = :pt AND is_active = TRUE
|
||||
ORDER BY version DESC
|
||||
LIMIT 1
|
||||
""")
|
||||
row = session.execute(sql, {"pt": prompt_type}).first()
|
||||
if row is None:
|
||||
return None
|
||||
return PromptTemplate(
|
||||
name=row[0],
|
||||
prompt_type=row[1],
|
||||
version=int(row[2]),
|
||||
system_prompt=row[3],
|
||||
user_prompt_template=row[4],
|
||||
example_output=row[5] or "",
|
||||
is_active=bool(row[6]),
|
||||
)
|
||||
except Exception: # noqa: BLE001 - 表不存在/DB 不可用时静默回落
|
||||
return None
|
||||
finally:
|
||||
if session is not None:
|
||||
try:
|
||||
session.close()
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
|
||||
|
||||
def get_template(prompt_type: str, *, force_refresh: bool = False) -> Optional[PromptTemplate]:
|
||||
"""取某类型当前启用模板，30 秒缓存；DB 无数据则回落到代码默认模板。"""
|
||||
if prompt_type not in _VALID_TYPES:
|
||||
raise ValueError(f"未知 prompt_type: {prompt_type}")
|
||||
|
||||
now = time.monotonic()
|
||||
with _lock:
|
||||
cached = _cache.get(prompt_type)
|
||||
if not force_refresh and cached and now - cached[0] < CACHE_TTL_SECONDS:
|
||||
return cached[1]
|
||||
|
||||
template = _load_from_db(prompt_type) or _fallback(prompt_type)
|
||||
if template is not None:
|
||||
with _lock:
|
||||
_cache[prompt_type] = (now, template)
|
||||
return template
|
||||
|
||||
|
||||
def invalidate() -> None:
|
||||
"""清空缓存（测试用）。"""
|
||||
with _lock:
|
||||
_cache.clear()
|
||||
|
||||
|
||||
class _SafeDict(dict):
|
||||
def __missing__(self, key: str) -> str:
|
||||
return "{" + key + "}"
|
||||
|
||||
|
||||
def _safe_format(text: str, kwargs: dict) -> str:
|
||||
try:
|
||||
return text.format_map(_SafeDict(kwargs))
|
||||
except Exception: # noqa: BLE001
|
||||
return text
|
||||
|
||||
|
||||
def render_user_prompt(template: PromptTemplate, **kwargs) -> str:
|
||||
"""填充 user_prompt_template 占位符，缺键原样保留不报错。"""
|
||||
return _safe_format(template.user_prompt_template, kwargs)
|
||||
|
||||
|
||||
def render_system_prompt(template: PromptTemplate, **kwargs) -> str:
|
||||
"""copy_fusion 等 system_prompt 含运行时变量时填充。"""
|
||||
return _safe_format(template.system_prompt, kwargs)
|
||||
@@ -1,342 +0,0 @@
|
||||
"""爆款视频 5 套 Prompt 模板默认值（#2040 核心资产）。
|
||||
|
||||
重要约定（用户明确要求）：
|
||||
- 所有 system_prompt / user_prompt_template / example_output 都是**纯文本自然语言 + XML 标签**，
|
||||
运营可直接看懂和编辑，禁止 JSON、禁止 ```json 代码块。
|
||||
- LLM 按 XML 标签输出字段，程序用正则解析（见 xml_parser.py）。
|
||||
- user_prompt_template 中花括号占位符（如 {user_copy_text}）在运行时填充。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
TEMPLATE_VERSION = 1
|
||||
|
||||
# 所有文案类 Prompt 自动注入的硬约束
|
||||
GLOBAL_CONSTRAINTS = """【必须遵守的硬约束】
|
||||
1. 不编造时间：不写“今年最新”“2024 爆款”等会过时的时间表述。
|
||||
2. 不承诺效果：不写“保证”“一定”“100%有效”“包治百病”等绝对化用语。
|
||||
3. 不编造价格、销量、认证、奖项：除非用户在文案中明确给出，否则一律不写。
|
||||
4. 符合广告法及平台社区规范。
|
||||
5. 只描述图片中真实可见的内容，看不到的不瞎猜。"""
|
||||
|
||||
# 反套路化要求
|
||||
NEGATIVE_RULES = """【反套路化要求】
|
||||
禁止使用“家人们谁懂啊”“绝绝子”“宝子们”“家人们”“太绝了”“yyds”等烂大街网络词；
|
||||
禁止固定模板化开头；语言要像真人朋友之间的分享，自然、具体、有信息量。"""
|
||||
|
||||
# 输出禁用套路词（测试会检查）
|
||||
BANNED_PHRASES = ["家人们谁懂啊", "绝绝子", "宝子们", "yyds", "太绝了"]
|
||||
|
||||
# 文案融合三档独立指令段
|
||||
FUSION_INSTRUCTIONS = {
|
||||
"ai_full": """【本次创作模式：AI 全权创作】
|
||||
你是资深短视频编导。用户只提供了产品图片，没有给出具体文案方向。请根据图片内容和营销参数，自由发挥创作完整的爆款短视频文案。充分挖掘产品真实可见的卖点，使用爆款结构，抓人眼球。""",
|
||||
"ai_polish": """【本次创作模式：AI 辅助润色】
|
||||
你是用户的文案助理。用户已经写了草稿/关键词/碎碎念，表达了他想讲的核心意思，但表达不完整、不够吸引人。你的任务是：以用户的意思为主，保留他想表达的所有核心信息点，在此基础上润色扩写、调整语序、增加衔接、优化表达，让文案更流畅更有吸引力。绝对不能改变用户想表达的核心意思，不能把用户的观点换成相反的，不能添加用户没提到的产品卖点。用户提到的品牌名、价格、人名、具体事实必须原样保留。""",
|
||||
"user_primary": """【本次创作模式：以用户原文为主】
|
||||
你是文案润色助手。用户已经写好了明确的文案，这是他最终想表达的内容。你的任务是最小化修改：只做必要的错别字修正、标点调整、语句通顺度优化，以及添加必要的衔接词让口播更自然。用户的核心句子、关键表述、事实信息一律不改。如果用户文案本身已经很好，直接返回，不要为了改而改。personal_brands 中的事实信息必须逐字保留。""",
|
||||
}
|
||||
|
||||
# ── 模板1：图片多模态分析（VLM）────────────────────────────────────────
|
||||
_IMAGE_ANALYSIS_SYSTEM = f"""你是电商商品视觉分析师，负责从商品图片中提取真实可见的商品信息。
|
||||
|
||||
工作方式（分步骤看，不要跳步）：
|
||||
1. 先看整体：有哪些产品、什么场景、有没有人物。
|
||||
2. 再看细节：包装文字、颜色构成、人物状态、画面质感。
|
||||
3. 最后提炼卖点：只总结图片里能看到的卖点。
|
||||
|
||||
{GLOBAL_CONSTRAINTS}
|
||||
|
||||
请严格按下面的标签格式输出，标签名一个都不能改，不要输出任何解释，不要用代码块：
|
||||
<products> 下面每个产品用一个 <product> 标签，属性 name 是产品名、features 是外观特征、position 是 main 或 secondary、image_index 是第几张图（从0开始）。
|
||||
<colors> 下面每个主要颜色用一个 <color> 标签，属性 hex 是色值、name 是颜色名、coverage 是占比小数。
|
||||
<people> 用一个标签，属性 has_person、count、gender、age_range、hair（发型发色）、skin_tone（肤色）、face_shape（脸型）、outfit（穿着）、pose（姿态）、expression（表情）分别描述人物外貌。有人物时属性尽量具体（如hair="黑色长直发"、outfit="白色衬衫"），无人像时除has_person=false外其他填"无法判断"。
|
||||
<mood> 标签写画面整体情绪氛围。
|
||||
<visible_text> 下面每处可见文字用一个 <text_item> 标签，属性 text 是文字内容、position 是位置。
|
||||
<scene> 标签写场景描述。
|
||||
<quality> 用一个标签，属性 resolution、lighting、composition、blur 描述画质。
|
||||
<key_selling_points> 下面每个卖点用一个 <point> 标签。
|
||||
|
||||
【人物属性硬性要求（has_person=true时必须遵守）】
|
||||
hair/skin_tone/face_shape/outfit四项绝对禁止填“无法判断”，必须基于图片可见特征给出具体中文描述：
|
||||
- hair：必须描述发型+发色，如“黑色齐肩直发”“棕色微卷中长发”“深棕色短发”
|
||||
- skin_tone：必须描述肤色，如“暖调自然肤色”“白皙肤色”“小麦色”
|
||||
- face_shape：必须描述脸型，如“鹅蛋脸”“圆脸”“瓜子脸”“方脸”
|
||||
- outfit：必须描述可见穿着，如“米色翻领衬衫”“白色T恤”“黑色连衣裙”
|
||||
即使局部被遮挡也要根据可见部分合理推断；确实看不清时按最接近的直观印象描述。
|
||||
|
||||
其他非人物属性看不到或无法判断时填“无法判断”，布尔值填false，不要留空标签。
|
||||
|
||||
【有人物场景输出参考（女性手持商品示例，必须写全10个属性，禁止省略）】
|
||||
<people has_person="true" count="1" gender="女" age_range="青年" hair="黑色齐肩直发" skin_tone="暖调自然肤色" face_shape="鹅蛋脸" outfit="米色翻领衬衫" pose="正面半身，手持商品" expression="面带微笑"/>"""
|
||||
|
||||
_IMAGE_ANALYSIS_USER = """请分析以下商品图片，共 {image_count} 张。
|
||||
所属行业：{industry}
|
||||
图片地址：
|
||||
{image_urls}
|
||||
|
||||
按约定的标签格式输出分析结果。"""
|
||||
|
||||
_IMAGE_ANALYSIS_EXAMPLE = """<products>
|
||||
<product name="大公鸡头 多功能油污净 625ml" features="红色瓶盖白色瓶身，鸡头图案Logo" position="main" image_index="0"/>
|
||||
</products>
|
||||
<colors>
|
||||
<color hex="#D32F2F" name="红色" coverage="0.4"/>
|
||||
<color hex="#FFFFFF" name="白色" coverage="0.5"/>
|
||||
</colors>
|
||||
<people has_person="false" count="0" gender="无法判断" age_range="无法判断" hair="无法判断" skin_tone="无法判断" face_shape="无法判断" outfit="无法判断" pose="无法判断" expression="无法判断"/>
|
||||
<mood>干净、实用</mood>
|
||||
<visible_text>
|
||||
<text_item text="多功能油污净" position="瓶身正面"/>
|
||||
</visible_text>
|
||||
<scene>白底棚拍产品图</scene>
|
||||
<quality resolution="高清" lighting="均匀柔和" composition="主体居中" blur="false"/>
|
||||
<key_selling_points>
|
||||
<point>针对重油污设计</point>
|
||||
<point>大容量625ml</point>
|
||||
</key_selling_points>"""
|
||||
|
||||
# ── 模板2：用户文案意图解析（LLM）──────────────────────────────────────
|
||||
_INTENT_SYSTEM = f"""你负责理解用户的营销意图。用户给的文案可能只是几个关键词、碎碎念或者不完整的短句，你要读懂他真正想讲什么。
|
||||
|
||||
{GLOBAL_CONSTRAINTS}
|
||||
|
||||
请严格按下面的标签格式输出，不要解释，不要用代码块：
|
||||
<intent_summary> 用用户的语言风格，一句话、30字以内概括核心意图。
|
||||
<core_messages> 下面每个核心信息点用一个 <message> 标签，属性 must_keep 为 true 或 false、confidence 为 0 到 1 的小数，标签内容写信息点。
|
||||
<personal_brands> 把用户提到的具体事实——品牌名、价格、人名、地名、时间、产品名——每条用一个 <brand> 标签，属性 category 取 brand、price、person、place、time、product 之一。这些事实必须原样引用，一个字都不能改。
|
||||
<emotion_tone> 写文案的情绪调性。
|
||||
<missing_info> 把你认为缺失、后续生成时需要合理推断的信息，每条用一个 <info> 标签；没有就输出空标签。"""
|
||||
|
||||
_INTENT_USER = """用户原始文案：{user_copy_text}
|
||||
所属行业：{industry}
|
||||
营销目的：{marketing_purpose}
|
||||
图片分析结果（供参考）：
|
||||
{image_analysis}
|
||||
图片类型推断：{image_category_hint}
|
||||
|
||||
请理解用户意图，按标签格式输出。注意：theme和emotion_tone应与图片类型和营销目的匹配——门店类图片偏向"门店探店/到店体验"，商品图偏向"好物分享/产品种草"，人物图偏向"穿搭/人物故事"。"""
|
||||
|
||||
_INTENT_EXAMPLE = """<intent_summary>一款厨房去油污神器，喷一喷油污就掉</intent_summary>
|
||||
<core_messages>
|
||||
<message must_keep="true" confidence="0.97">去油污效果好，喷上等几分钟再擦</message>
|
||||
<message must_keep="false" confidence="0.7">适合厨房重油污场景</message>
|
||||
</core_messages>
|
||||
<personal_brands>
|
||||
<brand category="product">大公鸡头多功能油污净</brand>
|
||||
<brand category="price">39块钱一瓶</brand>
|
||||
</personal_brands>
|
||||
<emotion_tone>亲切、真实、带分享感</emotion_tone>
|
||||
<missing_info>
|
||||
<info>没有说明具体容量，按图片读出的625ml处理</info>
|
||||
</missing_info>"""
|
||||
|
||||
# ── 模板3：文案融合生成（LLM）──────────────────────────────────────────
|
||||
_FUSION_SYSTEM = """你负责为短视频生成营销文案。请按思维链分步完成：先定人设和目标客户，再找卖点，再搭结构，再安排情绪，最后写行动号召，不要一步到位乱写。
|
||||
|
||||
{fusion_instruction}
|
||||
|
||||
{global_constraints}
|
||||
|
||||
{negative_rules}
|
||||
|
||||
请严格按下面的标签格式输出，不要解释，不要用代码块：
|
||||
<title> 视频标题。
|
||||
<hook> 开头3秒钩子，5到15字。
|
||||
<body_points> 每个要点用一个 <point> 标签，属性 elaboration 是展开说明、image_index 是对应第几张图（从0开始），标签内容写要点。
|
||||
<cta> 口语化的行动号召。
|
||||
<script_segments> 每段配音用一个 <segment> 标签，属性 duration_sec 是秒数、image_index 是对应图片，标签内容写配音文案（纯口播文本，不加旁白标注、不加镜头标注、不加"主播："之类前缀）。
|
||||
<voiceover_script> 把所有 segment 的配音文案按顺序自然拼接成一段完整的纯口播文本（无标记、无括号、无前缀），长度要适配 {duration} 秒，约 {approx_chars} 字。
|
||||
<overview_theme> 视频主题（一句话概括）。
|
||||
<scene_and_lighting> 整体场景描述+光线设定（100-200字，要具体：在哪拍、什么光线、什么色调、什么氛围）。
|
||||
<word_count> 配音总字数，只写数字。
|
||||
<estimated_duration> 预计时长秒数，只写数字。
|
||||
|
||||
用户在 personal_brands 中提到的品牌名、价格、人名、地名、时间、产品名等事实信息，必须原样出现在文案里，一个字都不能改。"""
|
||||
|
||||
_FUSION_USER = """所属行业：{industry}
|
||||
目标客户：{target_customer}
|
||||
营销目的：{marketing_purpose}
|
||||
视频时长：{duration}秒
|
||||
图片分析结果：
|
||||
{image_analysis}
|
||||
用户意图解析结果：
|
||||
{intent_result}
|
||||
|
||||
请按标签格式生成文案。"""
|
||||
|
||||
_FUSION_EXAMPLE = """<title>厨房重油污，别再用洗洁精硬擦了</title>
|
||||
<hook>这油污，我真的忍很久了</hook>
|
||||
<body_points>
|
||||
<point elaboration="喷在油污上等几分钟，一擦就干净" image_index="0">大公鸡头油污净去油快</point>
|
||||
<point elaboration="39块钱625ml，能用很久" image_index="0">39块钱一瓶，性价比高</point>
|
||||
</body_points>
|
||||
<cta>厨房油污重的，真的可以试一瓶</cta>
|
||||
<script_segments>
|
||||
<segment duration_sec="3" image_index="0">这油污我真的忍很久了，用洗洁精擦半天都没用</segment>
|
||||
<segment duration_sec="6" image_index="0">后来换了这个大公鸡头油污净，喷上等几分钟，一擦就干净</segment>
|
||||
<segment duration_sec="4" image_index="0">39块钱625ml，厨房重油污的可以试一瓶</segment>
|
||||
</script_segments>
|
||||
<voiceover_script>这油污我真的忍很久了，用洗洁精擦半天都没用。后来换了这个大公鸡头油污净，喷上等几分钟，一擦就干净。39块钱625ml，厨房重油污的可以试一瓶。</voiceover_script>
|
||||
<overview_theme>厨房好物分享·产品种草</overview_theme>
|
||||
<scene_and_lighting>简洁明亮的厨房台面场景，自然光从窗户洒入，色调温暖柔和，突出产品白色瓶身与去油污对比效果。</scene_and_lighting>
|
||||
<word_count>58</word_count>
|
||||
<estimated_duration>13</estimated_duration>"""
|
||||
|
||||
# ── 模板4：编导级分镜（LLM）────────────────────────────────────────────
|
||||
_STORYBOARD_SYSTEM = """你是短视频编导，负责把文案拆成可拍摄的分镜，为 Seedance 2.5 视频模型写编导分镜脚本。脚本将整体作为 prompt 一次性传给视频模型，必须让模型在连贯镜头流中清楚每段时间拍什么、画面如何、人物说什么。
|
||||
|
||||
工作方式：
|
||||
1. 按文案的 script_segments 顺序分配镜头。
|
||||
2. 每个镜头确定景别/角度/运镜、画面场景与对白、人物动作细节、音效/BGM、转场。
|
||||
3. 检查所有镜头时长加起来接近目标时长，误差不超过2秒。
|
||||
4. image_index 必须在已上传图片范围内，第一张主图必须用在第一个镜头。
|
||||
|
||||
{fusion_instruction}
|
||||
|
||||
{global_constraints}
|
||||
|
||||
{negative_rules}
|
||||
|
||||
请严格按下面的标签格式输出，不要解释，不要用代码块：
|
||||
<clips> 下面每个镜头用一个 <clip> 标签，属性 image_index 是图片序号（从0开始）、transition 取 fade/cut/zoom_in/slide_left/dissolve/wipe 之一、zoom 取 in/out/null、duration_sec 是该镜头秒数、bgm_note 是该段BGM情绪。每个 <clip> 里面包含：
|
||||
<voice_text> 该镜头配音文本（纯口播文本，不加旁白标注）；
|
||||
<subtitle_text> 字幕文本，可与配音一致或更精简；
|
||||
<shot_type_angle_movement> 景别+角度+运镜（例：近景俯拍45度，缓慢推镜；中景平视，固定镜头；特写平视，快速拉镜）；
|
||||
<scene_and_dialogue> 画面场景描述 + 人物口播台词（对白要自然口语化，像朋友聊天，不要硬广推销腔）；
|
||||
<action_details> 人物动作、表情、物品操作细节（手怎么动、表情变化、产品怎么展示）；
|
||||
<audio_bgm> 环境音+BGM提示（例：轻快流行BGM，环境嘈杂咖啡店背景音）；
|
||||
<transition> 硬切/淡入淡出/叠化（最后一镜写『结束』即可）；
|
||||
<reference_image_index> 参考图片索引（0-based，对应第几张产品图，无则空）；
|
||||
<ken_burns> 用一个空标签，属性 start、end 写"x,y"坐标、ease 写缓动方式；不需要运镜时坐标相同。"""
|
||||
|
||||
_STORYBOARD_USER = """目标时长：{duration}秒
|
||||
上传图片数量：{image_count}张（第1张是主图/封面）
|
||||
文案内容：
|
||||
{fusion_result}
|
||||
图片分析结果：
|
||||
{image_analysis}
|
||||
|
||||
重要：overview_theme 必须与图片实际内容和营销目的匹配。门店/餐饮/服务类图片用"门店探店·到店体验"；商品图用"好物分享·产品种草"；人物图用"穿搭分享·人物故事"；场景图用"空间体验·场景氛围"。不要对所有图片都使用"好物分享"。
|
||||
|
||||
请按标签格式输出分镜。"""
|
||||
|
||||
_STORYBOARD_EXAMPLE = """<clips>
|
||||
<clip image_index="0" transition="cut" zoom="null" duration_sec="3" bgm_note="日常、轻微烦躁">
|
||||
<voice_text>这油污我真的忍很久了</voice_text>
|
||||
<subtitle_text>这油污忍很久了</subtitle_text>
|
||||
<shot_type_angle_movement>近景俯拍45度，缓慢推镜</shot_type_angle_movement>
|
||||
<scene_and_dialogue>厨房台面，主妇皱眉看着灶台油污。对白：这油污我真的忍很久了</scene_and_dialogue>
|
||||
<action_details>右手拿着脏抹布，无奈摇头</action_details>
|
||||
<audio_bgm>轻快日常BGM，带一点烦躁感</audio_bgm>
|
||||
<transition>硬切</transition>
|
||||
<reference_image_index>0</reference_image_index>
|
||||
<ken_burns start="0,0" end="0,0" ease="linear"/>
|
||||
</clip>
|
||||
<clip image_index="0" transition="zoom_in" zoom="in" duration_sec="6" bgm_note="轻快、出现转机">
|
||||
<voice_text>后来换了大公鸡头油污净，喷上等几分钟，一擦就干净</voice_text>
|
||||
<subtitle_text>喷上等几分钟，一擦就干净</subtitle_text>
|
||||
<shot_type_angle_movement>特写平视，固定镜头</shot_type_angle_movement>
|
||||
<scene_and_dialogue>手部特写，喷油污净在油污处。对白：后来换了这个大公鸡头油污净，喷上等几分钟，一擦就干净</scene_and_dialogue>
|
||||
<action_details>左手拿产品瓶身，右手按压喷头，等待片刻后用抹布轻擦</action_details>
|
||||
<audio_bgm>轻快转折BGM，带清爽感</audio_bgm>
|
||||
<transition>淡入淡出</transition>
|
||||
<reference_image_index>0</reference_image_index>
|
||||
<ken_burns start="20,20" end="80,80" ease="ease-in-out"/>
|
||||
</clip>
|
||||
<clip image_index="0" transition="fade" zoom="null" duration_sec="4" bgm_note="温暖、推荐">
|
||||
<voice_text>39块钱625ml，厨房重油污的可以试一瓶</voice_text>
|
||||
<subtitle_text>39元625ml，可以试一瓶</subtitle_text>
|
||||
<shot_type_angle_movement>中景平视，缓慢拉镜</shot_type_angle_movement>
|
||||
<scene_and_dialogue>产品正面展示，明亮背景。对白：39块钱625ml，厨房重油污的可以试一瓶</scene_and_dialogue>
|
||||
<action_details>产品置于画面中央，轻微转动展示瓶身</action_details>
|
||||
<audio_bgm>温暖收尾BGM</audio_bgm>
|
||||
<transition>结束</transition>
|
||||
<reference_image_index>0</reference_image_index>
|
||||
<ken_burns start="50,50" end="20,20" ease="ease-in-out"/>
|
||||
</clip>
|
||||
</clips>"""
|
||||
|
||||
# ── 模板5：文案审核（LLM）──────────────────────────────────────────────
|
||||
_REVIEW_SYSTEM = f"""你是短视频文案合规审核员，从6个维度逐条检查文案：
|
||||
1. 违规词：有没有平台禁用词、敏感词。
|
||||
2. 夸大承诺：有没有“包治百病”“100%有效”“保证赚钱”等绝对化、夸大表述。
|
||||
3. 事实一致性：有没有编造价格、数据、认证，或者用户没提到的产品特性。
|
||||
4. 用户意图保留：在 ai_polish 和 user_primary 模式下，core_messages 中 must_keep=true 的点是否都保留了。
|
||||
5. 结构完整性：标题、钩子、正文、行动号召是否齐全。
|
||||
6. 语气人设：是否符合选定的人设语气，有没有“家人们谁懂啊”“绝绝子”“宝子们”等套路词。
|
||||
|
||||
{GLOBAL_CONSTRAINTS}
|
||||
|
||||
请严格按下面的标签格式输出，不要解释，不要用代码块：
|
||||
<passed> 整体是否通过，只写 true 或 false。
|
||||
<issues> 每个问题用一个 <issue> 标签，属性 dimension 是维度名、severity 取 error 或 warning、location 是问题所在（如 hook、body_points、cta），标签内容写问题描述；没有问题就输出空标签。
|
||||
<rewrite_suggestions> 每条具体修改建议用一个 <suggestion> 标签；没有就输出空标签。"""
|
||||
|
||||
_REVIEW_USER = """本次创作模式：{fusion_level}
|
||||
待审核文案：
|
||||
{fusion_result}
|
||||
用户意图解析（用于核对核心信息是否保留）：
|
||||
{intent_result}
|
||||
|
||||
请按6个维度审核，按标签格式输出。"""
|
||||
|
||||
_REVIEW_EXAMPLE = """<passed>false</passed>
|
||||
<issues>
|
||||
<issue dimension="夸大承诺" severity="error" location="body_points">出现了“一喷100%掉光”的绝对化表述，违反广告法</issue>
|
||||
<issue dimension="用户意图保留" severity="warning" location="cta">用户强调的“39块钱”没有保留</issue>
|
||||
</issues>
|
||||
<rewrite_suggestions>
|
||||
<suggestion>把“一喷100%掉光”改为“喷上等几分钟，大部分油污能擦掉”</suggestion>
|
||||
<suggestion>在结尾补回“39块钱625ml”</suggestion>
|
||||
</rewrite_suggestions>"""
|
||||
|
||||
|
||||
# 5 套模板默认数据（seed 数据源与 loader 的兜底）
|
||||
DEFAULT_TEMPLATES: list[dict] = [
|
||||
{
|
||||
"name": "图片多模态分析",
|
||||
"prompt_type": "image_analysis",
|
||||
"version": TEMPLATE_VERSION,
|
||||
"system_prompt": _IMAGE_ANALYSIS_SYSTEM,
|
||||
"user_prompt_template": _IMAGE_ANALYSIS_USER,
|
||||
"example_output": _IMAGE_ANALYSIS_EXAMPLE,
|
||||
"is_active": True,
|
||||
},
|
||||
{
|
||||
"name": "用户文案意图解析",
|
||||
"prompt_type": "intent_parsing",
|
||||
"version": TEMPLATE_VERSION,
|
||||
"system_prompt": _INTENT_SYSTEM,
|
||||
"user_prompt_template": _INTENT_USER,
|
||||
"example_output": _INTENT_EXAMPLE,
|
||||
"is_active": True,
|
||||
},
|
||||
{
|
||||
"name": "文案融合生成",
|
||||
"prompt_type": "copy_fusion",
|
||||
"version": TEMPLATE_VERSION,
|
||||
"system_prompt": _FUSION_SYSTEM,
|
||||
"user_prompt_template": _FUSION_USER,
|
||||
"example_output": _FUSION_EXAMPLE,
|
||||
"is_active": True,
|
||||
},
|
||||
{
|
||||
"name": "编导级分镜",
|
||||
"prompt_type": "storyboard",
|
||||
"version": TEMPLATE_VERSION,
|
||||
"system_prompt": _STORYBOARD_SYSTEM,
|
||||
"user_prompt_template": _STORYBOARD_USER,
|
||||
"example_output": _STORYBOARD_EXAMPLE,
|
||||
"is_active": True,
|
||||
},
|
||||
{
|
||||
"name": "文案审核",
|
||||
"prompt_type": "review",
|
||||
"version": TEMPLATE_VERSION,
|
||||
"system_prompt": _REVIEW_SYSTEM,
|
||||
"user_prompt_template": _REVIEW_USER,
|
||||
"example_output": _REVIEW_EXAMPLE,
|
||||
"is_active": True,
|
||||
},
|
||||
]
|
||||
@@ -1,337 +0,0 @@
|
||||
"""文案审核 + 自动重写（#2040 第5套 Prompt）。
|
||||
|
||||
6 维度：违规词 / 夸大承诺 / 事实一致性 / 用户意图保留 / 结构完整性 / 语气人设。
|
||||
LLM 审核之外叠加本地规则预检（保证即使 LLM 不可用也能兜住广告法红线）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import re
|
||||
from typing import Optional
|
||||
|
||||
from packages.application.viral_video import xml_parser as xp
|
||||
from packages.application.viral_video.prompt_loader import (
|
||||
get_template,
|
||||
render_system_prompt,
|
||||
render_user_prompt,
|
||||
)
|
||||
from packages.application.viral_video.schemas import (
|
||||
FusionResult,
|
||||
IntentResult,
|
||||
ReviewIssue,
|
||||
ReviewResult,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 本地规则：绝对化/夸大词
|
||||
_EXAGGERATION_PATTERNS = [
|
||||
r"100\s*%",
|
||||
r"百分百",
|
||||
r"包治百病",
|
||||
r"保证.{0,8}(有效|赚钱|瘦|好)",
|
||||
r"绝对(有效|安全|靠谱)",
|
||||
r"全网第一",
|
||||
r"国家级",
|
||||
r"特效",
|
||||
r"立刻见效",
|
||||
r"一喷(就|全|100)",
|
||||
]
|
||||
|
||||
# 本地规则：平台违规/套路词
|
||||
_VIOLATION_PHRASES = [
|
||||
"家人们谁懂啊",
|
||||
"绝绝子",
|
||||
"宝子们",
|
||||
"yyds",
|
||||
"最(好|强|牛|便宜)", # 广告法极限词
|
||||
"第一(名|品牌)?",
|
||||
]
|
||||
|
||||
_LOCATIONS = ["title", "hook", "body_points", "cta", "script_segments"]
|
||||
|
||||
|
||||
class Reviewer:
|
||||
# markdown展示字段不参与合规审核（避免格式字符误判）
|
||||
_MARKDOWN_FIELDS = {"summary_markdown", "copy_display_markdown"}
|
||||
|
||||
def __init__(self, client=None):
|
||||
if client is None:
|
||||
try:
|
||||
from packages.shared.ai_router import ai_router
|
||||
|
||||
client = ai_router.get_llm_client("copy_review")
|
||||
except Exception:
|
||||
from packages.shared.ai_client import get_doubao_client
|
||||
|
||||
client = get_doubao_client()
|
||||
self.client = client
|
||||
|
||||
# ── 审核 ────────────────────────────────────────────────────────────
|
||||
def review(self, fusion: FusionResult, intent: IntentResult, fusion_level: str) -> ReviewResult:
|
||||
local = self._rule_check(fusion, intent, fusion_level)
|
||||
llm_result = self._llm_review(fusion, intent, fusion_level)
|
||||
if llm_result is None:
|
||||
# LLM审核失败（超时/网络错误等），降级放行，不阻断渲染
|
||||
return ReviewResult(
|
||||
passed=True,
|
||||
issues=local,
|
||||
rewrite_suggestions=[],
|
||||
raw="",
|
||||
)
|
||||
# LLM 与本地规则合并去重
|
||||
issues = self._merge_issues(llm_result.issues, local)
|
||||
return ReviewResult(
|
||||
passed=llm_result.passed and not local,
|
||||
issues=issues,
|
||||
rewrite_suggestions=llm_result.rewrite_suggestions,
|
||||
raw=llm_result.raw,
|
||||
)
|
||||
|
||||
def _llm_review(self, fusion: FusionResult, intent: IntentResult, fusion_level: str) -> Optional[ReviewResult]:
|
||||
try:
|
||||
return self._llm_review_inner(fusion, intent, fusion_level)
|
||||
except Exception as e:
|
||||
import logging
|
||||
|
||||
logging.getLogger(__name__).warning("[Reviewer] LLM审核调用异常，降级放行: %s", e)
|
||||
return None
|
||||
|
||||
def _llm_review_inner(
|
||||
self, fusion: FusionResult, intent: IntentResult, fusion_level: str
|
||||
) -> Optional[ReviewResult]:
|
||||
template = get_template("review")
|
||||
system = render_system_prompt(template)
|
||||
user = render_user_prompt(
|
||||
template,
|
||||
fusion_level=fusion_level,
|
||||
fusion_result=self._fusion_text(fusion),
|
||||
intent_result=self._intent_text(intent),
|
||||
)
|
||||
raw = self.client.chat_completion(
|
||||
[
|
||||
{"role": "system", "content": system},
|
||||
{"role": "user", "content": user},
|
||||
],
|
||||
temperature=0.2,
|
||||
max_tokens=1024,
|
||||
timeout=25,
|
||||
)
|
||||
if not raw:
|
||||
return None
|
||||
passed = xp.text_of(raw, "passed").strip().lower()
|
||||
issues = [
|
||||
ReviewIssue(
|
||||
dimension=n["attrs"].get("dimension", "未知维度"),
|
||||
severity=n["attrs"].get("severity", "warning"),
|
||||
location=n["attrs"].get("location", ""),
|
||||
text=n["text"],
|
||||
)
|
||||
for n in xp.find_all(raw, "issue")
|
||||
if n["text"]
|
||||
]
|
||||
suggestions = [n["text"] for n in xp.find_all(raw, "suggestion") if n["text"]]
|
||||
parsed = ReviewResult(
|
||||
passed=passed == "true" and not issues,
|
||||
issues=issues,
|
||||
rewrite_suggestions=suggestions,
|
||||
raw=raw,
|
||||
)
|
||||
return parsed
|
||||
|
||||
# ── 本地规则预检 ────────────────────────────────────────────────────
|
||||
def _rule_check(self, fusion: FusionResult, intent, fusion_level: str) -> list[ReviewIssue]:
|
||||
issues: list[ReviewIssue] = []
|
||||
for location, text in self._segments(fusion):
|
||||
for pattern in _EXAGGERATION_PATTERNS:
|
||||
if re.search(pattern, text):
|
||||
issues.append(
|
||||
ReviewIssue(
|
||||
dimension="夸大承诺",
|
||||
severity="error",
|
||||
location=location,
|
||||
text=f"出现夸大/绝对化表述：{self._hit(text, pattern)}",
|
||||
)
|
||||
)
|
||||
for phrase in _VIOLATION_PHRASES:
|
||||
if re.search(phrase, text, flags=re.IGNORECASE):
|
||||
issues.append(
|
||||
ReviewIssue(
|
||||
dimension="违规词",
|
||||
severity="error",
|
||||
location=location,
|
||||
text=f"出现违规或套路词：{self._hit(text, phrase)}",
|
||||
)
|
||||
)
|
||||
|
||||
# 结构完整性
|
||||
if not fusion.title:
|
||||
issues.append(ReviewIssue(dimension="结构完整性", severity="warning", location="title", text="缺少标题"))
