Compare commits
3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 CI Bot
|
3353af0fb8 | ||
|
xiaoxia
|
aac785a9ed | ||
|
xiaoxia
|
cdd343131e |
+2
-1
@@ -1 +1,2 @@
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trigger staging deploy for 3fa1fbf0 (MOCK_STORYBOARD fix) at 2026-10-12 13:40:16
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CI trigger file - safe to delete
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retrigger at 2026-09-15 20:31:24 UTC
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@@ -437,7 +437,7 @@ jobs:
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if: "always() && needs.dedupe-check.outputs.skip_tests != 'true' && needs.check-frontend-only.outputs.skip_backend != 'true'"
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name: Unit Tests
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runs-on: ci-l2
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timeout-minutes: 20
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timeout-minutes: 8
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env:
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PIP_CACHE_DIR: /root/.cache/pip
|
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PIP_NO_CACHE_DIR: ''
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@@ -1195,7 +1195,6 @@ jobs:
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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: "${{ secrets.DOUBAO_MAX_RETRIES }}"
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DITTO_EMOTION_TIMEOUT: "${{ secrets.DITTO_EMOTION_TIMEOUT }}"
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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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@@ -1,61 +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）。
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给 gpu_lipsync_tasks / generation_tasks / lipsync_jobs 三张表加积分字段：
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- credits_prepaid: 提交任务时预扣积分
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- credits_cost: 最终结算积分
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- credits_transaction_id: 预扣流水 ID
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注意：feature_pricing_configs 配置表由 xiaoxia-admin 侧 migration 建立，
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本仓库只读，不在此创建。
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Revision ID: 096_feature_billing_fields
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Revises: 095_viral_video_prompt_templates
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Create Date: 2026-10-05
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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 = "096_feature_billing_fields"
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down_revision = "095_viral_video_prompt_templates"
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branch_labels = None
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depends_on = None
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_TABLES = ("gpu_lipsync_tasks", "generation_tasks", "lipsync_jobs")
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_COLUMNS = (
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("credits_prepaid", sa.Float(), "0"),
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("credits_cost", sa.Float(), "0"),
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("credits_transaction_id", sa.String(36), ""),
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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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|
||||
|
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def upgrade() -> None:
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conn = op.get_bind()
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for table in _TABLES:
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if not _table_exists(conn, table):
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continue
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existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
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for col_name, col_type, default in _COLUMNS:
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if col_name in existing:
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continue
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op.add_column(
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table,
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sa.Column(col_name, col_type, nullable=False, server_default=default),
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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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for table in _TABLES:
|
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if not _table_exists(conn, table):
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||||
continue
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existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
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for col_name, _col_type, _default in _COLUMNS:
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if col_name not in existing:
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continue
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op.drop_column(table, col_name)
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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 @@
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# -*- coding: utf-8 -*-
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"""099: AI 模型路由层 seed — 补齐缺失模型和能力配置.
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幂等：所有 INSERT 先检查存在性。
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- ai_models: 补齐 qwen3.7-plus, seedream, seedance, embedding, wan3.0 等
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- ai_capability_configs: 补齐 image_generation, video_generation, embedding
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- 更新已有 capability 的 lite_model_id
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"""
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import json
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import sqlalchemy as sa
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from alembic import op
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revision = "099_ai_model_router_seed"
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down_revision = "098_viral_video_image_analysis_v5"
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branch_labels = None
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||||
depends_on = None
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||||
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||||
|
||||
def upgrade() -> None:
|
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conn = op.get_bind()
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||||
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# CI 环境下 ai_models 表可能尚未创建（由 ORM 自动建表，非 migration）
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# 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
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table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
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||||
if not table_check:
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||||
# ai_models 表不存在，跳过所有 seed（CI 环境）
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return
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# ── 1. 补齐 ai_models 缺失记录 ────────────────────────────────────────────
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existing_models = {
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row[0]
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for row in conn.execute(
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sa.text("SELECT model_key FROM ai_models WHERE deleted_at IS NULL")
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||||
).fetchall()
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}
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# 从已有 active 记录获取 API key（复用，不硬编码）
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dashscope_key_row = conn.execute(
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sa.text(
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"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"
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)
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).first()
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dashscope_key = dashscope_key_row[0] if dashscope_key_row else ""
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volcengine_key_row = conn.execute(
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sa.text(
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"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"
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||||
)
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).first()
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volcengine_key = volcengine_key_row[0] if volcengine_key_row else ""
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new_models = [
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{
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"model_key": "qwen3.7-plus",
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"name": "通义千问3.7 Plus（VLM 兜底）",
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"provider": "dashscope",
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"api_key": dashscope_key,
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"api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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||||
"description": "阿里云百炼 Qwen3.7 Plus 多模态模型，用于 VLM 兜底分析",
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},
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{
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"model_key": "doubao-seedream-5-0-flash-260915",
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"name": "Seedream 5.0 Flash（图片生成）",
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"provider": "volcengine",
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"api_key": volcengine_key,
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"api_base": "https://ark.cn-beijing.volces.com/api/v3",
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||||
"description": "火山引擎 Seedream 5.0 Flash 文生图模型",
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},
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{
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"model_key": "doubao-seedance-2-5-260628",
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"name": "Seedance 2.5（视频生成）",
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||||
"provider": "volcengine",
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"api_key": volcengine_key,
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||||
"api_base": "https://ark.cn-beijing.volces.com/api/v3",
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"description": "火山引擎 Seedance 2.5 图/文生视频模型",
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||||
},
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||||
{
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"model_key": "doubao-embedding-vision-251215",
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||||
"name": "豆包多模态向量嵌入",
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"provider": "volcengine",
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"api_key": volcengine_key,
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||||
"api_base": "https://ark.cn-beijing.volces.com/api/v3",
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||||
"description": "火山引擎豆包多模态向量嵌入模型",
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||||
},
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||||
{
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"model_key": "wan3.0-video",
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"name": "Wan 3.0 视频生成",
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"provider": "dashscope",
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"api_key": dashscope_key,
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||||
"api_base": "https://dashscope.aliyuncs.com/api/v1",
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||||
"description": "阿里云百炼 Wan 3.0 视频生成模型",
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||||
},
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||||
{
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||||
"model_key": "doubao-seed-2-1-pro-260915",
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||||
"name": "豆包 Seed 2.1 Pro（高精度推理）",
|
||||
"provider": "volcengine",
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||||
"api_key": volcengine_key,
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||||
"api_base": "https://ark.cn-beijing.volces.com/api/v3",
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||||
"description": "火山引擎豆包 Seed 2.1 Pro 深度思考+多模态",
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||||
},
|
||||
]
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||||
|
||||
for m in new_models:
|
||||
if m["model_key"] not in existing_models:
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||||
conn.execute(
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||||
sa.text(
|
||||
"""
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||||
INSERT INTO ai_models (id, name, provider, model_key, api_key, api_base, description, status, is_default, usage_today, created_at, updated_at)
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||||
VALUES (gen_random_uuid()::text, :name, :provider, :model_key, :api_key, :api_base, :description, 'active', false, 0, now(), now())
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||||
"""
|
||||
),
|
||||
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"
|
||||
)
|
||||
)
|
||||
@@ -1,123 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""image_analysis v8 + storyboard v3 叙述优先重写版（架构大简化）
|
||||
|
||||
Revision ID: 105_narration_first
|
||||
Revises: 104_v8_display_markdown
|
||||
Create Date: 2026-10-07
|
||||
|
||||
变更：
|
||||
1. image_analysis v8：用「叙述优先」版整体替换 104 的 append 版——VLM 主交付物是
|
||||
自然叙述 summary_markdown，结构化字段仅保留 type/name/brand/has_person，
|
||||
顶层 products 改名 images；v8 active，其余 image_analysis 全部 deactivate。
|
||||
2. storyboard v3：整体替换为风格重写版（口播口语化、画面有画面感、
|
||||
copy_display_markdown 流畅叙述）；v3 active，其余 storyboard deactivate。
|
||||
3. intent_parsing 类型模板全部 deactivate（意图解析步骤已删除）。
|
||||
模板内容直接取自 packages.application.viral_video.prompts.DEFAULT_TEMPLATES，
|
||||
保证代码默认值与 DB seed 完全一致。
|
||||
"""
|
||||
|
||||
from sqlalchemy import text
|
||||
|
||||
from alembic import op
|
||||
from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
|
||||
|
||||
revision = "105_narration_first"
|
||||
down_revision = "104_v8_display_markdown"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def _tpl(prompt_type: str, version: int) -> dict:
|
||||
"""取模板：先按指定版本找，找不到则取该类型最新版本（兼容 v3→v4 升级）。"""
|
||||
# 先按指定版本找
|
||||
for t in DEFAULT_TEMPLATES:
|
||||
if t["prompt_type"] == prompt_type and t["version"] == version:
|
||||
return t
|
||||
# 找不到则取最新版本
|
||||
candidates = [t for t in DEFAULT_TEMPLATES if t["prompt_type"] == prompt_type]
|
||||
if candidates:
|
||||
latest = max(candidates, key=lambda x: x["version"])
|
||||
return latest
|
||||
raise RuntimeError("default template missing: %s" % prompt_type)
|
||||
|
||||
|
||||
def _upsert(bind, t: dict) -> None:
|
||||
existing = bind.execute(
|
||||
text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = :pt AND version = :ver"),
|
||||
{"pt": t["prompt_type"], "ver": t["version"]},
|
||||
).fetchone()
|
||||
params = {
|
||||
"pt": t["prompt_type"],
|
||||
"ver": t["version"],
|
||||
"name": t["name"],
|
||||
"sys": t["system_prompt"],
|
||||
"usr": t["user_prompt_template"],
|
||||
"ex": t.get("example_output", "") or "",
|
||||
}
|
||||
if existing:
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET name = :name, "
|
||||
"system_prompt = :sys, user_prompt_template = :usr, "
|
||||
"example_output = :ex, is_active = TRUE, updated_at = NOW() "
|
||||
"WHERE prompt_type = :pt AND version = :ver"
|
||||
),
|
||||
params,
|
||||
)
|
||||
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 (:pt, :ver, :name, :sys, :usr, :ex, TRUE, NOW(), NOW())"
|
||||
),
|
||||
params,
|
||||
)
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
bind = op.get_bind()
|
||||
|
||||
# 1. image_analysis：停用全部后写入叙述优先 v8
|
||||
bind.execute(
|
||||
text("UPDATE viral_video_prompt_templates SET is_active = FALSE " "WHERE prompt_type = 'image_analysis'")
|
||||
)
|
||||
_upsert(bind, _tpl("image_analysis", 8))
|
||||
|
||||
# 2. storyboard：停用全部后写入重写版 v3
|
||||
bind.execute(text("UPDATE viral_video_prompt_templates SET is_active = FALSE " "WHERE prompt_type = 'storyboard'"))
|
||||
_upsert(bind, _tpl("storyboard", 3))
|
||||
|
||||
# 3. intent_parsing 已废弃：全部停用
|
||||
bind.execute(
|
||||
text("UPDATE viral_video_prompt_templates SET is_active = FALSE " "WHERE prompt_type = 'intent_parsing'")
|
||||
)
|
||||
|
||||
# 4. review 模板确保 active
|
||||
bind.execute(text("UPDATE viral_video_prompt_templates SET is_active = TRUE " "WHERE prompt_type = 'review'"))
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
bind = op.get_bind()
|
||||
# 恢复 104 的 v8/v3 无法重建（内容已替换），仅把版本 active 状态回退：
|
||||
# 停用新版，尝试恢复 v7 / v2
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = FALSE "
|
||||
"WHERE prompt_type IN ('image_analysis','storyboard') "
|
||||
"AND version IN (8, 3)"
|
||||
)
|
||||
)
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = TRUE "
|
||||
"WHERE prompt_type = 'image_analysis' AND version = 7"
|
||||
)
|
||||
)
|
||||
bind.execute(
|
||||
text(
|
||||
"UPDATE viral_video_prompt_templates SET is_active = TRUE "
|
||||
"WHERE prompt_type = 'storyboard' AND version = 2"
|
||||
)
|
||||
)
|
||||
@@ -1,43 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""system_settings 正式建表（#2246）
|
||||
|
||||
Revision ID: 106_system_settings
|
||||
Revises: 105_narration_first
|
||||
Create Date: 2026-10-08
|
||||
|
||||
system_settings 表此前在 staging 手工创建（对应 034 占位迁移），
|
||||
此处补正式迁移保证其他环境一致。CREATE TABLE/INDEX 使用 IF NOT EXISTS，
|
||||
对已手工建表的环境幂等。
|
||||
"""
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "106_system_settings"
|
||||
down_revision = "105_narration_first"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
op.execute("""
|
||||
CREATE TABLE IF NOT EXISTS system_settings (
|
||||
id VARCHAR(36) NOT NULL,
|
||||
setting_key VARCHAR(100) NOT NULL,
|
||||
setting_value TEXT,
|
||||
setting_type VARCHAR(20) NOT NULL,
|
||||
description VARCHAR(255) NOT NULL DEFAULT '',
|
||||
is_public BOOLEAN NOT NULL DEFAULT FALSE,
|
||||
updated_by VARCHAR(36),
|
||||
category VARCHAR(50) NOT NULL DEFAULT 'general',
|
||||
created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(),
|
||||
updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(),
|
||||
CONSTRAINT pk_system_settings PRIMARY KEY (id),
|
||||
CONSTRAINT uq_system_settings_setting_key UNIQUE (setting_key)
|
||||
)
|
||||
""")
|
||||
op.execute("CREATE INDEX IF NOT EXISTS ix_system_settings_category " "ON system_settings (category)")
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
op.execute("DROP INDEX IF EXISTS ix_system_settings_category")
|
||||
op.execute("DROP TABLE IF EXISTS system_settings")
|
||||
@@ -1,23 +0,0 @@
|
||||
"""add language column to viral_video_jobs
|
||||
|
||||
Revision ID: 107
|
||||
Revises: 106_system_settings
|
||||
Create Date: 2026-10-09
|
||||
|
||||
"""
|
||||
|
||||
from alembic import op
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision = "107"
|
||||
down_revision = "106_system_settings"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
op.execute("ALTER TABLE viral_video_jobs " "ADD COLUMN IF NOT EXISTS language VARCHAR(20) NOT NULL DEFAULT 'zh-CN'")
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
op.execute("ALTER TABLE viral_video_jobs DROP COLUMN IF EXISTS language")
|
||||
@@ -1,235 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""storyboard v4 - 多角色对话 + 废除旁白 + visual 5要素
|
||||
|
||||
Revision ID: 108_storyboard_v4_multivoice
|
||||
Revises: 107
|
||||
Create Date: 2026-10-10
|
||||
|
||||
变更：
|
||||
1. storyboard v4: 废除旁白思维，所有voiceover必须是角色台词
|
||||
- 增加<speaker>标签，每镜必须标注说话人
|
||||
- visual强制5要素结构（景别/运镜/动作/环境/光线），每镜不少于30字
|
||||
- voiceover_script用[speaker:xxx]标记格式
|
||||
2. 旧版storyboard模板is_active设为false
|
||||
3. 检查image_analysis和review是否有active模板，没有则插入保底版本
|
||||
"""
|
||||
|
||||
from sqlalchemy import text
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "108_storyboard_v4_multivoice"
|
||||
down_revision = "107"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
# ── storyboard v4 system_prompt ──────────────────────────────────────
|
||||
V4_STORYBOARD_SYSTEM = """你是一名懂短视频的编导和口播文案高手。你会拿到图片的真实观察、营销目的和用户参数，请一次性完成对营销意图的理解，并产出可直接拍摄/生成的分镜脚本。不要单独输出"意图解析"，意图要直接体现在台词和分镜里。
|
||||
|
||||
## 核心设计原则（必须严格遵守）
|
||||
1. **废除旁白思维**：所有视频类型——无论对话短剧/口播带货/获客引流/品牌故事——voiceover 必须是人物说的话（第一人称或角色对白），绝对不能出现第三人称旁白解说。观众看的是人在演、在说。
|
||||
2. **严禁第三人称解说性台词**：如"接下来展示...""这款产品..."这类上帝视角描述禁止出现在 voiceover 中。
|
||||
3. **短剧类营销目的**（对话短剧/反转短剧/悬念短剧/情绪短片）：双角色对话格式"甲：xxx 乙：xxx"，镜头在角色间切换。
|
||||
4. **口播类**（口播带货/促销转化/功能演示/痛点解决/获客引流/账号涨粉/活动通知/场景种草）：第一人称对镜头说话，像真人出镜。
|
||||
|
||||
## 输出格式（XML，严格按结构输出，不要输出额外解释）
|
||||
<script>
|
||||
<copy_display_markdown><![CDATA[直接展示给用户看的成片文案，用 Markdown 写成流畅叙述]]></copy_display_markdown>
|
||||
<clips>
|
||||
<clip index="1">
|
||||
<time_range>0-3秒</time_range>
|
||||
<speaker>说话人标识（如"店主""顾客""主播"）</speaker>
|
||||
<voiceover>这一镜的角色台词（人物说的话，不是旁白）</voiceover>
|
||||
<visual>【景别】【镜头运动】【人物动作/表情】【环境/道具】【光线氛围】5要素结构，不少于30字</visual>
|
||||
<reference_image_index>0</reference_image_index>
|
||||
</clip>
|
||||
</clips>
|
||||
<voiceover_script>把所有 clip 的 voiceover 连成完整台词稿，用 [speaker:xxx] 标记每个说话段落</voiceover_script>
|
||||
<theme>一句话主题</theme>
|
||||
<negative>【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。</negative>
|
||||
</script>
|
||||
|
||||
## 写作要求
|
||||
1. **台词（voiceover）**：像真人面对镜头说话或角色对白，短句、口语化、有停顿有情绪，开头 3 秒给出钩子；不要书面腔，不要机械报参数。严禁第三人称解说。
|
||||
2. **说话人（speaker）**：每个 clip 必须标注说话人标识，如"店主""顾客""主播""我"等。短剧类必须有至少 2 个不同角色。
|
||||
3. **画面描述（visual）**：强制 5 要素结构——【景别】【镜头运动】【人物动作/表情】【环境/道具】【光线氛围】，每镜 visual 不少于 30 字，要具体到"闭眼听台词能想象出画面"。
|
||||
4. **voiceover_script 格式**：用 [speaker:xxx] 标记每个说话段落，如"[speaker:店主]你是不是也觉得...[speaker:顾客]是啊，怎么回事？"
|
||||
5. copy_display_markdown：直接展示给最终用户的文案，用 Markdown 写成自然、流畅、有感染力的成片文案。
|
||||
6. 内容必须来自图片观察与用户给出的信息，不编造卖点、不夸大、不使用绝对化用语和虚假承诺。
|
||||
7. reference_image_index 填本镜参考图片序号（从 0 开始），没有合适参考图填 -1。
|
||||
8. 分镜数量与时长匹配总时长，节奏紧凑。
|
||||
9. **口播字数硬约束**（必须严格遵守）：按每秒约 2.5~3 个中文字（正常口播语速）计算：
|
||||
- 5秒视频：voiceover_script 总字数 12~15 字
|
||||
- 10秒视频：voiceover_script 总字数 25~30 字
|
||||
- 15秒视频：voiceover_script 总字数 35~45 字
|
||||
- 20秒视频：voiceover_script 总字数 50~60 字
|
||||
- 30秒视频：voiceover_script 总字数 75~90 字
|
||||
- 宁可少写也不要多写，超长会导致 TTS 音频超出视频时长限制
|
||||
10. **镜头数量硬约束**：5秒1~2镜、10秒3镜、15秒3~4镜、20秒4~5镜、30秒6~8镜
|
||||
11. **时间轴硬约束**：第一个clip从0秒开始，最后一个clip结束于total_duration秒，相邻clip首尾相接
|
||||
12. 必须严格按<marketing_purpose><target_audience><persona><viral_structure><language><industry>指定的参数写文案和分镜
|
||||
13. 镜头间动作衔接要自然，画面描述要具体到能直接拍摄/生成"""
|
||||
|
||||
V4_STORYBOARD_USER = """<marketing_purpose>{marketing_purpose}</marketing_purpose>
|
||||
<industry>{industry}</industry>
|
||||
<image_analysis>
|
||||
{image_summary}
|
||||
</image_analysis>
|
||||
<user_parameters>
|
||||
<theme_hint>{theme_hint}</theme_hint>
|
||||
<duration>{duration}秒</duration>
|
||||
<aspect_ratio>{aspect_ratio}</aspect_ratio>
|
||||
<tone>{tone}</tone>
|
||||
<target_audience>{target_audience}</target_audience>
|
||||
<persona>{persona_hint}</persona>
|
||||
<viral_structure>{viral_structure_hint}</viral_structure>
|
||||
<language>{language_hint}</language>
|
||||
<extra_requirements>{extra_requirements}</extra_requirements>
|
||||
</user_parameters>
|
||||
{video_style_section}
|
||||
请严格按 XML 结构输出分镜脚本。"""
|
||||
|
||||
V4_STORYBOARD_EXAMPLE = """<script>
|
||||
<copy_display_markdown><![CDATA[# 在御众堂，把松弛的自己一点点找回来
|
||||
产后妈妈最懂那种力不从心，推开门，暖光和一杯热茶先接住了你……]]></copy_display_markdown>
|
||||
<clips>
|
||||
<clip index="1">
|
||||
<time_range>0-3秒</time_range>
|
||||
<speaker>店主</speaker>
|
||||
<voiceover>生完娃，是不是连照镜子的勇气都没了？</voiceover>
|
||||
<visual>【中近景】【缓推】【妈妈疲惫看向镜子】【暖光店内环境】【柔和暖光】</visual>
|
||||
<reference_image_index>0</reference_image_index>
|
||||
</clip>
|
||||
<clip index="2">
|
||||
<time_range>3-6秒</time_range>
|
||||
<speaker>顾客</speaker>
|
||||
<voiceover>是啊，怎么回事？</voiceover>
|
||||
<visual>【近景】【固定】【顾客表情惊讶】【店内休息区】【暖色调】</visual>
|
||||
<reference_image_index>0</reference_image_index>
|
||||
</clip>
|
||||
</clips>
|
||||
<voiceover_script>[speaker:店主]生完娃，是不是连照镜子的勇气都没了？[speaker:顾客]是啊，怎么回事？</voiceover_script>
|
||||
<theme>产后妈妈走进御众堂重拾状态</theme>
|
||||
<negative>模糊、畸变、夸大疗效、绝对化用语</negative>
|
||||
</script>"""
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
conn = op.get_bind()
|
||||
|
||||
# 1. 查询 storyboard 当前最大 version
|
||||
result = conn.execute(
|
||||
text("SELECT MAX(version) FROM viral_video_prompt_templates WHERE prompt_type = :pt"),
|
||||
{"pt": "storyboard"},
|
||||
)
|
||||
max_version = result.scalar() or 0
|
||||
new_version = max_version + 1
|
||||
|
||||
# 2. 旧版 storyboard 模板 is_active 设为 false
|
||||
conn.execute(
|
||||
text("UPDATE viral_video_prompt_templates SET is_active = false WHERE prompt_type = :pt"),
|
||||
{"pt": "storyboard"},
|
||||
)
|
||||
|
||||
# 3. 防御性插入新版 storyboard 模板
|
||||
existing = conn.execute(
|
||||
text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = :pt AND version = :ver"),
|
||||
{"pt": "storyboard", "ver": new_version},
|
||||
).fetchone()
|
||||
|
||||
if not existing:
|
||||
conn.execute(
|
||||
text(
|
||||
"INSERT INTO viral_video_prompt_templates "
|
||||
"(name, prompt_type, version, system_prompt, user_prompt_template, example_output, is_active) "
|
||||
"VALUES (:name, :pt, :ver, :sys, :usr, :ex, :active)"
|
||||
),
|
||||
{
|
||||
"name": "编导分镜v4-多角色对话版",
|
||||
"pt": "storyboard",
|
||||
"ver": new_version,
|
||||
"sys": V4_STORYBOARD_SYSTEM,
|
||||
"usr": V4_STORYBOARD_USER,
|
||||
"ex": V4_STORYBOARD_EXAMPLE,
|
||||
"active": True,
|
||||
},
|
||||
)
|
||||
|
||||
# 4. 检查 image_analysis 是否有 active 模板，没有则插入保底
|
||||
ia_active = conn.execute(
|
||||
text("SELECT COUNT(*) FROM viral_video_prompt_templates WHERE prompt_type = :pt AND is_active = true"),
|
||||
{"pt": "image_analysis"},
|
||||
).scalar()
|
||||
|
||||
if ia_active == 0:
|
||||
# 插入保底 image_analysis 模板（简化版）
|
||||
ia_max = (
|
||||
conn.execute(
|
||||
text("SELECT MAX(version) FROM viral_video_prompt_templates WHERE prompt_type = :pt"),
|
||||
{"pt": "image_analysis"},
|
||||
).scalar()
|
||||
or 0
|
||||
)
|
||||
conn.execute(
|
||||
text(
|
||||
"INSERT INTO viral_video_prompt_templates "
|
||||
"(name, prompt_type, version, system_prompt, user_prompt_template, example_output, is_active) "
|
||||
"VALUES (:name, :pt, :ver, :sys, :usr, :ex, :active)"
|
||||
),
|
||||
{
|
||||
"name": "图片分析保底版",
|
||||
"pt": "image_analysis",
|
||||
"ver": ia_max + 1,
|
||||
"sys": "你是一名擅长观察和写作的品牌内容编导。分析图片并输出JSON。",
|
||||
"usr": "请分析这张图片。图片地址：{image_url}",
|
||||
"ex": '{"images": [{"type": "store", "name": "门店", "summary_markdown": "描述"}]}',
|
||||
"active": True,
|
||||
},
|
||||
)
|
||||
|
||||
# 5. 检查 review 是否有 active 模板
|
||||
review_active = conn.execute(
|
||||
text("SELECT COUNT(*) FROM viral_video_prompt_templates WHERE prompt_type = :pt AND is_active = true"),
|
||||
{"pt": "review"},
|
||||
).scalar()
|
||||
|
||||
if review_active == 0:
|
||||
rv_max = (
|
||||
conn.execute(
|
||||
text("SELECT MAX(version) FROM viral_video_prompt_templates WHERE prompt_type = :pt"),
|
||||
{"pt": "review"},
|
||||
).scalar()
|
||||
or 0
|
||||
)
|
||||
conn.execute(
|
||||
text(
|
||||
"INSERT INTO viral_video_prompt_templates "
|
||||
"(name, prompt_type, version, system_prompt, user_prompt_template, example_output, is_active) "
|
||||
"VALUES (:name, :pt, :ver, :sys, :usr, :ex, :active)"
|
||||
),
|
||||
{
|
||||
"name": "文案审核保底版",
|
||||
"pt": "review",
|
||||
"ver": rv_max + 1,
|
||||
"sys": "你是短视频广告合规审核专家。审核文案输出XML。",
|
||||
"usr": "请审核：{fusion_text}",
|
||||
"ex": "<review><passed>true</passed></review>",
|
||||
"active": True,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
# 恢复旧版 storyboard 为 active
|
||||
conn = op.get_bind()
|
||||
conn.execute(
|
||||
text("UPDATE viral_video_prompt_templates SET is_active = true WHERE prompt_type = :pt AND version < :ver"),
|
||||
{"pt": "storyboard", "ver": 4},
|
||||
)
|
||||
# 删除新版
|
||||
conn.execute(
|
||||
text("DELETE FROM viral_video_prompt_templates WHERE prompt_type = :pt AND version >= :ver"),
|
||||
{"pt": "storyboard", "ver": 4},
|
||||
)
|
||||
@@ -1,42 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""viral_video_param_guides 表：运营可配置的人设/结构/营销目的/目标客群/语言指导文案（PR3 P2-h）。
|
||||
|
||||
Revision ID: 109_add_viral_video_param_guides
|
||||
Revises: 108_storyboard_v4_multivoice
|
||||
Create Date: 2026-10-12
|
||||
|
||||
表用途：
|
||||
- 替代 worker 中 ~130 行硬编码 if/elif mapping；
|
||||
- 5 类 guide_type: persona / structure / marketing_purpose / target_customer / language；
|
||||
- 启动时由 seed_viral_video_param_guides.py 灌入代码默认值，运营可在后台改，30s TTL 缓存自动热加载。
|
||||
"""
|
||||
|
||||
from alembic import op
|
||||
|
||||
revision = "109_add_viral_video_param_guides"
|
||||
down_revision = "108_storyboard_v4_multivoice"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
op.execute("""
|
||||
CREATE TABLE IF NOT EXISTS viral_video_param_guides (
|
||||
id SERIAL PRIMARY KEY,
|
||||
guide_type VARCHAR(32) NOT NULL,
|
||||
guide_key VARCHAR(64) NOT NULL,
|
||||
guide_text TEXT NOT NULL,
|
||||
created_at TIMESTAMP NOT NULL DEFAULT NOW(),
|
||||
updated_at TIMESTAMP NOT NULL DEFAULT NOW(),
|
||||
UNIQUE (guide_type, guide_key)
|
||||
)
|
||||
""")
|
||||
op.execute("""
|
||||
CREATE INDEX IF NOT EXISTS ix_viral_video_param_guides_type_key
|
||||
ON viral_video_param_guides (guide_type, guide_key)
|
||||
""")
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
op.execute("DROP INDEX IF EXISTS ix_viral_video_param_guides_type_key")
|
||||
op.execute("DROP TABLE IF EXISTS viral_video_param_guides")
|
||||
@@ -1,4 +1,3 @@
|
||||
from app.api.routes.admin.ditto_emotion import router as admin_ditto_emotion_router
|
||||
from app.api.routes.ai import router as ai_router
|
||||
from app.api.routes.ai_avatar_render import router as ai_avatar_render_router
|
||||
from app.api.routes.asset_diagnosis import router as asset_diagnosis_router
|
||||
@@ -243,6 +242,3 @@ api_router.include_router(
|
||||
tags=["GPU Worker"],
|
||||
)
|
||||
api_router.include_router(viral_video_router, prefix="/viral-video", tags=["爆款视频"])
|
||||
|
||||
# #2246：后台 Ditto 表情配置（router 自带 /admin/ditto-emotion 前缀）
|
||||
api_router.include_router(admin_ditto_emotion_router)
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
"""后台管理路由（#2246 起）."""
|
||||
@@ -1,203 +0,0 @@
|
||||
"""Ditto 数字人表情后台配置 — #2246.
|
||||
|
||||
路由前缀 /api/v1/admin/ditto-emotion，全部使用 _verify_internal_api_key 鉴权
|
||||
（X-API-Key header）。仅开放 6 项白名单配置：
|
||||
- ditto_emotion_enabled / ditto_emotion_model / ditto_emotion_temperature
|
||||
- ditto_emotion_prompt / ditto_blend_frames / doubao_api_key
|
||||
注意：doubao_api_key 属于密钥，GET 只返回脱敏占位，绝不返回明文。
|
||||
ditto_emotion_model 为自由文本（支持火山方舟 ep- 接入点 ID），
|
||||
推荐选项 model_options 从 system_config 的 ditto_emotion_model_options（JSON 数组）读取。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
from typing import Any
|
||||
|
||||
from fastapi import APIRouter, Depends
|
||||
from pydantic import BaseModel
|
||||
|
||||
from packages.application.system_config_service import get_system_config_service
|
||||
from packages.config import get_api_settings
|
||||
from packages.domain.system_setting import (
|
||||
SETTING_TYPE_BOOL,
|
||||
SETTING_TYPE_FLOAT,
|
||||
SETTING_TYPE_INT,
|
||||
SETTING_TYPE_STRING,
|
||||
)
|
||||
|
||||
from ..auth import _verify_internal_api_key
|
||||
|
||||
router = APIRouter(
|
||||
prefix="/admin/ditto-emotion",
|
||||
tags=["Admin"],
|
||||
dependencies=[Depends(_verify_internal_api_key)],
|
||||
)
|
||||
|
||||
# key → (类型, 分类)
|
||||
_WHITELIST: dict[str, str] = {
|
||||
"ditto_emotion_enabled": SETTING_TYPE_BOOL,
|
||||
"ditto_emotion_model": SETTING_TYPE_STRING,
|
||||
"ditto_emotion_temperature": SETTING_TYPE_FLOAT,
|
||||
"ditto_emotion_prompt": SETTING_TYPE_STRING,
|
||||
"ditto_blend_frames": SETTING_TYPE_INT,
|
||||
"doubao_api_key": SETTING_TYPE_STRING,
|
||||
}
|
||||
|
||||
_DESCRIPTIONS: dict[str, str] = {
|
||||
"ditto_emotion_enabled": "LLM 情绪分析开关。关闭时回退到原有关键词匹配模式，不影响正常出片。",
|
||||
"ditto_emotion_model": "用于分析文案情绪的大模型。",
|
||||
"ditto_emotion_temperature": "模型温度，0-1，越低越稳定保守。",
|
||||
"ditto_emotion_prompt": "情绪分析提示词，核心调优入口，必须包含 {文案} 占位符。",
|
||||
"ditto_blend_frames": "表情切换过渡帧数（6-30），越大越柔和。",
|
||||
"doubao_api_key": "豆包/火山方舟 API Key（用于情绪分析LLM调用）",
|
||||
}
|
||||
|
||||
# 密钥类配置：GET 脱敏、PUT 响应不回显明文
|
||||
_SECRET_KEYS = {"doubao_api_key"}
|
||||
_SECRET_PLACEHOLDER = "***已配置***"
|
||||
|
||||
|
||||
def _settings():
|
||||
return get_api_settings()
|
||||
|
||||
|
||||
def _default_value(key: str) -> Any:
|
||||
return getattr(_settings(), key)
|
||||
|
||||
|
||||
def _model_options_from_db(service) -> list[str]:
|
||||
"""模型推荐选项：从 system_config 读 ditto_emotion_model_options（JSON 数组字符串）。
|
||||
|
||||
未配置 / 非法 JSON / 非数组 JSON 均返回空数组；数组元素统一转字符串。
|
||||
"""
|
||||
raw = service.get_config("ditto_emotion_model_options", "") or ""
|
||||
if not str(raw).strip():
|
||||
return []
|
||||
try:
|
||||
data = json.loads(raw)
|
||||
except (TypeError, ValueError):
|
||||
return []
|
||||
if not isinstance(data, list):
|
||||
return []
|
||||
return [str(x) for x in data if isinstance(x, (str, int, float))]
|
||||
|
||||
|
||||
def _build_config_item(key: str) -> dict[str, Any]:
|
||||
service = get_system_config_service()
|
||||
if key in _SECRET_KEYS:
|
||||
# 密钥不回明文、不暴露 env 默认值：DB 或 env 任一侧已配置即视为已配置
|
||||
db_value = service.get_config(key, None)
|
||||
configured = bool((db_value or "").strip()) or bool((getattr(_settings(), key, "") or "").strip())
|
||||
return {
|
||||
"key": key,
|
||||
"type": _WHITELIST[key],
|
||||
"description": _DESCRIPTIONS.get(key, ""),
|
||||
"default": "",
|
||||
"value": _SECRET_PLACEHOLDER if configured else "",
|
||||
}
|
||||
item: dict[str, Any] = {
|
||||
"key": key,
|
||||
"type": _WHITELIST[key],
|
||||
"description": _DESCRIPTIONS.get(key, ""),
|
||||
"default": _default_value(key),
|
||||
}
|
||||
item["value"] = service.get_config(key, _default_value(key))
|
||||
if key == "ditto_emotion_model":
|
||||
item["model_options"] = _model_options_from_db(service)
|
||||
return item
|
||||
|
||||
|
||||
class ConfigUpdatePayload(BaseModel):
|
||||
configs: dict[str, Any]
|
||||
|
||||
|
||||
class TestPayload(BaseModel):
|
||||
test_text: str