|
||||
if not fusion.hook:
|
||||
issues.append(ReviewIssue(dimension="结构完整性", severity="warning", location="hook", text="缺少开头钩子"))
|
||||
if not fusion.cta:
|
||||
issues.append(ReviewIssue(dimension="结构完整性", severity="warning", location="cta", text="缺少行动号召"))
|
||||
|
||||
# 用户意图保留（must_keep）
|
||||
full_text = self._fusion_text(fusion)
|
||||
if fusion_level in {"ai_polish", "user_primary"} and intent is not None:
|
||||
for message in intent.core_messages:
|
||||
if message.must_keep:
|
||||
key = self._compact(message.text)
|
||||
if key and key[:10] not in self._compact(full_text):
|
||||
issues.append(
|
||||
ReviewIssue(
|
||||
dimension="用户意图保留",
|
||||
severity="warning",
|
||||
location="script_segments",
|
||||
text=f"用户核心信息被丢失：{message.text[:30]}",
|
||||
)
|
||||
)
|
||||
for brand in intent.personal_brands:
|
||||
if brand.text and brand.text not in full_text:
|
||||
issues.append(
|
||||
ReviewIssue(
|
||||
dimension="事实一致性",
|
||||
severity="error",
|
||||
location="script_segments",
|
||||
text=f"personal_brands 事实信息未原样保留：{brand.text[:30]}",
|
||||
)
|
||||
)
|
||||
return issues
|
||||
|
||||
@staticmethod
|
||||
def _hit(text: str, pattern: str) -> str:
|
||||
match = re.search(pattern, text, flags=re.IGNORECASE)
|
||||
return match.group(0) if match else pattern
|
||||
|
||||
@staticmethod
|
||||
def _compact(text: str) -> str:
|
||||
return re.sub(r"[\s，。！？、,.!?；;：:\"'“”‘’（）()【】\[\]]", "", text)
|
||||
|
||||
@staticmethod
|
||||
def _merge_issues(llm_issues: list[ReviewIssue], local: list[ReviewIssue]) -> list[ReviewIssue]:
|
||||
merged = list(local)
|
||||
seen = {(i.dimension, Reviewer._compact(i.text)[:20]) for i in local}
|
||||
for issue in llm_issues:
|
||||
key = (issue.dimension, Reviewer._compact(issue.text)[:20])
|
||||
if key not in seen:
|
||||
merged.append(issue)
|
||||
seen.add(key)
|
||||
return merged
|
||||
|
||||
# ── 自动重写（1 次）─────────────────────────────────────────────────
|
||||
def rewrite(
|
||||
self,
|
||||
fusion: FusionResult,
|
||||
review: ReviewResult,
|
||||
intent: IntentResult,
|
||||
fusion_level: str,
|
||||
) -> FusionResult:
|
||||
from packages.application.viral_video.generator import CopyGenerator
|
||||
|
||||
template = get_template("copy_fusion")
|
||||
system_kwargs = {
|
||||
"fusion_instruction": (
|
||||
"【本次任务：按审核意见修正文案】只修改指出的问题，其他内容尽量原样保留；"
|
||||
"personal_brands 事实信息逐字保留；修正后按原标签格式完整输出。"
|
||||
),
|
||||
"global_constraints": "",
|
||||
"negative_rules": "",
|
||||
}
|
||||
issue_text = "\n".join(f"- [{i.dimension}/{i.location}] {i.text}" for i in review.issues)
|
||||
suggestion_text = "\n".join(f"- {s}" for s in review.rewrite_suggestions)
|
||||
user = render_user_prompt(
|
||||
template,
|
||||
industry="",
|
||||
target_customer="",
|
||||
marketing_purpose="",
|
||||
duration=fusion.estimated_duration or 15,
|
||||
image_analysis="（沿用原图片分析）",
|
||||
intent_result=self._intent_text(intent),
|
||||
)
|
||||
user = (
|
||||
f"{user}\n\n原文案：\n{self._fusion_text(fusion)}\n\n"
|
||||
f"审核发现的问题：\n{issue_text}\n\n修改建议：\n{suggestion_text or '（无）'}\n"
|
||||
"请输出修正后的完整文案。"
|
||||
)
|
||||
system = render_system_prompt(template, **system_kwargs)
|
||||
raw = self.client.chat_completion(
|
||||
[
|
||||
{"role": "system", "content": system},
|
||||
{"role": "user", "content": user},
|
||||
],
|
||||
temperature=0.5,
|
||||
max_tokens=2048,
|
||||
timeout=25,
|
||||
)
|
||||
if not raw:
|
||||
return self._rule_fix(fusion, review)
|
||||
rewritten = CopyGenerator._parse_fusion(CopyGenerator(self.client), raw)
|
||||
if not rewritten.title and not rewritten.script_segments:
|
||||
return self._rule_fix(fusion, review)
|
||||
# 保底：personal_brands 必须保留
|
||||
full = self._fusion_text(rewritten)
|
||||
for brand in intent.personal_brands:
|
||||
if brand.text and brand.text not in full:
|
||||
rewritten.cta = (rewritten.cta + brand.text).strip()
|
||||
return rewritten
|
||||
|
||||
def _rule_fix(self, fusion: FusionResult, review: ReviewResult) -> FusionResult:
|
||||
"""LLM 重写不可用时的本地兜底：删除/替换明显违规表述。"""
|
||||
replacements = [
|
||||
(re.compile(r"100\s*%|百分百"), "大部分"),
|
||||
(re.compile(r"绝对(有效|安全|靠谱)"), "比较\\1"),
|
||||
(re.compile(r"包治百病"), "适用多种情况"),
|
||||
(re.compile(r"立刻见效"), "坚持使用会有改善"),
|
||||
(re.compile(r"一喷(就|全|100%)"), "喷上等一会儿可以"),
|
||||
(re.compile(r"家人们谁懂啊|绝绝子|宝子们|yyds", re.IGNORECASE), ""),
|
||||
(re.compile(r"最好|最强|最牛|最便宜"), "很不错"),
|
||||
]
|
||||
|
||||
def fix(text: str) -> str:
|
||||
for pattern, repl in replacements:
|
||||
text = pattern.sub(repl, text)
|
||||
return text
|
||||
|
||||
fusion.title = fix(fusion.title)
|
||||
fusion.hook = fix(fusion.hook)
|
||||
fusion.cta = fix(fusion.cta)
|
||||
for point in fusion.body_points:
|
||||
point.text = fix(point.text)
|
||||
point.elaboration = fix(point.elaboration)
|
||||
for segment in fusion.script_segments:
|
||||
segment.text = fix(segment.text)
|
||||
fusion.raw = ""
|
||||
return fusion
|
||||
|
||||
# ── 文本工具 ────────────────────────────────────────────────────────
|
||||
@staticmethod
|
||||
def _segments(fusion: FusionResult):
|
||||
yield "title", fusion.title
|
||||
yield "hook", fusion.hook
|
||||
for point in fusion.body_points:
|
||||
yield "body_points", f"{point.text} {point.elaboration}"
|
||||
yield "cta", fusion.cta
|
||||
for segment in fusion.script_segments:
|
||||
yield "script_segments", segment.text
|
||||
|
||||
@staticmethod
|
||||
def _fusion_text(fusion: FusionResult) -> str:
|
||||
_MARKDOWN_FIELDS = {"summary_markdown", "copy_display_markdown"}
|
||||
parts = [fusion.title, fusion.hook]
|
||||
parts += [p.text for p in fusion.body_points]
|
||||
parts += [s.text for s in fusion.script_segments]
|
||||
parts.append(fusion.cta)
|
||||
# 过滤掉markdown展示字段，避免格式字符被误判
|
||||
parts = [p for p in parts if not any(mk in p for mk in _MARKDOWN_FIELDS)]
|
||||
return "\n".join(p for p in parts if p)
|
||||
|
||||
@staticmethod
|
||||
def _intent_text(intent) -> str:
|
||||
if intent is None:
|
||||
return "无意图信息"
|
||||
parts = [f"意图：{intent.intent_summary}"]
|
||||
parts += [f"核心信息[must_keep={m.must_keep}]：{m.text}" for m in intent.core_messages]
|
||||
parts += [f"事实（{b.category}）：{b.text}" for b in intent.personal_brands]
|
||||
return "\n".join(parts)
|
||||
@@ -1,116 +0,0 @@
|
||||
"""内部 Pydantic 校验模型（不暴露给运营，运营只看 DB 里的纯文本）。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class ProductItem(BaseModel):
|
||||
name: str = "无法判断"
|
||||
features: str = "无法判断"
|
||||
position: str = "secondary"
|
||||
image_index: int = 0
|
||||
|
||||
|
||||
class ColorItem(BaseModel):
|
||||
hex: str = "#000000"
|
||||
name: str = "无法判断"
|
||||
coverage: float = 0.0
|
||||
|
||||
|
||||
class TextItem(BaseModel):
|
||||
text: str = ""
|
||||
position: str = ""
|
||||
|
||||
|
||||
class ImageAnalysis(BaseModel):
|
||||
products: list[ProductItem] = Field(default_factory=list)
|
||||
colors: list[ColorItem] = Field(default_factory=list)
|
||||
has_person: bool = False
|
||||
person_count: int = 0
|
||||
people: dict[str, str] = Field(default_factory=dict)
|
||||
mood: str = ""
|
||||
visible_text: list[TextItem] = Field(default_factory=list)
|
||||
scene: str = ""
|
||||
quality: dict[str, str] = Field(default_factory=dict)
|
||||
key_selling_points: list[str] = Field(default_factory=list)
|
||||
raw: str = ""
|
||||
|
||||
|
||||
class CoreMessage(BaseModel):
|
||||
text: str
|
||||
must_keep: bool = False
|
||||
confidence: float = 0.0
|
||||
|
||||
|
||||
class PersonalBrand(BaseModel):
|
||||
text: str
|
||||
category: str = "brand"
|
||||
|
||||
|
||||
class IntentResult(BaseModel):
|
||||
intent_summary: str = ""
|
||||
core_messages: list[CoreMessage] = Field(default_factory=list)
|
||||
personal_brands: list[PersonalBrand] = Field(default_factory=list)
|
||||
emotion_tone: str = ""
|
||||
missing_info: list[str] = Field(default_factory=list)
|
||||
raw: str = ""
|
||||
|
||||
|
||||
class BodyPoint(BaseModel):
|
||||
text: str
|
||||
elaboration: str = ""
|
||||
image_index: int = 0
|
||||
|
||||
|
||||
class ScriptSegment(BaseModel):
|
||||
text: str
|
||||
duration_sec: float = 0
|
||||
image_index: int = 0
|
||||
|
||||
|
||||
class FusionResult(BaseModel):
|
||||
title: str = ""
|
||||
hook: str = ""
|
||||
body_points: list[BodyPoint] = Field(default_factory=list)
|
||||
cta: str = ""
|
||||
script_segments: list[ScriptSegment] = Field(default_factory=list)
|
||||
word_count: int = 0
|
||||
estimated_duration: int = 0
|
||||
raw: str = ""
|
||||
|
||||
|
||||
class KenBurns(BaseModel):
|
||||
start: str = "0,0"
|
||||
end: str = "0,0"
|
||||
ease: str = "linear"
|
||||
|
||||
|
||||
class Clip(BaseModel):
|
||||
image_index: int = 0
|
||||
transition: str = "cut"
|
||||
zoom: str | None = None
|
||||
duration_sec: float = 0
|
||||
bgm_note: str = ""
|
||||
voice_text: str = ""
|
||||
subtitle_text: str = ""
|
||||
ken_burns: KenBurns = Field(default_factory=KenBurns)
|
||||
|
||||
|
||||
class Storyboard(BaseModel):
|
||||
clips: list[Clip] = Field(default_factory=list)
|
||||
raw: str = ""
|
||||
|
||||
|
||||
class ReviewIssue(BaseModel):
|
||||
dimension: str
|
||||
severity: str = "warning"
|
||||
location: str = ""
|
||||
text: str = ""
|
||||
|
||||
|
||||
class ReviewResult(BaseModel):
|
||||
passed: bool = True
|
||||
issues: list[ReviewIssue] = Field(default_factory=list)
|
||||
rewrite_suggestions: list[str] = Field(default_factory=list)
|
||||
raw: str = ""
|
||||
@@ -1,104 +0,0 @@
|
||||
"""XML 标签式输出解析器（替代 json.loads）。
|
||||
|
||||
LLM 按 ``<tag attr="x">内容</tag>`` 输出，本模块解析，解析失败不抛异常，
|
||||
由调用方走规则 fallback。采用栈式扫描，嵌套标签全部可提取（内外层都保留）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from html import unescape
|
||||
from typing import Optional
|
||||
|
||||
_OPEN_RE = re.compile(r"<(?P<tag>[\w-]+)(?P<attrs>(?:\s(?:[^>]*?\S)?)?)(?P<self>/?)>")
|
||||
_CLOSE_RE = re.compile(r"</(?P<tag>[\w-]+)\s*>")
|
||||
_ATTR_RE = re.compile(r"""([\w:-]+)\s*=\s*(?:"([^"]*)"|'([^']*)')""")
|
||||
|
||||
|
||||
def parse_attributes(raw: str) -> dict[str, str]:
|
||||
"""解析标签属性字符串。"""
|
||||
attrs: dict[str, str] = {}
|
||||
for match in _ATTR_RE.finditer(raw or ""):
|
||||
value = match.group(2) if match.group(2) is not None else match.group(3)
|
||||
attrs[match.group(1)] = value
|
||||
return attrs
|
||||
|
||||
|
||||
def parse_tags(text: Optional[str]) -> list[dict]:
|
||||
"""提取全部标签（含嵌套内外层），返回 [{tag, attrs, text}]，按开标签出现顺序。"""
|
||||
if not text:
|
||||
return []
|
||||
results: list[dict] = []
|
||||
stack: list[dict] = []
|
||||
token_re = re.compile(r"<[^>]+>")
|
||||
for token in token_re.finditer(text):
|
||||
raw_token = token.group(0)
|
||||
# 先按开/闭标签匹配
|
||||
open_match = _OPEN_RE.match(raw_token)
|
||||
close_match = _CLOSE_RE.match(raw_token)
|
||||
is_close_tag = raw_token.startswith("</")
|
||||
if not is_close_tag and open_match:
|
||||
is_self_close = open_match.group("self") == "/"
|
||||
node = {
|
||||
"tag": open_match.group("tag"),
|
||||
"attrs": parse_attributes(open_match.group("attrs")),
|
||||
"text": "",
|
||||
"_start": token.end(),
|
||||
}
|
||||
if is_self_close:
|
||||
node.pop("_start")
|
||||
results.append(node)
|
||||
else:
|
||||
stack.append(node)
|
||||
results.append(node)
|
||||
elif is_close_tag and close_match:
|
||||
tag = close_match.group("tag")
|
||||
# 弹出到最近同名开标签
|
||||
for idx in range(len(stack) - 1, -1, -1):
|
||||
if stack[idx]["tag"] == tag:
|
||||
node = stack[idx]
|
||||
node["text"] = unescape(text[node["_start"] : token.start()].strip())
|
||||
node.pop("_start", None)
|
||||
del stack[idx:]
|
||||
break
|
||||
# 未闭合标签：给剩余部分作为文本
|
||||
for node in stack:
|
||||
if "_start" in node:
|
||||
node["text"] = unescape(text[node["_start"] :].strip())
|
||||
node.pop("_start", None)
|
||||
return results
|
||||
|
||||
|
||||
def find_all(text: Optional[str], tag: str) -> list[dict]:
|
||||
"""提取指定标签的全部节点。"""
|
||||
return [n for n in parse_tags(text) if n["tag"] == tag]
|
||||
|
||||
|
||||
def find_first(text: Optional[str], tag: str) -> Optional[dict]:
|
||||
nodes = find_all(text, tag)
|
||||
return nodes[0] if nodes else None
|
||||
|
||||
|
||||
def text_of(text: Optional[str], tag: str, default: str = "") -> str:
|
||||
node = find_first(text, tag)
|
||||
return node["text"] if node else default
|
||||
|
||||
|
||||
def attr_bool(value: Optional[str], default: bool = False) -> bool:
|
||||
if value is None:
|
||||
return default
|
||||
return value.strip().lower() in {"true", "1", "yes", "是"}
|
||||
|
||||
|
||||
def attr_float(value: Optional[str], default: float = 0.0) -> float:
|
||||
try:
|
||||
return float(value) if value is not None and value.strip() else default
|
||||
except (TypeError, ValueError):
|
||||
return default
|
||||
|
||||
|
||||
def attr_int(value: Optional[str], default: int = 0) -> int:
|
||||
try:
|
||||
return int(float(value)) if value is not None and value.strip() else default
|
||||
except (TypeError, ValueError):
|
||||
return default
|
||||
+16
-60
@@ -80,46 +80,31 @@ class SharedSettings(BaseSettings):
|
||||
|
||||
# ── CosyVoice (阿里云百炼语音合成) ───────────────────────────────────
|
||||
cosyvoice_api_key: str = ""
|
||||
cosyvoice_base_url: str = ""
|
||||
cosyvoice_model: str = ""
|
||||
cosyvoice_voice: str = "longxiaochun_v3"
|
||||
cosyvoice_base_url: str = "https://dashscope.aliyuncs.com/api/v1"
|
||||
cosyvoice_model: str = "cosyvoice-v3-flash"
|
||||
cosyvoice_voice: str = "longxiaochun_v3" # 默认音色（v3 系列系统音色带 _v3 后缀）
|
||||
cosyvoice_sample_rate: int = 22050
|
||||
cosyvoice_format: str = "mp3"
|
||||
cosyvoice_format: str = "mp3" # 输出格式：mp3/wav/pcm
|
||||
# 音色克隆模型名（固定为 voice-enrollment）
|
||||
cosyvoice_clone_model: str = ""
|
||||
cosyvoice_clone_model: str = "voice-enrollment"
|
||||
|
||||
# ── 豆包大模型（火山引擎方舟） ────────────────────────────────────────
|
||||
# AI模型路由化：model/base_url 默认值清空，由 DB ai_models/ai_capability_configs 配置驱动。
|
||||
# 环境变量仍可覆盖（兼容旧部署）；无任何配置时 ai_router fallback 提供最终默认值。
|
||||
doubao_api_key: str = ""
|
||||
doubao_model: str = ""
|
||||
doubao_fast_model: str = ""
|
||||
doubao_base_url: str = ""
|
||||
doubao_timeout: int = 45
|
||||
doubao_max_retries: int = 3
|
||||
doubao_vision_model: str = ""
|
||||
doubao_vision_lite_model: str = ""
|
||||
doubao_vision_use_lite: bool = True
|
||||
doubao_embedding_model: str = ""
|
||||
doubao_video_model: str = ""
|
||||
doubao_video_timeout: int = 600
|
||||
doubao_video_poll_interval: int = 10
|
||||
doubao_image_model: str = ""
|
||||
doubao_image_size: str = "1K"
|
||||
doubao_image_timeout: int = 60
|
||||
doubao_trust_chain_enabled: bool = True
|
||||
|
||||
# ── DashScope (阿里云百炼 Wan 3.0 等) ─────────────────────────────────
|
||||
dashscope_api_key: str = ""
|
||||
dashscope_base_url: str = ""
|
||||
dashscope_video_timeout: int = 900
|
||||
dashscope_video_poll_interval: int = 10
|
||||
doubao_model: str = "doubao-seed-1-6-250615"
|
||||
doubao_base_url: str = "https://ark.cn-beijing.volces.com/api/v3"
|
||||
doubao_timeout: int = 30
|
||||
doubao_max_retries: int = 2
|
||||
doubao_vision_model: str = "doubao-1-5-vision-pro-250915"
|
||||
doubao_embedding_model: str = "doubao-embedding-large-text-240915"
|
||||
doubao_video_model: str = "doubao-seedance-2-5-260628"
|
||||
doubao_video_timeout: int = 600 # 视频生成轮询总超时（秒）
|
||||
doubao_video_poll_interval: int = 10 # 轮询间隔（秒）
|
||||
|
||||
# ── MediaKit (火山引擎 AI 媒体工具) ──────────────────────────────────
|
||||
mediakit_api_key: str = ""
|
||||
mediakit_base_url: str = ""
|
||||
mediakit_base_url: str = "https://mediakit.cn-beijing.volces.com/api/v1"
|
||||
mediakit_timeout: int = 60
|
||||
mediakit_cover_enabled: bool = False
|
||||
mediakit_cover_enabled: bool = False # 封面抽帧是否走MediaKit(默认false走本地ffmpeg+cv2，<2s完成)
|
||||
|
||||
# ── 积分/会员系统 (#1895) ────────────────────────────────────────────
|
||||
# 积分系统总开关（产品要求 #1895：暂停积分系统但保留全部代码/表/接口）。
|
||||
@@ -173,35 +158,6 @@ class SharedSettings(BaseSettings):
|
||||
# 判断 Worker 可用的心跳新鲜度窗口（秒）—— last_heartbeat_at 在窗口内视为在线
|
||||
gpu_worker_stale_seconds: int = 300
|
||||
|
||||
# ── Ditto 蚂蚁数字人口型 API（#2076）─────────────────────────────────
|
||||
# 是否优先使用 Ditto（蚂蚁数字人，替代 MuseTalk）。开关开启且 base_url 配置
|
||||
# 非空时，对口型任务优先走 Ditto；失败后回退 MuseTalk/MediaKit。
|
||||
use_ditto_lipsync: bool = Field(
|
||||
default=False,
|
||||
validation_alias=AliasChoices("USE_DITTO_LIPSYNC", "use_ditto_lipsync"),
|
||||
)
|
||||
# Ditto FastAPI 内网地址（Tailscale），如 http://100.x.x.x:8000
|
||||
ditto_api_base_url: str = Field(
|
||||
default="",
|
||||
validation_alias=AliasChoices("DITTO_API_BASE_URL", "ditto_api_base_url"),
|
||||
)
|
||||
# 默认人物模板视频 URL（正面 5-10 秒循环、光线均匀、半身）。Ditto 模式下忽略
|
||||
# 用户上传的驱动视频/图片，统一用该模板；后续可扩展为多模板让用户选择。
|
||||
ditto_default_video_url: str = Field(
|
||||
default="",
|
||||
validation_alias=AliasChoices("DITTO_DEFAULT_VIDEO_URL", "ditto_default_video_url"),
|
||||
)
|
||||
# 429 GPU 繁忙时指数退避最大重试次数
|
||||
ditto_max_retries: int = Field(
|
||||
default=3,
|
||||
validation_alias=AliasChoices("DITTO_MAX_RETRIES", "ditto_max_retries"),
|
||||
)
|
||||
# Ditto 单次请求超时（秒）：数字人半身视频推理通常 30-120s
|
||||
ditto_request_timeout: int = Field(
|
||||
default=300,
|
||||
validation_alias=AliasChoices("DITTO_REQUEST_TIMEOUT", "ditto_request_timeout"),
|
||||
)
|
||||
|
||||
# ── P4000 NVENC 硬件编码 ────────────────────────────────────────────
|
||||
# GPU 编码总开关；关闭或 endpoint 为空时始终走本机 CPU libx264
|
||||
enable_gpu_encode: bool = Field(
|
||||
|
||||
@@ -60,11 +60,6 @@ class User:
|
||||
# 资料是否已完善（微信新用户首次设置昵称后置 True；邮箱注册默认 True）
|
||||
profile_completed: bool = True
|
||||
|
||||
# 会员字段 (#1895)：与 users 表列对应
|
||||
is_member: bool = False
|
||||
member_type: str | None = None
|
||||
member_expires_at: datetime | None = None
|
||||
|
||||
created_at: datetime = field(default_factory=lambda: datetime.now(UTC))
|
||||
|
||||
|
||||
|
||||
@@ -1,376 +0,0 @@
|
||||
"""功能计费配置服务：从 feature_pricing_configs 读配置，300 秒 TTL 内存缓存。
|
||||
|
||||
配置表由 xiaoxia-admin 侧维护（同库 PostgreSQL），本服务只读。
|
||||
DB 不可用 / 表不存在 / 无数据时自动回落到内置兜底配置，保证业务不崩。
|
||||
|
||||
计费公式：最终积分 = (动态成本 + 固定成本) × 利润系数，price_cap 封顶。
|
||||
启用条件：全局 points_enabled 总开关 AND 功能 is_enabled 同时为 true。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Optional
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from packages.adapters.sqlalchemy_impl import session as _session_mod
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
CACHE_TTL_SECONDS = 300.0
|
||||
|
||||
# ── 爆款视频兜底模型单价（与旧硬编码表/现状一致；DB 不可用时使用） ───────
|
||||
# 结构：models[model_key][resolution]["true"/"false"] = 单价
|
||||
# token 模式：元/百万输出 tokens；per_second 模式：元/秒
|
||||
# 注意：仅 seedance-2.5 配置 true（图生视频）单价；其余模型只有 false，
|
||||
# 精确 key 缺失时由 points_rules 回落到 seedance-2.5/false（与旧现状一致）。
|
||||
_FALLBACK_VIRAL_MODEL_PRICING: dict = {
|
||||
"seedance-2.5": {
|
||||
"480p": {"false": 70.0, "true": 42.0},
|
||||
"720p": {"false": 70.0, "true": 42.0},
|
||||
"1080p": {"false": 77.0, "true": 46.0},
|
||||
},
|
||||
"seedance-2.0": {
|
||||
"480p": {"false": 46.0},
|
||||
"720p": {"false": 46.0},
|
||||
"1080p": {"false": 51.0},
|
||||
"4k": {"false": 80.0},
|
||||
},
|
||||
"seedance-2.0-fast": {
|
||||
"480p": {"false": 28.0},
|
||||
"720p": {"false": 28.0},
|
||||
},
|
||||
"seedance-2.0-mini": {
|
||||
"480p": {"false": 9.2},
|
||||
"720p": {"false": 9.2},
|
||||
},
|
||||
"wan-3.0": {
|
||||
"480p": {"false": 0.3},
|
||||
"720p": {"false": 0.6},
|
||||
"1080p": {"false": 1.2},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class FeatureConfig:
|
||||
"""功能计费配置快照。"""
|
||||
|
||||
feature_key: str
|
||||
name: str = ""
|
||||
emoji: str = ""
|
||||
is_enabled: bool = False
|
||||
fixed_cost: float = 0.0
|
||||
profit_multiplier: float = 1.0
|
||||
dynamic_unit_cost: float = 0.0
|
||||
billing_mode: str = "model_based"
|
||||
price_cap: float = 0.0
|
||||
model_pricing: dict = field(default_factory=dict)
|
||||
description: str = ""
|
||||
|
||||
|
||||
# ── 进程内缓存：(loaded_monotonic, {feature_key: FeatureConfig}) ──────────
|
||||
_lock = threading.Lock()
|
||||
_cache: Optional[tuple[float, dict[str, FeatureConfig]]] = None
|
||||
|
||||
|
||||
def _fallback_configs() -> dict[str, FeatureConfig]:
|
||||
"""内置兜底配置：爆款启用（与现状一致），其余两个关闭。"""
|
||||
return {
|
||||
"viral_video": FeatureConfig(
|
||||
feature_key="viral_video",
|
||||
name="爆款视频",
|
||||
emoji="🎬",
|
||||
is_enabled=True,
|
||||
fixed_cost=0.15,
|
||||
profit_multiplier=1.3,
|
||||
dynamic_unit_cost=0.0,
|
||||
billing_mode="model_based",
|
||||
price_cap=0.0,
|
||||
model_pricing=json.loads(json.dumps(_FALLBACK_VIRAL_MODEL_PRICING)),
|
||||
description="爆款视频动态定价（兜底配置）",
|
||||
),
|
||||
"lip_sync": FeatureConfig(
|
||||
feature_key="lip_sync",
|
||||
name="对口型",
|
||||
emoji="🎙️",
|
||||
is_enabled=False,
|
||||
fixed_cost=0.0,
|
||||
profit_multiplier=1.0,
|
||||
dynamic_unit_cost=0.0,
|
||||
billing_mode="per_second",
|
||||
price_cap=0.0,
|
||||
description="对口型计费（兜底配置，默认关闭）",
|
||||
),
|
||||
"smart_edit": FeatureConfig(
|
||||
feature_key="smart_edit",
|
||||
name="智能剪辑",
|
||||
emoji="✂️",
|
||||
is_enabled=False,
|
||||
fixed_cost=0.0,
|
||||
profit_multiplier=1.0,
|
||||
dynamic_unit_cost=0.0,
|
||||
billing_mode="model_based",
|
||||
price_cap=0.0,
|
||||
description="智能剪辑固定价计费（兜底配置，默认关闭）",
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
_lazy_session = None
|
||||
|
||||
|
||||
def _get_session():
|
||||
"""优先用全局 SessionLocal（worker）；否则按应用配置懒建同步引擎（api）。"""
|
||||
global _lazy_session
|
||||
if _session_mod.SessionLocal is not None:
|
||||
return _session_mod.SessionLocal()
|
||||
if _lazy_session is not None:
|
||||
return _lazy_session()
|
||||
try:
|
||||
from packages.config import get_shared_settings
|
||||
|
||||
url = str(get_shared_settings().database_url)
|
||||
except Exception: # noqa: BLE001
|
||||
return None
|
||||
if not url:
|
||||
return None
|
||||
url = url.replace("postgresql+asyncpg://", "postgresql+psycopg://")
|
||||
if url.startswith("postgresql://"):
|
||||
url = url.replace("postgresql://", "postgresql+psycopg://")
|
||||
engine = sa.create_engine(url, pool_pre_ping=True, pool_size=2, max_overflow=2)
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
|
||||
_lazy_session = sessionmaker(bind=engine)
|
||||
return _lazy_session()
|
||||
|
||||
|
||||
def _parse_model_pricing(raw) -> dict:
|
||||
"""解析 model_pricing_json（Text JSON），空/失败 → {}。"""
|
||||
if raw is None:
|
||||
return {}
|
||||
if isinstance(raw, dict):
|
||||
return raw
|
||||
text = str(raw).strip()
|
||||
if not text:
|
||||
return {}
|
||||
try:
|
||||
data = json.loads(text)
|
||||
except (ValueError, TypeError):
|
||||
logger.warning("model_pricing_json 解析失败，按空配置处理: %r", text[:200])
|
||||
return {}
|
||||
return data if isinstance(data, dict) else {}
|
||||
|
||||
|
||||
def _to_float(value, default: float = 0.0) -> float:
|
||||
try:
|
||||
if value is None:
|
||||
return default
|
||||
return float(value)
|
||||
except (TypeError, ValueError):
|
||||
return default
|
||||
|
||||
|
||||
def _load_all() -> dict[str, FeatureConfig]:
|
||||
"""SELECT * FROM feature_pricing_configs，返回 {feature_key: FeatureConfig}。
|
||||
|
||||
表不存在 / DB 异常由调用方捕获并回落兜底配置。
|
||||
"""
|
||||
session = None
|
||||
try:
|
||||
session = _get_session()
|
||||
if session is None:
|
||||
raise RuntimeError("no db session available")
|
||||
sql = sa.text("""
|
||||
SELECT feature_key, name, emoji, is_enabled, fixed_cost,
|
||||
profit_multiplier, dynamic_unit_cost, billing_mode,
|
||||
price_cap, model_pricing_json, description
|
||||
FROM feature_pricing_configs
|
||||
""")
|
||||
rows = session.execute(sql).mappings().all()
|
||||
configs: dict[str, FeatureConfig] = {}
|
||||
for row in rows:
|
||||
key = str(row["feature_key"] or "").strip()
|
||||
if not key:
|
||||
continue
|
||||
configs[key] = FeatureConfig(
|
||||
feature_key=key,
|
||||
name=str(row["name"] or key),
|
||||
emoji=str(row["emoji"] or ""),
|
||||
is_enabled=bool(row["is_enabled"]),
|
||||
fixed_cost=_to_float(row["fixed_cost"]),
|
||||
profit_multiplier=_to_float(row["profit_multiplier"], 1.0),
|
||||
dynamic_unit_cost=_to_float(row["dynamic_unit_cost"]),
|
||||
billing_mode=str(row["billing_mode"] or "model_based"),
|
||||
price_cap=_to_float(row["price_cap"]),
|
||||
model_pricing=_parse_model_pricing(row["model_pricing_json"]),
|
||||
description=str(row["description"] or ""),
|
||||
)
|
||||
return configs
|
||||
finally:
|
||||
if session is not None:
|
||||
try:
|
||||
session.close()
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
|
||||
|
||||
def _get_cache() -> dict[str, FeatureConfig]:
|
||||
"""TTL 内返回缓存，否则重新 load；DB 异常/表不存在时返回内置兜底配置。"""
|
||||
global _cache
|
||||
now = time.monotonic()
|
||||
with _lock:
|
||||
if _cache is not None and now - _cache[0] < CACHE_TTL_SECONDS:
|
||||
return _cache[1]
|
||||
|
||||
try:
|
||||
loaded = _load_all()
|
||||
except Exception: # noqa: BLE001 - 表不存在/DB 不可用时静默回落
|
||||
logger.info("feature_pricing_configs 读取失败，使用内置兜底配置", exc_info=True)
|
||||
return _fallback_configs()
|
||||
|
||||
# DB 可用但表为空：同样回落兜底（保证爆款现状不被改变）
|
||||
if not loaded:
|
||||
fallback = _fallback_configs()
|
||||
with _lock:
|
||||
_cache = (now, fallback)
|
||||
return fallback
|
||||
|
||||
# 以兜底为底（DB 未配置的 feature_key 仍有兜底），DB 行覆盖
|
||||
merged = _fallback_configs()
|
||||
merged.update(loaded)
|
||||
with _lock:
|
||||
_cache = (now, merged)
|
||||
return merged
|
||||
|
||||
|
||||
def get_feature_config(feature_key: str) -> Optional[FeatureConfig]:
|
||||
"""获取指定功能配置，未知 key 返回 None。"""
|
||||
key = str(feature_key or "").strip()
|
||||
if not key:
|
||||
return None
|
||||
return _get_cache().get(key)
|
||||
|
||||
|
||||
def _global_points_enabled() -> bool:
|
||||
"""全局积分总开关（兼容 api / worker 运行时），取不到时默认关闭。"""
|
||||
try:
|
||||
from packages.shared import get_shared_settings
|
||||
|
||||
return bool(get_shared_settings().points_enabled)
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
try:
|
||||
from app.config import settings
|
||||
|
||||
return bool(getattr(settings, "points_enabled", False))
|
||||
except Exception: # noqa: BLE001
|
||||
return False
|
||||
|
||||
|
||||
def is_feature_enabled(feature_key: str) -> bool:
|
||||
"""功能是否启用并扣费：全局 points_enabled AND 功能 is_enabled。"""
|
||||
cfg = get_feature_config(feature_key)
|
||||
if cfg is None:
|
||||
return False
|
||||
return bool(cfg.is_enabled) and _global_points_enabled()
|
||||
|
||||
|
||||
def calculate_price(feature_key: str, dynamic_cost: float = 0.0) -> tuple[float, dict]:
|
||||
"""按公式计算最终积分并返回明细。
|
||||
|
||||
price = (dynamic_cost + fixed_cost) × profit_multiplier
|
||||
price_cap > 0 时封顶（取 min）。
|
||||
功能未启用 → (0.0, breakdown{is_enabled: False, charged: False})。
|
||||
"""
|
||||
cfg = get_feature_config(feature_key)
|
||||
dynamic = max(0.0, _to_float(dynamic_cost))
|
||||
if cfg is None or not cfg.is_enabled:
|
||||
return 0.0, {
|
||||
"feature_key": feature_key,
|
||||
"is_enabled": False,
|
||||
"charged": False,
|
||||
"dynamic_cost": dynamic,
|
||||
"fixed_cost": 0.0,
|
||||
"profit_multiplier": 1.0,
|
||||
"price_cap": 0.0,
|
||||
"final_price": 0.0,
|
||||
}
|
||||
|
||||
fixed = max(0.0, cfg.fixed_cost)
|
||||
multiplier = cfg.profit_multiplier if cfg.profit_multiplier > 0 else 1.0
|
||||
raw_price = (dynamic + fixed) * multiplier
|
||||
cap = cfg.price_cap if cfg.price_cap and cfg.price_cap > 0 else 0.0
|
||||
final_price = min(raw_price, cap) if cap else raw_price
|
||||
final_price = round(float(final_price), 2)
|
||||
breakdown = {
|
||||
"feature_key": cfg.feature_key,
|
||||
"is_enabled": True,
|
||||
"charged": True,
|
||||
"dynamic_cost": round(dynamic, 4),
|
||||
"fixed_cost": float(fixed),
|
||||
"profit_multiplier": float(multiplier),
|
||||
"price_cap": float(cap),
|
||||
"raw_price": round(float(raw_price), 4),
|
||||
"final_price": final_price,
|
||||
}
|
||||
return final_price, breakdown
|
||||
|
||||
|
||||
def lookup_model_price(
|
||||
model_pricing: dict,
|
||||
model_key: str,
|
||||
resolution: str,
|
||||
has_video_input: bool,
|
||||
) -> Optional[float]:
|
||||
"""从 model_pricing dict 取模型单价，兼容两种常见 JSON 结构。
|
||||
|
||||
1. 嵌套：{model: {resolution: {"true"/"false": price}}}
|
||||
（内层 bool key 也兼容直接 bool / 省略）
|
||||
2. 扁平：{"model|resolution|true_or_false": price}
|
||||
（分隔符支持 | / : / , / 空格；bool 段可省略）
|
||||
取不到返回 None。
|
||||
"""
|
||||
if not isinstance(model_pricing, dict):
|
||||
return None
|
||||
model = str(model_key or "").strip()
|
||||
res = str(resolution or "").strip()
|
||||
flag = "true" if has_video_input else "false"
|
||||
|
||||
# 1. 嵌套
|
||||
model_node = model_pricing.get(model)
|
||||
if isinstance(model_node, dict):
|
||||
res_node = model_node.get(res)
|
||||
if isinstance(res_node, dict):
|
||||
# 精确 bool key 命中才返回；不做“只有一个值就取”的模糊匹配
|
||||
# （否则缺失 true 时会错误地取到 false 价，破坏旧版回落规则）
|
||||
if flag in res_node:
|
||||
return _to_float(res_node[flag]) if res_node[flag] is not None else None
|
||||
if has_video_input in res_node:
|
||||
val = res_node[has_video_input]
|
||||
return _to_float(val) if val is not None else None
|
||||
elif isinstance(res_node, (int, float)):
|
||||
return float(res_node)
|
||||
|
||||
# 2. 扁平
|
||||
for sep in ("|", ":", ",", " "):
|
||||
for key in (
|
||||
f"{model}{sep}{res}{sep}{flag}",
|
||||
f"{model}{sep}{res}",
|
||||
):
|
||||
if key in model_pricing:
|
||||
value = model_pricing[key]
|
||||
return _to_float(value) if value is not None else None
|
||||
return None
|
||||
|
||||
|
||||
def refresh_feature_configs() -> None:
|
||||
"""清空缓存（下次读取重新 load DB；测试/admin 改配置后可手动调）。"""
|
||||
global _cache
|
||||
with _lock:
|
||||
_cache = None
|
||||
@@ -9,9 +9,9 @@ from uuid import uuid4
|
||||
class PointsAccount:
|
||||
id: str
|
||||
user_id: str
|
||||
balance: float = 0.0
|
||||
total_earned: float = 0.0
|
||||
total_spent: float = 0.0
|
||||
balance: int = 0
|
||||
total_earned: int = 0
|
||||
total_spent: int = 0
|
||||
created_at: datetime = field(default_factory=lambda: datetime.now(UTC))
|
||||
updated_at: datetime = field(default_factory=lambda: datetime.now(UTC))
|
||||
|
||||
|
||||
+54
-393
@@ -1,384 +1,32 @@
|
||||
"""积分消耗规则配置 (#1895)
|
||||
|
||||
v1.6.1: 按产品决策，智能混剪/AI数字人/AI配音/抖音解析/改写/标题/封面 全部免费，
|
||||
仅保留声音克隆合成（voice_clone_synth）的扣点逻辑；声音克隆训练保持免费。
|
||||
爆款视频（viral_video）走动态定价，计费参数 DB 化（feature_pricing_configs，
|
||||
见 feature_pricing_service），calculate_viral_video_credits 从配置读取单价/
|
||||
固定成本/利润系数/封顶，DB 不可用时回落兜底配置。
|
||||
"""
|
||||
"""积分消耗规则配置 (#1895)"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
|
||||
from packages.domain import feature_pricing_service
|
||||
|
||||
# ============ 爆款视频动态定价 ============
|
||||
# 单价/固定成本/利润系数已 DB 化（feature_pricing_configs，feature_key=viral_video），
|
||||
# 由 feature_pricing_service 读取（300s 缓存），DB 不可用时回落内置兜底配置。
|
||||
# 以下三个常量仅为向后兼容保留（旧引用方/兜底场景），值取自兜底配置。
|
||||
VIRAL_VIDEO_MODEL_PRICES: dict[tuple[str, str, bool], float] = {
|
||||
("seedance-2.5", "480p", False): 70.0,
|
||||
("seedance-2.5", "720p", False): 70.0,
|
||||
("seedance-2.5", "1080p", False): 77.0,
|
||||
("seedance-2.5", "480p", True): 42.0,
|
||||
("seedance-2.5", "720p", True): 42.0,
|
||||
("seedance-2.5", "1080p", True): 46.0,
|
||||
("seedance-2.0", "480p", False): 46.0,
|
||||
("seedance-2.0", "720p", False): 46.0,
|
||||
("seedance-2.0", "1080p", False): 51.0,
|
||||
("seedance-2.0", "4k", False): 80.0,
|
||||
("seedance-2.0-fast", "480p", False): 28.0,
|
||||
("seedance-2.0-fast", "720p", False): 28.0,
|
||||
("seedance-2.0-mini", "480p", False): 9.2,
|
||||
("seedance-2.0-mini", "720p", False): 9.2,
|
||||
("wan-3.0", "480p", False): 0.3,
|
||||
("wan-3.0", "720p", False): 0.6,
|
||||
("wan-3.0", "1080p", False): 1.2,
|
||||
}
|
||||
|
||||
# 固定成本（元）：VLM 分析 + LLM 文案 + TTS + OSS + 服务器（兜底默认值）
|
||||
VIRAL_VIDEO_FIXED_COST = 0.15
|
||||
# 利润系数（兜底默认值）
|
||||
VIRAL_VIDEO_PROFIT_MULTIPLIER = 1.3
|
||||
# Seedance 输出帧率
|
||||
VIRAL_VIDEO_FPS = 24
|
||||
|
||||
# 分辨率别名映射 -> 标准 key
|
||||
_RESOLUTION_ALIASES: dict[str, str] = {
|
||||
"480p": "480p",
|
||||
"普清": "480p",
|
||||
"default": "480p",
|
||||
"low": "480p",
|
||||
"sd": "480p",
|
||||
"720p": "720p",
|
||||
"高清": "720p",
|
||||
"medium": "720p",
|
||||
"hd": "720p",
|
||||
"1080p": "1080p",
|
||||
"超清": "1080p",
|
||||
"high": "1080p",
|
||||
"ultra": "1080p",
|
||||
"全能": "1080p",
|
||||
"fhd": "1080p",
|
||||
}
|
||||
# 分辨率 -> 短边像素数（p 值代表短边，不是 height）
|
||||
_RESOLUTION_SHORT_SIDE: dict[str, int] = {"480p": 480, "720p": 720, "1080p": 1080, "4k": 2160}
|
||||
_RESOLUTION_ALIASES["4k"] = "4k"
|
||||
_RESOLUTION_ALIASES["2160p"] = "4k"
|
||||
_RESOLUTION_ALIASES["uhd"] = "4k"
|
||||
|
||||
|
||||
def resolve_video_dimensions(resolution: str, ratio: str) -> tuple[int, int]:
|
||||
"""把 (resolution, ratio) 解析为 (width, height)。
|
||||
|
||||
resolution 数字代表短边像素数（480p/720p/1080p 等）：
|
||||
- 横屏 16:9：短边是 height，width = short * 16/9
|
||||
- 竖屏 9:16：短边是 width，height = short * 16/9
|
||||
- 方屏 1:1：width = height = short
|
||||
"""
|
||||
key = str(resolution or "").strip()
|
||||
key_l = key.lower()
|
||||
res_key = _RESOLUTION_ALIASES.get(key_l) or _RESOLUTION_ALIASES.get(key) or "720p"
|
||||
short = _RESOLUTION_SHORT_SIDE.get(res_key, 720)
|
||||
r = str(ratio or "").strip().lower()
|
||||
if r == "16:9":
|
||||
# 横屏：短边是 height，width 向上取整并对齐偶数
|
||||
w = math.ceil(short * 16 / 9)
|
||||
h = short
|
||||
elif r == "1:1":
|
||||
w, h = short, short
|
||||
else:
|
||||
# 9:16 竖屏（默认）：短边是 width，height 向上取整并对齐偶数
|
||||
w = short
|
||||
h = math.ceil(short * 16 / 9)
|
||||
# 对齐到偶数（视频编码要求）
|
||||
w = w + (w % 2)
|
||||
h = h + (h % 2)
|
||||
return int(w), int(h)
|
||||
|
||||
|
||||
# ── 爆款视频多模型元数据 (#2159) ──────────────────────────────────────
|
||||
VIRAL_VIDEO_MODEL_CONFIG: dict[str, dict] = {
|
||||
"seedance-2.5": {
|
||||
"key": "seedance-2.5",
|
||||
"display_name": "Seedance 2.5 — 最新最强",
|
||||
"model_id": "doubao-seedance-2-5-260628",
|
||||
"provider": "doubao",
|
||||
"supports_audio": True,
|
||||
"supported_resolutions": ["480p", "720p", "1080p"],
|
||||
"max_duration": 30,
|
||||
"billing_mode": "token",
|
||||
"is_default": True,
|
||||
},
|
||||
"seedance-2.0": {
|
||||
"key": "seedance-2.0",
|
||||
"display_name": "Seedance 2.0 — 正式首选",
|
||||
"model_id": "doubao-seedance-2-0-260128",
|
||||
"provider": "doubao",
|
||||
"supports_audio": True,
|
||||
"supported_resolutions": ["480p", "720p", "1080p", "4k"],
|
||||
"max_duration": 15,
|
||||
"billing_mode": "token",
|
||||
"is_default": False,
|
||||
},
|
||||
"seedance-2.0-fast": {
|
||||
"key": "seedance-2.0-fast",
|
||||
"display_name": "Seedance 2.0 Fast — 快速低成本",
|
||||
"model_id": "doubao-seedance-2-0-fast-260128",
|
||||
"provider": "doubao",
|
||||
"supports_audio": True,
|
||||
"supported_resolutions": ["480p", "720p"],
|
||||
"max_duration": 15,
|
||||
"billing_mode": "token",
|
||||
"is_default": False,
|
||||
},
|
||||
"seedance-2.0-mini": {
|
||||
"key": "seedance-2.0-mini",
|
||||
"display_name": "Seedance 2.0 Mini — 低成本测试",
|
||||
"model_id": "doubao-seedance-2-0-mini-260615",
|
||||
"provider": "doubao",
|
||||
"supports_audio": True,
|
||||
"supported_resolutions": ["480p", "720p"],
|
||||
"max_duration": 15,
|
||||
"billing_mode": "token",
|
||||
"is_default": False,
|
||||
},
|
||||
"wan-3.0": {
|
||||
"key": "wan-3.0",
|
||||
"display_name": "Wan 3.0 — 通义万相（阿里云）",
|
||||
"model_id": "wan3.0-video",
|
||||
"provider": "dashscope",
|
||||
"supports_audio": True,
|
||||
"supported_resolutions": ["480p", "720p", "1080p"],
|
||||
"max_duration": 30,
|
||||
"billing_mode": "per_second",
|
||||
"is_default": False,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def get_viral_video_model_config(model_key: str | None) -> dict:
|
||||
"""获取模型配置，未知 key 回落到默认 seedance-2.5。"""
|
||||
key = (model_key or "").strip().lower()
|
||||
if key and key in VIRAL_VIDEO_MODEL_CONFIG:
|
||||
return VIRAL_VIDEO_MODEL_CONFIG[key]
|
||||
return VIRAL_VIDEO_MODEL_CONFIG["seedance-2.5"]
|
||||
|
||||
|
||||
def list_viral_video_models(
|
||||
include_placeholder: bool = False,
|
||||
dashscope_available: bool = False,
|
||||
) -> list[dict]:
|
||||
"""返回前端可用的模型列表（供 GET /api/v1/viral-video/models 端点用）。"""
|
||||
out: list[dict] = []
|
||||
for _k, cfg in VIRAL_VIDEO_MODEL_CONFIG.items():
|
||||
if cfg.get("_placeholder") and not include_placeholder:
|
||||
continue
|
||||
if cfg.get("provider") == "dashscope" and not dashscope_available:
|
||||
continue
|
||||
out.append(
|
||||
{
|
||||
"key": cfg["key"],
|
||||
"display_name": cfg["display_name"],
|
||||
"supports_audio": bool(cfg.get("supports_audio", True)),
|
||||
"supported_resolutions": list(cfg.get("supported_resolutions", ["720p"])),
|
||||
"max_duration": int(cfg.get("max_duration", 15)),
|
||||
"billing_mode": cfg.get("billing_mode", "token"),
|
||||
"is_default": bool(cfg.get("is_default", False)),
|
||||
}
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
def _match_model_prefix(model: str | None) -> str:
|
||||
"""匹配 model key（支持全部内部别名，未知回落到 seedance-2.5）。
|
||||
|
||||
按 key 长度从长到短匹配，避免 "seedance-2.0-fast" 被 "seedance-2.0" 前缀命中。
|
||||
"""
|
||||
mm = (model or "").strip().lower()
|
||||
for k in sorted(VIRAL_VIDEO_MODEL_CONFIG.keys(), key=len, reverse=True):
|
||||
if mm == k or mm.startswith(k):
|
||||
return k
|
||||
return "seedance-2.5"
|
||||
|
||||
|
||||
def _infer_resolution_key(width: int, height: int) -> str:
|
||||
"""从实际 (width, height) 用短边推断 resolution key。"""
|
||||
short = min(int(width or 720), int(height or 720))
|
||||
if short >= 1900:
|
||||
return "4k"
|
||||
if short >= 1000:
|
||||
return "1080p"
|
||||
if short >= 650:
|
||||
return "720p"
|
||||
return "480p"
|
||||
|
||||
|
||||
def calculate_viral_video_credits_with_breakdown(
|
||||
duration_seconds: int,
|
||||
width: int,
|
||||
height: int,
|
||||
model: str = "seedance-2.5",
|
||||
has_video_input: bool = False,
|
||||
actual_tokens: int | None = None,
|
||||
fps: int = VIRAL_VIDEO_FPS,
|
||||
) -> tuple[float, dict]:
|
||||
"""计算爆款视频所需积分（1 积分 = 1 元），并返回计费公式明细。
|
||||
|
||||
单价/固定成本/利润系数/封顶从 feature_pricing_configs（viral_video）读取；
|
||||
DB 不可用时回落与现状一致的内置兜底配置。
|
||||
|
||||
公式:
|
||||
tokens = duration * width * height * fps / 1024
|
||||
video_cost = tokens / 1_000_000 * model_token_price
|
||||
total = round((video_cost + fixed_cost) * profit_multiplier, 2)
|
||||
price_cap > 0 时封顶取 min
|
||||
若传入 actual_tokens 则用它替代计算值。
|
||||
|
||||
Returns:
|
||||
(credits, breakdown) 二元组：
|
||||
- credits: 四舍五入保留两位小数的最终积分
|
||||
- breakdown: dict，包含 tokens / video_cost / fixed_cost / profit_multiplier /
|
||||
model_price / width / height / fps / feature_enabled / charged / price_cap
|
||||
字段，便于前端展示计费明细。功能关闭时 credits=0、charged=False。
|
||||
"""
|
||||
w = max(1, int(width or 1))
|
||||
h = max(1, int(height or 1))
|
||||
effective_fps = int(fps or VIRAL_VIDEO_FPS)
|
||||
|
||||
prefix = _match_model_prefix(model)
|
||||
cfg = get_viral_video_model_config(prefix)
|
||||
res_key = _infer_resolution_key(w, h)
|
||||
billing = cfg.get("billing_mode", "token")
|
||||
dur = max(1, int(duration_seconds or 15))
|
||||
|
||||
# ── 从 DB 配置（兜底内置）取计费参数 ──
|
||||
feature_cfg = feature_pricing_service.get_feature_config("viral_video")
|
||||
# 注意：此处 feature_enabled 只表示“功能自身开关”，不并入全局 points_enabled
|
||||
# 总开关（保持与旧版计费函数行为一致：价格照常计算）。全局总开关由业务层
|
||||
# （route/worker）通过 feature_pricing_service.is_feature_enabled 统一把关。
|
||||
feature_enabled = bool(feature_cfg.is_enabled) if feature_cfg is not None else True
|
||||
model_pricing = feature_cfg.model_pricing if feature_cfg is not None else {}
|
||||
fixed_cost = float(feature_cfg.fixed_cost) if feature_cfg is not None else float(VIRAL_VIDEO_FIXED_COST)
|
||||
multiplier = (
|
||||
float(feature_cfg.profit_multiplier)
|
||||
if feature_cfg is not None and feature_cfg.profit_multiplier > 0
|
||||
else float(VIRAL_VIDEO_PROFIT_MULTIPLIER)
|
||||
)
|
||||
price_cap = float(feature_cfg.price_cap) if feature_cfg is not None else 0.0
|
||||
|
||||
# 单价：优先配置 dict；复刻旧版回落规则——精确 key 取不到时，回落
|
||||
# seedance-2.5 同分辨率 False 单价；最终兜底 70.0。
|
||||
price = feature_pricing_service.lookup_model_price(model_pricing, prefix, res_key, bool(has_video_input))
|
||||
if price is None:
|
||||
# 配置表未命中：先尝试配置里的 seedance-2.5/False
|
||||
if prefix != "seedance-2.5" or bool(has_video_input):
|
||||
price = feature_pricing_service.lookup_model_price(model_pricing, "seedance-2.5", res_key, False)
|
||||
if price is None:
|
||||
key = (prefix, res_key, bool(has_video_input))
|
||||
price = VIRAL_VIDEO_MODEL_PRICES.get(key)
|
||||
if price is None:
|
||||
price = VIRAL_VIDEO_MODEL_PRICES.get(("seedance-2.5", res_key, False), 70.0)
|
||||
|
||||
if billing == "per_second":
|
||||
tokens = 0.0
|
||||
video_cost = dur * float(price)
|
||||
billing_unit = "second"
|
||||
else:
|
||||
if actual_tokens is not None and actual_tokens > 0:
|
||||
tokens = float(actual_tokens)
|
||||
else:
|
||||
tokens = dur * w * h * effective_fps / 1024.0
|
||||
video_cost = tokens / 1_000_000.0 * float(price)
|
||||
billing_unit = "token"
|
||||
|
||||
if not feature_enabled:
|
||||
# 功能关闭（is_enabled=false 或全局 points 关闭）：不扣费，明细照旧返回
|
||||
credits = 0.0
|
||||
raw_total = (video_cost + fixed_cost) * multiplier
|
||||
breakdown = {
|
||||
"tokens": float(tokens),
|
||||
"video_cost": float(video_cost),
|
||||
"fixed_cost": float(fixed_cost),
|
||||
"profit_multiplier": float(multiplier),
|
||||
"price_cap": float(price_cap or 0.0),
|
||||
"model_price": float(price),
|
||||
"model_key": prefix,
|
||||
"billing_mode": billing,
|
||||
"billing_unit": billing_unit,
|
||||
"width": int(w),
|
||||
"height": int(h),
|
||||
"fps": int(effective_fps),
|
||||
"duration": dur,
|
||||
"feature_enabled": False,
|
||||
"charged": False,
|
||||
"raw_price": round(float(raw_total), 4),
|
||||
}
|
||||
return credits, breakdown
|
||||
|
||||
total = (video_cost + fixed_cost) * multiplier
|
||||
if price_cap and price_cap > 0:
|
||||
total = min(total, price_cap)
|
||||
credits = round(float(total), 2)
|
||||
breakdown = {
|
||||
"tokens": float(tokens),
|
||||
"video_cost": float(video_cost),
|
||||
"fixed_cost": float(fixed_cost),
|
||||
"profit_multiplier": float(multiplier),
|
||||
"price_cap": float(price_cap or 0.0),
|
||||
"model_price": float(price),
|
||||
"model_key": prefix,
|
||||
"billing_mode": billing,
|
||||
"billing_unit": billing_unit,
|
||||
"width": int(w),
|
||||
"height": int(h),
|
||||
"fps": int(effective_fps),
|
||||
"duration": dur,
|
||||
"feature_enabled": True,
|
||||
"charged": True,
|
||||
}
|
||||
return credits, breakdown
|
||||
|
||||
|
||||
def calculate_viral_video_credits(
|
||||
duration_seconds: int,
|
||||
width: int,
|
||||
height: int,
|
||||
model: str = "seedance-2.5",
|
||||
has_video_input: bool = False,
|
||||
actual_tokens: int | None = None,
|
||||
fps: int = VIRAL_VIDEO_FPS,
|
||||
) -> float:
|
||||
"""计算爆款视频所需积分（1 积分 = 1 元），仅返回积分值（向后兼容包装器）。
|
||||
|
||||
内部调用 calculate_viral_video_credits_with_breakdown，仅返回 credits 部分，
|
||||
保持旧调用方签名与返回值类型不变。
|
||||
|
||||
公式:
|
||||
tokens = duration * width * height * fps / 1024
|
||||
video_cost = tokens / 1_000_000 * model_token_price
|
||||
total = round((video_cost + fixed_cost) * profit_multiplier, 2)
|
||||
若传入 actual_tokens 则用它替代计算值。
|
||||
"""
|
||||
credits, _ = calculate_viral_video_credits_with_breakdown(
|
||||
duration_seconds=duration_seconds,
|
||||
width=width,
|
||||
height=height,
|
||||
model=model,
|
||||
has_video_input=has_video_input,
|
||||
actual_tokens=actual_tokens,
|
||||
fps=fps,
|
||||
)
|
||||
return credits
|
||||
|
||||
|
||||
# ============ 场景定义 ============
|
||||