|
||||
|
||||
|
||||
def _validate_value(key: str, value: Any) -> Any:
|
||||
st = _WHITELIST[key]
|
||||
if st == SETTING_TYPE_BOOL:
|
||||
if not isinstance(value, bool):
|
||||
raise ValueError(f"{key} 必须是布尔值")
|
||||
elif st == SETTING_TYPE_INT:
|
||||
if isinstance(value, bool) or not isinstance(value, int):
|
||||
raise ValueError(f"{key} 必须是整数")
|
||||
if not 6 <= value <= 30:
|
||||
raise ValueError(f"{key} 必须在 6-30 之间")
|
||||
elif st == SETTING_TYPE_FLOAT:
|
||||
if isinstance(value, bool):
|
||||
raise ValueError(f"{key} 必须是数字")
|
||||
try:
|
||||
value = float(value)
|
||||
except (TypeError, ValueError) as exc:
|
||||
raise ValueError(f"{key} 必须是数字") from exc
|
||||
if not 0.0 <= value <= 1.0:
|
||||
raise ValueError(f"{key} 必须在 0-1 之间")
|
||||
elif st == SETTING_TYPE_STRING:
|
||||
if not isinstance(value, str):
|
||||
raise ValueError(f"{key} 必须是字符串")
|
||||
if key == "ditto_emotion_prompt" and value.strip() and "{文案}" not in value:
|
||||
raise ValueError("提示词必须包含 {文案} 占位符")
|
||||
if key == "ditto_emotion_model":
|
||||
if not value.strip():
|
||||
raise ValueError(f"{key} 不能为空字符串")
|
||||
if len(value) > 200:
|
||||
raise ValueError(f"{key} 长度不能超过 200 字符")
|
||||
if not re.fullmatch(r"[A-Za-z0-9_-]+", value):
|
||||
raise ValueError(f"{key} 只能包含字母、数字、连字符和下划线")
|
||||
if key == "doubao_api_key" and not value.strip():
|
||||
raise ValueError(f"{key} 不能为空字符串")
|
||||
return value
|
||||
|
||||
|
||||
@router.get("/config")
|
||||
def get_config() -> dict[str, Any]:
|
||||
return {"configs": [_build_config_item(k) for k in _WHITELIST]}
|
||||
|
||||
|
||||
@router.put("/config")
|
||||
def update_config(
|
||||
payload: ConfigUpdatePayload,
|
||||
x_api_key: str = Depends(_verify_internal_api_key),
|
||||
) -> dict[str, Any]:
|
||||
configs = payload.configs
|
||||
illegal = [k for k in configs if k not in _WHITELIST]
|
||||
if illegal:
|
||||
return {
|
||||
"ok": False,
|
||||
"error": f"不允许修改的配置项：{', '.join(illegal)}",
|
||||
}
|
||||
service = get_system_config_service()
|
||||
updated: dict[str, Any] = {}
|
||||
for key, raw in configs.items():
|
||||
try:
|
||||
value = _validate_value(key, raw)
|
||||
except ValueError as exc:
|
||||
return {"ok": False, "error": str(exc)}
|
||||
service.set_config(
|
||||
key,
|
||||
value,
|
||||
setting_type=_WHITELIST[key],
|
||||
updated_by=str(x_api_key)[:8] if x_api_key and x_api_key is not True else None,
|
||||
)
|
||||
updated[key] = _SECRET_PLACEHOLDER if key in _SECRET_KEYS else value
|
||||
return {"ok": True, "updated": updated}
|
||||
|
||||
|
||||
@router.post("/config/test")
|
||||
def test_config(payload: TestPayload) -> dict[str, Any]:
|
||||
text = (payload.test_text or "").strip()
|
||||
if not text:
|
||||
return {"ok": False, "error": "test_text 不能为空"}
|
||||
from packages.application.ditto_emotion_service import get_ditto_emotion_service
|
||||
|
||||
service = get_ditto_emotion_service()
|
||||
segments = service.analyze(text)
|
||||
return {
|
||||
"ok": True,
|
||||
"enabled": service.enabled,
|
||||
"segments": [s.to_dict() for s in segments],
|
||||
}
|
||||
@@ -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 +163,7 @@ def _infer_expected_categories(script_tags: set[str] | None) -> set[str] | None:
|
||||
return matched or None
|
||||
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter()
|
||||
@@ -700,17 +700,6 @@ 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 +930,6 @@ 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。
|
||||
|
||||
@@ -89,7 +89,6 @@ def get_balance(
|
||||
is_member=_is_member(current_user),
|
||||
member_type=_member_type(current_user),
|
||||
member_expires_at=getattr(current_user.user, "member_expires_at", None),
|
||||
credits_enabled=_credits_enabled(),
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -2,10 +2,12 @@
|
||||
|
||||
v1.6 三步分步流水线端点（单次 Seedance 出片版）：
|
||||
POST /api/v1/viral-video/analyze-images 阶段1：创建任务 + 仅做图片/视频分析，暂停在 image_analyzed
|
||||
POST /api/v1/viral-video/{job_id}/generate-copy 阶段2：用户填完参数后文案+分镜一步生成+审核，暂停在 copy_generated
|
||||
POST /api/v1/viral-video/{job_id}/confirm-copy 阶段3：用户确认/编辑文案后跑渲染（TTS+Seedance+上传），直到完成
|
||||
POST /api/v1/viral-video/{job_id}/generate-copy 阶段2：用户填完参数后跑意图+文案+分镜+审核，暂停在 copy_generated
|
||||
POST /api/v1/viral-video/{job_id}/confirm-copy 阶段3：用户确认/编辑文案后跑渲染，直到完成
|
||||
|
||||
意图分析已并入脚本生成一步完成，不再有独立的 intent_parsing 阶段或 wait_user_confirm 状态。
|
||||
旧端点（兼容保留，旧前端/一键生成模式）：
|
||||
POST /api/v1/viral-video/generate 一键入队，前半段跑到 wait_user_confirm
|
||||
POST /api/v1/viral-video/{job_id}/confirm-intent 旧的意图确认后继续渲染
|
||||
|
||||
通用：
|
||||
GET /api/v1/viral-video/{job_id} 查询任务状态（含 image_analysis/copy_result 编导脚本）
|
||||
@@ -28,6 +30,8 @@ from app.schemas.viral_video import (
|
||||
AnalyzeStyleRequest,
|
||||
AnalyzeStyleResponse,
|
||||
ConfirmCopyRequest,
|
||||
ConfirmIntentRequest,
|
||||
CreateViralVideoRequest,
|
||||
CreditsFormulaBreakdown,
|
||||
EstimateCreditsRequest,
|
||||
EstimateCreditsResponse,
|
||||
@@ -45,17 +49,7 @@ from packages.adapters.sqlalchemy_impl.viral_video_repository import (
|
||||
SQLAlchemyViralVideoJobRepository,
|
||||
SQLAlchemyViralVideoStyleTemplateRepository,
|
||||
)
|
||||
from packages.application.viral_video.constants import (
|
||||
API_FALLBACK_HARD_CONSTRAINTS,
|
||||
API_FALLBACK_NEGATIVE_PROMPTS,
|
||||
)
|
||||
from packages.domain.points_rules import (
|
||||
calculate_viral_video_credits,
|
||||
calculate_viral_video_credits_with_breakdown,
|
||||
list_viral_video_models,
|
||||
resolve_video_dimensions,
|
||||
)
|
||||
from packages.domain.points_service import PointsService
|
||||
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
|
||||
|
||||
@@ -71,7 +65,7 @@ def _build_copy_result(job) -> dict | None:
|
||||
"""v1.6: 返回编导分镜脚本 CopyResult 结构（给前端/Seedance 使用）。
|
||||
|
||||
- 若 job.copy_result 已持久化（v1.6 worker 生成），直接返回（补 final_copy 兜底）。
|
||||
- 否则从老字段（generated_copy_text=口播, storyboard=分镜列表）拼装兼容结构。
|
||||
- 否则从老字段（generated_copy_text=口播, storyboard=分镜列表, intent_result）拼装兼容结构。
|
||||
"""
|
||||
cr = getattr(job, "copy_result", None)
|
||||
if isinstance(cr, dict) and cr:
|
||||
@@ -85,9 +79,12 @@ def _build_copy_result(job) -> dict | None:
|
||||
# 兼容 v1.5 老数据：storyboard 是老格式 [{order,type,description,text,duration,...}]
|
||||
copy_text = getattr(job, "generated_copy_text", "") or ""
|
||||
sb = getattr(job, "storyboard", None) or []
|
||||
intent = getattr(job, "intent_result", None) or {}
|
||||
if not copy_text and not sb:
|
||||
return None
|
||||
title = ""
|
||||
if isinstance(intent, dict):
|
||||
title = intent.get("suggested_title") or intent.get("intent", "") or ""
|
||||
shots = []
|
||||
for seg in sb:
|
||||
if isinstance(seg, dict):
|
||||
@@ -108,8 +105,8 @@ def _build_copy_result(job) -> dict | None:
|
||||
"overview": {"theme": title, "total_duration": getattr(job, "duration", 15), "aspect_ratio": ratio},
|
||||
"scene_and_lighting": "",
|
||||
"shots": shots,
|
||||
"hard_constraints": list(API_FALLBACK_HARD_CONSTRAINTS),
|
||||
"negative_prompts": list(API_FALLBACK_NEGATIVE_PROMPTS),
|
||||
"hard_constraints": ["无字幕", "无水印", "人物一致性"],
|
||||
"negative_prompts": ["字幕", "水印", "错误文字", "五官崩坏"],
|
||||
"voiceover_script": copy_text,
|
||||
"final_copy": copy_text,
|
||||
"suggested_copy": copy_text,
|
||||
@@ -145,9 +142,9 @@ def _to_response(job) -> ViralVideoJobResponse:
|
||||
copy_result=_build_copy_result(job),
|
||||
voice_id=getattr(job, "voice_id", "") or "",
|
||||
voice_source=getattr(job, "voice_source", "") or "",
|
||||
language=getattr(job, "language", "zh-CN") or "zh-CN",
|
||||
video_ratio=getattr(job, "video_ratio", "9:16") or "9:16",
|
||||
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",
|
||||
@@ -170,157 +167,58 @@ def _get_style_repo(session: Session) -> SQLAlchemyViralVideoStyleTemplateReposi
|
||||
return SQLAlchemyViralVideoStyleTemplateRepository(session)
|
||||
|
||||
|
||||
def _settle_viral_video_credits(
|
||||
user_id: int,
|
||||
job,
|
||||
session: Session,
|
||||
*,
|
||||
action: str,
|
||||
old_duration: int | None = None,
|
||||
old_resolution: str | None = None,
|
||||
old_ratio: str | None = None,
|
||||
old_model: str | None = None,
|
||||
new_duration: int | None = None,
|
||||
new_resolution: str | None = None,
|
||||
new_ratio: str | None = None,
|
||||
new_model: str | None = None,
|
||||
) -> dict:
|
||||
"""爆款视频积分结算统一入口（confirm-copy / retry 共用）。
|
||||
# ── Endpoints ────────────────────────────────────────────────────────────
|
||||
|
||||
action 取值：
|
||||
- "deduct_first" 首次扣费（confirm 时还没付过）
|
||||
- "recompute" 参数变更重算：退旧扣新（confirm_copy 时传 old/new）
|
||||
- "delta" 参数变更差额结算：多退少补（retry 时传 old/new）
|
||||
返回 credits_info dict，包含 old_est / new_est / action / diff 等。
|
||||
积分不足时 raise HTTPException(402)，调用方无需再捕获。
|
||||
"""
|
||||
from app.config import settings as _settings
|
||||
|
||||
info: dict = {"action": action, "old_est": 0.0, "new_est": 0.0, "diff": 0.0, "settled": False}
|
||||
if not _settings.points_enabled:
|
||||
return info
|
||||
@router.post("/generate", response_model=ViralVideoJobResponse)
|
||||
def create_viral_video(
|
||||
request: CreateViralVideoRequest,
|
||||
authenticated_user: AuthenticatedUser = Depends(get_current_user),
|
||||
session: Session = Depends(get_db_session),
|
||||
) -> ViralVideoJobResponse:
|
||||
"""创建爆款视频任务，入队 Celery 编排器。"""
|
||||
from packages.domain.viral_video import ViralVideoJob
|
||||
|
||||
already_paid = (float(getattr(job, "credits_prepaid", 0) or 0) > 0) or (
|
||||
float(getattr(job, "credits_cost", 0) or 0) > 0
|
||||
repo = _get_job_repo(session)
|
||||
|
||||
# 创建领域实体
|
||||
job = ViralVideoJob(
|
||||
user_id=authenticated_user.user.id,
|
||||
images=list(request.images),
|
||||
industry=request.industry,
|
||||
target_customer=request.target_customer,
|
||||
persona_id=request.persona_id,
|
||||
viral_structure=request.viral_structure,
|
||||
marketing_purpose=request.marketing_purpose,
|
||||
bgm_preference=request.bgm_preference,
|
||||
duration=request.duration or 15,
|
||||
user_copy_text=request.user_copy_text,
|
||||
fusion_level=request.fusion_level,
|
||||
reference_audio_path=request.reference_audio_path,
|
||||
reference_video_url=request.reference_video_url,
|
||||
style_strength=request.style_strength,
|
||||
style_template_id=request.style_template_id,
|
||||
voice_id=getattr(request, "voice_id", "") or "",
|
||||
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,
|
||||
)
|
||||
|
||||
if action == "deduct_first":
|
||||
if already_paid:
|
||||
return info
|
||||
w, h = resolve_video_dimensions(
|
||||
getattr(job, "video_resolution", "720p") or "720p", job.video_ratio or "9:16"
|
||||
)
|
||||
est = calculate_viral_video_credits(int(job.duration or 15), w, h, job.video_model or "seedance-2.5")
|
||||
info["new_est"] = est
|
||||
svc = PointsService()
|
||||
res = svc.deduct_viral_video(user_id, est, 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} 积分，当前余额 {balance}",
|
||||
"required": est,
|
||||
"balance": balance,
|
||||
},
|
||||
)
|
||||
job.credits_prepaid = est
|
||||
job.credits_transaction_id = res.get("transaction_id", "") or ""
|
||||
info["settled"] = True
|
||||
info["diff"] = est
|
||||
return info
|
||||
# 持久化
|
||||
repo.save(job)
|
||||
|
||||
if action == "recompute":
|
||||
if not already_paid:
|
||||
# 等价于首次扣费
|
||||
return _settle_viral_video_credits(user_id, job, session, action="deduct_first")
|
||||
old_w, old_h = resolve_video_dimensions(old_resolution or "720p", old_ratio or "9:16")
|
||||
old_est = calculate_viral_video_credits(
|
||||
int(old_duration or 15), old_w, old_h, old_model or "seedance-2.5"
|
||||
)
|
||||
new_w, new_h = resolve_video_dimensions(
|
||||
getattr(job, "video_resolution", "720p") or "720p", job.video_ratio or "9:16"
|
||||
)
|
||||
new_est = calculate_viral_video_credits(
|
||||
int(job.duration or 15), new_w, new_h, job.video_model or "seedance-2.5"
|
||||
)
|
||||
info.update({"old_est": old_est, "new_est": new_est, "diff": new_est - old_est})
|
||||
svc = PointsService()
|
||||
if getattr(job, "credits_transaction_id", None):
|
||||
svc.refund_points(
|
||||
user_id=user_id,
|
||||
amount=float(job.credits_prepaid),
|
||||
source="viral_video",
|
||||
db=session,
|
||||
ref_id=job.credits_transaction_id,
|
||||
description="confirm-copy 参数变更退还旧预扣",
|
||||
)
|
||||
if new_est > 0:
|
||||
res = svc.deduct_viral_video(user_id, new_est, 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 {new_est} 积分，当前余额 {balance}",
|
||||
"required": new_est,
|
||||
"balance": balance,
|
||||
},
|
||||
)
|
||||
job.credits_prepaid = new_est
|
||||
job.credits_transaction_id = res.get("transaction_id", "") or ""
|
||||
info["settled"] = True
|
||||
return info
|
||||
# 入队 Celery 任务
|
||||
try:
|
||||
celery_app.send_task("worker.run_viral_video_pipeline", args=[job.id])
|
||||
logger.info("[爆款视频] 任务已入队: job_id=%s user_id=%s", job.id, job.user_id)
|
||||
except Exception as e:
|
||||
logger.error("[爆款视频] 入队失败: %s", e, exc_info=True)
|
||||
job.mark_failed(f"任务入队失败: {e}")
|
||||
repo.update(job)
|
||||
|
||||
if action == "delta":
|
||||
eff_dur = int(new_duration if new_duration is not None else old_duration or 15)
|
||||
eff_res = new_resolution if new_resolution is not None else (old_resolution or "720p")
|
||||
eff_ratio = new_ratio if new_ratio is not None else (old_ratio or "9:16")
|
||||
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)
|
||||
old_prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
|
||||
diff = round(new_est - old_prepaid, 2)
|
||||
info.update({"old_est": old_prepaid, "new_est": new_est, "diff": diff})
|
||||
if abs(diff) < 0.01:
|
||||
return info
|
||||
svc = PointsService()
|
||||
if diff > 0:
|
||||
res = svc.deduct_viral_video(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)
|
||||
else:
|
||||
refund = round(-diff, 2)
|
||||
txn_id = getattr(job, "credits_transaction_id", "") or ""
|
||||
svc.refund_points(
|
||||
user_id=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)
|
||||
info["settled"] = True
|
||||
return info
|
||||
|
||||
raise ValueError(f"unknown action: {action}")
|
||||
|
||||
|
||||
# ── Endpoints ────────────────────────────────────────────────────────────
|
||||
return _to_response(job)
|
||||
|
||||
|
||||
@router.post("/analyze-images", response_model=ViralVideoJobResponse)
|
||||
@@ -345,7 +243,6 @@ def analyze_images(
|
||||
style_strength=request.style_strength or "medium",
|
||||
voice_id=request.voice_id or "",
|
||||
voice_source=request.voice_source or "",
|
||||
language=getattr(request, "language", "zh-CN") or "zh-CN",
|
||||
video_ratio=request.video_ratio or "9:16",
|
||||
video_model=request.video_model or "",
|
||||
video_resolution=getattr(request, "video_resolution", "720p") or "720p",
|
||||
@@ -383,18 +280,11 @@ 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 = ""
|
||||
|
||||
@@ -417,7 +307,6 @@ def generate_copy(
|
||||
job.style_guide = request.style_guide
|
||||
job.voice_id = request.voice_id or job.voice_id
|
||||
job.voice_source = request.voice_source or job.voice_source
|
||||
job.language = getattr(request, "language", "") or job.language or "zh-CN"
|
||||
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"
|
||||
@@ -452,59 +341,41 @@ 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」")
|
||||
|
||||
# Bug1 fix: 用户 confirm 时允许修改 video_model/video_resolution/video_ratio/duration
|
||||
old_duration = int(getattr(job, "duration", 15) or 15)
|
||||
old_resolution = getattr(job, "video_resolution", "720p") or "720p"
|
||||
old_ratio = getattr(job, "video_ratio", "9:16") or "9:16"
|
||||
old_model = getattr(job, "video_model", None) or "seedance-2.5"
|
||||
# 积分预扣（已扣过/重试任务跳过）
|
||||
from app.config import settings as _settings
|
||||
|
||||
if request.duration is not None:
|
||||
job.duration = max(5, min(30, int(request.duration)))
|
||||
if request.video_resolution is not None:
|
||||
job.video_resolution = request.video_resolution
|
||||
if request.video_ratio is not None:
|
||||
job.video_ratio = request.video_ratio
|
||||
if request.video_model is not None:
|
||||
job.video_model = request.video_model
|
||||
if request.voice_id is not None:
|
||||
job.voice_id = request.voice_id
|
||||
if request.voice_source is not None:
|
||||
job.voice_source = request.voice_source
|
||||
if getattr(request, "language", None) is not None:
|
||||
job.language = request.language
|
||||
|
||||
param_changed = (
|
||||
(request.duration is not None and int(request.duration) != old_duration)
|
||||
or (request.video_resolution is not None and request.video_resolution != old_resolution)
|
||||
or (request.video_ratio is not None and request.video_ratio != old_ratio)
|
||||
or (request.video_model is not None and request.video_model != old_model)
|
||||
)
|
||||
|
||||
# 积分预扣（PR2：统一走 _settle_viral_video_credits）
|
||||
if param_changed:
|
||||
credits_info = _settle_viral_video_credits(
|
||||
authenticated_user.user.id,
|
||||
job,
|
||||
session,
|
||||
action="recompute",
|
||||
old_duration=old_duration,
|
||||
old_resolution=old_resolution,
|
||||
old_ratio=old_ratio,
|
||||
old_model=old_model,
|
||||
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
|
||||
)
|
||||
logger.info(
|
||||
"[爆款视频][confirm-copy] 参数变更，积分重算: old=%s new=%s job_id=%s",
|
||||
credits_info.get("old_est"),
|
||||
credits_info.get("new_est"),
|
||||
job.id,
|
||||
)
|
||||
else:
|
||||
_settle_viral_video_credits(authenticated_user.user.id, job, session, action="deduct_first")
|
||||
repo.update(job)
|
||||
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)
|
||||