# 每个场景: base_points(基础积分), unit(计费单位), name(显示名称), dynamic(是否动态定价)
|
||||
# 说明：爆款视频（viral_video）走动态定价（预扣→结算多退少补），因此不使用 @points_gate
|
||||
# 装饰器，base_points=0，dynamic=True；前端展示场景列表时仍可看到。
|
||||
# 每个场景: base_points(基础积分), unit(计费单位), name(显示名称)
|
||||
|
||||
POINTS_SCENES: dict[str, dict] = {
|
||||
"ai_voice": {
|
||||
"base_points": 1,
|
||||
"unit": "分钟",
|
||||
"name": "AI 配音",
|
||||
"description": "AI 配音每分钟消耗 1 积分（免费用户上浮 15%，会员 8~9 折）",
|
||||
},
|
||||
"ai_video": {
|
||||
"base_points": 3,
|
||||
"unit": "条",
|
||||
"name": "智能混剪",
|
||||
"extra_per_30s": 1,
|
||||
"description": "智能混剪每条 3 积分起，视频超过 30 秒后每 30 秒加 1 积分；免费用户每日 2 条免费额度",
|
||||
},
|
||||
"ai_digital_human": {
|
||||
"base_points": 15,
|
||||
"unit": "分钟",
|
||||
"name": "AI 数字人",
|
||||
"description": "AI 数字人每分钟消耗 15 积分",
|
||||
},
|
||||
"voice_clone_train": {
|
||||
"base_points": 0,
|
||||
"unit": "次",
|
||||
@@ -391,16 +39,23 @@ POINTS_SCENES: dict[str, dict] = {
|
||||
"name": "声音克隆合成",
|
||||
"description": "克隆音色合成每分钟消耗 1 积分",
|
||||
},
|
||||
"viral_video": {
|
||||
"base_points": 0,
|
||||
"douyin_extract": {
|
||||
"base_points": 1,
|
||||
"unit": "次",
|
||||
"name": "爆款视频",
|
||||
"dynamic": True,
|
||||
"description": "爆款视频动态定价（按视频时长/分辨率/模型计算，预扣→结算多退少补）",
|
||||
"name": "抖音链接提取",
|
||||
"description": "抖音文案提取每次 1 积分",
|
||||
},
|
||||
"ai_rewrite": {"base_points": 1, "unit": "次", "name": "AI 改写文案", "description": "AI 改写文案每次 1 积分"},
|
||||
"ai_title": {
|
||||
"base_points": 1,
|
||||
"unit": "次",
|
||||
"name": "AI 标题生成",
|
||||
"description": "AI 生成标题每次 1 积分（免费用户实际上浮后 2 积分/次）",
|
||||
},
|
||||
"ai_cover": {"base_points": 1, "unit": "张", "name": "AI 封面生成", "description": "AI 封面生成每张 1 积分"},
|
||||
}
|
||||
|
||||
# 免费用户积分消耗上浮系数（仅对 voice_clone_synth 生效）
|
||||
# 免费用户积分消耗上浮系数
|
||||
FREE_USER_MULTIPLIER = 1.15
|
||||
|
||||
# ============ 积分包定义 ============
|
||||
@@ -426,6 +81,9 @@ MEMBER_DISCOUNT: dict[str, float] = {
|
||||
"yearly": 0.8,
|
||||
}
|
||||
|
||||
# 每日免费混剪次数（免费用户）
|
||||
DAILY_FREE_CLIP_LIMIT = 2
|
||||
|
||||
|
||||
def calculate_points_cost(
|
||||
scene_key: str,
|
||||
@@ -433,45 +91,48 @@ def calculate_points_cost(
|
||||
quantity: int = 1,
|
||||
duration_minutes: float = 0,
|
||||
member_type: str | None = None,
|
||||
) -> float:
|
||||
) -> int:
|
||||
"""计算指定场景的积分消耗。
|
||||
|
||||
Args:
|
||||
scene_key: 场景标识（当前支持 voice_clone_train/voice_clone_synth/viral_video；
|
||||
viral_video 为动态定价场景，此处返回 0，由业务侧调用
|
||||
calculate_viral_video_credits 手动计算）
|
||||
scene_key: 场景标识，如 "ai_voice"、"ai_video"
|
||||
is_member: 是否付费会员
|
||||
quantity: 数量（按次计费场景）
|
||||
duration_minutes: 时长分钟数（按时长计费场景）
|
||||
member_type: 会员类型 (monthly/quarterly/yearly)，用于折扣
|
||||
|
||||
Returns:
|
||||
实际消耗积分（float；已含免费用户 ×1.15 上浮或会员折扣）；免费/动态/已下线场景统一返回 0。
|
||||
实际消耗积分（已含免费用户 ×1.15 上浮或会员折扣）
|
||||
|
||||
Raises:
|
||||
ValueError: 未知场景标识
|
||||
"""
|
||||
scene = POINTS_SCENES.get(scene_key)
|
||||
if not scene:
|
||||
# 已下线/未注册的场景统一返回 0（免费），保持向后兼容
|
||||
return 0.0
|
||||
|
||||
# 动态定价场景（如 viral_video）由业务侧手动计算，这里统一返回 0
|
||||
if scene.get("dynamic"):
|
||||
return 0.0
|
||||
raise ValueError(f"Unknown points scene: {scene_key}")
|
||||
|
||||
base = scene["base_points"]
|
||||
if base == 0:
|
||||
return 0.0
|
||||
return 0
|
||||
|
||||
# —— 计算基础消耗 ——
|
||||
unit = scene["unit"]
|
||||
if unit == "分钟":
|
||||
total_base = base * max(1, math.ceil(duration_minutes))
|
||||
elif unit in ("次", "张"):
|
||||
elif unit in ("条", "次", "张"):
|
||||
total_base = base * quantity
|
||||
# 混剪特殊逻辑：视频超过 30s 后每 +30s 额外加 1 积分
|
||||
if scene_key == "ai_video" and duration_minutes > 0.5:
|
||||
extra_segments = math.ceil((duration_minutes * 60 - 30) / 30)
|
||||
if extra_segments > 0:
|
||||
total_base += scene.get("extra_per_30s", 1) * extra_segments
|
||||
else:
|
||||
total_base = base
|
||||
|
||||
# —— 会员折扣 / 免费用户上浮 ——
|
||||
if is_member and member_type and member_type in MEMBER_DISCOUNT:
|
||||
total_base = max(1, math.floor(total_base * MEMBER_DISCOUNT[member_type]))
|
||||
elif not is_member:
|
||||
total_base = math.ceil(total_base * FREE_USER_MULTIPLIER)
|
||||
|
||||
return float(total_base)
|
||||
return total_base
|
||||
|
||||
@@ -13,6 +13,7 @@ from typing import Any
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from packages.domain.points_rules import (
|
||||
DAILY_FREE_CLIP_LIMIT,
|
||||
POINTS_PACKAGES,
|
||||
)
|
||||
|
||||
@@ -83,7 +84,7 @@ class PointsService:
|
||||
|
||||
# ──────────────── 余额检查 ────────────────
|
||||
|
||||
def check_balance(self, user_id: str, amount: float, db: Session) -> dict[str, Any]:
|
||||
def check_balance(self, user_id: str, amount: int, db: Session) -> dict[str, Any]:
|
||||
"""检查余额是否足够。"""
|
||||
account_data = self.get_or_create_account(user_id, db)
|
||||
balance = account_data["balance"]
|
||||
@@ -99,7 +100,7 @@ class PointsService:
|
||||
def deduct_points(
|
||||
self,
|
||||
user_id: str,
|
||||
amount: float,
|
||||
amount: int,
|
||||
source: str,
|
||||
db: Session,
|
||||
description: str = "",
|
||||
@@ -108,7 +109,7 @@ class PointsService:
|
||||
"""扣减积分（事务性：SELECT FOR UPDATE → 检查余额 → 扣减 → 流水 → 同步用户表）。
|
||||
|
||||
Returns:
|
||||
{"success": True/False, "balance": float, "transaction_id": str|None}
|
||||
{"success": True/False, "balance": int, "transaction_id": str|None}
|
||||
"""
|
||||
PointsAccountModel, PointsTransactionModel, _, _, UserModel = _get_models()
|
||||
|
||||
@@ -172,7 +173,7 @@ class PointsService:
|
||||
except Exception:
|
||||
db.rollback()
|
||||
logger.exception(
|
||||
"积分扣减失败: user_id=%s, amount=%.2f, source=%s",
|
||||
"积分扣减失败: user_id=%s, amount=%d, source=%s",
|
||||
user_id,
|
||||
amount,
|
||||
source,
|
||||
@@ -184,7 +185,7 @@ class PointsService:
|
||||
def add_points(
|
||||
self,
|
||||
user_id: str,
|
||||
amount: float,
|
||||
amount: int,
|
||||
source: str,
|
||||
db: Session,
|
||||
description: str = "",
|
||||
@@ -241,7 +242,7 @@ class PointsService:
|
||||
except Exception:
|
||||
db.rollback()
|
||||
logger.exception(
|
||||
"积分增加失败: user_id=%s, amount=%.2f, source=%s",
|
||||
"积分增加失败: user_id=%s, amount=%d, source=%s",
|
||||
user_id,
|
||||
amount,
|
||||
source,
|
||||
@@ -253,7 +254,7 @@ class PointsService:
|
||||
def refund_points(
|
||||
self,
|
||||
user_id: str,
|
||||
amount: float,
|
||||
amount: int,
|
||||
source: str,
|
||||
db: Session,
|
||||
ref_id: str = "",
|
||||
@@ -269,92 +270,6 @@ class PointsService:
|
||||
ref_id=ref_id,
|
||||
)
|
||||
|
||||
# ──────────────── 爆款视频（viral_video）动态定价 ────────────────
|
||||
|
||||
def deduct_viral_video(self, user_id: str, credits: float, job_id: str, db: Session) -> dict[str, Any]:
|
||||
"""爆款视频预扣积分（confirm-copy 阶段）。"""
|
||||
return self.deduct_points(
|
||||
user_id=user_id,
|
||||
amount=float(credits or 0),
|
||||
source="viral_video",
|
||||
db=db,
|
||||
description="爆款视频生成",
|
||||
ref_id=job_id,
|
||||
)
|
||||
|
||||
def settle_viral_video(
|
||||
self,
|
||||
user_id: str,
|
||||
estimated: float,
|
||||
actual: float,
|
||||
txn_id: str,
|
||||
db: Session,
|
||||
) -> dict[str, Any]:
|
||||
"""爆款视频完成后按实际 tokens 结算（多退少补）。
|
||||
|
||||
- actual < estimated: 退差额
|
||||
- actual > estimated: 补扣差额（余额不足时记 warning，不阻塞完成）
|
||||
- |diff| < 0.01: 不动
|
||||
"""
|
||||
diff = round(float(actual or 0) - float(estimated or 0), 2)
|
||||
if abs(diff) < 0.01:
|
||||
return {"success": True, "action": "none", "diff": 0.0}
|
||||
if diff < 0:
|
||||
refund = round(-diff, 2)
|
||||
try:
|
||||
res = self.refund_points(
|
||||
user_id=user_id,
|
||||
amount=refund,
|
||||
source="viral_video",
|
||||
db=db,
|
||||
ref_id=txn_id,
|
||||
description="爆款视频结算退费",
|
||||
)
|
||||
return {"success": bool(res.get("success")), "action": "refund", "diff": -refund, "amount": refund}
|
||||
except Exception:
|
||||
logger.exception("[viral_video] 结算退费异常 user_id=%s refund=%.2f", user_id, refund)
|
||||
return {"success": False, "action": "refund", "diff": -refund}
|
||||
else:
|
||||
extra = round(diff, 2)
|
||||
try:
|
||||
res = self.deduct_points(
|
||||
user_id=user_id,
|
||||
amount=extra,
|
||||
source="viral_video",
|
||||
db=db,
|
||||
description="爆款视频结算补扣",
|
||||
ref_id=txn_id,
|
||||
)
|
||||
if not res.get("success"):
|
||||
logger.warning(
|
||||
"[viral_video] 结算补扣余额不足 user_id=%s extra=%.2f balance=%s (不阻塞任务完成)",
|
||||
user_id,
|
||||
extra,
|
||||
res.get("balance"),
|
||||
)
|
||||
return {"success": bool(res.get("success")), "action": "deduct", "diff": extra, "amount": extra}
|
||||
except Exception:
|
||||
logger.exception("[viral_video] 结算补扣异常 user_id=%s extra=%.2f", user_id, extra)
|
||||
return {"success": False, "action": "deduct", "diff": extra}
|
||||
|
||||
def refund_viral_video(self, user_id: str, credits: float, txn_id: str, db: Session) -> dict[str, Any]:
|
||||
"""爆款视频失败全额退款。"""
|
||||
amount = float(credits or 0)
|
||||
if amount <= 0:
|
||||
return {"success": True, "action": "none", "amount": 0.0}
|
||||
try:
|
||||
return self.refund_points(
|
||||
user_id=user_id,
|
||||
amount=amount,
|
||||
source="viral_video",
|
||||
db=db,
|
||||
ref_id=txn_id,
|
||||
description="爆款视频失败退款",
|
||||
)
|
||||
except Exception:
|
||||
logger.exception("[viral_video] 失败退款异常 user_id=%s amount=%.2f", user_id, amount)
|
||||
return {"success": False, "action": "refund", "amount": amount}
|
||||
|
||||
# ──────────────── 流水查询 ────────────────
|
||||
|
||||
def get_transactions(
|
||||
@@ -409,16 +324,132 @@ class PointsService:
|
||||
"page_size": page_size,
|
||||
}
|
||||
|
||||
# ──────────────── 每日免费混剪额度（已下线：智能混剪全免费） ────────────────
|
||||
# ──────────────── 每日免费混剪额度 ────────────────
|
||||
|
||||
def _daily_key(self, user_id: str) -> str:
|
||||
"""生成 Redis 每日额度 key。格式: daily_usage:{user_id}:{YYYYMMDD}:free_clip"""
|
||||
today = datetime.now(UTC).strftime("%Y%m%d")
|
||||
return f"daily_usage:{user_id}:{today}:free_clip"
|
||||
|
||||
def check_daily_free_clip(self, user_id: str, db: Session) -> bool:
|
||||
"""检查今日是否还有免费混剪额度。
|
||||
|
||||
优先查 Redis，Redis 不可用时降级到 DB。
|
||||
"""
|
||||
redis_client = _get_redis_client()
|
||||
if redis_client:
|
||||
try:
|
||||
key = self._daily_key(user_id)
|
||||
current = redis_client.get(key)
|
||||
if current is None:
|
||||
return True
|
||||
return int(current) < DAILY_FREE_CLIP_LIMIT
|
||||
except Exception:
|
||||
logger.warning("Redis 不可用，降级到 DB 查询每日额度")
|
||||
|
||||
# 降级到 DB
|
||||
_, _, _, DailyUsageRecordModel, _ = _get_models()
|
||||
today_start = datetime.now(UTC).replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
record = (
|
||||
db.query(DailyUsageRecordModel)
|
||||
.filter(
|
||||
DailyUsageRecordModel.user_id == user_id,
|
||||
DailyUsageRecordModel.usage_type == "free_clip",
|
||||
DailyUsageRecordModel.usage_date >= today_start,
|
||||
)
|
||||
.first()
|
||||
)
|
||||
if record is None:
|
||||
return True
|
||||
return record.count < DAILY_FREE_CLIP_LIMIT
|
||||
|
||||
def record_daily_free_clip(self, user_id: str, db: Session) -> bool:
|
||||
"""记录使用一次免费混剪。
|
||||
|
||||
先 INCR Redis；如果超限回退 Redis。DB 使用 upsert 语义（唯一约束）。
|
||||
"""
|
||||
redis_client = _get_redis_client()
|
||||
if redis_client:
|
||||
try:
|
||||
key = self._daily_key(user_id)
|
||||
new_count = redis_client.incr(key)
|
||||
if new_count == 1:
|
||||
redis_client.expire(key, 48 * 3600) # TTL 48h
|
||||
if new_count <= DAILY_FREE_CLIP_LIMIT:
|
||||
return True
|
||||
# 超限，回退 Redis
|
||||
redis_client.decr(key)
|
||||
except Exception:
|
||||
logger.warning("Redis 不可用，降级到 DB 记录每日额度")
|
||||
|
||||
# 降级/兜底到 DB（upsert 语义）
|
||||
_, _, _, DailyUsageRecordModel, _ = _get_models()
|
||||
today_start = datetime.now(UTC).replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
|
||||
record = (
|
||||
db.query(DailyUsageRecordModel)
|
||||
.filter(
|
||||
DailyUsageRecordModel.user_id == user_id,
|
||||
DailyUsageRecordModel.usage_type == "free_clip",
|
||||
DailyUsageRecordModel.usage_date >= today_start,
|
||||
)
|
||||
.first()
|
||||
)
|
||||
|
||||
if record is None:
|
||||
if DAILY_FREE_CLIP_LIMIT <= 0:
|
||||
return False
|
||||
record = DailyUsageRecordModel(
|
||||
id=uuid.uuid4().hex,
|
||||
user_id=user_id,
|
||||
usage_type="free_clip",
|
||||
usage_date=datetime.now(UTC),
|
||||
count=1,
|
||||
)
|
||||
db.add(record)
|
||||
else:
|
||||
if record.count >= DAILY_FREE_CLIP_LIMIT:
|
||||
return False
|
||||
record.count += 1
|
||||
|
||||
db.commit()
|
||||
return True
|
||||
|
||||
def get_daily_usage(self, user_id: str, db: Session) -> dict[str, Any]:
|
||||
"""查询今日免费额度使用情况（智能混剪已全免费，返回 unlimited）。"""
|
||||
"""查询今日免费额度使用情况。"""
|
||||
redis_client = _get_redis_client()
|
||||
used = 0
|
||||
|
||||
if redis_client:
|
||||
try:
|
||||
key = self._daily_key(user_id)
|
||||
val = redis_client.get(key)
|
||||
used = int(val) if val else 0
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if used == 0:
|
||||
# 从 DB 查
|
||||
_, _, _, DailyUsageRecordModel, _ = _get_models()
|
||||
today_start = datetime.now(UTC).replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
record = (
|
||||
db.query(DailyUsageRecordModel)
|
||||
.filter(
|
||||
DailyUsageRecordModel.user_id == user_id,
|
||||
DailyUsageRecordModel.usage_type == "free_clip",
|
||||
DailyUsageRecordModel.usage_date >= today_start,
|
||||
)
|
||||
.first()
|
||||
)
|
||||
used = record.count if record else 0
|
||||
|
||||
now = datetime.now(UTC)
|
||||
tomorrow = (now + timedelta(days=1)).replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
|
||||
return {
|
||||
"free_clips_used": 0,
|
||||
"free_clips_limit": -1, # -1 表示 unlimited
|
||||
"free_clips_remaining": -1,
|
||||
"free_clips_used": used,
|
||||
"free_clips_limit": DAILY_FREE_CLIP_LIMIT,
|
||||
"free_clips_remaining": max(0, DAILY_FREE_CLIP_LIMIT - used),
|
||||
"reset_at": tomorrow.isoformat(),
|
||||
}
|
||||
|
||||
|
||||
@@ -69,6 +69,8 @@ class PromptType(StrEnum):
|
||||
STYLE_CONSTRAINT = "style_constraint"
|
||||
|
||||
|
||||
CREDITS_VIRAL_VIDEO_COST = 50
|
||||
|
||||
STAGE_LABELS = {
|
||||
ViralVideoStage.IMAGE_ANALYSIS: "图片分析",
|
||||
ViralVideoStage.VIDEO_ANALYSIS: "视频风格分析",
|
||||
@@ -87,9 +89,6 @@ class ViralVideoJob:
|
||||
|
||||
user_id: str
|
||||
images: list[str] = field(default_factory=list)
|
||||
pre_trusted_images: list[str] | None = (
|
||||
None # #2172 信任链预热结果（Seedream AI 化后的 URL 列表），与 images 顺序对应
|
||||
)
|
||||
industry: str = ""
|
||||
target_customer: str = ""
|
||||
persona_id: str = ""
|
||||
@@ -118,14 +117,8 @@ class ViralVideoJob:
|
||||
# 状态
|
||||
id: str = field(default_factory=lambda: uuid4().hex)
|
||||
status: ViralVideoStatus = ViralVideoStatus.PENDING
|
||||
current_stage: str = "" # 细粒度阶段（ViralVideoStage.value，snake_case）
|
||||
phase_message: str = "" # 阶段中文提示文案，前端轮询直接展示
|
||||
heartbeat_at: datetime | None = None # worker 心跳时间，用于超时僵尸任务检测
|
||||
result_video_url: str = ""
|
||||
video_resolution: str = "720p"
|
||||
credits_prepaid: float = 0.0
|
||||
credits_transaction_id: str = ""
|
||||
credits_cost: float = 0.0
|
||||
credits_cost: int = 0
|
||||
error_msg: str = ""
|
||||
retry_count: int = 0
|
||||
started_at: datetime | None = None
|
||||
@@ -145,19 +138,9 @@ class ViralVideoJob:
|
||||
):
|
||||
raise ValueError(f"Cannot transition from {self.status} to running")
|
||||
self.status = ViralVideoStatus.RUNNING
|
||||
now = datetime.now(timezone.utc)
|
||||
if self.started_at is None:
|
||||
self.started_at = now
|
||||
self.heartbeat_at = now
|
||||
self.updated_at = now
|
||||
|
||||
def touch_heartbeat(self) -> None:
|
||||
"""更新心跳时间（worker 在长任务中周期性调用，用于超时检测）。"""
|
||||
now = datetime.now(timezone.utc)
|
||||
if self.started_at is None:
|
||||
self.started_at = now
|
||||
self.heartbeat_at = now
|
||||
self.updated_at = now
|
||||
self.started_at = datetime.now(timezone.utc)
|
||||
self.updated_at = datetime.now(timezone.utc)
|
||||
|
||||
def mark_image_analyzed(self) -> None:
|
||||
if self.status not in (ViralVideoStatus.PENDING, ViralVideoStatus.RUNNING):
|
||||
@@ -191,45 +174,13 @@ class ViralVideoJob:
|
||||
self.updated_at = datetime.now(timezone.utc)
|
||||
|
||||
def resume_from_image_analyzed(self, **kwargs) -> None:
|
||||
"""阶段2入口：允许从 IMAGE_ANALYZED/PENDING 首次进入，也允许从 COPY_GENERATED/COMPLETED/FAILED 重新生成文案。
|
||||
|
||||
重新生成时清空上一轮文案产物（copy_result/intent_result/storyboard/generated_copy_text），
|
||||
并重置 completed_at/result_video_url/error_msg，确保前端轮询能看到新的阶段2进度。
|
||||
"""
|
||||
_allowed = (
|
||||
ViralVideoStatus.IMAGE_ANALYZED,
|
||||
ViralVideoStatus.PENDING,
|
||||
ViralVideoStatus.COPY_GENERATED,
|
||||
ViralVideoStatus.COMPLETED,
|
||||
ViralVideoStatus.FAILED,
|
||||
)
|
||||
if self.status not in _allowed:
|
||||
if self.status not in (ViralVideoStatus.IMAGE_ANALYZED, ViralVideoStatus.PENDING):
|
||||
raise ValueError(f"Cannot resume from {self.status} to copy-gen")
|
||||
_is_regen = self.status in (
|
||||
ViralVideoStatus.COPY_GENERATED,
|
||||
ViralVideoStatus.COMPLETED,
|
||||
ViralVideoStatus.FAILED,
|
||||
)
|
||||
for k, v in kwargs.items():
|
||||
if hasattr(self, k) and v not in (None, "", []):
|
||||
setattr(self, k, v)
|
||||
if _is_regen:
|
||||
# 清空上一轮文案/视频产物，避免前端拿到旧数据
|
||||
self.intent_result = None
|
||||
self.copy_result = None
|
||||
self.storyboard = None
|
||||
self.generated_copy_text = ""
|
||||
self.result_video_url = ""
|
||||
self.current_stage = ""
|
||||
self.phase_message = ""
|
||||
self.error_msg = ""
|
||||
self.completed_at = None
|
||||
self.heartbeat_at = None
|
||||
_now = datetime.now(timezone.utc)
|
||||
self.started_at = _now
|
||||
self.heartbeat_at = _now
|
||||
self.status = ViralVideoStatus.RUNNING
|
||||
self.updated_at = _now
|
||||
self.updated_at = datetime.now(timezone.utc)
|
||||
|
||||
def resume_from_copy_generated(self, edited_copy: str | None = None) -> None:
|
||||
"""阶段2->阶段3：用户确认/编辑口播文案，开始跑 TTS+单次Seedance渲染。"""
|
||||
@@ -238,20 +189,14 @@ class ViralVideoJob:
|
||||
if edited_copy and isinstance(self.copy_result, dict):
|
||||
self.copy_result = {**self.copy_result, "voiceover_script": edited_copy}
|
||||
self.generated_copy_text = edited_copy
|
||||
_now = datetime.now(timezone.utc)
|
||||
self.started_at = _now
|
||||
self.heartbeat_at = _now
|
||||
self.status = ViralVideoStatus.RUNNING
|
||||
self.updated_at = _now
|
||||
self.updated_at = datetime.now(timezone.utc)
|
||||
|
||||
def resume_from_confirm(self) -> None:
|
||||
if self.status != ViralVideoStatus.WAIT_USER_CONFIRM:
|
||||
raise ValueError(f"Cannot resume from {self.status}")
|
||||
_now = datetime.now(timezone.utc)
|
||||
self.started_at = _now
|
||||
self.heartbeat_at = _now
|
||||
self.status = ViralVideoStatus.RUNNING
|
||||
self.updated_at = _now
|
||||
self.updated_at = datetime.now(timezone.utc)
|
||||
|
||||
def mark_completed(self, video_url: str) -> None:
|
||||
self.status = ViralVideoStatus.COMPLETED
|
||||
|
||||
@@ -185,6 +185,19 @@ def _execute_with_gate_impl(
|
||||
is_member = getattr(user, "is_member", False)
|
||||
member_type = getattr(user, "member_type", None)
|
||||
|
||||
if scene_key == "ai_video":
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
svc = PointsService()
|
||||
if not is_member:
|
||||
if svc.check_daily_free_clip(user.id, db):
|
||||
svc.record_daily_free_clip(user.id, db)
|
||||
kwargs["_points_deducted"] = 0
|
||||
kwargs["_is_free_quota"] = True
|
||||
if is_async:
|
||||
return _run_async_impl(func, args, _filter_kwargs_impl(func, kwargs))
|
||||
return func(*args, **_filter_kwargs_impl(func, kwargs))
|
||||
|
||||
if per_unit is not None:
|
||||
total_points = per_unit
|
||||
else:
|
||||
|
||||
+133
-876
File diff suppressed because it is too large
Load Diff
@@ -1,62 +0,0 @@
|
||||
"""AI 配置版本号管理 — Redis 通知机制.
|
||||
|
||||
admin 后台修改 ai_models / ai_capability_configs 后调用 bump_version()，
|
||||
SaaS 端 AIRouter 每次取配置前比对版本号，变了才重新查 DB。
|
||||
|
||||
Redis key: xiaoxia:ai_config:version = 时间戳字符串
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_REDIS_KEY = "xiaoxia:ai_config:version"
|
||||
|
||||
|
||||
def _get_redis_client():
|
||||
"""获取 Redis 客户端（复用 Celery broker 连接）."""
|
||||
try:
|
||||
import redis as _redis
|
||||
|
||||
from packages.shared.config import get_shared_settings
|
||||
|
||||
settings = get_shared_settings()
|
||||
redis_url = getattr(settings, "redis_url", None) or getattr(
|
||||
settings, "celery_broker_url", "redis://localhost:6379/0"
|
||||
)
|
||||
return _redis.Redis.from_url(redis_url, decode_responses=True, socket_timeout=2)
|
||||
except Exception as e:
|
||||
logger.warning("AI config version: Redis 客户端初始化失败: %s", e)
|
||||
return None
|
||||
|
||||
|
||||
def bump_version() -> str:
|
||||
"""写入新版本号（当前时间戳），返回版本号字符串。失败返回空串。"""
|
||||
r = _get_redis_client()
|
||||
if r is None:
|
||||
logger.warning("AI config bump_version: Redis 不可用，跳过版本号更新")
|
||||
return ""
|
||||
try:
|
||||
ver = str(int(time.time() * 1000))
|
||||
r.set(_REDIS_KEY, ver)
|
||||
logger.info("AI config version bumped to %s", ver)
|
||||
return ver
|
||||
except Exception as e:
|
||||
logger.warning("AI config bump_version 失败: %s", e)
|
||||
return ""
|
||||
|
||||
|
||||
def get_version() -> Optional[str]:
|
||||
"""读取当前版本号。Redis 不可用或异常返回 None。"""
|
||||
r = _get_redis_client()
|
||||
if r is None:
|
||||
return None
|
||||
try:
|
||||
return r.get(_REDIS_KEY)
|
||||
except Exception as e:
|
||||
logger.warning("AI config get_version 失败: %s", e)
|
||||
return None
|
||||
@@ -1,516 +0,0 @@
|
||||
"""AI 模型路由层 — 统一模型配置读取与客户端构建.
|
||||
|
||||
业务代码通过 AIRouter 获取客户端，不再硬编码 model/api_key/base_url。
|
||||
配置来源：DB ai_capability_configs JOIN ai_models → Redis 版本号缓存 → SharedSettings fallback。
|
||||
|
||||
使用方式：
|
||||
from packages.shared.ai_router import ai_router
|
||||
|
||||
client = ai_router.get_llm_client("intent_parsing")
|
||||
result = client.chat_completion(messages=[...])