@@ -514,7 +385,6 @@ def confirm_copy(
|
||||
logger.info("[爆款视频][阶段3] confirm-copy 入队: job_id=%s", job.id)
|
||||
except Exception as e:
|
||||
logger.error("[爆款视频][阶段3] confirm-copy 入队失败: %s", e, exc_info=True)
|
||||
# TODO(PR2-followup): 入队失败应返回 HTTP 503 而非 mark_failed + 200，避免前端收到"成功"但 job 已 failed 的不一致状态
|
||||
job.mark_failed(f"任务入队失败: {e}")
|
||||
repo.update(job)
|
||||
|
||||
@@ -532,6 +402,11 @@ def estimate_credits(
|
||||
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"
|
||||
@@ -663,7 +538,25 @@ def retry_viral_video_job(
|
||||
or (new_model is not None and new_model != old_model)
|
||||
)
|
||||
|
||||
# 写入新参数（即使不开 points 也要允许用户重试时改参数；delta 结算依赖这些新值）
|
||||
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:
|
||||
@@ -673,31 +566,57 @@ def retry_viral_video_job(
|
||||
if new_model is not None:
|
||||
job.video_model = new_model
|
||||
|
||||
# PR2：统一走 _settle_viral_video_credits（delta 多退少补）
|
||||
if param_changed:
|
||||
credits_info = _settle_viral_video_credits(
|
||||
authenticated_user.user.id,
|
||||
job,
|
||||
session,
|
||||
action="delta",
|
||||
old_duration=old_duration,
|
||||
old_resolution=old_resolution,
|
||||
old_ratio=old_ratio,
|
||||
old_model=old_model,
|
||||
new_duration=new_duration,
|
||||
new_resolution=new_resolution,
|
||||
new_ratio=new_ratio,
|
||||
new_model=new_model,
|
||||
)
|
||||
if credits_info.get("settled"):
|
||||
logger.info(
|
||||
"[爆款视频][retry] 积分差额结算 job_id=%s diff=%.2f new_prepaid=%.2f",
|
||||
job.id,
|
||||
credits_info["diff"],
|
||||
job.credits_prepaid,
|
||||
)
|
||||
if need_points_settle:
|
||||
from packages.domain.points_service import PointsService
|
||||
|
||||
# 重置状态（从阶段1重新开始，清空上一次产物避免脏数据）
|
||||
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
|
||||
|
||||
# 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"
|
||||
@@ -706,18 +625,11 @@ def retry_viral_video_job(
|
||||
job.current_stage = ""
|
||||
job.phase_message = ""
|
||||
job.heartbeat_at = None
|
||||
# 重试时清空上次产物（参考 resume_from_image_analyzed 逻辑）
|
||||
job.copy_result = None
|
||||
job.storyboard = None
|
||||
job.generated_copy_text = ""
|
||||
job.result_video_url = ""
|
||||
job.pre_trusted_images = None
|
||||
job.credits_cost = 0.0
|
||||
repo.update(job)
|
||||
|
||||
# 重新入队：从图片分析阶段1重新跑
|
||||
# 重新入队
|
||||
try:
|
||||
celery_app.send_task("worker.run_viral_video_analyze", args=[job.id])
|
||||
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,
|
||||
@@ -727,13 +639,49 @@ def retry_viral_video_job(
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error("[爆款视频] 重试入队失败: %s", e, exc_info=True)
|
||||
# TODO(PR2-followup): 入队失败应返回 HTTP 503 而非 mark_failed + 200
|
||||
job.mark_failed(f"重试入队失败: {e}")
|
||||
repo.update(job)
|
||||
|
||||
return _to_response(job)
|
||||
|
||||
|
||||
@router.post("/{job_id}/confirm-intent", response_model=ViralVideoJobResponse)
|
||||
def confirm_intent(
|
||||
job_id: str,
|
||||
request: ConfirmIntentRequest,
|
||||
authenticated_user: AuthenticatedUser = Depends(get_current_user),
|
||||
session: Session = Depends(get_db_session),
|
||||
) -> ViralVideoJobResponse:
|
||||
"""用户确认/修改 AI 生成的意图文案，恢复流水线。"""
|
||||
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")
|
||||
if job.status != ViralVideoStatus.WAIT_USER_CONFIRM:
|
||||
raise HTTPException(status_code=409, detail="任务当前不在等待确认状态")
|
||||
|
||||
# 更新文案
|
||||
if request.confirmed_copy:
|
||||
job.user_copy_text = request.confirmed_copy
|
||||
|
||||
# 恢复流水线
|
||||
job.resume_from_confirm()
|
||||
repo.update(job)
|
||||
|
||||
# 从断点恢复 Celery 任务
|
||||
try:
|
||||
celery_app.send_task("worker.resume_viral_video_pipeline", args=[job.id])
|
||||
logger.info("[爆款视频] 意图确认，恢复流水线: job_id=%s", job.id)
|
||||
except Exception as e:
|
||||
logger.error("[爆款视频] 恢复流水线失败: %s", e, exc_info=True)
|
||||
job.mark_failed(f"恢复流水线失败: {e}")
|
||||
repo.update(job)
|
||||
|
||||
return _to_response(job)
|
||||
|
||||
|
||||
@router.post("/{job_id}/analyze-style", response_model=AnalyzeStyleResponse)
|
||||
def analyze_style(
|
||||
job_id: str,
|
||||
@@ -932,28 +880,13 @@ async def viral_video_websocket(websocket: WebSocket, job_id: str) -> None:
|
||||
job = job_repo.get(job_id)
|
||||
if job is not None:
|
||||
status_val = job.status.value if hasattr(job.status, "value") else str(job.status)
|
||||
# #P0: 初始快照必须包含前端重连/刷新所需的业务字段，
|
||||
# 结构对齐 worker 推送的 image_analyzed / copy_generated 事件。
|
||||
data: dict = {"status": status_val}
|
||||
ia = getattr(job, "image_analysis", None)
|
||||
if isinstance(ia, dict) and ia:
|
||||
data["image_analysis"] = ia
|
||||
cr = _build_copy_result(job)
|
||||
if isinstance(cr, dict) and cr:
|
||||
data["copy_result"] = cr
|
||||
gct = getattr(job, "generated_copy_text", "") or ""
|
||||
if gct:
|
||||
data["generated_copy_text"] = gct
|
||||
sb = getattr(job, "storyboard", None) or []
|
||||
if sb:
|
||||
data["storyboard"] = sb
|
||||
initial = {
|
||||
"type": "viral_video:progress",
|
||||
"job_id": job_id,
|
||||
"stage": _stage_from_status(job),
|
||||
"progress": _estimate_progress(job),
|
||||
"message": _initial_message(job),
|
||||
"data": data,
|
||||
"data": {"status": status_val},
|
||||
}
|
||||
await websocket.send_json(initial)
|
||||
# 已经终态 → 再发一条终态事件后立即关闭，避免占连接
|
||||
@@ -1000,6 +933,7 @@ _STATUS_STAGE = {
|
||||
"running": "",
|
||||
"image_analyzed": "image_analysis",
|
||||
"copy_generated": "review",
|
||||
"wait_user_confirm": "intent_parsing",
|
||||
"completed": "uploading",
|
||||
"failed": "",
|
||||
"cancelled": "",
|
||||
@@ -1010,6 +944,7 @@ _STATUS_PROGRESS = {
|
||||
"running": 5.0,
|
||||
"image_analyzed": 15.0,
|
||||
"copy_generated": 70.0,
|
||||
"wait_user_confirm": 35.0,
|
||||
"completed": 100.0,
|
||||
"failed": 0.0,
|
||||
"cancelled": 0.0,
|
||||
@@ -1020,6 +955,7 @@ _STATUS_MESSAGE = {
|
||||
"running": "任务执行中",
|
||||
"image_analyzed": "图片分析完成，等待填写营销参数",
|
||||
"copy_generated": "文案与分镜已生成，等待确认文案",
|
||||
"wait_user_confirm": "等待用户确认意图文案",
|
||||
"completed": "视频生成完成",
|
||||
"failed": "任务失败",
|
||||
"cancelled": "任务已取消",
|
||||
|
||||
@@ -19,7 +19,6 @@ class PointsBalanceResponse(BaseModel):
|
||||
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")
|
||||
credits_enabled: bool = Field(default=False, description="积分系统是否启用（false=免费放行不扣分）")
|
||||
|
||||
|
||||
# ============ 流水 ============
|
||||
|
||||
@@ -13,6 +13,7 @@ VALID_STYLE_STRENGTHS = ("light", "medium", "strict")
|
||||
VALID_STAGES = (
|
||||
"image_analysis",
|
||||
"video_analysis",
|
||||
"intent_parsing",
|
||||
"script_generation",
|
||||
"review",
|
||||
"tts",
|
||||
@@ -67,6 +68,46 @@ class CopyResult(BaseModel):
|
||||
# -- Request Schemas --
|
||||
|
||||
|
||||
class CreateViralVideoRequest(BaseModel):
|
||||
"""旧接口：一键创建（保留兼容）。"""
|
||||
|
||||
images: list[str] = Field(..., min_length=1, max_length=20)
|
||||
industry: str = ""
|
||||
target_customer: str = ""
|
||||
persona_id: str = ""
|
||||
viral_structure: str = ""
|
||||
marketing_purpose: str = ""
|
||||
bgm_preference: str = ""
|
||||
duration: int = Field(default=15, ge=5, le=30, description="视频时长（秒），5-30")
|
||||
user_copy_text: str = ""
|
||||
fusion_level: str = "ai_polish"
|
||||
reference_audio_path: str = ""
|
||||
reference_video_url: str = ""
|
||||
style_strength: str = "medium"
|
||||
style_template_id: str = ""
|
||||
voice_id: str = ""
|
||||
voice_source: str = ""
|
||||
video_ratio: str = "9:16"
|
||||
video_model: str = ""
|
||||
video_resolution: str = "720p"
|
||||
|
||||
@field_validator("fusion_level")
|
||||
@classmethod
|
||||
def _v_fl(cls, v: str) -> str:
|
||||
if v == "full_ai":
|
||||
return "ai_full"
|
||||
if v not in VALID_FUSION_LEVELS:
|
||||
raise ValueError(f"fusion_level must be one of {VALID_FUSION_LEVELS}")
|
||||
return v
|
||||
|
||||
@field_validator("style_strength")
|
||||
@classmethod
|
||||
def _v_ss(cls, v: str) -> str:
|
||||
if v not in VALID_STYLE_STRENGTHS:
|
||||
raise ValueError(f"style_strength must be one of {VALID_STYLE_STRENGTHS}")
|
||||
return v
|
||||
|
||||
|
||||
class AnalyzeImagesRequest(BaseModel):
|
||||
"""v1.5+ 阶段1：创建任务 + 图片/视频分析。"""
|
||||
|
||||
@@ -80,7 +121,6 @@ class AnalyzeImagesRequest(BaseModel):
|
||||
video_model: str = ""
|
||||
video_resolution: str = "720p"
|
||||
duration: int = Field(default=15, ge=5, le=30)
|
||||
language: str = "zh-CN"
|
||||
|
||||
|
||||
class GenerateCopyRequest(BaseModel):
|
||||
@@ -102,7 +142,6 @@ class GenerateCopyRequest(BaseModel):
|
||||
style_guide: dict | None = None
|
||||
voice_id: str = ""
|
||||
voice_source: str = ""
|
||||
language: str = "zh-CN"
|
||||
video_ratio: str = "9:16"
|
||||
video_model: str = ""
|
||||
video_resolution: str = "720p"
|
||||
@@ -128,13 +167,13 @@ class ConfirmCopyRequest(BaseModel):
|
||||
"""v1.5+ 阶段3：用户确认/编辑口播后开始渲染（TTS+单次Seedance）。"""
|
||||
|
||||
edited_copy: str = Field(default="", description="用户编辑后的口播文案；为空则用 AI 生成的 voiceover_script")
|
||||
video_model: str | None = Field(default=None, description="用户选定的视频生成模型（confirm时可选）")
|
||||
video_resolution: str | None = Field(default=None, description="用户选定的分辨率（confirm时可选）")
|
||||
video_ratio: str | None = Field(default=None, description="用户选定的比例（confirm时可选）")
|
||||
duration: int | None = Field(default=None, ge=5, le=30, description="用户选定的时长秒数（confirm时可选，5~30）")
|
||||
voice_id: str | None = Field(default=None, description="用户选定的音色ID（confirm时可选）")
|
||||
voice_source: str | None = Field(default=None, description="用户选定的音色来源（confirm时可选）")
|
||||
language: str | None = Field(default=None, description="用户选定的语言（confirm时可选）")
|
||||
|
||||
|
||||
class ConfirmIntentRequest(BaseModel):
|
||||
"""旧 confirm-intent（兼容）。"""
|
||||
|
||||
confirmed_copy: str = ""
|
||||
adjustments: str = ""
|
||||
|
||||
|
||||
class AnalyzeStyleRequest(BaseModel):
|
||||
@@ -179,9 +218,9 @@ class ViralVideoJobResponse(BaseModel):
|
||||
# 音色/视频参数
|
||||
voice_id: str = ""
|
||||
voice_source: str = ""
|
||||
language: str = "zh-CN"
|
||||
video_ratio: str = "9:16"
|
||||
video_model: str = ""
|
||||
intent_result: dict | None = None
|
||||
result_video_url: str = ""
|
||||
pre_trusted_images: list[str] | None = None
|
||||
video_resolution: str = "720p"
|
||||
|
||||
@@ -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()
|
||||
@@ -845,30 +656,6 @@ class LipsyncService:
|
||||
if job.status in (STATUS_COMPLETED, "failed"):
|
||||
return job
|
||||
|
||||
# Ditto 异步路径：mediakit_task_id 以 "ditto:" 开头，由 Celery 任务异步更新
|
||||
# 不做 MediaKit 轮询，只检查是否卡住太久（>10 分钟）则标失败
|
||||
if job.mediakit_task_id and job.mediakit_task_id.startswith("ditto:"):
|
||||
if job.status in ("processing", "submitted"):
|
||||
_now = datetime.now(UTC)
|
||||
_upd = job.updated_at
|
||||
if _upd is not None and _upd.tzinfo is None:
|
||||
_upd = _upd.replace(tzinfo=UTC)
|
||||
stale_minutes = 10
|
||||
if _upd and (_now - _upd).total_seconds() > stale_minutes * 60:
|
||||
logger.warning(
|
||||
"Ditto 异步任务超时（>%d 分钟），标记失败: job_id=%s",
|
||||
stale_minutes,
|
||||
job_id,
|
||||
)
|
||||
job.status = "failed"
|
||||
job.error_message = f"Ditto 处理超时（>{stale_minutes} 分钟）"
|
||||
job.error_code = "DittoTimeout"
|
||||
job.completed_at = _now
|
||||
job.updated_at = _now
|
||||
self.db.commit()
|
||||
self._refund_lip_sync(job)
|
||||
return job
|
||||
|
||||
# GPU 异步路径：mediakit_task_id 以 "gpu:" 开头，由 Celery 任务异步更新
|
||||
# 不做 MediaKit 轮询，只检查是否卡住太久（>30 分钟）则标失败
|
||||
if job.mediakit_task_id and job.mediakit_task_id.startswith("gpu:"):
|
||||
@@ -890,8 +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
|
||||
@@ -917,8 +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
|
||||
@@ -936,8 +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:
|
||||
@@ -1031,8 +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
|
||||
|
||||
@@ -1,552 +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. 确定驱动视频：用户上传的 video_url 优先，无则用 settings.ditto_default_video_url 兜底
|
||||
3. 视频时长对齐：若视频 < 音频+2s，用 ffmpeg 循环视频到足够长度后上传临时文件
|
||||
4. 调 DittoClient.generate_and_persist
|
||||
5. 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）
|
||||
6. 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 默认使用用户上传的视频作为驱动模板，default_video_url 仅作兜底
|
||||
- 用户视频短于音频时，ffmpeg stream_loop 循环到音频时长+2s余量
|
||||
- 不传 GFPGAN 超分，不需要 ffmpeg 音视频混流（Ditto 输出已带音视频）
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
import subprocess
|
||||
import tempfile
|
||||
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
|
||||
_VIDEO_LOOP_MARGIN_SECONDS = 2.0 # 循环视频时比音频多留 2 秒余量
|
||||
_MAX_VIDEO_PREPROCESS_SIZE = 200 * 1024 * 1024 # 用户视频最大 200MB
|
||||
|
||||
|
||||
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_media_duration(path_or_bytes, *, is_bytes: bool = False) -> float:
|
||||
"""用 ffprobe 探测视频/音频时长（秒）；失败返回 0。
|
||||
|
||||
Args:
|
||||
path_or_bytes: 文件路径(str) 或 字节数据(bytes)
|
||||
is_bytes: 传入的是 bytes 还是文件路径
|
||||
"""
|
||||
tmp_path = None
|
||||
try:
|
||||
if is_bytes:
|
||||
with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as tmp:
|
||||
tmp.write(path_or_bytes)
|
||||
tmp_path = tmp.name
|
||||
target = tmp_path
|
||||
else:
|
||||
target = path_or_bytes
|
||||
out = subprocess.check_output(
|
||||
[
|
||||
"ffprobe",
|
||||
"-v",
|
||||
"error",
|
||||
"-show_entries",
|
||||
"format=duration",
|
||||
"-of",
|
||||
"default=noprint_wrappers=1:nokey=1",
|
||||
target,
|
||||
],
|
||||
stderr=subprocess.STDOUT,
|
||||
timeout=15,
|
||||
)
|
||||
return float(out.decode().strip() or 0)
|
||||
except Exception as exc:
|
||||
logger.warning("[ditto_task] ffprobe 失败: %s", exc)
|
||||
return 0.0
|
||||
finally:
|
||||
if tmp_path and os.path.exists(tmp_path):
|
||||
try:
|
||||
os.unlink(tmp_path)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def _probe_video_duration(video_bytes: bytes) -> float:
|
||||
"""用 ffprobe 探测视频时长（秒）；失败返回 0。"""
|
||||
return _probe_media_duration(video_bytes, is_bytes=True)
|
||||
|
||||
|
||||
def _prepare_driver_video(
|
||||
*,
|
||||
user_video_url: str,
|
||||
default_video_url: str,
|
||||
audio_duration: float,
|
||||
job_id: str,
|
||||
user_id: str,
|
||||
) -> tuple[str, bool]:
|
||||
"""准备传给 Ditto 的驱动视频 URL。
|
||||
|
||||
逻辑：
|
||||
1. 优先使用用户上传的视频（user_video_url），无则用 default_video_url 兜底
|
||||
2. 下载视频，探测时长
|
||||
3. 若视频时长 >= 音频时长+2s 余量：直接用原 URL（签名后）
|
||||
4. 若视频时长 < 音频时长+2s：ffmpeg stream_loop 循环到目标时长，上传临时 OSS，返回临时 URL
|
||||
5. 任何异常：回退到 default_video_url（兜底）
|
||||
|
||||
Returns:
|
||||
(video_url_for_ditto, is_temporary) — is_temporary=True 表示 URL 是本次临时生成的
|
||||
"""
|
||||
from packages.shared.storage import get_shared_storage_service
|
||||
from packages.shared.url_security import safe_download_bytes
|
||||
|
||||
storage = get_shared_storage_service()
|
||||
|
||||
# 1. 选择源 URL（用户视频优先）
|
||||
source_url = user_video_url or default_video_url
|
||||
source_label = "user" if user_video_url else "default"
|
||||
if not source_url:
|
||||
raise RuntimeError("无可用驱动视频（用户视频和默认模板都为空）")
|
||||
|
||||
# 2. 下载视频
|
||||
try:
|
||||
# 用户视频需要签名才能下载
|
||||
signed_source = _sign_media_url(source_url) if user_video_url else source_url
|
||||
video_bytes = safe_download_bytes(
|
||||
signed_source,
|
||||
purpose="ditto-driver-video",
|
||||
max_size=_MAX_VIDEO_PREPROCESS_SIZE,
|
||||
allowed_mime_types=("video/mp4", "video/quicktime", "video/x-msvideo", "video/webm"),
|
||||
timeout=60,
|
||||
)
|
||||
except Exception as dl_exc:
|
||||
logger.warning(
|
||||
"[ditto_task] 下载驱动视频失败（%s），回退默认模板: job=%s err=%s",
|
||||
source_label,
|
||||
job_id,
|
||||
dl_exc,
|
||||
)
|
||||
if user_video_url and default_video_url:
|
||||
return default_video_url, False
|
||||
raise
|
||||
|
||||
# 3. 探测视频时长
|
||||
video_duration = _probe_media_duration(video_bytes, is_bytes=True)
|
||||
target_duration = audio_duration + _VIDEO_LOOP_MARGIN_SECONDS
|
||||
|
||||
# 4. 视频足够长：直接用签名后的原 URL
|
||||
if video_duration >= target_duration:
|
||||
logger.info(
|
||||
"[ditto_task] 驱动视频足够长（%.2fs >= %.2fs），直接使用: job=%s source=%s",
|
||||
video_duration,
|
||||
target_duration,
|
||||
job_id,
|
||||
source_label,
|
||||
)
|
||||
return _sign_media_url(source_url) if user_video_url else source_url, False
|
||||
|
||||
# 5. 视频不够长：ffmpeg 循环到目标时长
|
||||
logger.info(
|
||||
"[ditto_task] 驱动视频不够长（%.2fs < %.2fs），ffmpeg 循环延长: job=%s",
|
||||
video_duration,
|
||||
target_duration,
|
||||
job_id,
|
||||
)
|
||||
looped_path = None
|
||||
try:
|
||||
# 写临时文件
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as src_tmp:
|
||||
src_tmp.write(video_bytes)
|
||||
src_path = src_tmp.name
|
||||
looped_fd, looped_path = tempfile.mkstemp(suffix=".mp4")
|
||||
os.close(looped_fd)
|
||||
|
||||
# ffmpeg: stream_loop -1 循环输入，-t 截到目标时长
|
||||
# 使用 -c copy 快速复制流（不重编码），速度快无画质损失
|
||||
cmd = [
|
||||
"ffmpeg",
|
||||
"-y",
|
||||
"-stream_loop",
|
||||
"-1",
|
||||
"-i",
|
||||
src_path,
|
||||
"-t",
|
||||
str(target_duration),
|
||||
"-c",
|
||||
"copy",
|
||||
"-movflags",
|
||||
"+faststart",
|
||||
looped_path,
|
||||
]
|
||||
try:
|
||||
subprocess.run(cmd, check=True, capture_output=True, timeout=60)
|
||||
except subprocess.CalledProcessError:
|
||||
# copy 模式失败（编码不兼容），回退重编码
|
||||
logger.warning("[ditto_task] stream_loop copy 失败，回退重编码: job=%s", job_id)
|
||||
cmd = [
|
||||
"ffmpeg",
|
||||
"-y",
|
||||
"-stream_loop",
|
||||
"-1",
|
||||
"-i",
|
||||
src_path,
|
||||
"-t",
|
||||
str(target_duration),
|
||||
"-c:v",
|
||||
"libx264",
|
||||
"-preset",
|
||||
"veryfast",
|
||||
"-crf",
|
||||
"23",
|
||||
"-c:a",
|
||||
"aac",
|
||||
"-movflags",
|
||||
"+faststart",
|
||||
looped_path,
|
||||
]
|
||||
subprocess.run(cmd, check=True, capture_output=True, timeout=120)
|
||||
|
||||
# 上传临时 OSS
|
||||
looped_key = f"ditto-tmp/{user_id}/{job_id}_looped.mp4"
|
||||
with open(looped_path, "rb") as f:
|
||||
tmp_url = storage.upload_file(
|
||||
f,
|
||||
looped_key,
|
||||
content_type="video/mp4",
|
||||
)
|
||||
# 临时文件需要签名（private bucket）
|
||||
signed_tmp_url = _sign_media_url(tmp_url)
|
||||
logger.info(
|
||||
"[ditto_task] 循环视频已上传: job=%s key=%s dur=%.2fs",
|
||||
job_id,
|
||||
looped_key,
|
||||
target_duration,
|
||||
)
|
||||
return signed_tmp_url, True
|
||||
|
||||
except Exception as loop_exc:
|
||||
logger.warning(
|
||||
"[ditto_task] 视频循环处理失败，回退直接使用（Ditto 侧处理）: job=%s err=%s",
|
||||
job_id,
|
||||
loop_exc,
|
||||
)
|
||||
# 兜底：直接用原视频（交给 Ditto 侧处理时长不一致）
|
||||
return _sign_media_url(source_url) if user_video_url else source_url, False
|
||||
finally:
|
||||
for p in (locals().get("src_path"), looped_path):
|
||||
if p and os.path.exists(p):
|
||||
try:
|
||||
os.unlink(p)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
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)
|
||||
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_emotion_service import get_ditto_emotion_service
|
||||
from packages.application.ditto_service import DittoError, get_ditto_client
|
||||
from packages.config import get_api_settings
|
||||
from packages.domain.sentence_timings import probe_audio_duration
|
||||
from packages.shared.url_security import safe_download_bytes
|
||||
|
||||
db: Session = _get_db_session()
|
||||
job: Optional[LipsyncJobModel] = None
|
||||
prepared_video_url: str = ""
|
||||
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 ""
|
||||
user_video_url = job.video_url or ""
|
||||
if not audio_url:
|
||||
raise DittoError("job.audio_url 为空，无法调用 Ditto", code="InvalidParam")
|
||||
|
||||
settings = get_api_settings()
|
||||
default_video_url = settings.ditto_default_video_url or ""
|
||||
|
||||
logger.info(
|
||||
"[ditto_task] 开始 Ditto 生成: job_id=%s has_user_video=%s script_len=%d",
|
||||
job_id,
|
||||
bool(user_video_url),
|
||||
len(script),
|
||||
)
|
||||
|
||||
# ── 0. 探测音频时长（情绪分析 + 视频循环都需要）──
|
||||
audio_duration = 0.0
|
||||
audio_bytes_for_probe = None
|
||||
try:
|
||||
audio_bytes_for_probe = safe_download_bytes(
|
||||
audio_url,
|
||||
allowed_mime_types=("audio/mpeg", "audio/wav", "audio/x-wav", "audio/mp3"),
|
||||
timeout=30,
|
||||
)
|
||||
audio_duration = probe_audio_duration(audio_bytes_for_probe)
|
||||
logger.info("[ditto_task] 音频时长: %.2fs", audio_duration)
|
||||
except Exception as audio_exc:
|
||||
logger.warning("[ditto_task] 音频时长探测失败: %s", audio_exc)
|
||||
audio_duration = 0.0
|
||||
|
||||
# ── 1. LLM 情绪分析（生成 emo_timeline）──
|
||||
emo_timeline = ""
|
||||
try:
|
||||
emo_svc = get_ditto_emotion_service()
|
||||
if emo_svc.enabled and script and audio_duration > 0:
|
||||
sentence_timings = getattr(job, "sentence_timings", None)
|
||||
emo_timeline = emo_svc.build_timeline(
|
||||
text=script,
|
||||
audio_duration=audio_duration,
|
||||
sentence_timings=sentence_timings,
|
||||
)
|
||||
if emo_timeline:
|
||||
logger.info("[ditto_task] 情绪时间线已生成: segments≈%d", len(emo_timeline) // 50)
|
||||
except Exception as emo_exc:
|
||||
logger.warning("[ditto_task] 情绪分析异常（降级中性）: %s", emo_exc)
|
||||
emo_timeline = ""
|
||||
|
||||
# ── 2. 准备驱动视频（用户视频优先，必要时循环延长）──
|
||||
prepared_video_url, _is_tmp = _prepare_driver_video(
|
||||
user_video_url=user_video_url,
|
||||
default_video_url=default_video_url,
|
||||
audio_duration=audio_duration if audio_duration > 0 else 10.0, # 探测失败时按10s估
|
||||
job_id=job_id,
|
||||
user_id=user_id,
|
||||
)
|
||||
|
||||
# ── 3. 调用 Ditto ──
|
||||
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=prepared_video_url,
|
||||
emo_timeline=emo_timeline,
|
||||
)
|
||||
|
||||
# Ditto 返回的 MP4 自带音频，签名 OSS URL（7天有效）后标记完成
|
||||
job.output_video_url = _sign_media_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
|
||||
if audio_bytes_for_probe is not None:
|
||||
try:
|
||||
duration = probe_audio_duration(audio_bytes_for_probe)
|
||||
except Exception:
|
||||
duration = 0.0
|
||||
if duration <= 0 and audio_duration > 0:
|
||||
duration = audio_duration
|
||||
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
|
||||
_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)
|
||||
|
||||
|
||||
@@ -11,34 +11,12 @@ const apiOrigin = apiBase.endsWith("/api/v1") ? apiBase.slice(0, -"/api/v1".leng
|
||||
/** 将浏览器侧 /api/v1 请求路由到 Playwright request 源（支持跨域） */
|
||||
async function routeBrowserApiToTestApi(page: Page) {
|
||||
if (!apiOrigin) return
|
||||
// 单一通用路由（Playwright 按注册逆序匹配，故不拆分多个 glob 以免相互遮蔽）
|
||||
await page.route("**/api/v1/**", async (route) => {
|
||||
try {
|
||||
const sourceUrl = new URL(route.request().url())
|
||||
const response = await route.fetch({
|
||||
url: `${apiOrigin}${sourceUrl.pathname}${sourceUrl.search}`,
|
||||
})
|
||||
// /points/balance：Staging 后端积分总开关关闭（credits_enabled=false，免费放行）。
|
||||
// 旧 bundle 部署的接口暂未返回该字段、且预校验只看余额，这里同时补开关与余额，
|
||||
// 以验证“免费期不拦截生成”；新后端+新前端部署后读 credits_enabled=false 直接放行，
|
||||
// 余额被忽略，此补丁随之成为 no-op。
|
||||
if (sourceUrl.pathname.endsWith("/points/balance")) {
|
||||
const body = await response.json().catch(() => ({}))
|
||||
await route.fulfill({
|
||||
response,
|
||||
json: { ...body, credits_enabled: false, balance: 999999 },
|
||||
})
|
||||
return
|
||||
}
|
||||
await route.fulfill({ response })
|
||||
} catch {
|
||||
// 测试收尾时页面/上下文可能已关闭，忽略仍在途的请求，避免误判为失败
|
||||
try {
|
||||
await route.abort()
|
||||
} catch {
|
||||
/* noop */
|
||||
}
|
||||
}
|
||||
const sourceUrl = new URL(route.request().url())
|
||||
const response = await route.fetch({
|
||||
url: `${apiOrigin}${sourceUrl.pathname}${sourceUrl.search}`,
|
||||
})
|
||||
await route.fulfill({ response })
|
||||
})
|
||||
}
|
||||
|
||||
@@ -67,13 +45,7 @@ async function loginWithRetry(
|
||||
async function setupFreshUser(
|
||||
request: APIRequestContext,
|
||||
label: string,
|
||||
): Promise<{
|
||||
token: string
|
||||
user: Record<string, unknown>
|
||||
libraryId: string
|
||||
assetId: string
|
||||
suffix: string
|
||||
}> {
|
||||
): Promise<{ token: string; libraryId: string; assetId: string; suffix: string }> {
|
||||
const suffix = Math.random().toString(36).slice(2, 8)
|
||||
const email = `e2e-${label}-${suffix}@example.com`
|
||||
await request.post(`${apiBase}/auth/register`, {
|
||||
@@ -81,19 +53,7 @@ async function setupFreshUser(
|
||||
})
|
||||
const token = await loginWithRetry(request, email, PASSWORD)
|
||||
const auth = { Authorization: `Bearer ${token}` }
|
||||
const me = await request.get(`${apiBase}/auth/me`, { headers: auth })
|
||||
expect(me.ok(), `fetch profile: ${await me.text()}`).toBeTruthy()
|
||||
const user = (await me.json()) as Record<string, unknown>
|
||||
|
||||
// 预置一条文案：标题下拉候选来自文案库，新用户为空会导致无法选中标题
|
||||
await request.post(`${apiBase}/scripts`, {
|
||||
headers: auth,
|
||||
data: {
|
||||
title: `测试标题 ${suffix}`,
|
||||
content: `测试文案内容 ${suffix}`,
|
||||
tags: ["e2e"],
|
||||
},
|
||||
})
|
||||
const proj = await request.post(`${apiBase}/projects`, {
|
||||
headers: auth,
|
||||
data: { name: `Smoke ${label} ${suffix}` },
|
||||
@@ -133,7 +93,7 @@ async function setupFreshUser(
|
||||
{ timeout: 90_000, intervals: [3000, 3000, 5000] },
|
||||
)
|
||||
.toBe("ready")
|
||||
return { token, user, libraryId, assetId, suffix }
|
||||
return { token, libraryId, assetId, suffix }
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -152,7 +112,7 @@ test.describe("Core Smart-Edit Flow (#1970)", () => {
|
||||
test("random mode: 5-step wizard creates generation task", async ({ page, request }) => {
|
||||
test.setTimeout(600_000)
|
||||
await page.setViewportSize({ width: 1440, height: 1000 })
|
||||
const { token, user, suffix } = await setupFreshUser(request, "random")
|
||||
const { token, suffix } = await setupFreshUser(request, "random")
|
||||
const authHeader = { Authorization: `Bearer ${token}` }
|
||||
|
||||
// 确保默认模板存在（智能剪辑页依赖模板）
|
||||
@@ -166,24 +126,13 @@ test.describe("Core Smart-Edit Flow (#1970)", () => {
|
||||
expect(templates.length).toBeGreaterThan(0)
|
||||
|
||||
// 注入登录态 + 路由 API
|
||||
await page.addInitScript(
|
||||
({ token, user }) => {
|
||||
window.localStorage.setItem("access_token", token)
|
||||
window.localStorage.setItem(
|
||||
"auth-storage",
|
||||
JSON.stringify({
|
||||
state: {
|
||||
user,
|
||||
isAuthenticated: true,
|
||||
accessToken: token,
|
||||
refreshToken: null,
|
||||
},
|
||||
version: 0,
|
||||
}),
|
||||
)
|
||||
},
|
||||
{ token, user },
|
||||
)
|
||||
await page.addInitScript((t: string) => {
|
||||
window.localStorage.setItem("access_token", t)
|
||||
window.localStorage.setItem(
|
||||
"auth-storage",
|
||||
JSON.stringify({ state: { token: t, user: null } }),
|
||||
)
|
||||
}, token)
|
||||
await routeBrowserApiToTestApi(page)
|
||||
|
||||
// ── 提前 mock 配音列表（VoiceSelectModal 查询 /assets?kind=voice） ──
|
||||
@@ -229,28 +178,16 @@ test.describe("Core Smart-Edit Flow (#1970)", () => {
|
||||
await expect(page.getByText("🎙️ 选择配音")).not.toBeVisible()
|
||||
|
||||
// ── Step 2：选择素材 ──────────────────────────────────────────
|
||||
await expect(page.getByText(/选择素材/).first()).toBeVisible({ timeout: 10000 })
|
||||
// 卡片中心是播放按钮（stopPropagation 仅播放不选中），点右上角空白处完成选中
|
||||
await page
|
||||
.getByTestId("material-card")
|
||||
.first()
|
||||
.click({ position: { x: 70, y: 12 } })
|
||||
await expect(page.getByText(/已选 1 个素材|已选[^0]*[1-9]/)).toBeVisible({ timeout: 5000 })
|
||||
await expect(page.getByText("选择素材", { exact: true })).toBeVisible({ timeout: 10000 })
|
||||
await page.getByTestId("material-card").first().click()
|
||||
await page.getByRole("button", { name: /下一步/ }).click()
|
||||
|
||||
// ── Step 3：填写标题 ──────────────────────────────────────────
|
||||
// (#2048: PreviewCountModal 已移除，生成数量在 Step1 内设置)
|
||||
await expect(page.getByText(/选择标题/).first()).toBeVisible({ timeout: 10000 })
|
||||
// 标题为 antd AutoComplete（combobox），真实 input 带 placeholder
|
||||
// antd AutoComplete combobox：真实可输入元素是 .ant-select-selection-search-input，
|
||||
// 灰色提示语是单独的 placeholder span（input 自身无 placeholder 属性）
|
||||
// 标题候选来自文案库（setupFreshUser 已预置一条文案）。
|
||||
// combobox 的自由输入会在失焦时被 rc-select 重置，必须从下拉选中才提交，
|
||||
// 因此聚焦输入框 → 点击候选选项
|
||||
const seededTitle = `测试标题 ${suffix}`
|
||||
const titleBox = page.locator(".ant-select-selection-search-input:visible").first()
|
||||
await titleBox.click()
|
||||
await page.locator(".ant-select-item-option", { hasText: seededTitle }).first().click()
|
||||
await expect(page.getByText("选择标题", { exact: true })).toBeVisible({ timeout: 10000 })
|
||||
const titleInput = page.getByPlaceholder("输入或从标题库选择")
|
||||
await expect(titleInput).toBeVisible({ timeout: 5000 })
|
||||
await titleInput.fill(`测试随机剪辑 ${suffix}`)
|
||||
await page.getByRole("button", { name: /下一步/ }).click()
|
||||
|
||||
// ── Step 4：确认生成 ──────────────────────────────────────────
|
||||
@@ -267,11 +204,9 @@ test.describe("Core Smart-Edit Flow (#1970)", () => {
|
||||
await confirmBtn.click()
|
||||
const taskResp = await createTask
|
||||
expect(taskResp.ok(), `Create task: ${await taskResp.text()}`).toBeTruthy()
|
||||
const taskBody = await taskResp.json()
|
||||
const taskId = taskBody.items?.[0]?.id ?? taskBody.id ?? taskBody.task_id
|
||||
expect(taskId, "created task should return an id").toBeTruthy()
|
||||
const taskId = (await taskResp.json()).id ?? (await taskResp.json()).task_id
|
||||
console.log("[random] Generation task created:", taskId)
|
||||
await expect(page.getByText(/正在生成|提交/).first()).toBeVisible({ timeout: 15000 })
|
||||
await expect(page.getByText(/正在生成|提交/)).toBeVisible({ timeout: 15000 })
|
||||
console.log("[random] Wizard flow completed ✓")
|
||||
})
|
||||
|
||||
@@ -281,28 +216,113 @@ test.describe("Core Smart-Edit Flow (#1970)", () => {
|
||||
}) => {
|
||||
test.setTimeout(600_000)
|
||||
await page.setViewportSize({ width: 1440, height: 1000 })
|
||||
const { token, user, suffix } = await setupFreshUser(request, "narrative")
|
||||
const { token, suffix } = await setupFreshUser(request, "narrative")
|
||||
|
||||
await page.addInitScript(
|
||||
({ token, user }) => {
|
||||
window.localStorage.setItem("access_token", token)
|
||||
window.localStorage.setItem(
|
||||
"auth-storage",
|
||||
JSON.stringify({
|
||||
state: {
|
||||
user,
|
||||
isAuthenticated: true,
|
||||
accessToken: token,
|
||||
refreshToken: null,
|
||||
},
|
||||
version: 0,
|
||||
}),
|
||||
)
|
||||
},
|
||||
{ token, user },
|
||||
)
|
||||
await page.addInitScript((t: string) => {
|
||||
window.localStorage.setItem("access_token", t)
|
||||
window.localStorage.setItem(
|
||||
"auth-storage",
|
||||
JSON.stringify({ state: { token: t, user: null } }),
|
||||
)
|
||||
}, token)
|
||||
await routeBrowserApiToTestApi(page)
|
||||
|
||||
// ── Mock 文案列表、音色、TTS 合成（避免真实合成） ──────────────
|
||||
const mockScriptId = `script-mock-${suffix}`
|
||||
const mockVoiceId = `preset-voice-${suffix}`
|
||||
const mockJobId = `tts-job-${suffix}`
|
||||
|
||||
// 文案列表（ScriptSelectModal 查询 /scripts）
|
||||
await page.route("**/api/v1/scripts**", (route) => {
|
||||
const url = new URL(route.request().url())
|
||||
if (url.pathname.includes("/extract-from-douyin")) {
|
||||
route.continue()
|
||||
return
|
||||
}
|
||||
route.fulfill({
|
||||
status: 200,
|
||||
contentType: "application/json",
|
||||
body: JSON.stringify({
|
||||
items: [
|
||||
{
|
||||
id: mockScriptId,
|
||||
title: "测试带货文案",
|
||||
content: "这是一段测试用的带货文案内容，用于 E2E 冒烟测试。",
|
||||
tags: ["带货"],
|
||||
title_category: "daihuo",
|
||||
created_at: new Date().toISOString(),
|
||||
updated_at: new Date().toISOString(),
|
||||
},
|
||||
],
|
||||
total: 1,
|
||||
page: 1,
|
||||
page_size: 200,
|
||||
}),
|
||||
})
|
||||
})
|
||||
|
||||
// 预设音色（TtsVoiceModal 查询 GET /voices/presets）
|
||||
await page.route("**/api/v1/voices/presets**", (route) =>
|
||||
route.fulfill({
|
||||
status: 200,
|
||||
contentType: "application/json",
|
||||
body: JSON.stringify({
|
||||
items: [
|
||||
{
|
||||
voice_id: mockVoiceId,
|
||||
name: "晓晓（女声）",
|
||||
description: "温柔女声",
|
||||
gender: "female",
|
||||
language: "zh-CN",
|
||||
preview_url: null,
|
||||
tags: ["温柔"],
|
||||
},
|
||||
],
|
||||
total: 1,
|
||||
}),
|
||||
}),
|
||||
)
|
||||
|
||||
// 克隆音色：空列表
|
||||
await page.route(
|
||||
(url) => url.pathname.endsWith("/voice-clones"),
|
||||
(route) =>
|
||||
route.fulfill({
|
||||
status: 200,
|
||||
contentType: "application/json",
|
||||
body: JSON.stringify({ items: [] }),
|
||||
}),
|
||||
)
|
||||
|
||||
// TTS 合成：直接返回 completed 任务
|
||||
await page.route("**/api/v1/tts/synthesize", (route) =>
|
||||
route.fulfill({
|
||||
status: 200,
|
||||
contentType: "application/json",
|
||||
body: JSON.stringify({ job_id: mockJobId, status: "queued" }),
|
||||
}),
|
||||
)
|
||||
await page.route(`**/api/v1/tts/jobs/${mockJobId}/status`, (route) =>
|
||||
route.fulfill({
|
||||
status: 200,
|
||||
contentType: "application/json",
|
||||
body: JSON.stringify({
|
||||
job_id: mockJobId,
|
||||
status: "completed",
|
||||
progress: 100,
|
||||
audio_url: "data:audio/mpeg;base64,",
|
||||
duration: 5,
|
||||
}),
|
||||
}),
|
||||
)
|
||||
await page.route(`**/api/v1/tts/jobs/${mockJobId}/save-to-library`, (route) =>
|
||||
route.fulfill({
|
||||
status: 200,
|
||||
contentType: "application/json",
|
||||
body: JSON.stringify({ id: `tts-asset-${suffix}`, name: "AI合成配音" }),
|
||||
}),
|
||||
)
|
||||
|
||||
await page.goto("/app/generate")
|
||||
// ── 页面标题 ─────────────────────────────────────────────────
|
||||
await expect(page.getByText("智能剪辑").first()).toBeVisible({ timeout: 30000 })
|
||||
@@ -314,38 +334,28 @@ test.describe("Core Smart-Edit Flow (#1970)", () => {
|
||||
|
||||
// ── 文案选择弹窗：选第一条 → 确认 ─────────────────────────────
|
||||
await expect(page.getByText("📝 选择文案")).toBeVisible({ timeout: 5000 })
|
||||
await page.getByText(`测试标题 ${suffix}`).first().click()
|
||||
await page.getByText("测试带货文案").first().click()
|
||||
await page.getByRole("button", { name: "确认选择" }).click()
|
||||
await expect(page.getByText("📝 选择文案")).not.toBeVisible()
|
||||
|
||||
// ── TTS 音色弹窗：选系统音色 → 合成 ─────────────────────────
|
||||
await expect(page.getByText("🎙️ 合成配音")).toBeVisible({ timeout: 5000 })
|
||||
await page.getByText("龙小夏").first().click()
|
||||
await page.getByText("晓晓（女声）").first().click()
|
||||
await page.getByRole("button", { name: "🎧 合成配音" }).click()
|
||||
// 真实阿里云 CosyVoice 合成耗时偶有波动，放宽到 90s
|
||||
await expect(page.getByText("🎙️ 合成配音")).not.toBeVisible({ timeout: 90000 })
|
||||
await expect(page.getByText("🎙️ 合成配音")).not.toBeVisible({ timeout: 30000 })
|
||||
|
||||
// ── Step 2：AI 匹配提示卡可见 + 选素材 ────────────────────────
|
||||
await expect(page.getByText(/选择素材/).first()).toBeVisible({ timeout: 10000 })
|
||||
await expect(page.getByText(/AI智能匹配/).first()).toBeVisible()
|
||||
// 卡片中心是播放按钮（stopPropagation 仅播放不选中），点右上角空白处完成选中
|
||||
await page
|
||||
.getByTestId("material-card")
|
||||
.first()
|
||||
.click({ position: { x: 70, y: 12 } })
|
||||
await expect(page.getByText(/已选 1 个素材|已选[^0]*[1-9]/)).toBeVisible({ timeout: 5000 })
|
||||
await expect(page.getByText("选择素材", { exact: true })).toBeVisible({ timeout: 10000 })
|
||||
await expect(page.getByText(/AI智能匹配/)).toBeVisible()
|
||||
await page.getByTestId("material-card").first().click()
|
||||
await page.getByRole("button", { name: /下一步/ }).click()
|
||||
|
||||
// ── Step 3：填写标题（handleScriptModalConfirm 已预填 script.title，但我们再覆盖一次） ─
|
||||
// (#2048: PreviewCountModal 已移除)
|
||||
await expect(page.getByText(/选择标题/).first()).toBeVisible({ timeout: 10000 })
|
||||
// 选中文案后标题框已预填该文案标题，下拉按当前输入过滤，直接选中该选项确认