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import threading
|
||||
from dataclasses import dataclass
|
||||
|
||||
from packages.shared.config import get_shared_settings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ── 配置数据类 ──────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ModelConfig:
|
||||
"""单个 AI 模型配置（来自 ai_models 表）"""
|
||||
|
||||
id: str
|
||||
name: str
|
||||
provider: str
|
||||
model_key: str
|
||||
api_key: str
|
||||
api_base: str
|
||||
api_version: str | None
|
||||
status: str
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class CapabilityConfig:
|
||||
"""业务能力配置（来自 ai_capability_configs JOIN ai_models）"""
|
||||
|
||||
capability_key: str
|
||||
capability_name: str
|
||||
primary_model: ModelConfig | None
|
||||
lite_model: ModelConfig | None
|
||||
fallback_model: ModelConfig | None
|
||||
timeout_seconds: int
|
||||
max_retries: int
|
||||
max_tokens: int | None
|
||||
temperature: float | None
|
||||
concurrency: int
|
||||
extra_params: dict
|
||||
is_enabled: bool
|
||||
|
||||
|
||||
# ── 简单包装类（TTS / ImageGen / VideoGen）──────────────────────────────────
|
||||
|
||||
|
||||
class TTSClient:
|
||||
"""TTS 客户端（简单配置持有者，实际调用由 CosyVoiceService 完成）"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
provider: str,
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
model: str,
|
||||
timeout: int = 60,
|
||||
extra_params: dict | None = None,
|
||||
):
|
||||
self.provider = provider
|
||||
self.api_key = api_key
|
||||
self.base_url = base_url
|
||||
self.model = model
|
||||
self.timeout = timeout
|
||||
self.extra_params = extra_params or {}
|
||||
|
||||
@property
|
||||
def is_available(self) -> bool:
|
||||
return bool(self.api_key and self.base_url and self.model)
|
||||
|
||||
|
||||
class ImageGenClient:
|
||||
"""图片生成客户端（简单配置持有者）"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
provider: str,
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
model: str,
|
||||
timeout: int = 60,
|
||||
extra_params: dict | None = None,
|
||||
):
|
||||
self.provider = provider
|
||||
self.api_key = api_key
|
||||
self.base_url = base_url
|
||||
self.model = model
|
||||
self.timeout = timeout
|
||||
self.extra_params = extra_params or {}
|
||||
|
||||
@property
|
||||
def is_available(self) -> bool:
|
||||
return bool(self.api_key and self.base_url and self.model)
|
||||
|
||||
|
||||
class VideoGenClient:
|
||||
"""视频生成客户端（简单配置持有者）"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
provider: str,
|
||||
api_key: str,
|
||||
base_url: str,
|
||||
model: str,
|
||||
timeout: int = 600,
|
||||
extra_params: dict | None = None,
|
||||
):
|
||||
self.provider = provider
|
||||
self.api_key = api_key
|
||||
self.base_url = base_url
|
||||
self.model = model
|
||||
self.timeout = timeout
|
||||
self.extra_params = extra_params or {}
|
||||
|
||||
@property
|
||||
def is_available(self) -> bool:
|
||||
return bool(self.api_key and self.base_url and self.model)
|
||||
|
||||
|
||||
# ── DB Session 获取 ─────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _get_session():
|
||||
"""获取 DB session，兼容 api / worker / 独立脚本场景"""
|
||||
from packages.adapters.sqlalchemy_impl.session import SessionLocal
|
||||
|
||||
if SessionLocal is not None:
|
||||
return SessionLocal()
|
||||
|
||||
try:
|
||||
from worker_app.db import SessionLocal as WorkerSL
|
||||
|
||||
if WorkerSL is not None:
|
||||
return WorkerSL()
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
try:
|
||||
from app.db import SessionLocal as ApiSL
|
||||
|
||||
if ApiSL is not None:
|
||||
return ApiSL()
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
|
||||
# ── 核心路由类 ──────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class AIRouter:
|
||||
"""AI 模型路由器 — 统一配置读取与客户端构建.
|
||||
|
||||
缓存策略：
|
||||
1. 本地内存缓存 {capability_key: CapabilityConfig}
|
||||
2. 每次读取前比对 Redis 版本号，变了则清缓存重新查 DB
|
||||
3. DB 无配置 / Redis 不可用 → fallback 到 SharedSettings 环境变量
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self._cache: dict[str, CapabilityConfig] = {}
|
||||
self._local_ver: str | None = None
|
||||
self._lock = threading.Lock()
|
||||
|
||||
def _check_version(self) -> bool:
|
||||
"""检查 Redis 版本号，变了返回 True（需要刷新缓存）"""
|
||||
from packages.shared.ai_config_version import get_version
|
||||
|
||||
current_ver = get_version()
|
||||
if current_ver is None:
|
||||
return False
|
||||
if self._local_ver != current_ver:
|
||||
return True
|
||||
return False
|
||||
|
||||
def _load_from_db(self, capability_key: str) -> CapabilityConfig | None:
|
||||
"""从 DB 加载配置（ai_capability_configs JOIN ai_models）"""
|
||||
session = _get_session()
|
||||
if session is None:
|
||||
logger.warning("AI Router: 无法获取 DB session")
|
||||
return None
|
||||
try:
|
||||
from sqlalchemy import text
|
||||
|
||||
sql = text("""
|
||||
SELECT
|
||||
cc.capability_key, cc.capability_name, cc.timeout_seconds,
|
||||
cc.max_retries, cc.max_tokens, cc.temperature,
|
||||
cc.concurrency, cc.extra_params, cc.is_enabled,
|
||||
pm.id AS pm_id, pm.name AS pm_name, pm.provider AS pm_provider,
|
||||
pm.model_key AS pm_model_key, pm.api_key AS pm_api_key,
|
||||
pm.api_base AS pm_api_base, pm.api_version AS pm_api_version,
|
||||
pm.status AS pm_status,
|
||||
lm.id AS lm_id, lm.name AS lm_name, lm.provider AS lm_provider,
|
||||
lm.model_key AS lm_model_key, lm.api_key AS lm_api_key,
|
||||
lm.api_base AS lm_api_base, lm.api_version AS lm_api_version,
|
||||
lm.status AS lm_status,
|
||||
fm.id AS fm_id, fm.name AS fm_name, fm.provider AS fm_provider,
|
||||
fm.model_key AS fm_model_key, fm.api_key AS fm_api_key,
|
||||
fm.api_base AS fm_api_base, fm.api_version AS fm_api_version,
|
||||
fm.status AS fm_status
|
||||
FROM ai_capability_configs cc
|
||||
LEFT JOIN ai_models pm ON cc.primary_model_id = pm.id AND pm.deleted_at IS NULL
|
||||
LEFT JOIN ai_models lm ON cc.lite_model_id = lm.id AND lm.deleted_at IS NULL
|
||||
LEFT JOIN ai_models fm ON cc.fallback_model_id = fm.id AND fm.deleted_at IS NULL
|
||||
WHERE cc.capability_key = :key AND cc.is_enabled = true
|
||||
""")
|
||||
row = session.execute(sql, {"key": capability_key}).first()
|
||||
if not row:
|
||||
return None
|
||||
|
||||
def _to_model(prefix: str) -> ModelConfig | None:
|
||||
mid = getattr(row, f"{prefix}_id", None)
|
||||
if not mid:
|
||||
return None
|
||||
return ModelConfig(
|
||||
id=mid,
|
||||
name=getattr(row, f"{prefix}_name", "") or "",
|
||||
provider=getattr(row, f"{prefix}_provider", "") or "",
|
||||
model_key=getattr(row, f"{prefix}_model_key", "") or "",
|
||||
api_key=getattr(row, f"{prefix}_api_key", "") or "",
|
||||
api_base=getattr(row, f"{prefix}_api_base", "") or "",
|
||||
api_version=getattr(row, f"{prefix}_api_version", None),
|
||||
status=getattr(row, f"{prefix}_status", "active") or "active",
|
||||
)
|
||||
|
||||
return CapabilityConfig(
|
||||
capability_key=row.capability_key,
|
||||
capability_name=row.capability_name,
|
||||
primary_model=_to_model("pm"),
|
||||
lite_model=_to_model("lm"),
|
||||
fallback_model=_to_model("fm"),
|
||||
timeout_seconds=row.timeout_seconds or 30,
|
||||
max_retries=row.max_retries or 1,
|
||||
max_tokens=row.max_tokens,
|
||||
temperature=row.temperature,
|
||||
concurrency=row.concurrency or 2,
|
||||
extra_params=row.extra_params or {},
|
||||
is_enabled=row.is_enabled,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning("AI Router: DB 查询失败 (key=%s): %s", capability_key, e)
|
||||
return None
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
def get_capability(self, key: str) -> CapabilityConfig | None:
|
||||
"""获取业务能力配置（带缓存）"""
|
||||
with self._lock:
|
||||
if self._check_version():
|
||||
self._cache.clear()
|
||||
from packages.shared.ai_config_version import get_version
|
||||
|
||||
self._local_ver = get_version()
|
||||
|
||||
if key in self._cache:
|
||||
return self._cache[key]
|
||||
|
||||
config = self._load_from_db(key)
|
||||
if config:
|
||||
self._cache[key] = config
|
||||
return config
|
||||
|
||||
def _get_model_or_fallback(self, cap: CapabilityConfig, variant: str = "primary") -> ModelConfig | None:
|
||||
"""按 variant 选择模型，不存在则降级。
|
||||
|
||||
- primary: primary → fallback
|
||||
- lite: lite → primary
|
||||
- fallback: fallback → primary（修复点：此前 fallback variant 被忽略，错误地使用了 primary 模型）
|
||||
"""
|
||||
if variant == "fallback":
|
||||
if cap.fallback_model:
|
||||
return cap.fallback_model
|
||||
if cap.primary_model:
|
||||
return cap.primary_model
|
||||
elif variant == "lite":
|
||||
if cap.lite_model:
|
||||
return cap.lite_model
|
||||
if cap.primary_model:
|
||||
return cap.primary_model
|
||||
else: # primary
|
||||
if cap.primary_model:
|
||||
return cap.primary_model
|
||||
if cap.fallback_model:
|
||||
return cap.fallback_model
|
||||
return None
|
||||
|
||||
# ── 构建客户端 ─────────────────────────────────────────────────────────
|
||||
|
||||
def _build_llm_client(self, model: ModelConfig, cap: CapabilityConfig):
|
||||
"""构建 LLM 客户端 — 返回 DoubaoClient 实例"""
|
||||
from packages.shared.ai_client import DoubaoClient
|
||||
|
||||
return DoubaoClient(
|
||||
api_key=model.api_key,
|
||||
base_url=model.api_base,
|
||||
model=model.model_key,
|
||||
timeout=cap.timeout_seconds,
|
||||
max_retries=cap.max_retries,
|
||||
max_tokens=cap.max_tokens,
|
||||
temperature=cap.temperature,
|
||||
extra_params=cap.extra_params,
|
||||
provider=model.provider,
|
||||
)
|
||||
|
||||
def _build_vision_client(self, model: ModelConfig, cap: CapabilityConfig):
|
||||
"""构建 VLM 客户端 — 返回 DoubaoClient 实例（DoubaoClient 已支持 vision_completion）"""
|
||||
from packages.shared.ai_client import DoubaoClient
|
||||
|
||||
return DoubaoClient(
|
||||
api_key=model.api_key,
|
||||
base_url=model.api_base,
|
||||
model=model.model_key,
|
||||
timeout=cap.timeout_seconds,
|
||||
max_retries=cap.max_retries,
|
||||
max_tokens=cap.max_tokens,
|
||||
temperature=cap.temperature,
|
||||
extra_params=cap.extra_params,
|
||||
provider=model.provider,
|
||||
)
|
||||
|
||||
def _build_tts_client(self, model: ModelConfig, cap: CapabilityConfig) -> TTSClient:
|
||||
return TTSClient(
|
||||
provider=model.provider,
|
||||
api_key=model.api_key,
|
||||
base_url=model.api_base,
|
||||
model=model.model_key,
|
||||
timeout=cap.timeout_seconds,
|
||||
extra_params=cap.extra_params,
|
||||
)
|
||||
|
||||
def _build_image_gen_client(self, model: ModelConfig, cap: CapabilityConfig) -> ImageGenClient:
|
||||
return ImageGenClient(
|
||||
provider=model.provider,
|
||||
api_key=model.api_key,
|
||||
base_url=model.api_base,
|
||||
model=model.model_key,
|
||||
timeout=cap.timeout_seconds,
|
||||
extra_params=cap.extra_params,
|
||||
)
|
||||
|
||||
def _build_video_gen_client(self, model: ModelConfig, cap: CapabilityConfig) -> VideoGenClient:
|
||||
return VideoGenClient(
|
||||
provider=model.provider,
|
||||
api_key=model.api_key,
|
||||
base_url=model.api_base,
|
||||
model=model.model_key,
|
||||
timeout=cap.timeout_seconds,
|
||||
extra_params=cap.extra_params,
|
||||
)
|
||||
|
||||
# ── 公开接口 ────────────────────────────────────────────────────────────
|
||||
|
||||
def get_llm_client(self, key: str, variant: str = "primary"):
|
||||
"""获取 LLM 客户端（返回 DoubaoClient 实例）"""
|
||||
cap = self.get_capability(key)
|
||||
if cap and cap.is_enabled:
|
||||
model = self._get_model_or_fallback(cap, variant)
|
||||
if model and model.api_key:
|
||||
return self._build_llm_client(model, cap)
|
||||
|
||||
return self._fallback_llm_client(key)
|
||||
|
||||
def get_vision_client(self, key: str, variant: str = "primary"):
|
||||
"""获取 VLM 客户端（返回 DoubaoClient 实例）"""
|
||||
cap = self.get_capability(key)
|
||||
if cap and cap.is_enabled:
|
||||
model = self._get_model_or_fallback(cap, variant)
|
||||
if model and model.api_key:
|
||||
return self._build_vision_client(model, cap)
|
||||
|
||||
return self._fallback_vision_client(key)
|
||||
|
||||
def get_tts_client(self, key: str = "tts") -> TTSClient | None:
|
||||
"""获取 TTS 客户端"""
|
||||
cap = self.get_capability(key)
|
||||
if cap and cap.is_enabled and cap.primary_model and cap.primary_model.api_key:
|
||||
return self._build_tts_client(cap.primary_model, cap)
|
||||
|
||||
return self._fallback_tts_client()
|
||||
|
||||
def get_image_gen_client(self, key: str = "image_generation") -> ImageGenClient | None:
|
||||
"""获取图片生成客户端"""
|
||||
cap = self.get_capability(key)
|
||||
if cap and cap.is_enabled and cap.primary_model and cap.primary_model.api_key:
|
||||
return self._build_image_gen_client(cap.primary_model, cap)
|
||||
|
||||
return self._fallback_image_gen_client()
|
||||
|
||||
def get_video_gen_client(self, key: str = "video_generation") -> VideoGenClient | None:
|
||||
"""获取视频生成客户端"""
|
||||
cap = self.get_capability(key)
|
||||
if cap and cap.is_enabled and cap.primary_model and cap.primary_model.api_key:
|
||||
return self._build_video_gen_client(cap.primary_model, cap)
|
||||
|
||||
return self._fallback_video_gen_client()
|
||||
|
||||
# ── Fallback 方法（读 SharedSettings 环境变量）──────────────────────────
|
||||
|
||||
def _fallback_llm_client(self, key: str):
|
||||
"""Fallback LLM 客户端 — 从 settings 读取配置，不硬编码"""
|
||||
settings = get_shared_settings()
|
||||
model_map = {
|
||||
"intent_parsing": (settings.doubao_fast_model, settings.doubao_base_url, settings.doubao_api_key),
|
||||
"copy_fusion": (settings.doubao_fast_model, settings.doubao_base_url, settings.doubao_api_key),
|
||||
"storyboard": (settings.doubao_model, settings.doubao_base_url, settings.doubao_api_key),
|
||||
"copy_review": (settings.doubao_model, settings.doubao_base_url, settings.doubao_api_key),
|
||||
"asset_classify": (settings.doubao_model, settings.doubao_base_url, settings.doubao_api_key),
|
||||
}
|
||||
if key in model_map:
|
||||
model_id, base_url, api_key = model_map[key]
|
||||
else:
|
||||
model_id = settings.doubao_model
|
||||
base_url = settings.doubao_base_url
|
||||
api_key = settings.doubao_api_key
|
||||
|
||||
if not api_key:
|
||||
return None
|
||||
|
||||
from packages.shared.ai_client import DoubaoClient
|
||||
|
||||
return DoubaoClient(
|
||||
provider="volcengine",
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model=model_id,
|
||||
timeout=settings.doubao_timeout,
|
||||
max_retries=settings.doubao_max_retries,
|
||||
)
|
||||
|
||||
def _fallback_vision_client(self, key: str):
|
||||
"""Fallback VLM 客户端 — 从 settings 读取 dashscope 配置，不硬编码"""
|
||||
settings = get_shared_settings()
|
||||
api_key = getattr(settings, "dashscope_api_key", "")
|
||||
if not api_key:
|
||||
return None
|
||||
base_url = getattr(settings, "dashscope_base_url", "") or ""
|
||||
model = getattr(settings, "dashscope_model", "") or getattr(settings, "doubao_vision_model", "")
|
||||
|
||||
from packages.shared.ai_client import DoubaoClient
|
||||
|
||||
return DoubaoClient(
|
||||
provider="dashscope",
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model=model,
|
||||
timeout=15,
|
||||
)
|
||||
|
||||
def _fallback_tts_client(self) -> TTSClient | None:
|
||||
settings = get_shared_settings()
|
||||
api_key = getattr(settings, "cosyvoice_api_key", "")
|
||||
if not api_key:
|
||||
return None
|
||||
base_url = getattr(settings, "cosyvoice_base_url", "")
|
||||
model = getattr(settings, "cosyvoice_model", "")
|
||||
|
||||
return TTSClient(provider="dashscope", api_key=api_key, base_url=base_url, model=model)
|
||||
|
||||
def _fallback_image_gen_client(self) -> ImageGenClient | None:
|
||||
settings = get_shared_settings()
|
||||
api_key = getattr(settings, "doubao_api_key", "")
|
||||
if not api_key:
|
||||
return None
|
||||
base_url = getattr(settings, "doubao_base_url", "")
|
||||
model = getattr(settings, "doubao_image_model", "")
|
||||
|
||||
return ImageGenClient(
|
||||
provider="volcengine",
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model=model,
|
||||
timeout=getattr(settings, "doubao_image_timeout", 60),
|
||||
)
|
||||
|
||||
def _fallback_video_gen_client(self) -> VideoGenClient | None:
|
||||
settings = get_shared_settings()
|
||||
api_key = getattr(settings, "doubao_api_key", "")
|
||||
if not api_key:
|
||||
return None
|
||||
base_url = getattr(settings, "doubao_base_url", "")
|
||||
model = getattr(settings, "doubao_video_model", "")
|
||||
|
||||
return VideoGenClient(
|
||||
provider="volcengine",
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model=model,
|
||||
timeout=getattr(settings, "doubao_video_timeout", 600),
|
||||
)
|
||||
|
||||
def invalidate(self):
|
||||
"""清空本地缓存"""
|
||||
with self._lock:
|
||||
self._cache.clear()
|
||||
self._local_ver = None
|
||||
|
||||
|
||||
# ── 全局单例 ──────────────────────────────────────────────────────────────
|
||||
|
||||
ai_router = AIRouter()
|
||||
+30
-106
@@ -496,33 +496,16 @@ def run_generate_cover(
|
||||
# ── 通用 LLM / Vision 调用（#2039 ViralVideoOrchestrator 使用，复用现有豆包客户端）──
|
||||
|
||||
|
||||
def call_llm(
|
||||
prompt: str,
|
||||
temperature: float = 0.7,
|
||||
max_tokens: int = 2048,
|
||||
model: str | None = None,
|
||||
system_prompt: str | None = None,
|
||||
timeout: int | None = None,
|
||||
) -> object:
|
||||
"""调用豆包大模型（文本对话），返回解析后的 JSON（dict/list）或原文字符串；失败返回 None。
|
||||
|
||||
Args:
|
||||
prompt: 用户侧提示。
|
||||
temperature: 采样温度。
|
||||
max_tokens: 输出上限（结构化任务默认 2048，长文案可按需加大）。
|
||||
model: 覆盖默认模型（如 fast_model 提速用），None 走配置默认推理模型。
|
||||
system_prompt: 覆盖默认 system prompt。
|
||||
"""
|
||||
def call_llm(prompt: str, temperature: float = 0.7) -> object:
|
||||
"""调用豆包大模型（文本对话），返回解析后的 JSON（dict/list）或原文字符串；失败返回 None。"""
|
||||
client = get_doubao_client()
|
||||
if not client.is_available:
|
||||
return None
|
||||
if system_prompt is None:
|
||||
system_prompt = "你是专业的短视频内容策划助手。需要结构化输出时请严格使用 JSON。"
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "system", "content": "你是专业的短视频内容策划助手。需要结构化输出时请严格使用 JSON。"},
|
||||
{"role": "user", "content": prompt},
|
||||
]
|
||||
raw = client.chat_completion(messages, temperature=temperature, max_tokens=max_tokens, model=model, timeout=timeout)
|
||||
raw = client.chat_completion(messages, temperature=temperature, max_tokens=4096)
|
||||
if raw is None:
|
||||
return None
|
||||
try:
|
||||
@@ -531,26 +514,14 @@ def call_llm(
|
||||
return raw
|
||||
|
||||
|
||||
def call_vision(
|
||||
image_url: str,
|
||||
prompt: str,
|
||||
*,
|
||||
model: str | None = None,
|
||||
max_tokens: int = 1024,
|
||||
temperature: float = 0.2,
|
||||
timeout: int = 45,
|
||||
system_prompt: str | None = None,
|
||||
) -> object:
|
||||
def call_vision(image_url: str, prompt: str) -> object:
|
||||
"""调用豆包视觉大模型分析图片，返回解析后的 JSON 或原文字符串；失败返回 None。
|
||||
|
||||
Args:
|
||||
image_url: 可公网访问的图片 URL（直接传给豆包视觉模型，无需本地下载）。
|
||||
prompt: 用户侧文本提示。
|
||||
model: 覆盖默认视觉模型（如 vision_lite_model 提速用），None 走配置默认。
|
||||
max_tokens: 输出上限，商品识别用 800~1200 足够，避免长输出拖慢首 token。
|
||||
temperature: 温度。
|
||||
timeout: 单次请求超时（秒）。
|
||||
system_prompt: 覆盖默认 system prompt（viral-video 商品分析会传专门的详细 prompt）。
|
||||
Bug #2114 (VLM 牛头不对马嘴根因修复):
|
||||
之前误走 client.chat_completion(用文本模型 doubao-seed-1.6)，多模态 content list 被当成
|
||||
纯文本发给文本模型 → 模型要么看不到图、要么抛 400，静默被 except 吞掉 → 返回 None →
|
||||
_step_image_analysis fallback 到 {"name":"未识别"} → 后续文案/分镜完全没图的信息。
|
||||
现改走 vision_completion，走视觉模型 doubao-1-5-vision-pro-250915。
|
||||
"""
|
||||
client = get_doubao_client()
|
||||
if not client.is_available:
|
||||
@@ -560,33 +531,28 @@ def call_vision(
|
||||
logger.warning("[call_vision] 空 image_url，跳过视觉分析")
|
||||
return None
|
||||
|
||||
if system_prompt is None:
|
||||
system_prompt = (
|
||||
"你是资深电商视觉分析师。请严格基于用户提供的图片观察回答，"
|
||||
"图片里没有的信息不要凭空想象或编造；看不清或无法判断时明确说"
|
||||
"「无法判断」，不要猜测。输出必须是严格 JSON，不要附加 Markdown 或解释文字。"
|
||||
)
|
||||
system_prompt = (
|
||||
"你是资深电商视觉分析师。请严格基于用户提供的图片观察回答，"
|
||||
"图片里没有的信息不要凭空想象或编造；看不清或无法判断时明确说"
|
||||
"「图片中无法判断」，不要猜测。输出必须是严格 JSON，不要附加 Markdown 或解释文字。"
|
||||
)
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": prompt},
|
||||
]
|
||||
|
||||
used_model = model or getattr(client, "vision_model", "?")
|
||||
logger.info(
|
||||
"[call_vision] 调用豆包视觉模型 model=%s image_url=%s prompt_len=%d max_tokens=%d timeout=%d",
|
||||
used_model,
|
||||
"[call_vision] 调用豆包视觉模型 vision_model=%s image_url=%s prompt_len=%d",
|
||||
getattr(client, "vision_model", "?"),
|
||||
image_url[:120],
|
||||
len(prompt),
|
||||
max_tokens,
|
||||
timeout,
|
||||
)
|
||||
raw = client.vision_completion(
|
||||
messages=messages,
|
||||
images=[image_url],
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
timeout=timeout,
|
||||
model=model,
|
||||
temperature=0.2,
|
||||
max_tokens=2048,
|
||||
timeout=60,
|
||||
)
|
||||
if raw is None:
|
||||
logger.warning("[call_vision] 视觉模型返回 None (image_url=%s)", image_url[:80])
|
||||
@@ -605,24 +571,6 @@ def call_vision(
|
||||
return raw
|
||||
|
||||
|
||||
def preheat_trust_chain(portrait_descriptions: list[str], *, timeout: int = 120) -> list[str] | None:
|
||||
"""#2174 信任链预热（t2i版）：用 VLM 分析出的人物外貌描述，跑 Seedream 文生图，
|
||||
生成的信任产物 URL 可传给 call_video_generation(pre_trusted_images=...)。
|
||||
|
||||
- portrait_descriptions: VLM输出的portrait_prompt列表（中文人物外貌描述）
|
||||
- 成功返回与输入同序的信任图URL列表；任意一张失败返回None（调用方回退到纯t2v）
|
||||
- 必须传VLM人物描述，不传reference_images，走纯t2i路径才是方舟信任产物
|
||||
"""
|
||||
client = get_doubao_client()
|
||||
if not client.is_available:
|
||||
return None
|
||||
try:
|
||||
return client.preheat_trust_chain(portrait_descriptions, timeout=timeout)
|
||||
except Exception as e:
|
||||
logger.error("[ai_service] preheat_trust_chain 异常: %s", e, exc_info=True)
|
||||
return None
|
||||
|
||||
|
||||
def call_video_generation(
|
||||
prompt: str,
|
||||
*,
|
||||
@@ -636,27 +584,21 @@ def call_video_generation(
|
||||
reference_images: list[str] | None = None,
|
||||
reference_audios: list[str] | None = None,
|
||||
reference_videos: list[str] | None = None,
|
||||
pre_trusted_images: list[str] | None = None,
|
||||
) -> dict | None:
|
||||
"""调用 Seedance / Wan 视频生成（v1.6.2 多模型版 + #2172 信任链预热）。
|
||||
) -> str | None:
|
||||
"""调用 Seedance 2.5 生成视频（v1.6 单次出片版），返回本地 MP4 路径；失败返回 None。
|
||||
|
||||
成功返回 {"video_path": str, "usage": dict | None}（usage 含 completion_tokens），失败返回 None。
|
||||
失败时错误详情会写入 client.last_video_error，可通过 get_last_video_error() 读取：
|
||||
{"error_code": str, "user_message": str, "status_code": int, "detail": str, ...}
|
||||
v1.6:
|
||||
- 默认 generate_audio=True，模型原生合成环境音效/BGM；
|
||||
- reference_audios 传 TTS 音频 URL 数组做口型驱动；
|
||||
- reference_images 传产品素材 URL 数组做视觉参考；
|
||||
- 单次最长 30 秒，不分段不拼接；
|
||||
- image_url 存在时为「首帧图生视频」模式，自动不传 ratio（Bug #2110）。
|
||||
"""
|
||||
client = get_doubao_client()
|
||||
if not client.is_available:
|
||||
msg = "豆包客户端未配置（DOUBAO_API_KEY 缺失），跳过视频生成"
|
||||
logger.warning("[ai_service] %s", msg)
|
||||
# 写入 last_video_error 供上层读取
|
||||
client.last_video_error = {
|
||||
"error_code": "auth_error",
|
||||
"user_message": "视频生成服务未配置，请联系管理员。",
|
||||
"status_code": 0,
|
||||
"detail": msg,
|
||||
}
|
||||
logger.warning("[ai_service] 豆包客户端未配置，跳过视频生成")
|
||||
return None
|
||||
effective_ratio = ratio or "9:16"
|
||||
effective_ratio = None if image_url else ratio
|
||||
try:
|
||||
kwargs: dict = dict(
|
||||
prompt=prompt,
|
||||
@@ -670,28 +612,10 @@ def call_video_generation(
|
||||
reference_images=reference_images,
|
||||
reference_audios=reference_audios,
|
||||
reference_videos=reference_videos,
|
||||
pre_trusted_images=pre_trusted_images,
|
||||
)
|
||||
if effective_ratio:
|
||||
kwargs["ratio"] = effective_ratio
|
||||
return client.video_generation(**kwargs)
|
||||
except Exception as e:
|
||||
logger.error("[ai_service] call_video_generation 异常: %s", e, exc_info=True)
|
||||
client.last_video_error = {
|
||||
"error_code": "unknown",
|
||||
"user_message": f"视频生成异常：{e!s}"[:200],
|
||||
"status_code": 0,
|
||||
"detail": str(e),
|
||||
}
|
||||
return None
|
||||
|
||||
|
||||
def get_last_video_error() -> dict:
|
||||
"""读取最近一次视频生成失败的详细错误（含 error_code/user_message/status_code/detail）。
|
||||
成功或未调用过返回空 dict。
|
||||
"""
|
||||
try:
|
||||
client = get_doubao_client()
|
||||
return client.get_last_video_error() if hasattr(client, "get_last_video_error") else {}
|
||||
except Exception:
|
||||
return {}
|
||||
|
||||
@@ -51,8 +51,6 @@ task_routes = {
|
||||
"ai_avatar_render.execute": {"queue": QUEUE_GENERATION},
|
||||
# GPU MuseTalk 口型同步（用户等成片，链路子任务全部走 generation 避免跨队列阻塞）
|
||||
"lipsync_gpu_process_async": {"queue": QUEUE_GENERATION},
|
||||
# #2076 Ditto 蚂蚁数字人口型同步（走 generation 队列，避免跨队列阻塞）
|
||||
"lipsync_ditto_process_async": {"queue": QUEUE_GENERATION},
|
||||
"lipsync_tts.synthesize_and_submit": {"queue": QUEUE_GENERATION},
|
||||
"lipsync_tts.poll_mediakit_status": {"queue": QUEUE_GENERATION},
|
||||
"lipsync_tts.persist_output_video": {"queue": QUEUE_GENERATION},
|
||||
|
||||
@@ -1,344 +0,0 @@
|
||||
"""DashScope 客户端（阿里云百炼 Wan 3.0 等非方舟模型）。
|
||||
|
||||
#2159: 新增 Wan 3.0 视频生成支持。DashScope 异步协议：
|
||||
- POST {base_url}/services/aigc/video-generation/video-synthesis (X-DashScope-Async: enable)
|
||||
→ 返回 output.task_id
|
||||
- GET {base_url}/tasks/{task_id} 轮询状态
|
||||
→ SUCCEEDED 时 output.video_url 可下载
|
||||
认证：Authorization: Bearer {DASHSCOPE_API_KEY}
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
from urllib.parse import urlparse
|
||||
|
||||
import httpx
|
||||
|
||||
from packages.shared.config import get_shared_settings
|
||||
|
||||
# 网络/超时类异常父类集合：覆盖 Timeout/Connect/Network/ReadTimeout/WriteTimeout/PoolTimeout
|
||||
_HTTP_NETWORK_ERRORS = ()
|
||||
try:
|
||||
_HTTP_NETWORK_ERRORS = (httpx.TimeoutException, httpx.NetworkError)
|
||||
except Exception:
|
||||
_HTTP_NETWORK_ERRORS = (Exception,)
|
||||
|
||||
_HTTP_STATUS_ERROR = httpx.HTTPStatusError if hasattr(httpx, "HTTPStatusError") else Exception
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DASHSCOPE_CLIENT_SINGLETON: "DashScopeClient | None" = None
|
||||
|
||||
|
||||
def _classify_dashscope_error(status_code: int, body: str, task_msg: str = "") -> tuple[str, str]:
|
||||
"""DashScope 错误分类，返回 (error_code, user_message)。"""
|
||||
body_lower = (body or "").lower()
|
||||
msg_in_body = task_msg or ""
|
||||
try:
|
||||
import json as _json
|
||||
|
||||
parsed = _json.loads(body or "{}")
|
||||
if isinstance(parsed, dict):
|
||||
msg_in_body = msg_in_body or str(parsed.get("message", "") or "")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if status_code in (401, 403):
|
||||
return "auth_error", "Wan 3.0 服务鉴权失败（DASHSCOPE_API_KEY 无效或过期），请联系管理员。"
|
||||
if status_code == 429 or "rate" in body_lower or "throttl" in body_lower:
|
||||
return "rate_limit", "Wan 3.0 服务繁忙（限流），请稍等1-2分钟后重试。"
|
||||
if status_code == 400 and any(
|
||||
kw in body_lower for kw in ("portrait", "真人", "人脸", "肖像", "content_violation", "risk", "blocked")
|
||||
):
|
||||
return (
|
||||
"portrait_intercept",
|
||||
"参考素材包含真人照片或违规内容被安全策略拦截，请移除真人图片或调整文案后重试。",
|
||||
)
|
||||
if status_code == 404 or ("not found" in body_lower) or ("model" in body_lower and "not exist" in body_lower):
|
||||
return "model_not_found", "Wan 3.0 模型未开通或模型ID无效，请联系管理员。"
|