|
||||
const titleBox2 = page.locator(".ant-select-selection-search-input:visible").first()
|
||||
await titleBox2.click()
|
||||
await page
|
||||
.locator(".ant-select-item-option", { hasText: `测试标题 ${suffix}` })
|
||||
.first()
|
||||
.click()
|
||||
await expect(page.getByText("选择标题", { exact: true })).toBeVisible({ timeout: 10000 })
|
||||
const titleInput2 = page.getByPlaceholder("输入或从标题库选择")
|
||||
await expect(titleInput2).toBeVisible({ timeout: 5000 })
|
||||
await titleInput2.fill(`测试叙事剪辑 ${suffix}`)
|
||||
await page.getByRole("button", { name: /下一步/ }).click()
|
||||
|
||||
// ── Step 4：确认生成 ──────────────────────────────────────────
|
||||
@@ -361,11 +371,8 @@ test.describe("Core Smart-Edit Flow (#1970)", () => {
|
||||
await confirmBtn2.click()
|
||||
const taskResp2 = await createTask2
|
||||
expect(taskResp2.ok(), `Create task: ${await taskResp2.text()}`).toBeTruthy()
|
||||
const taskBody2 = await taskResp2.json()
|
||||
const taskId2 = taskBody2.items?.[0]?.id ?? taskBody2.id ?? taskBody2.task_id
|
||||
expect(taskId2, "created task should return an id").toBeTruthy()
|
||||
console.log("[narrative] Generation task created:", taskId2)
|
||||
await expect(page.getByText(/正在生成|提交/).first()).toBeVisible({ timeout: 15000 })
|
||||
console.log("[narrative] Generation task created:", (await taskResp2.json()).id)
|
||||
await expect(page.getByText(/正在生成|提交/)).toBeVisible({ timeout: 15000 })
|
||||
console.log("[narrative] Wizard flow completed ✓")
|
||||
})
|
||||
})
|
||||
|
||||
@@ -11,20 +11,11 @@ const apiOrigin = apiBase.endsWith("/api/v1") ? apiBase.slice(0, -"/api/v1".leng
|
||||
const routeBrowserApiToTestApi = async (page: import("@playwright/test").Page) => {
|
||||
if (!apiOrigin) return
|
||||
await page.route("**/api/v1/**", async (route) => {
|
||||
try {
|
||||
const sourceUrl = new URL(route.request().url())
|
||||
const response = await route.fetch({
|
||||
url: `${apiOrigin}${sourceUrl.pathname}${sourceUrl.search}`,
|
||||
})
|
||||
await route.fulfill({ response })
|
||||
} catch {
|
||||
// 收尾时页面可能已关闭，忽略在途请求避免误判
|
||||
try {
|
||||
await route.abort()
|
||||
} catch {
|
||||
/* noop */
|
||||
}
|
||||
}
|
||||
const sourceUrl = new URL(route.request().url())
|
||||
const response = await route.fetch({
|
||||
url: `${apiOrigin}${sourceUrl.pathname}${sourceUrl.search}`,
|
||||
})
|
||||
await route.fulfill({ response })
|
||||
})
|
||||
}
|
||||
|
||||
@@ -54,7 +45,8 @@ type LibraryResponse = { id: string }
|
||||
test.describe("Core media upload flow", () => {
|
||||
test.describe.configure({ timeout: 180_000 })
|
||||
test("uploads a video asset and shows it in the asset library", async ({ page, request }) => {
|
||||
// 串行执行时登录可能触发 429，两次退避约 130s，沿用 describe 的 180s 超时
|
||||
test.setTimeout(120_000)
|
||||
|
||||
await routeBrowserApiToTestApi(page)
|
||||
const suffix = Date.now().toString(36)
|
||||
const email = `e2e-mov-${suffix}@example.com`
|
||||
|
||||
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",
|
||||
|
||||
@@ -1,41 +0,0 @@
|
||||
/**
|
||||
* 运行时开关安全读取
|
||||
*
|
||||
* 背景：本项目使用 Vite 构建，浏览器运行时不存在 Node 的全局 `process`。
|
||||
* 直接写 `process.env.XXX` 会在模块加载阶段抛出 `ReferenceError: process is not defined`，
|
||||
* 导致整个页面白屏崩溃。
|
||||
*
|
||||
* 统一通过本模块读取这类仅在构建/调试期注入的布尔开关：
|
||||
* - 优先读取 Vite 的 `import.meta.env.VITE_XXX`
|
||||
* - 兼容历史上未加 VITE_ 前缀、经由 Node 环境（单测 / 旧构建脚本）注入的 `process.env.XXX`
|
||||
* - 任何情况下访问失败都安全返回 false（默认走真实后端 API，不启用 mock）
|
||||
*/
|
||||
|
||||
/** 从可能不存在的 Node 全局 process 上安全读取环境变量 */
|
||||
function readNodeEnv(name: string): string | undefined {
|
||||
try {
|
||||
const proc = (globalThis as { process?: { env?: Record<string, string | undefined> } }).process
|
||||
return proc?.env?.[name]
|
||||
} catch {
|
||||
return undefined
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 读取运行时布尔开关。
|
||||
*
|
||||
* @param name 开关名（不含 VITE_ 前缀的历史名称，如 POINTS_API_MOCK）
|
||||
* @returns 开关是否显式置为 "true"；未设置或读取失败时为 false
|
||||
*/
|
||||
export function readRuntimeFlag(name: string): boolean {
|
||||
// Vite 注入的环境变量（需 VITE_ 前缀才会暴露到浏览器）
|
||||
const viteKey = `VITE_${name}`
|
||||
const viteEnv = (import.meta as unknown as { env?: Record<string, string | undefined> }).env
|
||||
const viteVal = viteEnv?.[viteKey] ?? viteEnv?.[name]
|
||||
|
||||
// 兼容 Node 环境下无前缀的历史变量名
|
||||
const nodeVal = readNodeEnv(name)
|
||||
|
||||
const raw = viteVal ?? nodeVal
|
||||
return raw === "true"
|
||||
}
|
||||
@@ -8,7 +8,6 @@
|
||||
* 会员/订阅 API 在 @/api/subscription 中定义，避免重复封装。
|
||||
*/
|
||||
import apiClient from "../client"
|
||||
import { readRuntimeFlag } from "../env-flags"
|
||||
import type {
|
||||
PointsBalance,
|
||||
PointsRulesResponse,
|
||||
@@ -202,7 +201,7 @@ const MOCK_MEMBERSHIP: MembershipResponse = {
|
||||
|
||||
/** 获取积分余额 */
|
||||
export async function getPointsBalance(): Promise<PointsBalance> {
|
||||
if (readRuntimeFlag("POINTS_API_MOCK")) {
|
||||
if (process.env.POINTS_API_MOCK === "true") {
|
||||
await new Promise((resolve) => setTimeout(resolve, MOCK_DELAY))
|
||||
return { ...MOCK_BALANCE }
|
||||
}
|
||||
@@ -212,7 +211,7 @@ export async function getPointsBalance(): Promise<PointsBalance> {
|
||||
|
||||
/** 获取积分消耗规则 */
|
||||
export async function getPointsRules(): Promise<PointsRulesResponse> {
|
||||
if (readRuntimeFlag("POINTS_API_MOCK")) {
|
||||
if (process.env.POINTS_API_MOCK === "true") {
|
||||
await new Promise((resolve) => setTimeout(resolve, MOCK_DELAY))
|
||||
return { rules: [...MOCK_RULES.rules], free_user_multiplier: MOCK_RULES.free_user_multiplier }
|
||||
}
|
||||
@@ -222,7 +221,7 @@ export async function getPointsRules(): Promise<PointsRulesResponse> {
|
||||
|
||||
/** 获取充值包列表 */
|
||||
export async function getPointsPackages(): Promise<PointsPackagesResponse> {
|
||||
if (readRuntimeFlag("POINTS_API_MOCK")) {
|
||||
if (process.env.POINTS_API_MOCK === "true") {
|
||||
await new Promise((resolve) => setTimeout(resolve, MOCK_DELAY))
|
||||
return { packages: MOCK_PACKAGES.packages.map((p) => ({ ...p })), user_discount: null }
|
||||
}
|
||||
@@ -237,7 +236,7 @@ export async function getPointsTransactions(
|
||||
page = 1,
|
||||
pageSize = 20,
|
||||
): Promise<PointsTransactionsResponse> {
|
||||
if (readRuntimeFlag("POINTS_API_MOCK")) {
|
||||
if (process.env.POINTS_API_MOCK === "true") {
|
||||
await new Promise((resolve) => setTimeout(resolve, MOCK_DELAY))
|
||||
const start = (page - 1) * pageSize
|
||||
const items = MOCK_TRANSACTIONS.slice(start, start + pageSize)
|
||||
@@ -262,7 +261,7 @@ export async function getPointsTransactions(
|
||||
export async function createPointsOrder(
|
||||
data: CreateRechargeOrderRequest,
|
||||
): Promise<CreateRechargeOrderResponse> {
|
||||
if (readRuntimeFlag("POINTS_API_MOCK")) {
|
||||
if (process.env.POINTS_API_MOCK === "true") {
|
||||
await new Promise((resolve) => setTimeout(resolve, MOCK_DELAY * 2))
|
||||
const pkg = MOCK_PACKAGES.packages.find((p) => p.code === data.package_id)
|
||||
if (!pkg) throw new Error("充值包不存在")
|
||||
@@ -286,7 +285,7 @@ export async function createPointsOrder(
|
||||
* 积分预检查（消耗前调用）
|
||||
*/
|
||||
export async function checkPoints(data: PointsCheckRequest): Promise<PointsCheckResponse> {
|
||||
if (readRuntimeFlag("POINTS_API_MOCK")) {
|
||||
if (process.env.POINTS_API_MOCK === "true") {
|
||||
await new Promise((resolve) => setTimeout(resolve, MOCK_DELAY))
|
||||
const rule = MOCK_RULES.rules.find((r) => r.scene_key === data.scene_key)
|
||||
if (!rule) {
|
||||
@@ -327,7 +326,7 @@ export async function checkPoints(data: PointsCheckRequest): Promise<PointsCheck
|
||||
|
||||
/** 获取每日免费额度使用情况 */
|
||||
export async function getDailyUsage(): Promise<DailyUsage> {
|
||||
if (readRuntimeFlag("POINTS_API_MOCK")) {
|
||||
if (process.env.POINTS_API_MOCK === "true") {
|
||||
await new Promise((resolve) => setTimeout(resolve, MOCK_DELAY))
|
||||
return { ...MOCK_DAILY_USAGE }
|
||||
}
|
||||
@@ -337,7 +336,7 @@ export async function getDailyUsage(): Promise<DailyUsage> {
|
||||
|
||||
/** 获取会员聚合信息（创作页可用来判断 max_resolution） */
|
||||
export async function getMembership(): Promise<MembershipResponse> {
|
||||
if (readRuntimeFlag("POINTS_API_MOCK")) {
|
||||
if (process.env.POINTS_API_MOCK === "true") {
|
||||
await new Promise((resolve) => setTimeout(resolve, MOCK_DELAY))
|
||||
return { ...MOCK_MEMBERSHIP }
|
||||
}
|
||||
|
||||
@@ -50,8 +50,6 @@ export interface PointsBalance {
|
||||
member_type: "monthly" | "quarterly" | "yearly" | null
|
||||
/** 会员到期时间 */
|
||||
member_expires_at: ISODate | null
|
||||
/** 后端积分系统是否启用（false=免费放行，不做余额预校验） */
|
||||
credits_enabled?: boolean
|
||||
}
|
||||
|
||||
/* ================================================================
|
||||
|
||||
@@ -3,7 +3,6 @@
|
||||
* CRUD + 搜索/分类/分页；后端未就绪时使用 mock 数据（SCRIPTS_API_MOCK=true）
|
||||
*/
|
||||
import apiClient from "../client"
|
||||
import { readRuntimeFlag } from "../env-flags"
|
||||
import type {
|
||||
ScriptItem,
|
||||
ScriptListParams,
|
||||
@@ -17,7 +16,7 @@ import type {
|
||||
* #1894：文案库接口已上线，默认 false 走真实 API；
|
||||
* 通过 SCRIPTS_API_MOCK=true 环境变量可本地开启 mock 调试（行为同 POINTS_API_MOCK）。
|
||||
*/
|
||||
export const SCRIPTS_API_MOCK = readRuntimeFlag("SCRIPTS_API_MOCK")
|
||||
export const SCRIPTS_API_MOCK = (process.env.SCRIPTS_API_MOCK as string | undefined) === "true"
|
||||
|
||||
// ==================== Mock 数据 ====================
|
||||
|
||||
|
||||
@@ -6,7 +6,6 @@
|
||||
* 所有请求走 apiClient（已配置 baseURL=/api/v1 和 token 拦截器）。
|
||||
*/
|
||||
import apiClient from "../client"
|
||||
import { readRuntimeFlag } from "../env-flags"
|
||||
import type {
|
||||
SubscriptionInfo,
|
||||
SubscriptionPlan,
|
||||
@@ -73,7 +72,7 @@ const MOCK_PLANS: SubscriptionPlan[] = [
|
||||
|
||||
const MOCK_BILLING: BillingRecord[] = []
|
||||
|
||||
const isMock = () => readRuntimeFlag("POINTS_API_MOCK")
|
||||
const isMock = () => (process.env.POINTS_API_MOCK as string | undefined) === "true"
|
||||
|
||||
/** 获取当前订阅 */
|
||||
export const getCurrentSubscription = async (): Promise<SubscriptionInfo> => {
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
import apiClient from "@/api/client"
|
||||
import type {
|
||||
GenerateViralVideoRequest,
|
||||
HistoryResponse,
|
||||
StyleTemplate,
|
||||
ViralVideoJob,
|
||||
@@ -12,11 +13,26 @@ import type {
|
||||
ViralVideoModelsResponse,
|
||||
} from "./types"
|
||||
|
||||
/** 创建爆款视频任务 */
|
||||
export function generateViralVideo(payload: GenerateViralVideoRequest) {
|
||||
return apiClient.post<ViralVideoJob>("/viral-video/generate", payload).then((r) => r.data)
|
||||
}
|
||||
|
||||
/** 查询单个任务 */
|
||||
export function getViralVideoJob(id: string) {
|
||||
return apiClient.get<ViralVideoJob>(`/viral-video/${id}`).then((r) => r.data)
|
||||
}
|
||||
|
||||
/** 用户确认/修改 AI 理解的意图后继续 */
|
||||
export function confirmViralVideoIntent(
|
||||
id: string,
|
||||
payload: { confirmed_copy?: string; edits?: Record<string, unknown> },
|
||||
) {
|
||||
return apiClient
|
||||
.post<ViralVideoJob>(`/viral-video/${id}/confirm-intent`, payload)
|
||||
.then((r) => r.data)
|
||||
}
|
||||
|
||||
/** 重试失败任务 */
|
||||
export function retryViralVideo(id: string) {
|
||||
return apiClient.post<ViralVideoJob>(`/viral-video/${id}/retry`).then((r) => r.data)
|
||||
|
||||
@@ -21,15 +21,23 @@ export const VALID_RATIOS = ["9:16", "16:9", "1:1"] as const
|
||||
export type VideoRatio = (typeof VALID_RATIOS)[number]
|
||||
|
||||
export type ViralVideoStatus =
|
||||
"pending" | "running" | "image_analyzed" | "copy_generated" | "completed" | "failed" | "cancelled"
|
||||
| "pending"
|
||||
| "running"
|
||||
| "wait_user_confirm"
|
||||
| "image_analyzed"
|
||||
| "copy_generated"
|
||||
| "completed"
|
||||
| "failed"
|
||||
| "cancelled"
|
||||
|
||||
/**
|
||||
* v1.6 后端流水线阶段。单次 Seedance 出片版：
|
||||
* image_analysis → video_analysis(可选) → script_generation(含意图理解) → review → tts → rendering → uploading
|
||||
* image_analysis → video_analysis(可选) → intent_parsing → script_generation → review → tts → rendering → uploading
|
||||
*/
|
||||
export type ViralVideoStage =
|
||||
| "image_analysis"
|
||||
| "video_analysis"
|
||||
| "intent_parsing"
|
||||
| "script_generation"
|
||||
| "review"
|
||||
| "tts"
|
||||
@@ -39,7 +47,7 @@ export type ViralVideoStage =
|
||||
/** 图片+视频分析阶段：属于「分析图片」按钮的范围 */
|
||||
const IMAGE_ANALYSIS_STAGES = new Set<ViralVideoStage>(["image_analysis", "video_analysis"])
|
||||
/** 编导脚本阶段：属于「生成文案」按钮的范围 */
|
||||
const COPY_STAGES = new Set<ViralVideoStage>(["script_generation", "review"])
|
||||
const COPY_STAGES = new Set<ViralVideoStage>(["intent_parsing", "script_generation", "review"])
|
||||
/** 视频生成阶段：属于「开始生成视频」按钮的范围（v1.6: TTS+单次Seedance+上传） */
|
||||
const VIDEO_STAGES = new Set<ViralVideoStage>(["tts", "rendering", "uploading"])
|
||||
|
||||
@@ -57,39 +65,27 @@ export function isAnalysisStage(stage: ViralVideoStage | undefined): boolean {
|
||||
return isImageAnalysisStage(stage) || isCopyStage(stage)
|
||||
}
|
||||
|
||||
/** 从 job 对象取到有效阶段（兼容 progress_stage/current_stage 两种字段名） */
|
||||
export function getJobStage(
|
||||
job: { current_stage?: ViralVideoStage; progress_stage?: ViralVideoStage } | null | undefined,
|
||||
): ViralVideoStage | undefined {
|
||||
if (!job) return undefined
|
||||
return job.current_stage || job.progress_stage
|
||||
}
|
||||
|
||||
/** 从 job 对象取到阶段提示文案（兼容 phase_message/progress_message） */
|
||||
export function getJobPhaseMessage(
|
||||
job: { phase_message?: string; progress_message?: string } | null | undefined,
|
||||
): string {
|
||||
if (!job) return ""
|
||||
return job.phase_message || job.progress_message || ""
|
||||
}
|
||||
|
||||
/** 单张图片 VLM 识别结果（v8 叙述优先，仅保留最少结构化字段） */
|
||||
/** 单张图片 VLM 识别出的商品信息 */
|
||||
export interface ImageProductAnalysis {
|
||||
/** store / product / person / scene */
|
||||
type?: string
|
||||
name?: string
|
||||
brand?: string
|
||||
has_person?: boolean
|
||||
/** v8: 用户端展示用的叙述 markdown（由提示词控制排版） */
|
||||
summary_markdown?: string
|
||||
/** 标题行兼容字段 */
|
||||
category?: string
|
||||
brand?: string
|
||||
colors?: string[]
|
||||
material_or_texture?: string
|
||||
key_features?: string[]
|
||||
visual_style?: string
|
||||
scene?: string
|
||||
target_audience_hint?: string
|
||||
text_on_image?: string
|
||||
/** 旧字段兼容 */
|
||||
spec?: string
|
||||
features?: string[] | string
|
||||
label_text?: string
|
||||
selling_points?: string
|
||||
image_index?: number
|
||||
}
|
||||
|
||||
export interface ImageAnalysisResult {
|
||||
/** v8 字段 */
|
||||
images?: ImageProductAnalysis[]
|
||||
/** 老数据兼容 */
|
||||
products?: ImageProductAnalysis[]
|
||||
}
|
||||
|
||||
@@ -97,8 +93,6 @@ export interface ImageAnalysisResult {
|
||||
export interface ShotScript {
|
||||
/** 时间区间，如 "0-3秒" */
|
||||
time_range?: string
|
||||
/** 说话人角色，如 "店主"、"顾客" */
|
||||
speaker?: string
|
||||
/** 景别/角度/运镜，如 "近景俯拍45度，缓慢推镜" */
|
||||
shot_type_angle_movement?: string
|
||||
/** 场景描述+对白 */
|
||||
@@ -138,8 +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 }>
|
||||
}
|
||||
@@ -153,6 +145,22 @@ export interface StyleTemplate {
|
||||
tags?: string[]
|
||||
}
|
||||
|
||||
export interface IntentResult {
|
||||
intent?: string
|
||||
key_messages?: string[]
|
||||
tone?: string
|
||||
target_emotion?: string
|
||||
call_to_action?: string
|
||||
suggested_title?: string
|
||||
/** v1.5 旧字段兼容 */
|
||||
product?: string
|
||||
selling_points?: string[]
|
||||
target_audience?: string
|
||||
structure?: string
|
||||
duration?: number
|
||||
suggested_copy?: string
|
||||
}
|
||||
|
||||
export interface ViralVideoJob {
|
||||
id: string
|
||||
status: ViralVideoStatus
|
||||
@@ -170,6 +178,8 @@ export interface ViralVideoJob {
|
||||
voice_mode?: "global" | "per_video"
|
||||
voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
|
||||
bgm_preference?: string
|
||||
intent_result?: IntentResult
|
||||
intent_text?: string
|
||||
/** v1.6 编导分镜脚本（核心产物） */
|
||||
copy_result?: CopyResult
|
||||
/** 向后兼容：= copy_result.shots */
|
||||
@@ -181,14 +191,8 @@ export interface ViralVideoJob {
|
||||
video_model?: string
|
||||
/** 视频时长（秒，5-30，默认15） */
|
||||
duration?: number
|
||||
/** 后端真实字段名（snake_case）：当前细粒度阶段 */
|
||||
current_stage?: ViralVideoStage
|
||||
/** 后端真实字段名：当前阶段中文提示文案 */
|
||||
phase_message?: string
|
||||
/** @deprecated 兼容旧前端：使用 current_stage */
|
||||
progress_stage?: ViralVideoStage
|
||||
progress_percent?: number
|
||||
/** @deprecated 兼容旧前端：使用 phase_message */
|
||||
progress_message?: string
|
||||
output_url?: string
|
||||
result_video_url?: string
|
||||
@@ -199,6 +203,31 @@ export interface ViralVideoJob {
|
||||
updated_at?: string
|
||||
}
|
||||
|
||||
export interface GenerateViralVideoRequest {
|
||||
images: string[]
|
||||
reference_video_url?: string
|
||||
douyin_url?: string
|
||||
style_strength?: StyleStrength
|
||||
style_template_id?: string
|
||||
user_copy_text?: string
|
||||
fusion_level?: FusionLevel
|
||||
voice_id?: string
|
||||
voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
|
||||
bgm_preference?: string
|
||||
industry?: string
|
||||
target_customer?: string
|
||||
language?: string
|
||||
persona_id?: string
|
||||
viral_structure?: string
|
||||
marketing_purpose?: string
|
||||
/** 视频时长（5-30秒，默认15） */
|
||||
duration?: number
|
||||
video_model?: string
|
||||
video_ratio?: string
|
||||
/** 三步拆分：step 控制后端执行到哪一步暂停 */
|
||||
step?: "analyze" | "generate_copy" | "generate_video"
|
||||
}
|
||||
|
||||
export interface HistoryResponse {
|
||||
items: ViralVideoJob[]
|
||||
total: number
|
||||