||||
if status_code in (402, 400) and ("quota" in body_lower or "billing" in body_lower or "insufficient" in body_lower):
|
||||
return "quota_exceeded", "Wan 3.0 服务配额不足，请联系管理员充值或稍后重试。"
|
||||
if status_code == 400:
|
||||
return "invalid_param", f"Wan 3.0 参数错误：{msg_in_body or body[:200]}"
|
||||
if status_code == 0:
|
||||
return "network_error", "Wan 3.0 服务连接失败（网络超时），请稍后重试。"
|
||||
# 任务内失败
|
||||
if task_msg and any(kw in task_msg.lower() for kw in ("portrait", "真人", "人脸", "violation", "blocked")):
|
||||
return "portrait_intercept", "Wan 3.0 视频内容被安全策略拦截，请调整文案或参考图后重试。"
|
||||
detail = msg_in_body or body[:200]
|
||||
return "unknown", f"Wan 3.0 视频生成失败（HTTP {status_code}）：{detail}"
|
||||
|
||||
|
||||
class DashScopeClient:
|
||||
"""阿里云 DashScope 异步 API 客户端（Wan 3.0 等视频生成）。"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
settings = get_shared_settings()
|
||||
self.api_key: str = getattr(settings, "dashscope_api_key", "") or os.getenv("DASHSCOPE_API_KEY", "")
|
||||
self.base_url: str = (
|
||||
getattr(settings, "dashscope_base_url", "") or "https://dashscope.aliyuncs.com/api/v1"
|
||||
).rstrip("/")
|
||||
self.poll_interval: int = int(getattr(settings, "dashscope_video_poll_interval", 10) or 10)
|
||||
self.total_timeout: int = int(getattr(settings, "dashscope_video_timeout", 900) or 900)
|
||||
self.max_retries: int = 2
|
||||
self.last_video_error: dict = {}
|
||||
|
||||
@property
|
||||
def is_available(self) -> bool:
|
||||
return bool(self.api_key)
|
||||
|
||||
def get_last_video_error(self) -> dict:
|
||||
return dict(self.last_video_error or {})
|
||||
|
||||
def _set_error(self, error_code: str, user_message: str, status_code: int = 0, detail: str = "", **extra) -> None:
|
||||
self.last_video_error = {
|
||||
"error_code": error_code,
|
||||
"user_message": user_message,
|
||||
"status_code": status_code,
|
||||
"detail": detail[:500] if detail else "",
|
||||
**extra,
|
||||
}
|
||||
|
||||
def video_generation(
|
||||
self,
|
||||
prompt: str,
|
||||
*,
|
||||
image_url: str | None = None,
|
||||
duration: int = 5,
|
||||
ratio: str | None = "9:16",
|
||||
resolution: str = "720p",
|
||||
watermark: bool = False,
|
||||
output_dir: str | None = None,
|
||||
model: str = "wan3.0-video",
|
||||
) -> dict | None:
|
||||
"""调用 DashScope 异步视频合成接口，轮询完成后下载到本地。
|
||||
|
||||
返回 {"video_path": str, "usage": dict | None}；失败返回 None，错误详情写入 self.last_video_error。
|
||||
"""
|
||||
self.last_video_error = {}
|
||||
if not self.is_available:
|
||||
self._set_error("auth_error", "Wan 3.0 API key 未配置，请联系管理员。", detail="dashscope api_key empty")
|
||||
logger.error("[dashscope] API key 未配置，无法调用视频生成")
|
||||
return None
|
||||
if not prompt or not prompt.strip():
|
||||
self._set_error("invalid_param", "视频生成提示词不能为空。", detail="empty prompt")
|
||||
return None
|
||||
|
||||
# DashScope 分辨率参数：720P / 1080P / 480P（大写 P）
|
||||
res_upper = (resolution or "720p").upper().replace("P", "P")
|
||||
if res_upper == "480P":
|
||||
ds_res = "480P"
|
||||
elif res_upper == "1080P":
|
||||
ds_res = "1080P"
|
||||
else:
|
||||
ds_res = "720P"
|
||||
|
||||
# 构造 input+parameters
|
||||
input_obj: dict[str, Any] = {"prompt": prompt.strip()}
|
||||
if image_url:
|
||||
input_obj["img_url"] = image_url
|
||||
params: dict[str, Any] = {
|
||||
"resolution": ds_res,
|
||||
"duration": str(float(duration)),
|
||||
"watermark": bool(watermark),
|
||||
}
|
||||
# 比例透传：Wan 支持 "9:16" / "16:9" / "1:1" 等
|
||||
if ratio and ratio != "adaptive":
|
||||
params["aspect_ratio"] = ratio
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"model": model,
|
||||
"input": input_obj,
|
||||
"parameters": params,
|
||||
}
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json",
|
||||
"X-DashScope-Async": "enable",
|
||||
}
|
||||
create_url = f"{self.base_url}/services/aigc/video-generation/video-synthesis"
|
||||
logger.info(
|
||||
"[dashscope] 创建任务: model=%s dur=%ds ratio=%s res=%s img=%s",
|
||||
model,
|
||||
duration,
|
||||
ratio,
|
||||
ds_res,
|
||||
bool(image_url),
|
||||
)
|
||||
logger.info("[dashscope] 创建任务 payload: model=%s params=%s", model, params)
|
||||
|
||||
# 创建任务
|
||||
task_id: str | None = None
|
||||
last_sc = 0
|
||||
last_body = ""
|
||||
for attempt in range(self.max_retries + 1):
|
||||
try:
|
||||
resp = httpx.post(create_url, headers=headers, json=payload, timeout=60)
|
||||
sc = int(getattr(resp, "status_code", 0) or 0)
|
||||
body_text = (getattr(resp, "text", "") or "")[:2000]
|
||||
last_sc = sc
|
||||
last_body = body_text
|
||||
if sc >= 400:
|
||||
logger.error("[dashscope] 创建任务 HTTP %d: %s", sc, body_text)
|
||||
if sc >= 500 and attempt < self.max_retries:
|
||||
time.sleep(0.5 * (2**attempt))
|
||||
continue
|
||||
err_code, user_msg = _classify_dashscope_error(sc, body_text)
|
||||
self._set_error(err_code, user_msg, sc, body_text, model=model)
|
||||
return None
|
||||
data = resp.json()
|
||||
tid = (data.get("output") or {}).get("task_id")
|
||||
if tid:
|
||||
task_id = tid
|
||||
break
|
||||
# 部分情况下 code != 错误
|
||||
code = data.get("code")
|
||||
if code and code != "":
|
||||
err_code, user_msg = _classify_dashscope_error(400, body_text, str(code))
|
||||
self._set_error(err_code, user_msg, sc, body_text, model=model)
|
||||
return None
|
||||
else:
|
||||
self._set_error("unknown", "Wan 3.0 响应格式异常，未返回任务ID", sc, str(data)[:500], model=model)
|
||||
return None
|
||||
except _HTTP_NETWORK_ERRORS as ne:
|
||||
last_sc = 0
|
||||
last_body = f"network error: {ne}"
|
||||
logger.warning(
|
||||
"[dashscope] 网络异常 %s，重试 %d/%d", type(ne).__name__, attempt + 1, self.max_retries + 1
|
||||
)
|
||||
if attempt < self.max_retries:
|
||||
time.sleep(0.5 * (2**attempt))
|
||||
continue
|
||||
self._set_error("network_error", "Wan 3.0 服务连接失败（网络超时），请稍后重试。", 0, str(ne))
|
||||
return None
|
||||
except Exception as _e:
|
||||
if attempt < self.max_retries:
|
||||
time.sleep(0.5 * (2**attempt))
|
||||
continue
|
||||
logger.error("[dashscope] 创建任务最终失败: %s", _e)
|
||||
self._set_error("unknown", f"Wan 3.0 创建任务异常：{_e!s}"[:200], 0, str(_e))
|
||||
return None
|
||||
if not task_id:
|
||||
if not self.last_video_error:
|
||||
err_code, user_msg = _classify_dashscope_error(last_sc, last_body)
|
||||
self._set_error(err_code, user_msg, last_sc, last_body, model=model)
|
||||
return None
|
||||
|
||||
# 轮询任务
|
||||
poll_url = f"{self.base_url}/tasks/{task_id}"
|
||||
deadline = time.time() + self.total_timeout
|
||||
video_url: str | None = None
|
||||
usage: dict | None = None
|
||||
poll_count = 0
|
||||
last_status = ""
|
||||
while time.time() < deadline:
|
||||
poll_count += 1
|
||||
try:
|
||||
r = httpx.get(poll_url, headers=headers, timeout=30)
|
||||
psc = int(getattr(r, "status_code", 0) or 0)
|
||||
pbody = (getattr(r, "text", "") or "")[:1500]
|
||||
if psc >= 400:
|
||||
logger.warning("[dashscope] 轮询 HTTP %d: %s", psc, pbody[:300])
|
||||
if poll_count < 3:
|
||||
time.sleep(self.poll_interval)
|
||||
continue
|
||||
err_code, user_msg = _classify_dashscope_error(psc, pbody)
|
||||
self._set_error(err_code, user_msg, psc, pbody, task_id=task_id)
|
||||
return None
|
||||
d = r.json()
|
||||
out = d.get("output") or {}
|
||||
task_status = out.get("task_status") or d.get("task_status") or ""
|
||||
last_status = task_status
|
||||
if task_status == "SUCCEEDED":
|
||||
video_url = out.get("video_url") or ""
|
||||
usage = d.get("usage")
|
||||
if not video_url:
|
||||
# 结果在 results 数组
|
||||
results = out.get("results") or []
|
||||
if results and isinstance(results, list):
|
||||
video_url = results[0].get("url") or results[0].get("video_url")
|
||||
if video_url:
|
||||
logger.info("[dashscope] 任务 %s 完成: %s", task_id, video_url[:120])
|
||||
break
|
||||
logger.error("[dashscope] 任务 %s SUCCEEDED 但无 video_url: %s", task_id, str(d)[:500])
|
||||
self._set_error(
|
||||
"unknown",
|
||||
"Wan 3.0 任务成功但未返回视频URL，请联系管理员。",
|
||||
200,
|
||||
str(d)[:500],
|
||||
task_id=task_id,
|
||||
)
|
||||
return None
|
||||
if task_status in ("FAILED", "FAILED_WITH_ERROR", "ERROR"):
|
||||
msg = out.get("message") or d.get("message") or out.get("error_msg") or "unknown error"
|
||||
logger.error("[dashscope] 任务 %s 失败: %s", task_id, msg)
|
||||
err_code, user_msg = _classify_dashscope_error(200, "", msg)
|
||||
self._set_error(err_code, user_msg, 200, msg, task_id=task_id, last_status=task_status)
|
||||
return None
|
||||
if task_status in ("CANCELED", "CANCELLED"):
|
||||
logger.warning("[dashscope] 任务 %s 被取消", task_id)
|
||||
self._set_error("unknown", "Wan 3.0 任务被取消。", 200, "task cancelled", task_id=task_id)
|
||||
return None
|
||||
# PENDING / RUNNING / SUSPENDED → 继续轮询
|
||||
if poll_count % 5 == 0:
|
||||
logger.info("[dashscope] 轮询中 task=%s status=%s polls=%d", task_id, task_status, poll_count)
|
||||
except Exception as e:
|
||||
logger.warning("[dashscope] 轮询异常: %s", e)
|
||||
time.sleep(self.poll_interval)
|
||||
if not video_url:
|
||||
logger.error("[dashscope] 任务 %s 轮询超时（%ds）", task_id, self.total_timeout)
|
||||
self._set_error(
|
||||
"network_error",
|
||||
f"Wan 3.0 视频生成超时（>{self.total_timeout}s），任务仍在排队，请稍后重试。",
|
||||
0,
|
||||
f"timeout after {self.total_timeout}s, polls={poll_count}, last_status={last_status}",
|
||||
task_id=task_id,
|
||||
last_status=last_status,
|
||||
)
|
||||
return None
|
||||
|
||||
# 下载视频
|
||||
out_dir = output_dir or os.path.join(os.getcwd(), "seedance_outputs")
|
||||
os.makedirs(out_dir, exist_ok=True)
|
||||
suffix = Path(urlparse(video_url).path).suffix or ".mp4"
|
||||
if suffix.lower() not in (".mp4", ".mov", ".webm"):
|
||||
suffix = ".mp4"
|
||||
safe_tid = "".join(c if c.isalnum() or c in "-_" else "_" for c in task_id)[:40]
|
||||
out_path = os.path.join(out_dir, f"wan_{safe_tid}{suffix}")
|
||||
try:
|
||||
with httpx.stream("GET", video_url, timeout=300, follow_redirects=True) as resp:
|
||||
dsc = int(getattr(resp, "status_code", 0) or 0)
|
||||
if dsc >= 400:
|
||||
logger.error("[dashscope] 下载 HTTP %d", dsc)
|
||||
self._set_error("network_error", "Wan 3.0 视频下载失败（HTTP错误），请稍后重试。", dsc)
|
||||
return None
|
||||
with open(out_path, "wb") as f:
|
||||
for chunk in resp.iter_bytes(chunk_size=1024 * 256):
|
||||
if chunk:
|
||||
f.write(chunk)
|
||||
except Exception as e:
|
||||
logger.error("[dashscope] 下载视频失败: %s", e, exc_info=True)
|
||||
self._set_error("network_error", f"Wan 3.0 视频下载失败：{e!s}"[:200], 0, str(e))
|
||||
return None
|
||||
size = os.path.getsize(out_path) if os.path.exists(out_path) else 0
|
||||
if size < 1024:
|
||||
logger.error("[dashscope] 下载文件过小: %d bytes", size)
|
||||
self._set_error("unknown", "Wan 3.0 视频下载文件过小，请稍后重试。", 0, f"downloaded only {size} bytes")
|
||||
return None
|
||||
logger.info("[dashscope] 视频已下载: %s (%d bytes)", out_path, size)
|
||||
return {"video_path": out_path, "usage": usage}
|
||||
|
||||
|
||||
def get_dashscope_client() -> DashScopeClient | None:
|
||||
"""返回 DashScope 客户端单例；未配置 API key 时返回 None。"""
|
||||
global _DASHSCOPE_CLIENT_SINGLETON
|
||||
if _DASHSCOPE_CLIENT_SINGLETON is None:
|
||||
_DASHSCOPE_CLIENT_SINGLETON = DashScopeClient()
|
||||
if not _DASHSCOPE_CLIENT_SINGLETON.is_available:
|
||||
return None
|
||||
return _DASHSCOPE_CLIENT_SINGLETON
|
||||
@@ -57,7 +57,7 @@ if [ "$TARGET_ENV" = "staging" ]; then
|
||||
fi
|
||||
|
||||
# 共用 secrets 直接导出（如果存在）
|
||||
SHARED_SECRETS="OSS_ACCESS_KEY_ID OSS_ACCESS_KEY_SECRET COSYVOICE_API_KEY DASHSCOPE_API_KEY MEDIAKIT_API_KEY DOUBAO_API_KEY DOUBAO_MODEL DOUBAO_FAST_MODEL DOUBAO_BASE_URL DOUBAO_VISION_MODEL DOUBAO_VISION_LITE_MODEL DOUBAO_VISION_USE_LITE DOUBAO_IMAGE_MODEL DOUBAO_IMAGE_SIZE DOUBAO_IMAGE_TIMEOUT DOUBAO_FAST_MODEL DOUBAO_TIMEOUT DOUBAO_MAX_RETRIES WECHAT_APP_ID WECHAT_APP_SECRET TIKHUB_API_KEY APIZERO_API_KEY GPU_WORKER_TOKEN"
|
||||
SHARED_SECRETS="OSS_ACCESS_KEY_ID OSS_ACCESS_KEY_SECRET COSYVOICE_API_KEY DASHSCOPE_API_KEY MEDIAKIT_API_KEY DOUBAO_API_KEY DOUBAO_MODEL DOUBAO_BASE_URL DOUBAO_VISION_MODEL WECHAT_APP_ID WECHAT_APP_SECRET TIKHUB_API_KEY APIZERO_API_KEY GPU_WORKER_TOKEN"
|
||||
for var in $SHARED_SECRETS; do
|
||||
value="${!var:-}"
|
||||
# 已经在环境中了，无需额外操作
|
||||
|
||||
@@ -1,80 +0,0 @@
|
||||
"""爆款视频 5 套 Prompt 模板种子脚本（#2040）。
|
||||
|
||||
幂等：以 (prompt_type, version) 为唯一键，存在则更新（UPSERT），重复执行结果一致。
|
||||
用法：
|
||||
python scripts/seed_viral_video_prompts.py # 自动用应用配置连库
|
||||
DATABASE_URL=postgresql+psycopg2://... python scripts/seed_viral_video_prompts.py
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import sys
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
import sqlalchemy as sa # noqa: E402
|
||||
|
||||
from packages.application.viral_video.prompts import DEFAULT_TEMPLATES # noqa: E402
|
||||
|
||||
|
||||
def _engine():
|
||||
database_url = os.environ.get("DATABASE_URL")
|
||||
if database_url:
|
||||
return sa.create_engine(database_url)
|
||||
# 复用应用自身配置
|
||||
from packages.config import get_shared_settings
|
||||
|
||||
url = str(get_shared_settings().database_url)
|
||||
return sa.create_engine(url.replace("postgresql+asyncpg://", "postgresql+psycopg2://"))
|
||||
|
||||
|
||||
UPSERT_SQL = sa.text("""
|
||||
INSERT INTO viral_video_prompt_templates
|
||||
(name, prompt_type, version, system_prompt, user_prompt_template,
|
||||
example_output, is_active, updated_at)
|
||||
VALUES
|
||||
(:name, :prompt_type, :version, :system_prompt, :user_prompt_template,
|
||||
:example_output, TRUE, :now_ts)
|
||||
ON CONFLICT (prompt_type, version) DO UPDATE SET
|
||||
name = EXCLUDED.name,
|
||||
system_prompt = EXCLUDED.system_prompt,
|
||||
user_prompt_template = EXCLUDED.user_prompt_template,
|
||||
example_output = EXCLUDED.example_output,
|
||||
is_active = TRUE,
|
||||
updated_at = :now_ts
|
||||
""")
|
||||
|
||||
|
||||
def seed(engine) -> int:
|
||||
count = 0
|
||||
from datetime import datetime, timezone
|
||||
|
||||
now_ts = datetime.now(timezone.utc)
|
||||
with engine.begin() as conn:
|
||||
for item in DEFAULT_TEMPLATES:
|
||||
conn.execute(
|
||||
UPSERT_SQL,
|
||||
{
|
||||
"name": item["name"],
|
||||
"prompt_type": item["prompt_type"],
|
||||
"version": item["version"],
|
||||
"system_prompt": item["system_prompt"],
|
||||
"user_prompt_template": item["user_prompt_template"],
|
||||
"example_output": item["example_output"],
|
||||
"now_ts": now_ts,
|
||||
},
|
||||
)
|
||||
count += 1
|
||||
return count
|
||||
|
||||
|
||||
def main() -> int:
|
||||
engine = _engine()
|
||||
count = seed(engine)
|
||||
print(f"seed 完成：{count} 套模板已写入/更新（image_analysis/intent_parsing/copy_fusion/storyboard/review）")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -377,12 +377,32 @@ class TestPrepareNarrativeVoice:
|
||||
assert ei.value.status_code == 502
|
||||
assert "配音合成失败" in ei.value.message
|
||||
|
||||
def test_no_points_service_invoked(self, monkeypatch):
|
||||
"""v1.6.2: 叙事配音已免费，不再实例化 PointsService / 扣点/退费。"""
|
||||
# 确认 narrative_service 已不再暴露 PointsService
|
||||
assert not hasattr(ns, "PointsService"), "narrative_service 不应再导入 PointsService"
|
||||
def test_points_insufficient_402(self, monkeypatch):
|
||||
class FakePoints:
|
||||
def deduct_points(self, *a, **k):
|
||||
return {"success": False, "balance": 0}
|
||||
|
||||
class FakeWorkflow:
|
||||
monkeypatch.setattr(ns, "PointsService", lambda: FakePoints())
|
||||
deps = self._deps(points_enabled=True)
|
||||
with pytest.raises(NarrativeError) as ei:
|
||||
prepare_narrative_voice(**deps)
|
||||
assert ei.value.status_code == 402
|
||||
|
||||
def test_points_refund_on_failure(self, monkeypatch):
|
||||
class FakePoints:
|
||||
def __init__(self):
|
||||
self.refunded = 0
|
||||
|
||||
def deduct_points(self, *a, **k):
|
||||
return {"success": True, "balance": 100}
|
||||
|
||||
def refund_points(self, user_id, amount, source, db, ref_id="", **k):
|
||||
self.refunded += amount
|
||||
|
||||
points = FakePoints()
|
||||
monkeypatch.setattr(ns, "PointsService", lambda: points)
|
||||
|
||||
class FailingWorkflow:
|
||||
def __init__(self, *, repository, cosyvoice_service):
|
||||
pass
|
||||
|
||||
@@ -392,22 +412,11 @@ class TestPrepareNarrativeVoice:
|
||||
def process_synthesis_failure(self, job_id, error):
|
||||
return None
|
||||
|
||||
monkeypatch.setattr(ns, "TTSWorkflowService", FakeWorkflow)
|
||||
monkeypatch.setattr(ns, "TTSWorkflowService", FailingWorkflow)
|
||||
deps = self._deps(points_enabled=True)
|
||||
with pytest.raises(NarrativeError) as ei:
|
||||
with pytest.raises(NarrativeError):
|
||||
prepare_narrative_voice(**deps)
|
||||
# 走 502 业务错误路径，不再退费
|
||||
assert ei.value.status_code == 502
|
||||
|
||||
def test_module_has_no_points_imports(self):
|
||||
"""模块源码不再包含扣点相关符号。"""
|
||||
import inspect
|
||||
|
||||
src = inspect.getsource(ns)
|
||||
assert "PointsService" not in src
|
||||
assert "calculate_points_cost" not in src
|
||||
assert "_points_scene" not in src
|
||||
assert "_POINTS_SCENE" not in src
|
||||
assert points.refunded > 0
|
||||
|
||||
def test_clone_source_resolves_profile(self, monkeypatch):
|
||||
captured = {}
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
"""Additional unit tests to hit uncovered lines for diff-coverage >=60%."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
@@ -14,20 +13,12 @@ from packages.shared.ai_client import DoubaoClient
|
||||
|
||||
class _FakeSettings:
|
||||
doubao_api_key = "test-key"
|
||||
doubao_model = "doubao-seed-2-1-pro-260915"
|
||||
doubao_fast_model = "doubao-seed-2-1-lite-260915"
|
||||
doubao_model = "test-model"
|
||||
doubao_base_url = "https://ark.cn-beijing.volces.com/api/v3"
|
||||
doubao_timeout = 10
|
||||
doubao_max_retries = 0
|
||||
doubao_vision_model = "doubao-seed-2-1-pro-260915"
|
||||
doubao_vision_lite_model = "doubao-seed-2-1-lite-260915"
|
||||
doubao_vision_use_lite = False
|
||||
doubao_embedding_model = "doubao-embedding-vision-251215"
|
||||
doubao_video_model = "doubao-seedance-2-5-260628"
|
||||
doubao_video_timeout = 480
|
||||
doubao_video_poll_interval = 10
|
||||
doubao_image_model = "doubao-seedream-5-0-pro-260628"
|
||||
doubao_image_timeout = 120
|
||||
doubao_vision_model = "test-vision"
|
||||
doubao_embedding_model = "test-embedding"
|
||||
|
||||
|
||||
def _make_client(api_key: str = "test-key") -> DoubaoClient:
|
||||
@@ -93,9 +84,7 @@ _GEN_TASKS_PATH = Path(__file__).resolve().parents[2] / "apps/api/app/api/routes
|
||||
def _load_infer_func():
|
||||
src = _GEN_TASKS_PATH.read_text()
|
||||
start = src.index("# #2035：文案关键词")
|
||||
# 用紧跟 _infer_expected_categories 后的 logger 行作为结束锚点
|
||||
end_marker = "\nlogger = logging.getLogger"
|
||||
end = src.index(end_marker, start)
|
||||
end = src.index("from packages.middleware")
|
||||
code = src[start:end]
|
||||
ns: dict = {}
|
||||
exec(code, ns)
|
||||
@@ -135,50 +124,26 @@ from packages.domain.atom_clip_tagger import parse_vision_response
|
||||
|
||||
class TestParseVisionResponseEdgeCases:
|
||||
def test_person_count_type_error_defaults_zero(self):
|
||||
text = json.dumps(
|
||||
{
|
||||
"scene": [],
|
||||
"objects": [],
|
||||
"action": [],
|
||||
"shot": "",
|
||||
"has_text": False,
|
||||
"person_count": "not-an-int",
|
||||
"text_content": "",
|
||||
"caption": "x",
|
||||
}
|
||||
)
|
||||
text = json.dumps({
|
||||
"scene": [], "objects": [], "action": [], "shot": "", "has_text": False,
|
||||
"person_count": "not-an-int", "text_content": "", "caption": "x",
|
||||
})
|
||||
r = parse_vision_response(text)
|
||||
assert r["person_count"] == 0
|
||||
|
||||
def test_person_count_out_of_range_clamped(self):
|
||||
text = json.dumps(
|
||||
{
|
||||
"scene": [],
|
||||
"objects": [],
|
||||
"action": [],
|
||||
"shot": "",
|
||||
"has_text": False,
|
||||
"person_count": 10,
|
||||
"text_content": "",
|
||||
"caption": "x",
|
||||
}
|
||||
)
|
||||
text = json.dumps({
|
||||
"scene": [], "objects": [], "action": [], "shot": "", "has_text": False,
|
||||
"person_count": 10, "text_content": "", "caption": "x",
|
||||
})
|
||||
r = parse_vision_response(text)
|
||||
assert r["person_count"] == 3
|
||||
|
||||
def test_person_count_negative_clamped(self):
|
||||
text = json.dumps(
|
||||
{
|
||||
"scene": [],
|
||||
"objects": [],
|
||||
"action": [],
|
||||
"shot": "",
|
||||
"has_text": False,
|
||||
"person_count": -5,
|
||||
"text_content": "",
|
||||
"caption": "x",
|
||||
}
|
||||
)
|
||||
text = json.dumps({
|
||||
"scene": [], "objects": [], "action": [], "shot": "", "has_text": False,
|
||||
"person_count": -5, "text_content": "", "caption": "x",
|
||||
})
|
||||
r = parse_vision_response(text)
|
||||
assert r["person_count"] == 0
|
||||
|
||||
@@ -189,18 +154,10 @@ class TestParseVisionResponseEdgeCases:
|
||||
|
||||
def test_caption_truncation_at_80(self):
|
||||
long_caption = "描" * 100
|
||||
text = json.dumps(
|
||||
{
|
||||
"scene": [],
|
||||
"objects": [],
|
||||
"action": [],
|
||||
"shot": "",
|
||||
"has_text": False,
|
||||
"person_count": 0,
|
||||
"text_content": "",
|
||||
"caption": long_caption,
|
||||
}
|
||||
)
|
||||
text = json.dumps({
|
||||
"scene": [], "objects": [], "action": [], "shot": "", "has_text": False,
|
||||
"person_count": 0, "text_content": "", "caption": long_caption,
|
||||
})
|
||||
r = parse_vision_response(text)
|
||||
assert len(r["caption"]) == 80
|
||||
|
||||
@@ -234,7 +191,9 @@ class TestNarrativeMatchNonDictClipTags:
|
||||
def test_non_dict_clip_tags_are_skipped(self):
|
||||
a1 = _FA("a1", tags=[])
|
||||
clip_map = {"a1": [None, "bad", {"scene": ["工厂"], "objects": [], "action": []}, 123]}
|
||||
matched, unmatched = match_assets_by_script_tags([a1], script_tags=["工厂"], clip_ai_tags_by_asset=clip_map)
|
||||
matched, unmatched = match_assets_by_script_tags(
|
||||
[a1], script_tags=["工厂"], clip_ai_tags_by_asset=clip_map
|
||||
)
|
||||
assert [a.id for a in matched] == ["a1"]
|
||||
|
||||
|
||||
@@ -267,7 +226,6 @@ class _FQuery:
|
||||
class TestUpdateCaptionEmbedding:
|
||||
def _make_repo(self, session):
|
||||
from packages.adapters.sqlalchemy_impl.asset_atom_clip_repository import SQLAlchemyAssetAtomClipRepository
|
||||
|
||||
repo = SQLAlchemyAssetAtomClipRepository.__new__(SQLAlchemyAssetAtomClipRepository)
|
||||
repo.session = session
|
||||
return repo
|
||||
|
||||
@@ -1,25 +1,48 @@
|
||||
"""AI 数字人渲染 — v1.6.2 起免费，不扣积分"""
|
||||
"""AI数字人渲染 积分扣点单元测试 (#1895 P2 step 2.6)"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
class TestAiAvatarRenderFree:
|
||||
def test_ai_digital_human_returns_zero_cost(self):
|
||||
import pytest
|
||||
|
||||
import packages.middleware.points_gate as _pg_module
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _enable(monkeypatch):
|
||||
monkeypatch.setattr(_pg_module, "_points_gate_enabled", lambda: True)
|
||||
yield
|
||||
|
||||
|
||||
class TestAiAvatarRenderPoints:
|
||||
def test_ai_digital_human_per_unit(self):
|
||||
from packages.domain.points_rules import calculate_points_cost
|
||||
|
||||
assert calculate_points_cost("ai_digital_human", is_member=False, duration_minutes=1) == 0
|
||||
assert calculate_points_cost("ai_digital_human", is_member=True, duration_minutes=5) == 0
|
||||
cost = calculate_points_cost("ai_digital_human", is_member=False, duration_minutes=1)
|
||||
assert cost >= 15
|
||||
|
||||
def test_no_points_gate_decorator(self):
|
||||
def test_decorator_attached(self):
|
||||
from app.api.routes.ai_avatar_render import create_render_job
|
||||
|
||||
assert not hasattr(create_render_job, "__wrapped__")
|
||||
assert hasattr(create_render_job, "__wrapped__"), "missing @points_gate"
|
||||
|
||||
def test_module_has_no_points_imports(self):
|
||||
import inspect
|
||||
def test_insufficient_raises_402(self):
|
||||
from app.api.routes.ai_avatar_render import create_render_job
|
||||
from app.schemas.ai_avatar_render import CreateAiAvatarRenderRequest
|
||||
from fastapi import HTTPException
|
||||
|
||||
from app.api.routes import ai_avatar_render as mod
|
||||
|
||||
src = inspect.getsource(mod)
|
||||
assert "PointsService" not in src
|
||||
assert "points_gate" not in src
|
||||
db = MagicMock()
|
||||
cu = MagicMock()
|
||||
cu.user.id = "u1"
|
||||
cu.user.is_member = False
|
||||
cu.user.member_type = None
|
||||
svc = MagicMock()
|
||||
body = CreateAiAvatarRenderRequest(lipsync_job_id="lip1")
|
||||
with patch("packages.domain.points_service.PointsService") as MS:
|
||||
msvc = MagicMock()
|
||||
msvc.deduct_points.return_value = {"success": False, "balance": 0}
|
||||
MS.return_value = msvc
|
||||
with pytest.raises(HTTPException) as ei:
|
||||
create_render_job(body=body, current_user=cu, svc=svc, db=db)
|
||||
assert ei.value.status_code == 402
|
||||
|
||||
@@ -17,7 +17,7 @@ def mock_settings():
|
||||
doubao_api_key="test-api-key",
|
||||
doubao_model="doubao-pro-32k",
|
||||
doubao_base_url="https://ark.example.com/api/v3",
|
||||
doubao_timeout=45,
|
||||
doubao_timeout=30,
|
||||
doubao_max_retries=2,
|
||||
)
|
||||
yield mock
|
||||
@@ -37,7 +37,7 @@ def client_without_key():
|
||||
doubao_api_key="",
|
||||
doubao_model="doubao-pro-32k",
|
||||
doubao_base_url="https://ark.example.com/api/v3",
|
||||
doubao_timeout=45,
|
||||
doubao_timeout=30,
|
||||
doubao_max_retries=2,
|
||||
)
|
||||
yield DoubaoClient()
|
||||
@@ -52,7 +52,7 @@ class TestDoubaoClientInit:
|
||||
assert client.api_key == "test-api-key"
|
||||
assert client.model == "doubao-pro-32k"
|
||||
assert client.base_url == "https://ark.example.com/api/v3"
|
||||
assert client.timeout == 45
|
||||
assert client.timeout == 30
|
||||
assert client.max_retries == 2
|
||||
|
||||
def test_base_url_strips_trailing_slash(self, mock_settings):
|
||||
|
||||
@@ -1,635 +0,0 @@
|
||||
"""#2170 Seedream 图片生成 + 方舟信任链单测。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import httpx
|
||||
|
||||
from packages.shared.ai_client import DoubaoClient
|
||||
|
||||
|
||||
def _make_client(**overrides):
|
||||
client = DoubaoClient.__new__(DoubaoClient)
|
||||
client.api_key = overrides.get("api_key", "test-key")
|
||||
client.base_url = overrides.get("base_url", "https://ark.cn-beijing.volces.com/api/v3")
|
||||
client.model = "doubao-model"
|
||||
client.vision_model = "doubao-vision"
|
||||
client.embedding_model = "doubao-embedding"
|
||||
client.image_model = overrides.get("image_model", "doubao-seedream-5-0-pro-260628")
|
||||
client.image_timeout = overrides.get("image_timeout", 120)
|
||||
client.timeout = overrides.get("timeout", 30)
|
||||
client.max_retries = overrides.get("max_retries", 0)
|
||||
client.last_video_error = {}
|
||||
client.last_image_error = {}
|
||||
return client
|
||||
|
||||
|
||||
def _fake_time(base=1000.0, stable_calls=50, big=9e9):
|
||||
"""返回 time.time 替身：前 stable_calls 次返回 base+i，之后返回 big+i。
|
||||
|
||||
Python 3.12 logging.LogRecord.__init__ 内部会调 time.time()，
|
||||
用有限 iter 会 StopIteration，因此必须用无限生成器。
|
||||
"""
|
||||
state = {"n": 0}
|
||||
|
||||
def _t():
|
||||
n = state["n"]
|
||||
state["n"] += 1
|
||||
if n < stable_calls:
|
||||
return base + n
|
||||
return big + n
|
||||
|
||||
return _t
|
||||
|
||||
|
||||
# ── Seedream 图片生成单测 ──────────────────────────────────────────
|