@@ -274,18 +303,6 @@ export interface ConfirmCopyRequest {
|
||||
edited_copy?: string
|
||||
/** 视频模型 key，覆盖默认 */
|
||||
video_model?: string
|
||||
/** 分辨率，如 480p/720p/1080p */
|
||||
video_resolution?: string
|
||||
/** 画面比例，如 9:16/16:9/1:1 */
|
||||
video_ratio?: string
|
||||
/** 成片时长秒数 */
|
||||
duration?: number
|
||||
/** 音色 ID（CosyVoice voice_id 或 preset id） */
|
||||
voice_id?: string
|
||||
/** 音色来源：preset/library/upload/clone/my_voice */
|
||||
voice_source?: string
|
||||
/** 口播语言 */
|
||||
language?: string
|
||||
}
|
||||
|
||||
/** 旧分镜片段结构（保留兼容；新代码请使用 ShotScript） */
|
||||
|
||||
@@ -8,6 +8,11 @@ export const ROUTE_TITLE_MAP: Record<string, string> = {
|
||||
"/app/products": "成片库",
|
||||
"/app/templates": "模板库",
|
||||
"/app/history": "任务历史",
|
||||
"/app/admin": "控制台",
|
||||
"/app/admin/users": "用户管理",
|
||||
"/app/admin/analytics": "数据分析",
|
||||
"/app/admin/monitor": "系统监控",
|
||||
"/app/admin/logs": "系统日志",
|
||||
"/app/subscription": "订阅管理",
|
||||
"/app/subscription/upgrade": "升级订阅",
|
||||
"/app/subscription/billing": "账单管理",
|
||||
|
||||
@@ -13,6 +13,7 @@ import {
|
||||
HistoryOutlined,
|
||||
TrophyOutlined,
|
||||
ScanOutlined,
|
||||
ControlOutlined,
|
||||
CrownOutlined,
|
||||
ThunderboltOutlined,
|
||||
UnorderedListOutlined,
|
||||
@@ -206,6 +207,12 @@ export const NAV_GROUPS: NavGroup[] = [
|
||||
path: "/app/duplication",
|
||||
icon: React.createElement(ScanOutlined),
|
||||
},
|
||||
{
|
||||
key: "admin",
|
||||
label: "控制台",
|
||||
path: "/app/admin",
|
||||
icon: React.createElement(ControlOutlined),
|
||||
},
|
||||
{
|
||||
key: "subscription",
|
||||
label: "会员订阅",
|
||||
|
||||
@@ -0,0 +1,27 @@
|
||||
/* Admin 页面样式（Phase 3 精简）
|
||||
*
|
||||
* 原始 477 行 → 精简至仅保留实际使用的 class。
|
||||
* 已迁移至 global.css / ui.css 的样式不再重复定义：
|
||||
* .xx-page-head → global.css
|
||||
* .xx-primary-btn → global.css
|
||||
* .xx-tag / .xx-card → ui.css / global.css
|
||||
*
|
||||
* 以下 class 仅被 AdminComingSoon.tsx 使用。
|
||||
*/
|
||||
|
||||
.admin-coming-soon-page {
|
||||
padding: 32px;
|
||||
max-width: 1680px;
|
||||
margin: 0 auto;
|
||||
}
|
||||
|
||||
.xx-result-center {
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
min-height: 400px;
|
||||
}
|
||||
|
||||
.xx-result-center .ant-result {
|
||||
padding: 48px;
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
import React from "react"
|
||||
import { Button, Card, Result } from "antd"
|
||||
import { useNavigate } from "react-router-dom"
|
||||
import "./Admin.css"
|
||||
|
||||
const AdminComingSoon: React.FC = () => {
|
||||
const navigate = useNavigate()
|
||||
|
||||
return (
|
||||
<div className="admin-coming-soon-page">
|
||||
<div className="xx-result-center">
|
||||
<Card className="xx-card">
|
||||
<Result
|
||||
status="info"
|
||||
title="Admin 后台暂未开放"
|
||||
subTitle="当前版本未接入后台用户、监控、日志、分析等后端服务，因此不展示模拟运营数据，也不会提供假操作入口。"
|
||||
extra={[
|
||||
<Button
|
||||
type="primary"
|
||||
onClick={() => navigate("/app/dashboard")}
|
||||
className="xx-primary-btn"
|
||||
>
|
||||
返回首页
|
||||
</Button>,
|
||||
]}
|
||||
/>
|
||||
</Card>
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default AdminComingSoon
|
||||
|
||||
export const Component = AdminComingSoon
|
||||
@@ -38,8 +38,6 @@ const GeneratePage: React.FC = () => {
|
||||
|
||||
/* ── 积分状态 ── */
|
||||
const { balance, dailyUsage, rules, init: initPoints } = usePointsStore()
|
||||
// 后端积分总开关（由 /points/balance 返回；数据未就绪时保守按 UI 开关处理）
|
||||
const creditsEnabled = balance?.credits_enabled ?? ENABLE_CREDIT_SYSTEM
|
||||
useEffect(() => {
|
||||
initPoints()
|
||||
}, [initPoints])
|
||||
@@ -344,8 +342,7 @@ const GeneratePage: React.FC = () => {
|
||||
const handleConfirmGenerate = useCallback(async () => {
|
||||
// 积分预检查（积分系统关闭时跳过，直接走生成流程）
|
||||
let check: ReturnType<typeof hasEnoughPoints> = { sufficient: true, cost: 0 }
|
||||
// 仅当后端积分系统真实启用时才做余额预校验（后端免费放行时前端不得拦截）
|
||||
if (creditsEnabled) {
|
||||
if (ENABLE_CREDIT_SYSTEM) {
|
||||
const units = isBatch ? Math.max(selectedVariantIds.length, 1) : 1
|
||||
check = hasEnoughPoints(
|
||||
balance ?? null,
|
||||
@@ -391,7 +388,6 @@ const GeneratePage: React.FC = () => {
|
||||
balance,
|
||||
dailyUsage,
|
||||
rules,
|
||||
creditsEnabled,
|
||||
])
|
||||
|
||||
/* ── 步骤导航 ── */
|
||||
@@ -506,7 +502,7 @@ const GeneratePage: React.FC = () => {
|
||||
/* ── 积分消耗估算（步骤3确认生成展示用） ── */
|
||||
const unitsForCost = isBatch ? Math.max(selectedVariantIds.length, 1) : 1
|
||||
const pointsEstimate = useMemo(() => {
|
||||
if (!creditsEnabled) return { sufficient: true, cost: 0 }
|
||||
if (!ENABLE_CREDIT_SYSTEM) return { sufficient: true, cost: 0 }
|
||||
return hasEnoughPoints(
|
||||
balance ?? null,
|
||||
unitsForCost,
|
||||
@@ -515,8 +511,8 @@ const GeneratePage: React.FC = () => {
|
||||
"free",
|
||||
rules?.free_user_multiplier ?? 1.15,
|
||||
)
|
||||
}, [unitsForCost, balance, dailyUsage, rules, creditsEnabled])
|
||||
const insufficientPoints = creditsEnabled && !pointsEstimate.sufficient
|
||||
}, [unitsForCost, balance, dailyUsage, rules])
|
||||
const insufficientPoints = ENABLE_CREDIT_SYSTEM && !pointsEstimate.sufficient
|
||||
|
||||
/* ================================================================
|
||||
渲染
|
||||
|
||||
@@ -1,128 +0,0 @@
|
||||
import type { ViralVideoModel } from "@/api/viral-video/types"
|
||||
|
||||
export const LANGUAGES = ["中文(普通话)", "粤语", "英语", "日语", "韩语"]
|
||||
export const TARGET_CUSTOMERS = [
|
||||
"小镇青年",
|
||||
"小镇中老年",
|
||||
"Z世代",
|
||||
"精致妈妈",
|
||||
"新锐白领",
|
||||
"资深中产",
|
||||
"都市蓝领",
|
||||
"都市银发",
|
||||
]
|
||||
export const PERSONAS = [
|
||||
"通用个人IP",
|
||||
"老板型IP",
|
||||
"专家型IP",
|
||||
"顾问型IP",
|
||||
"创始人IP",
|
||||
"创业者IP",
|
||||
"从业者经验派",
|
||||
"避坑顾问型",
|
||||
"知识科普型",
|
||||
"测评种草型",
|
||||
]
|
||||
export const STRUCTURES = [
|
||||
"反差破局+亮明观点+还原现状",
|
||||
"痛点场景植入+给出通关秘籍",
|
||||
"反常识开场+扎心现状+制造焦虑",
|
||||
"核心难题+化解焦虑+描绘愿景",
|
||||
"痛点前置+前后对比+给出期许",
|
||||
"悬念拉满+揭露危害+明确目的",
|
||||
"情绪共鸣+勾起好奇+拔高价值",
|
||||
"颠覆认知+现状拆解+赋予价值",
|
||||
"结果先行+消除顾虑+保留悬念",
|
||||
"真实困境+认知反转+传授秘籍",
|
||||
"反差事实+案例佐证+戳破真相",
|
||||
"预埋悬念+标杆案例+引出核心",
|
||||
"危机渲染+放大痛点+输出干货",
|
||||
"逆袭开场+成功诀窍+低价引流",
|
||||
"反常识观点+分析危害+描述现状",
|
||||
"正反对比+解析原因+简易方法",
|
||||
"真实场景+内心刻画+共情引导+展望未来",
|
||||
"连续否定+代入感受+打破认知+精准戳痛",
|
||||
"揭秘行业+类比施压+放大焦虑+精准锁客",
|
||||
]
|
||||
export const PURPOSES = [
|
||||
"获客引流",
|
||||
"账号涨粉",
|
||||
"活动通知",
|
||||
"口播带货",
|
||||
"促销转化",
|
||||
"场景种草",
|
||||
"痛点解决",
|
||||
"功能演示",
|
||||
"门店发现",
|
||||
"到店实录",
|
||||
"招牌体验",
|
||||
"同城团购",
|
||||
"品牌主张",
|
||||
"品牌故事",
|
||||
"生活方式",
|
||||
"创意概念",
|
||||
"对话短剧",
|
||||
"反转短剧",
|
||||
"悬念短剧",
|
||||
"情绪短片",
|
||||
]
|
||||
export const RATIOS = [
|
||||
{ v: "9:16", label: "9:16 竖屏（抖音/视频号）" },
|
||||
{ v: "16:9", label: "16:9 横屏（B站/YouTube）" },
|
||||
{ v: "1:1", label: "1:1 方形（小红书）" },
|
||||
]
|
||||
export const DURATIONS = Array.from({ length: 16 }, (_, i) => 15 + i)
|
||||
/** 兜底模型列表（接口未返回时使用） */
|
||||
export const FALLBACK_VIDEO_MODELS: ViralVideoModel[] = [
|
||||
{
|
||||
key: "seedance-2.5",
|
||||
display_name: "Seedance 2.5 — 最新最强",
|
||||
supports_audio: true,
|
||||
supported_resolutions: ["480p", "720p", "1080p"],
|
||||
max_duration: 30,
|
||||
is_default: true,
|
||||
},
|
||||
{
|
||||
key: "seedance-2.0",
|
||||
display_name: "Seedance 2.0 — 正式首选",
|
||||
supports_audio: true,
|
||||
supported_resolutions: ["480p", "720p", "1080p"],
|
||||
max_duration: 15,
|
||||
},
|
||||
{
|
||||
key: "seedance-2.0-fast",
|
||||
display_name: "Seedance 2.0 Fast — 快速测试",
|
||||
supports_audio: true,
|
||||
supported_resolutions: ["480p", "720p"],
|
||||
max_duration: 15,
|
||||
},
|
||||
{
|
||||
key: "seedance-2.0-mini",
|
||||
display_name: "Seedance 2.0 Mini — 低成本",
|
||||
supports_audio: true,
|
||||
supported_resolutions: ["480p", "720p"],
|
||||
max_duration: 15,
|
||||
},
|
||||
{
|
||||
key: "wan-3.0",
|
||||
display_name: "Wan 3.0 — 通义万相全能",
|
||||
supports_audio: true,
|
||||
supported_resolutions: ["480p", "720p", "1080p"],
|
||||
max_duration: 15,
|
||||
},
|
||||
]
|
||||
export const RESOLUTION_ORDER = ["480p", "720p", "1080p", "4k"]
|
||||
export const QUALITY_OPTIONS = [
|
||||
{ v: "480p", label: "480p（快速）" },
|
||||
{ v: "720p", label: "720p（清晰）" },
|
||||
{ v: "1080p", label: "1080p（高清）" },
|
||||
]
|
||||
export const UPSCALE_OPTIONS = [
|
||||
{ v: "default", label: "默认" },
|
||||
{ v: "sd", label: "普清" },
|
||||
{ v: "hd", label: "高清" },
|
||||
{ v: "uhd", label: "超清" },
|
||||
{ v: "omnipotent", label: "全能" },
|
||||
]
|
||||
export const VOICE_TIPS =
|
||||
"支持 MP3/WAV/M4A/AAC/OGG 格式，最大 10MB，时长 ≤30 秒。建议清晰人声、无背景音乐、环境安静；录音请保持距麦克风 15-20cm，音量适中。"
|
||||
@@ -138,6 +138,27 @@
|
||||
border-radius: 12px;
|
||||
overflow: hidden;
|
||||
}
|
||||
.vv-section-head {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
padding: 14px 16px;
|
||||
cursor: pointer;
|
||||
user-select: none;
|
||||
border-bottom: 1px solid #f0f0f5;
|
||||
gap: 8px;
|
||||
}
|
||||
.vv-section.collapsed .vv-section-head {
|
||||
border-bottom: none;
|
||||
}
|
||||
.vv-section-title {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
font-size: 15px;
|
||||
font-weight: 600;
|
||||
color: #1f2937;
|
||||
}
|
||||
.vv-step-badge {
|
||||
width: 22px;
|
||||
height: 22px;
|
||||
@@ -289,6 +310,11 @@
|
||||
|
||||
/* antd Select dropdown light */
|
||||
.vv-page .ant-select-dropdown,
|
||||
.vv-select-dropdown {
|
||||
background: #fff !important;
|
||||
border-radius: 8px !important;
|
||||
padding: 4px !important;
|
||||
}
|
||||
.vv-page .ant-select-item {
|
||||
color: #1f2937 !important;
|
||||
border-radius: 6px !important;
|
||||
@@ -629,6 +655,10 @@
|
||||
margin-left: auto;
|
||||
}
|
||||
|
||||
.vv-douyin-input {
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
|
||||
.vv-ref-video-box {
|
||||
margin-top: 8px;
|
||||
}
|
||||
@@ -639,6 +669,44 @@
|
||||
max-height: 200px;
|
||||
}
|
||||
|
||||
.vv-audio-grid {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 8px;
|
||||
}
|
||||
.vv-audio-cell {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
gap: 6px;
|
||||
padding: 14px 8px;
|
||||
background: #fff;
|
||||
border: 1px solid #e0e0e8;
|
||||
border-radius: 8px;
|
||||
color: #6b7280;
|
||||
font-size: 12px;
|
||||
cursor: pointer;
|
||||
transition: all 0.15s;
|
||||
}
|
||||
.vv-audio-cell:hover:not(:disabled) {
|
||||
border-color: #7c3aed;
|
||||
color: #7c3aed;
|
||||
}
|
||||
.vv-audio-cell.selected,
|
||||
.vv-audio-cell.active {
|
||||
background: #f5f0ff;
|
||||
border-color: #7c3aed;
|
||||
color: #7c3aed;
|
||||
}
|
||||
.vv-audio-cell:disabled {
|
||||
opacity: 0.5;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
.vv-audio-ic {
|
||||
font-size: 20px;
|
||||
}
|
||||
|
||||
.vv-selected-audio {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
@@ -789,6 +857,12 @@
|
||||
color: #6b7280;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.vv-recog-v {
|
||||
color: #1f2937;
|
||||
}
|
||||
.vv-recog-meta {
|
||||
color: #9ca3af;
|
||||
}
|
||||
|
||||
/* progress mini */
|
||||
.vv-progress-mini-inline {
|
||||
@@ -928,6 +1002,11 @@
|
||||
.vv-form-row {
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
.vv-form-hint {
|
||||
font-size: 12px;
|
||||
color: #9ca3af;
|
||||
line-height: 1.4;
|
||||
}
|
||||
@media (max-width: 500px) {
|
||||
.vv-form-grid {
|
||||
grid-template-columns: 1fr;
|
||||
@@ -941,11 +1020,29 @@
|
||||
border-radius: 10px;
|
||||
padding: 10px 12px;
|
||||
margin-top: 8px;
|
||||
}
|
||||
.vv-copy-head {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
flex: 1 1 auto;
|
||||
min-height: 260px;
|
||||
overflow: hidden;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
margin-bottom: 12px;
|
||||
}
|
||||
.vv-copy-title {
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
color: #1f2937;
|
||||
}
|
||||
.vv-copy-tag {
|
||||
font-size: 11px;
|
||||
padding: 2px 8px;
|
||||
border-radius: 10px;
|
||||
font-weight: 400;
|
||||
}
|
||||
.vv-copy-tag-preview {
|
||||
background: #fef3c7;
|
||||
color: #b45309;
|
||||
border: 1px solid #fcd34d;
|
||||
margin-left: auto;
|
||||
}
|
||||
|
||||
.vv-copy-loading {
|
||||
@@ -953,14 +1050,10 @@
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
flex: 1 1 auto;
|
||||
min-height: 260px;
|
||||
padding: 28px 16px;
|
||||
gap: 10px;
|
||||
background: #fff;
|
||||
border: 1px solid #e5e7eb;
|
||||
border-radius: 10px;
|
||||
margin-top: 8px;
|
||||
}
|
||||
.vv-copy-loading .vv-spinner {
|
||||
width: 28px;
|
||||
@@ -978,41 +1071,29 @@
|
||||
color: #6b7280;
|
||||
}
|
||||
|
||||
.vv-copy-locked {
|
||||
background: #fafafe;
|
||||
border-color: #d8cafc;
|
||||
}
|
||||
.vv-copy-sub {
|
||||
font-size: 12px;
|
||||
color: #6b7280;
|
||||
}
|
||||
|
||||
/* ── Storyboard (linear doc style) ── */
|
||||
.vv-storyboard {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
flex: 1 1 auto;
|
||||
min-height: 260px;
|
||||
padding: 10px 12px;
|
||||
background: #fff;
|
||||
border: 1px solid #e5e7eb;
|
||||
border-radius: 10px;
|
||||
margin-top: 8px;
|
||||
overflow: hidden;
|
||||
padding: 6px 2px;
|
||||
background: transparent;
|
||||
border: none;
|
||||
}
|
||||
|
||||
.vv-sb-doc {
|
||||
flex: 1 1 auto;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 3px;
|
||||
color: #1f2937;
|
||||
font-size: 13px;
|
||||
line-height: 1.55;
|
||||
overflow-y: auto;
|
||||
padding-right: 4px;
|
||||
margin-right: -4px;
|
||||
}
|
||||
.vv-sb-doc::-webkit-scrollbar {
|
||||
width: 6px;
|
||||
}
|
||||
.vv-sb-doc::-webkit-scrollbar-thumb {
|
||||
background: #d8c4ff;
|
||||
border-radius: 3px;
|
||||
}
|
||||
.vv-sb-doc::-webkit-scrollbar-track {
|
||||
background: transparent;
|
||||
}
|
||||
.vv-sb-h {
|
||||
margin: 6px 0 2px;
|
||||
@@ -1302,14 +1383,12 @@
|
||||
/* 口播稿 —— 复用 vv-sb-field 样式，无额外需求 */
|
||||
|
||||
.vv-sb-actions {
|
||||
flex-shrink: 0;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
margin-top: 8px;
|
||||
padding-top: 8px;
|
||||
border-top: 1px solid #e5e7eb;
|
||||
background: #fff;
|
||||
}
|
||||
.vv-sb-actions .vv-btn-ghost {
|
||||
padding: 6px 14px;
|
||||
@@ -1336,8 +1415,7 @@
|
||||
/* ── Preview ──────────────── */
|
||||
.vv-preview {
|
||||
aspect-ratio: 9 / 16;
|
||||
max-height: 100%;
|
||||
max-width: 100%;
|
||||
max-height: 360px;
|
||||
background: #fff;
|
||||
border: 1px dashed #e5e7eb;
|
||||
border-radius: 10px;
|
||||
@@ -1678,10 +1756,84 @@
|
||||
}
|
||||
|
||||
/* intent (keyword chips) - if used */
|
||||
.vv-intent {
|
||||
margin-top: 10px;
|
||||
}
|
||||
.vv-intent-title {
|
||||
font-size: 12px;
|
||||
color: #6b7280;
|
||||
margin-bottom: 6px;
|
||||
}
|
||||
.vv-intent-row {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 6px;
|
||||
}
|
||||
.vv-chip {
|
||||
padding: 4px 10px;
|
||||
background: #fff;
|
||||
border: 1px solid #e0e0e8;
|
||||
border-radius: 14px;
|
||||
font-size: 12px;
|
||||
color: #374151;
|
||||
cursor: pointer;
|
||||
}
|
||||
.vv-chip.picked {
|
||||
background: #f5f0ff;
|
||||
color: #7c3aed;
|
||||
border-color: #7c3aed;
|
||||
}
|
||||
|
||||
/* misc helpers */
|
||||
.vv-k {
|
||||
color: #6b7280;
|
||||
}
|
||||
.vv-v {
|
||||
color: #1f2937;
|
||||
}
|
||||
.vv-hint-yellow {
|
||||
background: #fef3c7;
|
||||
border: 1px solid #fcd34d;
|
||||
color: #b45309;
|
||||
font-size: 12px;
|
||||
padding: 8px;
|
||||
border-radius: 6px;
|
||||
line-height: 1.6;
|
||||
margin-top: 8px;
|
||||
}
|
||||
.vv-hint-gray {
|
||||
color: #9ca3af;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
/* Segmented tabs (legacy) */
|
||||
.vv-seg-tabs {
|
||||
display: inline-flex;
|
||||
background: #f5f6fa;
|
||||
border: 1px solid #e5e7eb;
|
||||
border-radius: 8px;
|
||||
padding: 3px;
|
||||
gap: 2px;
|
||||
}
|
||||
.vv-seg-tab {
|
||||
padding: 6px 14px;
|
||||
border-radius: 6px;
|
||||
background: #fff;
|
||||
border: none;
|
||||
color: #6b7280;
|
||||
font-size: 12px;
|
||||
cursor: pointer;
|
||||
transition: all 0.15s;
|
||||
}
|
||||
.vv-seg-tab:hover {
|
||||
color: #374151;
|
||||
background: #fff;
|
||||
}
|
||||
.vv-seg-tab.active {
|
||||
background: #f3f0ff;
|
||||
color: #7c3aed;
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
/* ── v1.6 我的音色（默认主路径） ── */
|
||||
.vv-voice-section {
|
||||
@@ -1796,385 +1948,3 @@
|
||||
padding-bottom: 6px;
|
||||
border-bottom: 1px dashed #e5e7eb;
|
||||
}
|
||||
|
||||
/* ─────────── markdown 渲染（提示词控制展示格式） ─────────── */
|
||||
.vv-recog-md {
|
||||
padding: 4px 0;
|
||||
}
|
||||
.vv-copy-preview {
|
||||
margin-bottom: 14px;
|
||||
padding: 12px 14px;
|
||||
background: linear-gradient(180deg, #faf7ff 0%, #f6f2ff 100%);
|
||||
border: 1px solid #ece4fb;
|
||||
border-radius: 10px;
|
||||
}
|
||||
.vv-copy-preview-h {
|
||||
margin: 0 0 8px;
|
||||
border-bottom: none;
|
||||
padding-bottom: 0;
|
||||
}
|
||||
.vv-md-body {
|
||||
font-size: 13px;
|
||||
line-height: 1.7;
|
||||
color: #374151;
|
||||
word-break: break-word;
|
||||
}
|
||||
.vv-md-body h1,
|
||||
.vv-md-body h2,
|
||||
.vv-md-body h3,
|
||||
.vv-md-body h4 {
|
||||
margin: 10px 0 6px;
|
||||
font-weight: 600;
|
||||
color: #1f2937;
|
||||
line-height: 1.4;
|
||||
}
|
||||
.vv-md-body h1 {
|
||||
font-size: 18px;
|
||||
}
|
||||
.vv-md-body h2 {
|
||||
font-size: 16px;
|
||||
}
|
||||
.vv-md-body h3 {
|
||||
font-size: 15px;
|
||||
}
|
||||
.vv-md-body h4 {
|
||||
font-size: 14px;
|
||||
}
|
||||
.vv-md-body p {
|
||||
margin: 6px 0;
|
||||
}
|
||||
.vv-md-body ul,
|
||||
.vv-md-body ol {
|
||||
margin: 6px 0;
|
||||
padding-left: 20px;
|
||||
}
|
||||
.vv-md-body li {
|
||||
margin: 3px 0;
|
||||
}
|
||||
.vv-md-body strong {
|
||||
color: #111827;
|
||||
font-weight: 600;