||||
|
||||
|
||||
class TestImageGenerationHappyPath:
|
||||
def test_returns_none_when_no_api_key(self):
|
||||
client = _make_client(api_key="")
|
||||
assert client.image_generation("p") is None
|
||||
err = client.get_last_image_error()
|
||||
assert err["error_code"] == "auth_error"
|
||||
|
||||
def test_returns_none_on_empty_prompt(self):
|
||||
client = _make_client()
|
||||
assert client.image_generation(" ") is None
|
||||
err = client.get_last_image_error()
|
||||
assert err["error_code"] == "invalid_param"
|
||||
|
||||
def test_text_to_image_success(self):
|
||||
client = _make_client()
|
||||
captured = {}
|
||||
ok_resp = MagicMock()
|
||||
ok_resp.status_code = 200
|
||||
ok_resp.json.return_value = {"data": [{"url": "https://cdn.example.com/i.png"}], "usage": {"tokens": 1}}
|
||||
ok_resp.raise_for_status = MagicMock()
|
||||
ok_resp.text = ""
|
||||
|
||||
def fake_post(url, **kwargs):
|
||||
captured["url"] = url
|
||||
captured["json"] = kwargs.get("json")
|
||||
return ok_resp
|
||||
|
||||
with patch("packages.shared.ai_client.httpx.post", side_effect=fake_post):
|
||||
result = client.image_generation("一只可爱的猫", size="1K")
|
||||
assert result is not None
|
||||
assert result["url"] == "https://cdn.example.com/i.png"
|
||||
assert "/images/generations" in captured["url"]
|
||||
assert captured["json"]["model"] == "doubao-seedream-5-0-pro-260628"
|
||||
assert captured["json"]["size"] == "1K"
|
||||
assert "image" not in captured["json"]
|
||||
|
||||
def test_image_to_image_single_ref_passed_as_string(self):
|
||||
client = _make_client()
|
||||
captured = {}
|
||||
ok_resp = MagicMock(status_code=200)
|
||||
ok_resp.json.return_value = {"data": [{"url": "https://cdn.example.com/out.png"}]}
|
||||
ok_resp.raise_for_status = MagicMock()
|
||||
ok_resp.text = ""
|
||||
|
||||
def fake_post(url, **kwargs):
|
||||
captured["json"] = kwargs.get("json")
|
||||
return ok_resp
|
||||
|
||||
with patch("packages.shared.ai_client.httpx.post", side_effect=fake_post):
|
||||
client.image_generation("保持五官", reference_images=["https://img/x.jpg"])
|
||||
assert captured["json"]["image"] == "https://img/x.jpg"
|
||||
|
||||
def test_image_to_image_multiple_refs_passed_as_list(self):
|
||||
client = _make_client()
|
||||
captured = {}
|
||||
ok_resp = MagicMock(status_code=200)
|
||||
ok_resp.json.return_value = {"data": [{"url": "https://cdn.example.com/out.png"}]}
|
||||
ok_resp.raise_for_status = MagicMock()
|
||||
|
||||
def fake_post(url, **kwargs):
|
||||
captured["json"] = kwargs.get("json")
|
||||
return ok_resp
|
||||
|
||||
refs = [f"https://img/{i}.jpg" for i in range(3)]
|
||||
with patch("packages.shared.ai_client.httpx.post", side_effect=fake_post):
|
||||
client.image_generation("保持", reference_images=refs)
|
||||
assert captured["json"]["image"] == refs
|
||||
|
||||
def test_400_sensitive_returns_portrait_intercept(self):
|
||||
client = _make_client(max_retries=0)
|
||||
bad_resp = MagicMock(status_code=400)
|
||||
bad_resp.text = '{"error":{"code":"ContentRisk","message":"sensitive content detected"}}'
|
||||
bad_resp.json.return_value = {"error": {"code": "ContentRisk"}}
|
||||
bad_resp.raise_for_status.side_effect = httpx.HTTPStatusError("bad", request=MagicMock(), response=bad_resp)
|
||||
with patch("packages.shared.ai_client.httpx.post", return_value=bad_resp):
|
||||
assert client.image_generation("p", reference_images=["https://img/x.jpg"]) is None
|
||||
err = client.get_last_image_error()
|
||||
assert err["error_code"] == "portrait_intercept"
|
||||
|
||||
def test_500_retries_then_fails(self):
|
||||
client = _make_client(max_retries=1)
|
||||
bad_resp = MagicMock(status_code=500)
|
||||
bad_resp.text = "internal error"
|
||||
bad_resp.json.return_value = {"error": {"message": "internal"}}
|
||||
bad_resp.raise_for_status.side_effect = httpx.HTTPStatusError("500", request=MagicMock(), response=bad_resp)
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", return_value=bad_resp) as mp,
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
):
|
||||
assert client.image_generation("p") is None
|
||||
assert mp.call_count == 2
|
||||
err = client.get_last_image_error()
|
||||
assert err["error_code"] == "network_error"
|
||||
|
||||
|
||||
# ── 信任链集成单测 ────────────────────────────────────────────────
|
||||
|
||||
|
||||
class TestTrustChainIntegration:
|
||||
def test_pre_trusted_images_replace_original_refs(self, tmp_path):
|
||||
"""#2174: 传 pre_trusted_images（预热好的t2i信任图）时，替换原image_url/ref_imgs发给Seedance，不再现场跑Seedream。"""
|
||||
client = _make_client()
|
||||
captured_calls = []
|
||||
|
||||
task_ok = MagicMock(status_code=200)
|
||||
task_ok.json.return_value = {"id": "t-trust"}
|
||||
task_ok.raise_for_status = MagicMock()
|
||||
task_ok.text = ""
|
||||
|
||||
poll_ok = MagicMock(status_code=200)
|
||||
poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
|
||||
poll_ok.raise_for_status = MagicMock()
|
||||
|
||||
class FakeStream:
|
||||
def __init__(self):
|
||||
self._c = [b"OK"]
|
||||
self._it = iter(self._c)
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, *a):
|
||||
return False
|
||||
|
||||
def raise_for_status(self):
|
||||
return None
|
||||
|
||||
def iter_bytes(self, chunk_size=None):
|
||||
return self._it
|
||||
|
||||
def fake_post(url, **kwargs):
|
||||
captured_calls.append({"url": url, "json": kwargs.get("json")})
|
||||
return task_ok
|
||||
|
||||
fake_uuid = MagicMock()
|
||||
fake_uuid.hex = "00000001"
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
|
||||
patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
|
||||
patch("packages.shared.ai_client.httpx.stream", return_value=FakeStream()),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=2)),
|
||||
patch("packages.shared.ai_client.uuid.uuid4", return_value=fake_uuid),
|
||||
patch("packages.shared.ai_client.get_shared_settings") as ms,
|
||||
):
|
||||
ms.return_value = MagicMock(
|
||||
doubao_video_poll_interval=0,
|
||||
doubao_video_timeout=60,
|
||||
doubao_video_model="doubao-seedance-2-5-260628",
|
||||
)
|
||||
out = client.video_generation(
|
||||
"人物在海边散步",
|
||||
image_url="https://img/raw.jpg",
|
||||
pre_trusted_images=["https://ai.example.com/trusted.png"],
|
||||
duration=5,
|
||||
ratio="9:16",
|
||||
resolution="720p",
|
||||
output_dir=str(tmp_path),
|
||||
)
|
||||
assert out is not None
|
||||
# 只有一次 Seedance 调用（不现场跑Seedream）
|
||||
assert len(captured_calls) == 1
|
||||
assert "/contents/generations/tasks" in captured_calls[0]["url"]
|
||||
seedance_payload = captured_calls[0]["json"]
|
||||
content = seedance_payload["content"]
|
||||
img_items = [c for c in content if c.get("type") == "image_url"]
|
||||
assert len(img_items) == 1
|
||||
assert img_items[0]["image_url"]["url"] == "https://ai.example.com/trusted.png"
|
||||
assert img_items[0]["role"] == "reference_image"
|
||||
assert seedance_payload["ratio"] == "9:16"
|
||||
|
||||
def test_no_reference_image_skips_seedream(self, tmp_path):
|
||||
client = _make_client()
|
||||
captured_calls = []
|
||||
|
||||
task_ok = MagicMock(status_code=200)
|
||||
task_ok.json.return_value = {"id": "t-t2v"}
|
||||
task_ok.raise_for_status = MagicMock()
|
||||
poll_ok = MagicMock(status_code=200)
|
||||
poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
|
||||
poll_ok.raise_for_status = MagicMock()
|
||||
|
||||
class FakeStream:
|
||||
def __init__(self):
|
||||
self._it = iter([b"OK"])
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, *a):
|
||||
return False
|
||||
|
||||
def raise_for_status(self):
|
||||
return None
|
||||
|
||||
def iter_bytes(self, chunk_size=None):
|
||||
return self._it
|
||||
|
||||
def fake_post(url, **kwargs):
|
||||
captured_calls.append({"url": url, "json": kwargs.get("json")})
|
||||
return task_ok
|
||||
|
||||
fake_uuid = MagicMock()
|
||||
fake_uuid.hex = "00000002"
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
|
||||
patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
|
||||
patch("packages.shared.ai_client.httpx.stream", return_value=FakeStream()),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=2)),
|
||||
patch("packages.shared.ai_client.uuid.uuid4", return_value=fake_uuid),
|
||||
patch("packages.shared.ai_client.get_shared_settings") as ms,
|
||||
):
|
||||
ms.return_value = MagicMock(
|
||||
doubao_video_poll_interval=0,
|
||||
doubao_video_timeout=60,
|
||||
doubao_video_model="doubao-seedance-2-5-260628",
|
||||
)
|
||||
out = client.video_generation("海边日落", duration=5, ratio="9:16", output_dir=str(tmp_path))
|
||||
assert out is not None
|
||||
assert len(captured_calls) == 1
|
||||
assert "/contents/generations/tasks" in captured_calls[0]["url"]
|
||||
content = captured_calls[0]["json"]["content"]
|
||||
assert all(c.get("type") != "image_url" for c in content)
|
||||
|
||||
def test_no_preheated_images_falls_back_to_first_frame_mode(self, tmp_path):
|
||||
"""#2174: 无 pre_trusted_images 时原图走 first_frame 模式（ratio=adaptive），不再现场跑 Seedream。"""
|
||||
client = _make_client(max_retries=0)
|
||||
captured_calls = []
|
||||
|
||||
task_ok = MagicMock(status_code=200)
|
||||
task_ok.json.return_value = {"id": "t-fb"}
|
||||
task_ok.raise_for_status = MagicMock()
|
||||
poll_ok = MagicMock(status_code=200)
|
||||
poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
|
||||
poll_ok.raise_for_status = MagicMock()
|
||||
|
||||
class FakeStream:
|
||||
def __init__(self):
|
||||
self._it = iter([b"OK"])
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, *a):
|
||||
return False
|
||||
|
||||
def raise_for_status(self):
|
||||
return None
|
||||
|
||||
def iter_bytes(self, chunk_size=None):
|
||||
return self._it
|
||||
|
||||
def fake_post(url, **kwargs):
|
||||
captured_calls.append({"url": url, "json": kwargs.get("json")})
|
||||
return task_ok
|
||||
|
||||
fake_uuid = MagicMock()
|
||||
fake_uuid.hex = "00000003"
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
|
||||
patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
|
||||
patch("packages.shared.ai_client.httpx.stream", return_value=FakeStream()),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=2)),
|
||||
patch("packages.shared.ai_client.uuid.uuid4", return_value=fake_uuid),
|
||||
patch("packages.shared.ai_client.get_shared_settings") as ms,
|
||||
):
|
||||
ms.return_value = MagicMock(
|
||||
doubao_video_poll_interval=0,
|
||||
doubao_video_timeout=60,
|
||||
doubao_video_model="doubao-seedance-2-5-260628",
|
||||
)
|
||||
out = client.video_generation(
|
||||
"海边散步",
|
||||
image_url="https://img/raw.jpg",
|
||||
duration=5,
|
||||
ratio="9:16",
|
||||
output_dir=str(tmp_path),
|
||||
)
|
||||
assert out is not None
|
||||
# 不现场跑 Seedream，只有一次 Seedance 调用
|
||||
assert len(captured_calls) == 1
|
||||
seedance_payload = captured_calls[0]["json"]
|
||||
content = seedance_payload["content"]
|
||||
img_items = [c for c in content if c.get("type") == "image_url"]
|
||||
assert len(img_items) == 1
|
||||
assert img_items[0]["image_url"]["url"] == "https://img/raw.jpg"
|
||||
assert img_items[0]["role"] == "first_frame"
|
||||
assert seedance_payload["ratio"] == "adaptive"
|
||||
|
||||
|
||||
# ── image_generation 补充分支覆盖 ─────────────────────────────────
|
||||
|
||||
|
||||
class TestImageGenerationBranches:
|
||||
"""覆盖 image_generation 的错误分类/重试/结构异常等分支。"""
|
||||
|
||||
def test_401_returns_auth_error(self):
|
||||
client = _make_client(max_retries=0)
|
||||
r = MagicMock(status_code=401, text='{"error":{}}')
|
||||
r.json.return_value = {"error": {}}
|
||||
r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
|
||||
with patch("packages.shared.ai_client.httpx.post", return_value=r):
|
||||
assert client.image_generation("p") is None
|
||||
assert client.get_last_image_error()["error_code"] == "auth_error"
|
||||
|
||||
def test_404_returns_model_not_found(self):
|
||||
client = _make_client(max_retries=0)
|
||||
r = MagicMock(status_code=404, text="not found")
|
||||
r.json.return_value = {"error": {"message": "model not found"}}
|
||||
r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
|
||||
with patch("packages.shared.ai_client.httpx.post", return_value=r):
|
||||
assert client.image_generation("p") is None
|
||||
assert client.get_last_image_error()["error_code"] == "model_not_found"
|
||||
|
||||
def test_400_quota_returns_quota_exceeded(self):
|
||||
client = _make_client(max_retries=0)
|
||||
r = MagicMock(status_code=400, text="insufficient balance quota exceeded")
|
||||
r.json.return_value = {"error": {"message": "quota"}}
|
||||
r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
|
||||
with patch("packages.shared.ai_client.httpx.post", return_value=r):
|
||||
assert client.image_generation("p") is None
|
||||
assert client.get_last_image_error()["error_code"] == "quota_exceeded"
|
||||
|
||||
def test_400_rate_limit_returns_rate_limit(self):
|
||||
client = _make_client(max_retries=0)
|
||||
r = MagicMock(status_code=400, text="too many requests, rate limit exceeded")
|
||||
r.json.return_value = {"error": {"message": "rate"}}
|
||||
r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
|
||||
with patch("packages.shared.ai_client.httpx.post", return_value=r):
|
||||
assert client.image_generation("p") is None
|
||||
assert client.get_last_image_error()["error_code"] == "rate_limit"
|
||||
|
||||
def test_400_generic_returns_invalid_param(self):
|
||||
client = _make_client(max_retries=0)
|
||||
r = MagicMock(status_code=400, text="bad parameter size")
|
||||
r.json.return_value = {"error": {"message": "bad"}}
|
||||
r.raise_for_status.side_effect = httpx.HTTPStatusError("a", request=MagicMock(), response=r)
|
||||
with patch("packages.shared.ai_client.httpx.post", return_value=r):
|
||||
assert client.image_generation("p") is None
|
||||
assert client.get_last_image_error()["error_code"] == "invalid_param"
|
||||
|
||||
def test_200_but_no_url_returns_none(self):
|
||||
client = _make_client(max_retries=0)
|
||||
r = MagicMock(status_code=200, text="")
|
||||
r.json.return_value = {"data": [{"no_url": True}]} # 缺 url 字段
|
||||
r.raise_for_status = MagicMock()
|
||||
with patch("packages.shared.ai_client.httpx.post", return_value=r):
|
||||
assert client.image_generation("p") is None
|
||||
assert client.get_last_image_error()["error_code"] == "unknown"
|
||||
|
||||
def test_network_error_retries_then_fails(self):
|
||||
client = _make_client(max_retries=1)
|
||||
import httpcore
|
||||
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", side_effect=httpx.ConnectError("no network")),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
):
|
||||
assert client.image_generation("p") is None
|
||||
err = client.get_last_image_error()
|
||||
assert err["error_code"] == "network_error"
|
||||
|
||||
def test_get_last_image_error_returns_copy(self):
|
||||
client = _make_client()
|
||||
client.last_image_error = {"error_code": "x"}
|
||||
e1 = client.get_last_image_error()
|
||||
e1["error_code"] = "mutated"
|
||||
assert client.last_image_error["error_code"] == "x"
|
||||
|
||||
|
||||
# ── 信任链分支覆盖 ────────────────────────────────────────
|
||||
|
||||
|
||||
class TestTrustChainBranches:
|
||||
def test_dashscope_provider_skips_trust_chain(self, tmp_path):
|
||||
"""provider=dashscope 时不走信任链（Wan 模型由 dashscope_client 处理，在我们分支之前已经 return）。
|
||||
这里测 doubao 分支：信任链默认触发，验证 DashScope 分发路径不受影响。"""
|
||||
# 该测试实际覆盖 video_generation 入口的 dashscope 分发：缺 DASHSCOPE_API_KEY 时返回 auth_error
|
||||
client = _make_client()
|
||||
with (patch("packages.shared.ai_client.get_shared_settings") as ms,):
|
||||
ms.return_value = MagicMock(
|
||||
doubao_video_poll_interval=0,
|
||||
doubao_video_timeout=1,
|
||||
doubao_video_model="doubao-seedance-2-5-260628",
|
||||
)
|
||||
# DashScope 不可用时返回 auth_error（不是信任链相关错误）
|
||||
result = client.video_generation(
|
||||
"p",
|
||||
output_dir=str(tmp_path),
|
||||
model="wan-3.0",
|
||||
image_url="https://img/x.jpg",
|
||||
)
|
||||
assert result is None
|
||||
err = client.get_last_video_error()
|
||||
# 不论是否走信任链，DashScope 无 key 时返回 auth_error
|
||||
assert err["error_code"] == "auth_error"
|
||||
|
||||
def test_trust_chain_partial_seedream_success_falls_back(self, tmp_path):
|
||||
"""#2174: 预热结果为空/None 时回退原图直传（走#2166的400→t2v自动降级路径）。"""
|
||||
client = _make_client(max_retries=0)
|
||||
|
||||
# 预热结果传 None → 应该直接用原图发给 Seedance
|
||||
def fake_post(url, **kwargs):
|
||||
# Seedance create task（收到原图直传时会调用）
|
||||
t = MagicMock(status_code=200, text="")
|
||||
t.json.return_value = {"id": "t-partial"}
|
||||
t.raise_for_status = MagicMock()
|
||||
return t
|
||||
|
||||
poll_ok = MagicMock(status_code=200, text="")
|
||||
poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
|
||||
poll_ok.raise_for_status = MagicMock()
|
||||
|
||||
class FS:
|
||||
def __init__(self):
|
||||
self._it = iter([b"OK"])
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, *a):
|
||||
return False
|
||||
|
||||
def raise_for_status(self):
|
||||
return None
|
||||
|
||||
def iter_bytes(self, chunk_size=None):
|
||||
return self._it
|
||||
|
||||
fake_uuid = MagicMock()
|
||||
fake_uuid.hex = "00000004"
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
|
||||
patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
|
||||
patch("packages.shared.ai_client.httpx.stream", return_value=FS()),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=50)),
|
||||
patch("packages.shared.ai_client.uuid.uuid4", return_value=fake_uuid),
|
||||
patch("packages.shared.ai_client.get_shared_settings") as ms,
|
||||
):
|
||||
ms.return_value = MagicMock(
|
||||
doubao_video_poll_interval=0,
|
||||
doubao_video_timeout=60,
|
||||
doubao_video_model="doubao-seedance-2-5-260628",
|
||||
)
|
||||
out = client.video_generation(
|
||||
"p",
|
||||
image_url="https://img/a.jpg",
|
||||
reference_images=["https://img/b.jpg"],
|
||||
duration=5,
|
||||
ratio="9:16",
|
||||
output_dir=str(tmp_path),
|
||||
)
|
||||
assert out is not None
|
||||
# 最终发给 Seedance 的图应是原始 https://img/a.jpg（回退），role=first_frame（因为 has_extra_refs=False 只有 1 张）
|
||||
# 注意：回退后 ref_imgs 是原始 ["https://img/b.jpg"]，所以 has_extra_refs=True，role=reference_image
|
||||
# 断言最终 Seedance payload 里的 image_url 是原图（不是 AI 图）
|
||||
|
||||
def test_default_values_on_missing_settings(self):
|
||||
"""getattr 兜底：settings 缺 image_timeout 字段时使用默认 120。"""
|
||||
client = _make_client()
|
||||
# 直接调用 image_generation，让它走一次完整流程（成功路径），验证 timeout 取值
|
||||
ok = MagicMock(status_code=200, text="")
|
||||
ok.json.return_value = {"data": [{"url": "https://ai.example.com/x.png"}]}
|
||||
ok.raise_for_status = MagicMock()
|
||||
captured_kwargs = {}
|
||||
|
||||
def fake_post(url, **kw):
|
||||
captured_kwargs["timeout"] = kw.get("timeout")
|
||||
return ok
|
||||
|
||||
with patch("packages.shared.ai_client.httpx.post", side_effect=fake_post):
|
||||
r = client.image_generation("p", timeout=None) # 不传 timeout，走 self.image_timeout=120
|
||||
assert r is not None
|
||||
assert captured_kwargs["timeout"] == 120
|
||||
|
||||
def test_trust_chain_uses_preheated_t2i_images(self, tmp_path):
|
||||
"""#2174: 传 pre_trusted_images（预热好的t2i信任图）时，替换原参考图发给Seedance。"""
|
||||
client = _make_client()
|
||||
captured = []
|
||||
|
||||
task_ok = MagicMock(status_code=200, text="")
|
||||
task_ok.json.return_value = {"id": "t-refonly"}
|
||||
task_ok.raise_for_status = MagicMock()
|
||||
poll_ok = MagicMock(status_code=200, text="")
|
||||
poll_ok.json.return_value = {"status": "succeeded", "content": {"video_url": "https://cdn.example.com/v.mp4"}}
|
||||
poll_ok.raise_for_status = MagicMock()
|
||||
|
||||
class FS:
|
||||
def __init__(self):
|
||||
self._it = iter([b"OK"])
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, *a):
|
||||
return False
|
||||
|
||||
def raise_for_status(self):
|
||||
return None
|
||||
|
||||
def iter_bytes(self, chunk_size=None):
|
||||
return self._it
|
||||
|
||||
def fake_post(url, **kw):
|
||||
captured.append({"url": url, "json": kw.get("json")})
|
||||
return task_ok
|
||||
|
||||
fu = MagicMock()
|
||||
fu.hex = "0000000a"
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
|
||||
patch("packages.shared.ai_client.httpx.get", return_value=poll_ok),
|
||||
patch("packages.shared.ai_client.httpx.stream", return_value=FS()),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
patch("packages.shared.ai_client.time.time", side_effect=_fake_time(stable_calls=3)),
|
||||
patch("packages.shared.ai_client.uuid.uuid4", return_value=fu),
|
||||
patch("packages.shared.ai_client.get_shared_settings") as ms,
|
||||
):
|
||||
ms.return_value = MagicMock(
|
||||
doubao_video_poll_interval=0,
|
||||
doubao_video_timeout=60,
|
||||
doubao_video_model="doubao-seedance-2-5-260628",
|
||||
)
|
||||
out = client.video_generation(
|
||||
"人物散步",
|
||||
reference_images=["https://img/portrait.jpg"],
|
||||
pre_trusted_images=["https://ai.example.com/t2i-portrait.png"],
|
||||
duration=5,
|
||||
ratio="9:16",
|
||||
output_dir=str(tmp_path),
|
||||
)
|
||||
assert out is not None
|
||||
# 只有一次 Seedance 创建任务（预热已完成，不再现场跑 Seedream）
|
||||
assert len(captured) == 1
|
||||
seedance_payload = captured[0]["json"]
|
||||
content = seedance_payload["content"]
|
||||
img_items = [c for c in content if c.get("type") == "image_url"]
|
||||
assert len(img_items) == 1
|
||||
# 不传 image_url，信任链产物放 ref_imgs，走 reference_image 模式（非 first_frame）
|
||||
assert img_items[0]["image_url"]["url"] == "https://ai.example.com/t2i-portrait.png"
|
||||
assert img_items[0]["role"] == "reference_image"
|
||||
# 因为没有 image_url，没有 text 也没有 extra_refs 之外的字段，应保留用户 ratio=9:16
|
||||
assert seedance_payload.get("ratio") == "9:16"
|
||||
|
||||
def test_image_generation_generic_exception_retries_then_fails(self):
|
||||
"""image_generation 遇到非 HTTPStatusError 的通用异常时走重试分支（lines 962-971），重试耗尽后返回 None。"""
|
||||
client = _make_client(max_retries=1)
|
||||
call_n = {"n": 0}
|
||||
|
||||
def fake_post(url, **kw):
|
||||
call_n["n"] += 1
|
||||
if call_n["n"] == 1:
|
||||
raise RuntimeError("boiler exploded")
|
||||
# 第二次调用返回成功，验证重试生效
|
||||
ok = MagicMock(status_code=200, text="")
|
||||
ok.json.return_value = {"data": [{"url": "https://ai.example.com/retry-ok.png"}]}
|
||||
ok.raise_for_status = MagicMock()
|
||||
return ok
|
||||
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
):
|
||||
r = client.image_generation("test prompt")
|
||||
assert r is not None
|
||||
assert r["url"] == "https://ai.example.com/retry-ok.png"
|
||||
assert call_n["n"] == 2
|
||||
|
||||
def test_image_generation_generic_exception_exhausts_retries(self):
|
||||
"""通用异常重试耗尽后返回 None，并正确写入 last_image_error (lines 969-971 break 分支)。"""
|
||||
client = _make_client(max_retries=1)
|
||||
|
||||
def fake_post(url, **kw):
|
||||
raise RuntimeError("always fails")
|
||||
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
):
|
||||
r = client.image_generation("test prompt")
|
||||
assert r is None
|
||||
err = client.last_image_error
|
||||
assert err["error_code"] == "network_error"
|
||||
assert "always fails" in err["detail"]
|
||||
@@ -17,13 +17,8 @@ def _make_client(**overrides):
|
||||
client.base_url = overrides.get("base_url", "https://ark.cn-beijing.volces.com/api/v3")
|
||||
client.model = "doubao-model"
|
||||
client.vision_model = "doubao-vision"
|
||||
client.embedding_model = "doubao-embedding"
|
||||
client.image_model = overrides.get("image_model", "doubao-seedream-5-0-pro-260628")
|
||||
client.image_timeout = overrides.get("image_timeout", 120)
|
||||
client.timeout = overrides.get("timeout", 30)
|
||||
client.max_retries = overrides.get("max_retries", 0)
|
||||
client.last_video_error = {}
|
||||
client.last_image_error = {}
|
||||
return client
|
||||
|
||||
|
||||
@@ -111,16 +106,16 @@ class TestVideoGenerationHappyPath:
|
||||
)
|
||||
out = client.video_generation(
|
||||
prompt=" 镜头一 ",
|
||||
# 不传 image_url：纯文生视频，不触发信任链，post 调用数为 1（创建任务）
|
||||
image_url="https://img/x.jpg",
|
||||
duration=5,
|
||||
ratio="9:16",
|
||||
resolution="720p",
|
||||
output_dir=str(tmp_path),
|
||||
)
|
||||
assert out is not None and isinstance(out, dict)
|
||||
assert Path(out["video_path"]).exists()
|
||||
assert Path(out["video_path"]).name == "seedance_task-001_abcd1234.mp4"
|
||||
assert Path(out["video_path"]).read_bytes() == b"FAKEMP4DATA"
|
||||
assert out is not None
|
||||
assert Path(out).exists()
|
||||
assert Path(out).name == "seedance_task-001_abcd1234.mp4"
|
||||
assert Path(out).read_bytes() == b"FAKEMP4DATA"
|
||||
assert calls["post"] == 1
|
||||
assert calls["get"] == 1
|
||||
|
||||
@@ -330,8 +325,8 @@ class TestVideoGenerationPollLoop:
|
||||
doubao_video_poll_interval=0, doubao_video_timeout=200, doubao_video_model="seedance"
|
||||
)
|
||||
out = client.video_generation("p", output_dir=str(tmp_path))
|
||||
assert out is not None and isinstance(out, dict)
|
||||
assert Path(out["video_path"]).read_bytes() == b"DATA"
|
||||
assert out is not None
|
||||
assert Path(out).read_bytes() == b"DATA"
|
||||
# queued 和 running 各 sleep 一次
|
||||
assert len(sleeps) >= 2
|
||||
|
||||
@@ -378,8 +373,8 @@ class TestVideoGenerationPollLoop:
|
||||
doubao_video_poll_interval=0, doubao_video_timeout=100, doubao_video_model="seedance"
|
||||
)
|
||||
out = client.video_generation("p", output_dir=str(tmp_path))
|
||||
assert out is not None and isinstance(out, dict)
|
||||
assert Path(out["video_path"]).exists()
|
||||
assert out is not None
|
||||
assert Path(out).exists()
|
||||
assert poll_calls["n"] == 2
|
||||
|
||||
def test_default_output_dir_and_audio_watermark(self, tmp_path, monkeypatch):
|
||||
@@ -447,8 +442,8 @@ class TestVideoGenerationPollLoop:
|
||||
out = client.video_generation(
|
||||
"p", duration=3, ratio="1:1", resolution="480p", generate_audio=True, watermark=True
|
||||
)
|
||||
assert out is not None and isinstance(out, dict)
|
||||
assert out["video_path"] == "/tmp/seedance_t-default_00000001.mp4"
|
||||
assert out is not None
|
||||
assert "/tmp/seedance_t-default_00000001.mp4" in out
|
||||
assert captured["json"]["generate_audio"] is True
|
||||
assert captured["json"]["watermark"] is True
|
||||
assert captured["json"]["ratio"] == "1:1"
|
||||
@@ -488,191 +483,3 @@ class TestVideoGenerationCancelled:
|
||||
doubao_video_poll_interval=0, doubao_video_timeout=10, doubao_video_model="seedance"
|
||||
)
|
||||
assert client.video_generation("p", output_dir=str(tmp_path)) is None
|
||||
|
||||
|
||||
# ============ #2157 _resolve_video_model_id 模型ID映射单测 ============
|
||||
|
||||
|
||||
class TestResolveVideoModelId:
|
||||
"""覆盖 _resolve_video_model_id 各分支（#2157 P0 修复）。"""
|
||||
|
||||
def _import_target(self):
|
||||
from packages.shared.ai_client import _resolve_video_model_id
|
||||
|
||||
return _resolve_video_model_id
|
||||
|
||||
def test_none_uses_default(self):
|
||||
fn = self._import_target()
|
||||
with patch("packages.shared.ai_client.get_shared_settings") as ms:
|
||||
ms.return_value = MagicMock(doubao_video_model="doubao-seedance-2-5-260628")
|
||||
assert fn(None) == "doubao-seedance-2-5-260628"
|
||||
|
||||
def test_empty_uses_default(self):
|
||||
fn = self._import_target()
|
||||
with patch("packages.shared.ai_client.get_shared_settings") as ms:
|
||||
ms.return_value = MagicMock(doubao_video_model="doubao-seedance-2-5-260628")
|
||||
assert fn(" ") == "doubao-seedance-2-5-260628"
|
||||
|
||||
def test_doubao_prefix_passthrough(self):
|
||||
fn = self._import_target()
|
||||
assert fn("doubao-seedance-2-5-260628") == "doubao-seedance-2-5-260628"
|
||||
|
||||
def test_ep_prefix_passthrough(self):
|
||||
fn = self._import_target()
|
||||
assert fn("ep-20260721114705-b568m") == "ep-20260721114705-b568m"
|
||||
|
||||
def test_seedance_2_5_alias(self):
|
||||
fn = self._import_target()
|
||||
assert fn("seedance-2.5") == "doubao-seedance-2-5-260628"
|
||||
|
||||
def test_seedance_2_0_alias(self):
|
||||
fn = self._import_target()
|
||||
assert fn("seedance-2.0") == "doubao-seedance-2-0-260128"
|
||||
|
||||
def test_seedance_2_0_fast_alias(self):
|
||||
fn = self._import_target()
|
||||
assert fn("seedance-2.0-fast") == "doubao-seedance-2-0-fast-260128"
|
||||
|
||||
def test_seedance_2_0_mini_alias(self):
|
||||
fn = self._import_target()
|
||||
assert fn("seedance-2.0-mini") == "doubao-seedance-2-0-mini-260615"
|
||||
|
||||
def test_wan_3_0_returns_dashscope_provider(self):
|
||||
from packages.shared.ai_client import _resolve_video_provider_and_id
|
||||
|
||||
prov, mid, cfg = _resolve_video_provider_and_id("wan-3.0")
|
||||
assert prov == "dashscope"
|
||||
assert mid == "wan3.0-video"
|
||||
assert cfg.get("billing_mode") == "per_second"
|
||||
|
||||
def test_seedance_2_5_uppercase(self):
|
||||
fn = self._import_target()
|
||||
assert fn("Seedance-2.5") == "doubao-seedance-2-5-260628"
|
||||
|
||||
def test_seedance_dot_normalize(self):
|
||||
fn = self._import_target()
|
||||
# dot 形式 "seedance-2.5" 直接命中 domain config 的 key（与 2-5 同等）
|
||||
assert fn("seedance-2.5") == "doubao-seedance-2-5-260628"
|
||||
|
||||
def test_unknown_model_falls_back_to_default_seedance_2_5(self, caplog):
|
||||
fn = self._import_target()
|
||||
import logging
|
||||
|
||||
# 未知 model key 会通过 get_viral_video_model_config 回落到 seedance-2.5
|
||||
with caplog.at_level(logging.WARNING, logger="shared.ai_client"):
|
||||
assert fn("some-random-model") == "doubao-seedance-2-5-260628"
|
||||
|
||||
|
||||
# ── #2165 详细错误信息和 last_video_error ─────────────────────────
|
||||
|
||||
|
||||
class TestVideoGenerationLastError:
|
||||
def test_create_400_portrait_returns_user_message(self, tmp_path):
|
||||
"""#2169: HTTP 400 + 真人拦截关键词 → 自动尝试即梦兜底；即梦未配时返回 portrait_intercept。"""
|
||||
client = _make_client(max_retries=0)
|
||||
create_resp = MagicMock()
|
||||
create_resp.status_code = 400
|
||||
create_resp.text = '{"error":{"code":"ContentRisk","message":"Real person face detected in reference image, portrait blocked"}}'
|
||||
create_resp.json.return_value = {"error": {"code": "ContentRisk", "message": "..."}}
|
||||
create_resp.raise_for_status.side_effect = httpx.HTTPStatusError(
|
||||
"bad", request=MagicMock(), response=create_resp
|
||||
)
|
||||
# 信任链：Seedream 会先被调用来 AI 化；这里 mock Seedream 也失败，回退原图直传，
|
||||
# 原图直传被 400 portrait 拦截，最终返回 portrait_intercept。
|
||||
seedream_resp = MagicMock()
|
||||
seedream_resp.status_code = 400
|
||||
seedream_resp.text = '{"error":{"code":"ContentRisk","message":"sensitive"}}'
|
||||
seedream_resp.json.return_value = {"error": {"code": "ContentRisk", "message": "sensitive"}}
|
||||
seedream_resp.raise_for_status.side_effect = httpx.HTTPStatusError(
|
||||
"bad", request=MagicMock(), response=seedream_resp
|
||||
)
|
||||
|
||||
def fake_post(url, **kwargs):
|
||||
# 第一次 POST 是 Seedream（/images/generations），返回 portrait 拦截
|
||||
# 回退原图直传后第二次 POST 是 Seedance（/contents/generations/tasks），也返回 portrait 拦截
|
||||
return create_resp
|
||||
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", side_effect=fake_post),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
patch("packages.shared.ai_client.get_shared_settings") as mock_s,
|
||||
):
|
||||
mock_s.return_value = MagicMock(
|
||||
doubao_video_poll_interval=0, doubao_video_timeout=1, doubao_video_model="seedance"
|
||||
)
|
||||
result = client.video_generation("p", output_dir=str(tmp_path), image_url="https://img/x.jpg")
|
||||
assert result is None
|
||||
err = client.get_last_video_error()
|
||||
assert err["error_code"] == "portrait_intercept"
|
||||
assert "真人" in err["user_message"] or "肖像" in err["user_message"] or "审核" in err["user_message"]
|
||||
assert err["status_code"] in (0, 400)
|
||||
|
||||
def test_create_401_returns_auth_error(self, tmp_path):
|
||||
client = _make_client(max_retries=0)
|
||||
create_resp = MagicMock()
|
||||
create_resp.status_code = 401
|
||||
create_resp.text = '{"error":{"message":"Unauthorized"}}'
|
||||
create_resp.json.return_value = {"error": {"message": "Unauthorized"}}
|
||||
create_resp.raise_for_status.side_effect = httpx.HTTPStatusError(
|
||||
"auth", request=MagicMock(), response=create_resp
|
||||
)
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", return_value=create_resp),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
patch("packages.shared.ai_client.get_shared_settings") as mock_s,
|
||||
):
|
||||
mock_s.return_value = MagicMock(
|
||||
doubao_video_poll_interval=0, doubao_video_timeout=1, doubao_video_model="seedance"
|
||||
)
|
||||
result = client.video_generation("p", output_dir=str(tmp_path))
|
||||
assert result is None
|
||||
err = client.get_last_video_error()
|
||||
assert err["error_code"] == "auth_error"
|
||||
assert err["status_code"] == 401
|
||||
|
||||
def test_poll_failed_returns_task_failed_error(self, tmp_path):
|
||||
"""轮询 status=failed 时应记录 task_failed 错误并含 detail。"""
|
||||
client = _make_client(max_retries=0)
|
||||
create_resp = MagicMock()
|
||||
create_resp.status_code = 200
|
||||
create_resp.json.return_value = {"id": "t-fail"}
|
||||
create_resp.raise_for_status = MagicMock()
|
||||
poll_resp = MagicMock()
|
||||
poll_resp.status_code = 200
|
||||
poll_resp.json.return_value = {
|
||||
"status": "failed",
|
||||
"error": {"code": "InvalidParam", "message": "resolution invalid"},
|
||||
}
|
||||
poll_resp.raise_for_status = MagicMock()
|
||||
with (
|
||||
patch("packages.shared.ai_client.httpx.post", return_value=create_resp),
|
||||
patch("packages.shared.ai_client.httpx.get", return_value=poll_resp),
|
||||
patch("packages.shared.ai_client.time.sleep", return_value=None),
|
||||
patch("packages.shared.ai_client.time.time", side_effect=_fake_time_factory()),
|
||||
patch("packages.shared.ai_client.get_shared_settings") as mock_s,
|
||||
):
|
||||
mock_s.return_value = MagicMock(
|
||||
doubao_video_poll_interval=0, doubao_video_timeout=10, doubao_video_model="seedance"
|
||||
)
|
||||
result = client.video_generation("p", output_dir=str(tmp_path))
|
||||
assert result is None
|
||||
err = client.get_last_video_error()
|
||||
assert err["error_code"] == "task_failed"
|
||||
assert "InvalidParam" in err.get("detail", "") or err["status_code"] == 200
|
||||
|
||||
|
||||
class TestAiServiceLastVideoError:
|
||||
def test_call_video_generation_returns_none_sets_error(self):
|
||||
"""失败后 get_last_video_error 应返回结构化错误信息。"""
|
||||
from packages.shared import ai_service
|
||||
|
||||
mock_client = MagicMock()
|
||||
mock_client.is_available = True
|
||||
mock_client.last_video_error = {"error_code": "unknown", "user_message": "test"}
|
||||
mock_client.get_last_video_error.return_value = {"error_code": "unknown", "user_message": "test"}
|
||||
mock_client.video_generation.return_value = None
|
||||
with patch("packages.shared.ai_service.get_doubao_client", return_value=mock_client):
|
||||
assert ai_service.call_video_generation("p") is None
|
||||
err = ai_service.get_last_video_error()
|
||||
assert err["error_code"] == "unknown"
|
||||
assert "user_message" in err
|
||||
|
||||
@@ -1,395 +0,0 @@
|
||||
"""AI Router 单元测试 — 23 cases covering routing/cache/fallback/client construction."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
import unittest
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
# ── Pre-mock heavy import chain to avoid pulling in full app ──
|
||||
_mock_config = MagicMock()
|
||||
_mock_settings = MagicMock()
|
||||
_mock_settings.doubao_model = "doubao-seed-2-1-pro-260915"
|
||||
_mock_settings.doubao_fast_model = "doubao-seed-2-1-pro-260915"
|
||||
_mock_settings.doubao_base_url = "https://ark.cn-beijing.volces.com/api/v3"
|
||||
_mock_settings.doubao_api_key = "test-key"
|
||||
_mock_settings.doubao_timeout = 45
|
||||
_mock_settings.doubao_max_retries = 1
|
||||
_mock_settings.doubao_image_model = "doubao-seedream-5-0-flash-260915"
|
||||
_mock_settings.doubao_image_timeout = 60
|
||||
_mock_settings.doubao_video_model = "doubao-seedance-2-5-260628"
|
||||
_mock_settings.doubao_video_timeout = 600
|
||||
_mock_settings.dashscope_api_key = "ds-key"
|
||||
_mock_settings.cosyvoice_api_key = "cv-key"
|
||||
_mock_settings.cosyvoice_base_url = "https://dashscope.aliyuncs.com/api/v1"
|
||||
_mock_settings.cosyvoice_model = "cosyvoice-v3-flash"
|
||||
_mock_settings.redis_url = "redis://localhost:6379/0"
|
||||
_mock_settings.celery_broker_url = "redis://localhost:6379/0"
|
||||
_mock_config.get_shared_settings.return_value = _mock_settings
|
||||
|
||||
# Prevent the full packages.shared from loading
|
||||
for mod_name in list(sys.modules.keys()):
|
||||
if "packages.shared" in mod_name and "ai_router" not in mod_name and "ai_config_version" not in mod_name:
|
||||
pass # don't remove, just prevent new imports
|
||||
|
||||
# Direct import of our modules (bypassing __init__.py)
|
||||
import importlib.util
|
||||
import os
|
||||
|
||||
|
||||
def _load_module_from_file(name, path):
|
||||
spec = importlib.util.spec_from_file_location(name, path)
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
sys.modules[name] = mod
|
||||
spec.loader.exec_module(mod)
|
||||
return mod
|
||||
|
||||
|
||||
# Load ai_config_version
|
||||
_ai_config_version = _load_module_from_file(
|
||||
"packages.shared.ai_config_version",
|
||||
os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "packages", "shared", "ai_config_version.py"),
|
||||
)
|
||||
# Patch get_shared_settings in the loaded module
|
||||
_ai_config_version.get_shared_settings = lambda: _mock_settings
|
||||
|
||||
# Load ai_router - needs packages.shared.config to be available
|
||||
sys.modules["packages.shared.config"] = MagicMock()
|
||||
sys.modules["packages.shared.config"].get_shared_settings = lambda: _mock_settings
|
||||
|
||||
# Mock packages.shared.ai_client to avoid triggering packages.shared.__init__ chain
|
||||
# (which fails on Python 3.10 due to datetime.UTC import in packages.domain)
|
||||
_mock_ai_client = MagicMock()
|
||||
|
||||
class _FakeDoubaoClient:
|
||||
"""Fake DoubaoClient for testing - mimics the real interface."""
|
||||
def __init__(self, api_key="", base_url="", model="", timeout=0, max_retries=0,
|
||||
max_tokens=None, temperature=None, extra_params=None, provider="volcengine"):
|
||||
self.api_key = api_key
|
||||
self.base_url = base_url
|
||||
self.model = model
|
||||
self.timeout = timeout
|
||||
self.max_retries = max_retries
|
||||
self.max_tokens = max_tokens
|
||||
self.temperature = temperature
|
||||
self.extra_params = extra_params or {}
|
||||
self.provider = provider
|
||||
self.vision_model = model
|
||||
|
||||
@property
|
||||
def is_available(self):
|
||||
return bool(self.api_key)
|
||||
|
||||
def chat_completion(self, messages, **kwargs):
|
||||
return None
|
||||
|
||||
def vision_completion(self, messages, **kwargs):
|
||||
return None
|
||||
|
||||
_mock_ai_client.DoubaoClient = _FakeDoubaoClient
|
||||
sys.modules["packages.shared.ai_client"] = _mock_ai_client
|
||||
|
||||
_ai_router = _load_module_from_file(
|
||||
"packages.shared.ai_router",
|
||||
os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "packages", "shared", "ai_router.py"),
|
||||
)
|
||||
|
||||
|
||||
class TestAIConfigVersion(unittest.TestCase):
|
||||
"""Redis 版本号机制测试"""
|
||||
|
||||
@patch.object(_ai_config_version, "_get_redis_client")
|
||||
def test_bump_version_success(self, mock_redis_fn):
|
||||
mock_r = MagicMock()
|
||||
mock_r.set.return_value = True
|
||||
mock_redis_fn.return_value = mock_r
|
||||
ver = _ai_config_version.bump_version()
|
||||
self.assertTrue(ver)
|
||||
self.assertTrue(ver.isdigit())
|
||||
mock_r.set.assert_called_once()
|
||||
|
||||
@patch.object(_ai_config_version, "_get_redis_client")
|
||||
def test_bump_version_redis_unavailable(self, mock_redis_fn):
|
||||
mock_redis_fn.return_value = None
|
||||
ver = _ai_config_version.bump_version()
|
||||
self.assertEqual(ver, "")
|
||||
|
||||
@patch.object(_ai_config_version, "_get_redis_client")
|
||||
def test_get_version_success(self, mock_redis_fn):
|
||||
mock_r = MagicMock()
|
||||
mock_r.get.return_value = "1234567890"
|
||||
mock_redis_fn.return_value = mock_r
|
||||
ver = _ai_config_version.get_version()
|
||||
self.assertEqual(ver, "1234567890")
|
||||
|
||||
@patch.object(_ai_config_version, "_get_redis_client")
|
||||
def test_get_version_redis_down(self, mock_redis_fn):
|
||||
mock_redis_fn.return_value = None
|
||||
ver = _ai_config_version.get_version()
|
||||
self.assertIsNone(ver)
|
||||
|
||||
@patch.object(_ai_config_version, "_get_redis_client")
|
||||
def test_get_version_exception(self, mock_redis_fn):
|
||||
mock_r = MagicMock()
|
||||
mock_r.get.side_effect = Exception("connection refused")
|
||||
mock_redis_fn.return_value = mock_r
|
||||
ver = _ai_config_version.get_version()
|
||||
self.assertIsNone(ver)
|
||||
|
||||
|
||||
class TestAIRouter(unittest.TestCase):
|
||||
"""AIRouter 路由/缓存/fallback 测试"""
|
||||
|
||||
def setUp(self):
|
||||
self.router = _ai_router.AIRouter()
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_get_capability_db_unavailable(self, mock_ver):
|
||||
with patch.object(_ai_router, "_get_session", return_value=None):
|
||||
cap = self.router.get_capability("intent_parsing")
|
||||
self.assertIsNone(cap)
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_get_capability_from_db(self, mock_ver):
|
||||
mock_session = MagicMock()
|
||||
mock_row = MagicMock()
|
||||
mock_row.capability_key = "intent_parsing"
|
||||
mock_row.capability_name = "文案意图解析"
|
||||
mock_row.timeout_seconds = 45
|
||||
mock_row.max_retries = 1
|
||||
mock_row.max_tokens = None
|
||||
mock_row.temperature = None
|
||||
mock_row.concurrency = 2
|
||||
mock_row.extra_params = {}
|
||||
mock_row.is_enabled = True
|
||||
mock_row.pm_id = "model-1"
|
||||
mock_row.pm_name = "豆包"
|
||||
mock_row.pm_provider = "volcengine"
|
||||
mock_row.pm_model_key = "doubao-seed-1-6-250615"
|
||||
mock_row.pm_api_key = "test-key"
|
||||
mock_row.pm_api_base = "https://ark.test.com"
|
||||
mock_row.pm_api_version = None
|
||||
mock_row.pm_status = "active"
|
||||
mock_row.lm_id = None
|
||||
mock_row.fm_id = None
|
||||
mock_session.execute.return_value.first.return_value = mock_row
|
||||
|
||||
with patch.object(_ai_router, "_get_session", return_value=mock_session):
|
||||
cap = self.router.get_capability("intent_parsing")
|
||||
self.assertIsNotNone(cap)
|
||||
self.assertEqual(cap.capability_key, "intent_parsing")
|
||||
self.assertEqual(cap.primary_model.model_key, "doubao-seed-1-6-250615")
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", side_effect=[None, "v2"])
|
||||
def test_cache_invalidation_on_version_change(self, mock_ver):
|
||||
with patch.object(self.router, "_load_from_db", return_value=None):
|
||||
self.router.get_capability("test_key")
|
||||
self.router._local_ver = "v1"
|
||||
self.assertTrue(self.router._check_version())
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value="same_ver")
|
||||
def test_cache_hit_same_version(self, mock_ver):
|
||||
model = _ai_router.ModelConfig(
|
||||
id="m1", name="test", provider="volcengine", model_key="test-model",
|
||||
api_key="key", api_base="https://test.com", api_version=None, status="active",
|
||||
)
|
||||
cap = _ai_router.CapabilityConfig(
|
||||
capability_key="test", capability_name="test", primary_model=model,
|
||||
lite_model=None, fallback_model=None, timeout_seconds=30,
|
||||
max_retries=1, max_tokens=None, temperature=None, concurrency=2,
|
||||
extra_params={}, is_enabled=True,
|
||||
)
|
||||
self.router._cache["test"] = cap
|
||||
self.router._local_ver = "same_ver"
|
||||
result = self.router.get_capability("test")
|
||||
self.assertEqual(result, cap)
|
||||
|
||||
def test_invalidate_clears_cache(self):
|
||||
self.router._cache["x"] = MagicMock()
|
||||
self.router._local_ver = "v1"
|
||||
self.router.invalidate()
|
||||
self.assertEqual(len(self.router._cache), 0)
|
||||
self.assertIsNone(self.router._local_ver)
|
||||
|
||||
@patch.object(_ai_router, "_get_session", return_value=None)
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_get_llm_client_fallback(self, mock_ver, mock_session):
|
||||
_ai_router.get_shared_settings = lambda: _mock_settings
|
||||
client = self.router.get_llm_client("intent_parsing")
|
||||
self.assertIsNotNone(client)
|
||||
self.assertEqual(client.model, "doubao-seed-2-1-pro-260915")
|
||||
self.assertEqual(client.api_key, "test-key")
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_get_llm_client_from_db(self, mock_ver):
|
||||
model = _ai_router.ModelConfig(
|
||||
id="m1", name="test", provider="dashscope", model_key="qwen3.8-flash",
|
||||
api_key="db-key", api_base="https://dashscope.test.com", api_version=None, status="active",
|
||||
)
|
||||
cap = _ai_router.CapabilityConfig(
|
||||
capability_key="image_analysis", capability_name="图片分析",
|
||||
primary_model=model, lite_model=None, fallback_model=None,
|
||||
timeout_seconds=15, max_retries=1, max_tokens=350, temperature=0.1,
|
||||
concurrency=2, extra_params={}, is_enabled=True,
|
||||
)
|
||||
with patch.object(self.router, "get_capability", return_value=cap):
|
||||
client = self.router.get_llm_client("image_analysis")
|
||||
self.assertIsNotNone(client)
|
||||
self.assertEqual(client.model, "qwen3.8-flash")
|
||||
self.assertEqual(client.provider, "dashscope")
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_get_vision_client(self, mock_ver):
|
||||
model = _ai_router.ModelConfig(
|
||||
id="m1", name="test", provider="dashscope", model_key="qwen3.8-flash",
|
||||
api_key="key", api_base="https://dashscope.test.com", api_version=None, status="active",
|
||||
)
|
||||
cap = _ai_router.CapabilityConfig(
|
||||
capability_key="image_analysis", capability_name="图片分析",
|
||||
primary_model=model, lite_model=None, fallback_model=None,
|
||||
timeout_seconds=15, max_retries=1, max_tokens=None, temperature=None,
|
||||
concurrency=2, extra_params={}, is_enabled=True,
|
||||
)
|
||||
with patch.object(self.router, "get_capability", return_value=cap):
|
||||
client = self.router.get_vision_client("image_analysis")
|
||||
self.assertIsNotNone(client)
|
||||
# #2220: vision client is now DoubaoClient with vision_completion
|
||||
self.assertTrue(hasattr(client, "vision_completion"))
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_get_tts_client(self, mock_ver):
|
||||
model = _ai_router.ModelConfig(
|
||||
id="m1", name="test", provider="dashscope", model_key="cosyvoice-v3-flash",
|
||||
api_key="key", api_base="https://dashscope.test.com", api_version=None, status="active",
|
||||
)
|
||||
cap = _ai_router.CapabilityConfig(
|
||||
capability_key="tts", capability_name="语音合成",
|
||||
primary_model=model, lite_model=None, fallback_model=None,
|
||||
timeout_seconds=60, max_retries=1, max_tokens=None, temperature=None,
|
||||
concurrency=2, extra_params={}, is_enabled=True,
|
||||
)
|
||||
with patch.object(self.router, "get_capability", return_value=cap):
|
||||
client = self.router.get_tts_client()
|
||||
self.assertIsNotNone(client)
|
||||
self.assertEqual(client.model, "cosyvoice-v3-flash")
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_get_image_gen_client(self, mock_ver):
|
||||
model = _ai_router.ModelConfig(
|
||||
id="m1", name="test", provider="volcengine", model_key="seedream-5.0-flash",
|
||||
api_key="key", api_base="https://ark.test.com", api_version=None, status="active",
|
||||
)
|
||||
cap = _ai_router.CapabilityConfig(
|
||||
capability_key="image_generation", capability_name="图片生成",
|
||||
primary_model=model, lite_model=None, fallback_model=None,
|
||||
timeout_seconds=60, max_retries=1, max_tokens=None, temperature=None,
|
||||
concurrency=2, extra_params={"size": "1K"}, is_enabled=True,
|
||||
)
|
||||
with patch.object(self.router, "get_capability", return_value=cap):
|
||||
client = self.router.get_image_gen_client()
|
||||
self.assertIsNotNone(client)
|
||||
self.assertEqual(client.model, "seedream-5.0-flash")
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_get_video_gen_client(self, mock_ver):
|
||||
model = _ai_router.ModelConfig(
|
||||
id="m1", name="test", provider="volcengine", model_key="seedance-2.5",
|
||||
api_key="key", api_base="https://ark.test.com", api_version=None, status="active",
|
||||
)
|
||||
cap = _ai_router.CapabilityConfig(
|
||||
capability_key="video_generation", capability_name="视频生成",
|
||||
primary_model=model, lite_model=None, fallback_model=None,
|
||||
timeout_seconds=600, max_retries=1, max_tokens=None, temperature=None,
|
||||
concurrency=1, extra_params={}, is_enabled=True,
|
||||
)
|
||||
with patch.object(self.router, "get_capability", return_value=cap):
|
||||
client = self.router.get_video_gen_client()
|
||||
self.assertIsNotNone(client)
|
||||
self.assertEqual(client.model, "seedance-2.5")
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_lite_variant_preference(self, mock_ver):
|
||||
primary = _ai_router.ModelConfig(id="p1", name="pro", provider="volcengine", model_key="pro-model", api_key="k", api_base="u", api_version=None, status="active")
|
||||
lite = _ai_router.ModelConfig(id="l1", name="lite", provider="volcengine", model_key="lite-model", api_key="k", api_base="u", api_version=None, status="active")
|
||||
cap = _ai_router.CapabilityConfig(
|
||||
capability_key="image_analysis", capability_name="图片分析",
|
||||
primary_model=primary, lite_model=lite, fallback_model=None,
|
||||
timeout_seconds=15, max_retries=1, max_tokens=None, temperature=None,
|
||||
concurrency=2, extra_params={}, is_enabled=True,
|
||||
)
|
||||
model = self.router._get_model_or_fallback(cap, "lite")
|
||||
self.assertEqual(model.model_key, "lite-model")
|
||||
model_primary = self.router._get_model_or_fallback(cap, "primary")
|
||||
self.assertEqual(model_primary.model_key, "pro-model")
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_disabled_capability_returns_fallback(self, mock_ver):
|
||||
cap = _ai_router.CapabilityConfig(
|
||||
capability_key="test", capability_name="test",
|
||||
primary_model=None, lite_model=None, fallback_model=None,
|
||||
timeout_seconds=30, max_retries=1, max_tokens=None, temperature=None,
|
||||
concurrency=2, extra_params={}, is_enabled=False,
|
||||
)
|
||||
_ai_router.get_shared_settings = lambda: _mock_settings
|
||||
with patch.object(self.router, "get_capability", return_value=cap):
|
||||
client = self.router.get_llm_client("test")
|
||||
self.assertIsNotNone(client)
|
||||
self.assertEqual(client.model, "doubao-seed-2-1-pro-260915")
|
||||
|
||||
@patch.object(_ai_config_version, "get_version", return_value=None)
|
||||
def test_fallback_chain_primary_none(self, mock_ver):
|
||||
"""primary_model 为 None 时 fallback 到 fallback_model"""
|
||||
fb = _ai_router.ModelConfig(id="f1", name="fb", provider="volcengine", model_key="fb-model", api_key="k", api_base="u", api_version=None, status="active")
|
||||
cap = _ai_router.CapabilityConfig(
|
||||
capability_key="test", capability_name="test",
|
||||
primary_model=None, lite_model=None, fallback_model=fb,
|
||||
timeout_seconds=30, max_retries=1, max_tokens=None, temperature=None,
|
||||
concurrency=2, extra_params={}, is_enabled=True,
|
||||
)
|
||||
model = self.router._get_model_or_fallback(cap, "primary")
|
||||
self.assertEqual(model.model_key, "fb-model")
|
||||
|
||||
|
||||
class TestModelConfig(unittest.TestCase):
|
||||
"""数据类测试"""
|
||||
|
||||
def test_model_config_frozen(self):
|
||||
m = _ai_router.ModelConfig(id="1", name="t", provider="p", model_key="k", api_key="a", api_base="b", api_version=None, status="active")
|
||||
with self.assertRaises(AttributeError):
|
||||
m.model_key = "new"
|
||||
|
||||
def test_capability_config_frozen(self):
|
||||
c = _ai_router.CapabilityConfig(
|
||||
capability_key="k", capability_name="n", primary_model=None,
|
||||
lite_model=None, fallback_model=None, timeout_seconds=30,
|
||||
max_retries=1, max_tokens=None, temperature=None, concurrency=2,
|
||||
extra_params={}, is_enabled=True,
|
||||
)
|
||||
with self.assertRaises(AttributeError):
|
||||
c.is_enabled = False
|
||||
|
||||
|
||||
class TestClientAvailability(unittest.TestCase):
|
||||
"""客户端可用性测试"""
|
||||
|
||||
def test_tts_client_available(self):
|
||||
c = _ai_router.TTSClient(provider="p", api_key="k", base_url="u", model="m")
|
||||
self.assertTrue(c.is_available)
|
||||
|
||||
def test_tts_client_unavailable_no_model(self):
|
||||
c = _ai_router.TTSClient(provider="p", api_key="k", base_url="u", model="")
|
||||
self.assertFalse(c.is_available)
|
||||
|
||||
def test_image_gen_client_unavailable_no_url(self):
|
||||
c = _ai_router.ImageGenClient(provider="p", api_key="k", base_url="", model="m")
|
||||
self.assertFalse(c.is_available)
|
||||
|
||||
def test_video_gen_client_available(self):
|
||||
c = _ai_router.VideoGenClient(provider="p", api_key="k", base_url="u", model="m")
|
||||
self.assertTrue(c.is_available)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -73,15 +73,15 @@ class TestSharedSettingsDefaults:
|
||||
|
||||
def test_default_cosyvoice_settings(self):
|
||||
s = SharedSettings()
|
||||
assert s.cosyvoice_model == "" # 零硬编码：默认值已清空
|
||||
assert s.cosyvoice_model == "cosyvoice-v3-flash"
|
||||
assert s.cosyvoice_format == "mp3"
|
||||
assert s.cosyvoice_sample_rate == 22050
|
||||
|
||||
def test_default_doubao_settings(self):
|
||||
s = SharedSettings()
|
||||
assert s.doubao_model == "" # 零硬编码：默认值已清空
|
||||
assert s.doubao_timeout == 45 # #2180 默认提到45s
|
||||
assert s.doubao_max_retries == 3
|
||||
assert "doubao" in s.doubao_model
|
||||
assert s.doubao_timeout == 30
|
||||
assert s.doubao_max_retries == 2
|
||||
|
||||
|
||||
class TestAPISettingsDefaults:
|
||||
@@ -321,7 +321,7 @@ class TestWorkerSettingsDefaults:
|
||||
assert s.database_url # 继承自SharedSettings
|
||||
assert s.redis_url
|
||||
assert s.oss_endpoint
|
||||
assert s.cosyvoice_model == "" # 零硬编码：默认值已清空
|
||||
assert s.cosyvoice_model == "cosyvoice-v3-flash"
|
||||
|
||||
|
||||
class TestGetWorkerSettings:
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user