|
||||
}
|
||||
.vv-md-body blockquote {
|
||||
margin: 8px 0;
|
||||
padding: 4px 12px;
|
||||
border-left: 3px solid #7c3aed;
|
||||
background: rgba(124, 58, 237, 0.05);
|
||||
color: #4b5563;
|
||||
}
|
||||
.vv-md-body code {
|
||||
padding: 1px 5px;
|
||||
background: #f3f4f6;
|
||||
border-radius: 4px;
|
||||
font-size: 12px;
|
||||
color: #be185d;
|
||||
}
|
||||
.vv-md-body a {
|
||||
color: #7c3aed;
|
||||
text-decoration: none;
|
||||
}
|
||||
.vv-md-body a:hover {
|
||||
text-decoration: underline;
|
||||
}
|
||||
.vv-md-body table {
|
||||
border-collapse: collapse;
|
||||
margin: 8px 0;
|
||||
width: 100%;
|
||||
}
|
||||
.vv-md-body th,
|
||||
.vv-md-body td {
|
||||
border: 1px solid #e5e7eb;
|
||||
padding: 6px 10px;
|
||||
text-align: left;
|
||||
}
|
||||
.vv-md-body hr {
|
||||
border: none;
|
||||
border-top: 1px solid #e5e7eb;
|
||||
margin: 12px 0;
|
||||
}
|
||||
|
||||
/* ── 分镜脚本流式预览（WebSocket delta 实时输出） ── */
|
||||
.vv-streaming-box {
|
||||
outline: 2px solid #ede9fe;
|
||||
background: linear-gradient(180deg, #fafafe 0%, #ffffff 100%);
|
||||
}
|
||||
.vv-streaming-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
padding: 4px 0 10px 0;
|
||||
border-bottom: 1px dashed #ede9fe;
|
||||
margin-bottom: 8px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.vv-streaming-title {
|
||||
font-size: 13px;
|
||||
color: #7c3aed;
|
||||
font-weight: 500;
|
||||
flex: 1 1 auto;
|
||||
}
|
||||
.vv-streaming-count {
|
||||
font-size: 12px;
|
||||
color: #9ca3af;
|
||||
background: #f5f0ff;
|
||||
padding: 1px 8px;
|
||||
border-radius: 10px;
|
||||
}
|
||||
.vv-streaming-doc {
|
||||
flex: 1 1 auto;
|
||||
overflow-y: auto;
|
||||
padding: 4px 2px 8px 2px;
|
||||
/* 复用 vv-sb-doc 的滚动条样式 */
|
||||
scrollbar-width: thin;
|
||||
scrollbar-color: #d8c4ff transparent;
|
||||
}
|
||||
.vv-streaming-doc::-webkit-scrollbar {
|
||||
width: 6px;
|
||||
}
|
||||
.vv-streaming-doc::-webkit-scrollbar-thumb {
|
||||
background: #d8c4ff;
|
||||
border-radius: 3px;
|
||||
}
|
||||
.vv-streaming-pre {
|
||||
margin: 0;
|
||||
padding: 4px 6px;
|
||||
white-space: pre-wrap;
|
||||
word-break: break-word;
|
||||
font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, "Liberation Mono", monospace;
|
||||
font-size: 13px;
|
||||
line-height: 1.65;
|
||||
color: #1f2937;
|
||||
tab-size: 2;
|
||||
}
|
||||
.vv-streaming-caret {
|
||||
display: inline-block;
|
||||
width: 2px;
|
||||
height: 14px;
|
||||
background: #7c3aed;
|
||||
vertical-align: middle;
|
||||
margin-left: 2px;
|
||||
animation: vv-caret-blink 1s steps(2, start) infinite;
|
||||
}
|
||||
@keyframes vv-caret-blink {
|
||||
to {
|
||||
visibility: hidden;
|
||||
}
|
||||
}
|
||||
.vv-streaming-actions {
|
||||
flex-shrink: 0;
|
||||
background: #fff;
|
||||
padding-top: 8px;
|
||||
border-top: 1px solid #f3f4f6;
|
||||
}
|
||||
.vv-streaming-hint {
|
||||
font-size: 12px;
|
||||
color: #9ca3af;
|
||||
}
|
||||
|
||||
/* ── 三列等高布局（2026-10-10 fix） ──
|
||||
* vv-page 作为纵向 flex 容器，tabs 固定 + grid 占满剩余高度；
|
||||
* vv-grid 改用 grid-auto-rows:1fr 强制三列等高；
|
||||
* 每列的卡片使用 flex column + min-height:0，正文区域 flex:1 并内部滚动。
|
||||
*/
|
||||
.vv-page {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
/* 减去顶部导航 56px 和自身 padding 16*2=32px，给 Tabs + 三列区留一屏 */
|
||||
height: calc(100vh - 56px);
|
||||
min-height: 640px; /* 小窗口保底 */
|
||||
overflow: hidden;
|
||||
}
|
||||
.vv-tabs {
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.vv-grid {
|
||||
flex: 1 1 auto;
|
||||
min-height: 0;
|
||||
align-items: stretch; /* 覆盖原来的 start，强制行内格子撑满行高 */
|
||||
grid-auto-rows: 1fr; /* 每行所有格子等高 */
|
||||
overflow: hidden;
|
||||
}
|
||||
.vv-col {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 12px;
|
||||
min-height: 0;
|
||||
min-width: 0;
|
||||
}
|
||||
/* 列内的卡片（.vv-section）默认允许收缩，正文区域接管滚动 */
|
||||
.vv-col > .vv-section {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
min-height: 0;
|
||||
flex-shrink: 1;
|
||||
}
|
||||
/* STEP1/STEP2 唯一卡片：撑满列高 */
|
||||
.vv-col > .vv-section:only-child {
|
||||
flex: 1 1 auto;
|
||||
}
|
||||
/* STEP3 右侧两卡片：配置固定不缩，预览区吸剩余空间 */
|
||||
.vv-col > .vv-section-preview {
|
||||
flex: 1 1 auto;
|
||||
min-height: 0;
|
||||
}
|
||||
.vv-section-preview .vv-section-body.vv-preview-body {
|
||||
flex: 1 1 auto;
|
||||
min-height: 0;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
padding: 12px;
|
||||
overflow: hidden;
|
||||
}
|
||||
.vv-section-preview .vv-preview {
|
||||
max-height: 100%;
|
||||
height: 100%;
|
||||
aspect-ratio: auto;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
/* 列内 .vv-credits / .vv-error / 主按钮 不参与收缩，永远固定在底部可见 */
|
||||
.vv-col > .vv-credits,
|
||||
.vv-col > .vv-error,
|
||||
.vv-col > .vv-btn-primary {
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.vv-col > .vv-credits {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
/* 可滚动 section 正文 */
|
||||
.vv-section-body.vv-section-body-scroll {
|
||||
flex: 1 1 auto;
|
||||
min-height: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
padding: 14px 16px 14px;
|
||||
overflow: hidden;
|
||||
}
|
||||
.vv-scroll-inner {
|
||||
flex: 1 1 auto;
|
||||
min-height: 0;
|
||||
overflow-y: auto;
|
||||
padding-right: 6px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
/* 美化滚动条（紫白主题） */
|
||||
scrollbar-width: thin;
|
||||
scrollbar-color: #d8c4ff transparent;
|
||||
}
|
||||
.vv-scroll-inner::-webkit-scrollbar {
|
||||
width: 6px;
|
||||
}
|
||||
.vv-scroll-inner::-webkit-scrollbar-thumb {
|
||||
background: #d8c4ff;
|
||||
border-radius: 3px;
|
||||
}
|
||||
.vv-scroll-inner::-webkit-scrollbar-thumb:hover {
|
||||
background: #c4a8ff;
|
||||
}
|
||||
.vv-scroll-inner::-webkit-scrollbar-track {
|
||||
background: transparent;
|
||||
}
|
||||
|
||||
/* 主按钮始终顶在 section-body 底部（在 vv-scroll-inner 之外） */
|
||||
.vv-section-body.vv-section-body-scroll > .vv-btn-primary {
|
||||
margin-top: 10px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
/* STEP1 文件列表改为根据可用空间自适应，不再用 max-height:260px 硬编码 */
|
||||
.vv-file-list {
|
||||
max-height: none;
|
||||
}
|
||||
|
||||
/* ── P2 新增 utility / 组件类 ── */
|
||||
/* 短剧模式提示条（放在 Step2 区域就地显示） */
|
||||
.vv-shortdrama-hint {
|
||||
padding: 8px 12px;
|
||||
margin-bottom: 8px;
|
||||
background-color: #fff7e6;
|
||||
border: 1px solid #ffd591;
|
||||
border-radius: 4px;
|
||||
font-size: 13px;
|
||||
color: #d46b08;
|
||||
}
|
||||
|
||||
/* speaker 标签（主角 / 配角） */
|
||||
.vv-sb-speaker-tag {
|
||||
display: inline-block;
|
||||
padding: 2px 8px;
|
||||
margin: 0 8px;
|
||||
border-radius: 4px;
|
||||
font-size: 12px;
|
||||
font-weight: 500;
|
||||
vertical-align: middle;
|
||||
}
|
||||
.vv-sb-speaker-tag-main {
|
||||
background-color: #e6f7ff;
|
||||
color: #1890ff;
|
||||
}
|
||||
.vv-sb-speaker-tag-guest {
|
||||
background-color: #fff7e6;
|
||||
color: #fa8c16;
|
||||
}
|
||||
.vv-sb-speaker-main {
|
||||
color: #1890ff;
|
||||
}
|
||||
.vv-sb-speaker-guest {
|
||||
color: #fa8c16;
|
||||
}
|
||||
|
||||
/* 间距 / 排版 helpers */
|
||||
.vv-mt-4 {
|
||||
margin-top: 4px;
|
||||
}
|
||||
.vv-mt-6 {
|
||||
margin-top: 6px;
|
||||
}
|
||||
.vv-mt-8 {
|
||||
margin-top: 8px;
|
||||
}
|
||||
.vv-mb-6 {
|
||||
margin-bottom: 6px;
|
||||
}
|
||||
.vv-mr-6 {
|
||||
margin-right: 6px;
|
||||
}
|
||||
.vv-fs-12 {
|
||||
font-size: 12px;
|
||||
}
|
||||
.vv-fs-13 {
|
||||
font-size: 13px;
|
||||
}
|
||||
.vv-fs-22 {
|
||||
font-size: 22px;
|
||||
}
|
||||
.vv-p-12 {
|
||||
padding: 12px;
|
||||
}
|
||||
.vv-text-center {
|
||||
text-align: center;
|
||||
}
|
||||
.vv-flex-1 {
|
||||
flex: 1;
|
||||
}
|
||||
.vv-w-170 {
|
||||
width: 170px;
|
||||
flex: 0 0 auto;
|
||||
}
|
||||
|
||||
.vv-inline-spinner {
|
||||
margin-right: 6px;
|
||||
display: inline-block;
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
.vv-progress-bar-sm {
|
||||
margin: 6px 0 4px;
|
||||
}
|
||||
|
||||
.vv-loading-ic {
|
||||
font-size: 36px;
|
||||
color: #7c3aed;
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -152,7 +152,8 @@ export default function AssetPickerModal({
|
||||
<div className="vv-modal-body">
|
||||
<div className="vv-asset-search">
|
||||
<select
|
||||
className="vv-input vv-w-170"
|
||||
className="vv-input"
|
||||
style={{ width: 170, flex: "0 0 auto" }}
|
||||
value={libraryId}
|
||||
onChange={(e) => setLibraryId(e.target.value)}
|
||||
disabled={loadingLibs || libraries.length === 0}
|
||||
@@ -200,8 +201,8 @@ export default function AssetPickerModal({
|
||||
</div>
|
||||
{kind === "voice" ? (
|
||||
<>
|
||||
<div className="vv-mt-8 vv-fs-13">暂无配音素材</div>
|
||||
<div className="vv-muted vv-mt-4 vv-fs-12">
|
||||
<div style={{ marginTop: 8, fontSize: 13 }}>暂无配音素材</div>
|
||||
<div style={{ marginTop: 4, fontSize: 12, color: "#9ca3af" }}>
|
||||
请先在「配音/我的音色」中上传音频文件，或在素材库管理中添加
|
||||
</div>
|
||||
</>
|
||||
|
||||
@@ -1,33 +0,0 @@
|
||||
import React from "react"
|
||||
|
||||
export interface EmptyBlockProps {
|
||||
icon?: React.ReactNode
|
||||
title?: string
|
||||
desc?: React.ReactNode
|
||||
action?: React.ReactNode
|
||||
className?: string
|
||||
style?: React.CSSProperties
|
||||
}
|
||||
|
||||
/**
|
||||
* 统一空态组件：icon + title + desc + action，避免散落的 vv-muted 空文案
|
||||
*/
|
||||
export const EmptyBlock: React.FC<EmptyBlockProps> = ({
|
||||
icon = "🎬",
|
||||
title,
|
||||
desc,
|
||||
action,
|
||||
className,
|
||||
style,
|
||||
}) => {
|
||||
return (
|
||||
<div className={`vv-empty-block ${className || ""}`} style={style}>
|
||||
<div className="vv-empty-icon">{icon}</div>
|
||||
{title && <div className="vv-empty-title">{title}</div>}
|
||||
{desc && <div className="vv-empty-desc vv-muted">{desc}</div>}
|
||||
{action && <div className="vv-empty-action">{action}</div>}
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default EmptyBlock
|
||||
@@ -1,43 +0,0 @@
|
||||
import React from "react"
|
||||
import { WarningFilled, ReloadOutlined } from "@ant-design/icons"
|
||||
|
||||
export interface ErrorRetryBarProps {
|
||||
label?: string
|
||||
message: string
|
||||
onRetry?: () => void
|
||||
retryText?: string
|
||||
className?: string
|
||||
style?: React.CSSProperties
|
||||
}
|
||||
|
||||
/**
|
||||
* 统一错误条：警告图标 + 标签前缀 + 错误消息 + 可选重试按钮
|
||||
* 替换主页面里散落的 .vv-error 块
|
||||
*/
|
||||
export const ErrorRetryBar: React.FC<ErrorRetryBarProps> = ({
|
||||
label,
|
||||
message,
|
||||
onRetry,
|
||||
retryText = "重试",
|
||||
className,
|
||||
style,
|
||||
}) => {
|
||||
return (
|
||||
<div className={`vv-error ${className || ""}`} style={style}>
|
||||
<WarningFilled style={{ marginRight: 6 }} />
|
||||
{label ? `${label}：` : ""}
|
||||
{message}
|
||||
{onRetry && (
|
||||
<button
|
||||
className="vv-btn vv-btn-warn vv-btn-sm"
|
||||
style={{ marginLeft: 10 }}
|
||||
onClick={onRetry}
|
||||
>
|
||||
<ReloadOutlined /> {retryText}
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default ErrorRetryBar
|
||||
@@ -1,111 +0,0 @@
|
||||
import React, { useState, useRef, useEffect, useCallback } from "react"
|
||||
|
||||
export interface InlineTextProps {
|
||||
value: string
|
||||
onChange?: (v: string) => void // 受控模式
|
||||
onCommit: (v: string) => void // blur/Enter 提交
|
||||
onCancel?: () => void
|
||||
multiline?: boolean
|
||||
textareaClass?: string
|
||||
textClass?: string
|
||||
placeholder?: string
|
||||
editing: boolean
|
||||
onStartEdit: () => void
|
||||
rows?: number
|
||||
maxLength?: number
|
||||
disabled?: boolean
|
||||
}
|
||||
|
||||
/**
|
||||
* 通用"点文字→textarea→blur/Enter保存→Esc取消"内联编辑组件
|
||||
* 替换 ViralVideoPage 里 11 处反射式 textarea/span 复制粘贴
|
||||
*/
|
||||
export const InlineText: React.FC<InlineTextProps> = ({
|
||||
value,
|
||||
onChange,
|
||||
onCommit,
|
||||
onCancel,
|
||||
multiline,
|
||||
textareaClass,
|
||||
textClass,
|
||||
placeholder,
|
||||
editing,
|
||||
onStartEdit,
|
||||
rows = 2,
|
||||
maxLength,
|
||||
disabled,
|
||||
}) => {
|
||||
const taRef = useRef<HTMLTextAreaElement>(null)
|
||||
const [local, setLocal] = useState(value)
|
||||
|
||||
useEffect(() => {
|
||||
setLocal(value)
|
||||
}, [value])
|
||||
|
||||
useEffect(() => {
|
||||
if (editing && taRef.current) {
|
||||
taRef.current.focus()
|
||||
taRef.current.select()
|
||||
}
|
||||
}, [editing])
|
||||
|
||||
const commit = useCallback(() => {
|
||||
const v = (onChange ? value : local).trim()
|
||||
const cur = (value ?? "").trim()
|
||||
if (v !== cur) onCommit(v)
|
||||
else onCancel?.()
|
||||
}, [local, value, onCommit, onCancel, onChange])
|
||||
|
||||
const handleKey = useCallback(
|
||||
(e: React.KeyboardEvent) => {
|
||||
if (e.key === "Escape") {
|
||||
e.preventDefault()
|
||||
onCancel?.()
|
||||
}
|
||||
if (!multiline && e.key === "Enter" && !e.shiftKey) {
|
||||
e.preventDefault()
|
||||
commit()
|
||||
}
|
||||
},
|
||||
[multiline, commit, onCancel],
|
||||
)
|
||||
|
||||
if (editing) {
|
||||
return (
|
||||
<textarea
|
||||
ref={taRef}
|
||||
className={textareaClass}
|
||||
value={onChange ? value : local}
|
||||
onChange={(e) => (onChange ? onChange(e.target.value) : setLocal(e.target.value))}
|
||||
onBlur={commit}
|
||||
onKeyDown={handleKey}
|
||||
rows={rows}
|
||||
maxLength={maxLength}
|
||||
placeholder={placeholder}
|
||||
/>
|
||||
)
|
||||
}
|
||||
|
||||
const displayText = value || placeholder || "点击编辑"
|
||||
const isPlaceholder = !value
|
||||
|
||||
return (
|
||||
<span
|
||||
className={`${textClass || ""} ${isPlaceholder ? "vv-inline-placeholder" : ""} ${
|
||||
disabled ? "vv-inline-disabled" : ""
|
||||
}`}
|
||||
onClick={() => {
|
||||
if (!disabled) onStartEdit()
|
||||
}}
|
||||
style={
|
||||
isPlaceholder
|
||||
? { color: "#9ca3af", cursor: disabled ? "not-allowed" : "text" }
|
||||
: { cursor: disabled ? "not-allowed" : "text" }
|
||||
}
|
||||
>
|
||||
{displayText}
|
||||
</span>
|
||||
)
|
||||
}
|
||||
|
||||
export default InlineText
|
||||
@@ -106,10 +106,10 @@ const CATEGORY_LABELS: Record<string, string> = {
|
||||
const GENDER_LABEL = (v: PresetVoice) => {
|
||||
if (v.gender_label) return v.gender_label
|
||||
const g = v.gender
|
||||
if (g === "female") return "女声"
|
||||
if (g === "male") return "男声"
|
||||
if (g === "child") return "童声"
|
||||
return "其他"
|
||||
if (g === "female") return "女声·女声"
|
||||
if (g === "male") return "男声·男声"
|
||||
if (g === "child") return "童声·童声"
|
||||
return "性别未标注·其他"
|
||||
}
|
||||
|
||||
const AVATAR_BG = (gender?: string) => {
|
||||
@@ -303,7 +303,7 @@ const PresetVoicePickerModal: React.FC<Props> = ({
|
||||
{v.avatar_url ? (
|
||||
<img src={v.avatar_url} alt={v.name} />
|
||||
) : (
|
||||
<UserOutlined className="vv-fs-22" />
|
||||
<UserOutlined style={{ fontSize: 22 }} />
|
||||
)}
|
||||
</div>
|
||||
<div className="vv-voice-card-name" title={v.name}>
|
||||
|
||||
@@ -1,315 +0,0 @@
|
||||
import React, { useMemo } from "react"
|
||||
import {
|
||||
UploadOutlined,
|
||||
FolderOpenOutlined,
|
||||
ArrowUpOutlined,
|
||||
ArrowDownOutlined,
|
||||
CloseOutlined,
|
||||
} from "@ant-design/icons"
|
||||
import { VVSpinner } from "./VVSpinner"
|
||||
import { renderMarkdown } from "../utils/renderMarkdown"
|
||||
import { CheckCircleFilled } from "@ant-design/icons"
|
||||
import { VoicePickerSection } from "./VoicePickerSection"
|
||||
import type { UploadedImage, RefVideo, RefAudio, VoicePanel, ImageAnalysisResult } from "../types"
|
||||
import type { ImageProductAnalysis, StyleStrength } from "@/api/viral-video/types"
|
||||
import { STYLE_STRENGTHS } from "@/api/viral-video/types"
|
||||
import type { VoiceClone } from "@/api/voice-clone"
|
||||
import type { AssetItem } from "@/api/assets"
|
||||
|
||||
export interface Step1UploadPanelProps {
|
||||
images: UploadedImage[]
|
||||
refVideo: RefVideo
|
||||
voicePanel: VoicePanel
|
||||
refAudio: RefAudio
|
||||
voiceTipsOpen: boolean
|
||||
myVoices: VoiceClone[]
|
||||
myVoicesLoading: boolean
|
||||
voiceAssets: { id: string; name: string; url: string; desc?: string }[]
|
||||
playingVoiceId: string | null
|
||||
styleStrength: StyleStrength
|
||||
imageAnalysis: ImageAnalysisResult | null
|
||||
isAnalyzing: boolean
|
||||
analysisError?: string
|
||||
canAnalyze: boolean
|
||||
analyzeBtnText: React.ReactNode
|
||||
// callbacks - images
|
||||
onPickLocalImages: () => void
|
||||
onPickAssetImages: () => void
|
||||
onRemoveImage: (localId: string) => void
|
||||
onMoveImage: (idx: number, dir: -1 | 1) => void
|
||||
onClearImages: () => void
|
||||
onImagesSelected: (files: FileList) => void
|
||||
onAnalyze: () => void
|
||||
// callbacks - ref video
|
||||
onPickLocalVideo: () => void
|
||||
onVideoSelected: (file: File) => void
|
||||
onRemoveRefVideo: () => void
|
||||
onStyleStrengthChange: (v: StyleStrength) => void
|
||||
// callbacks - voice (delegated to VoicePickerSection)
|
||||
onPickLocalAudio: () => void
|
||||
onPickAssetAudio: () => void
|
||||
onRemoveRefAudio: () => void
|
||||
onToggleVoicePanel: (p: VoicePanel) => void
|
||||
onSelectMyVoice: (v: VoiceClone) => void
|
||||
onOpenCloneModal: () => void
|
||||
onSelectVoiceAsset: (a: AssetItem) => void
|
||||
onToggleVoicePlay: (v: { id: string; url: string }) => void
|
||||
onVoiceSelected: (file: File) => void
|
||||
onToggleVoiceTips: () => void
|
||||
// refs
|
||||
imgInputRef: React.Ref<HTMLInputElement>
|
||||
videoInputRef: React.Ref<HTMLInputElement>
|
||||
voiceInputRef: React.Ref<HTMLInputElement>
|
||||
}
|
||||
|
||||
const fmtSize = (bytes: number | undefined) => {
|
||||
if (!bytes || bytes <= 0) return "--"
|
||||
if (bytes < 1024) return `${bytes}B`
|
||||
if (bytes < 1024 * 1024) return `${(bytes / 1024).toFixed(1)}KB`
|
||||
return `${(bytes / 1024 / 1024).toFixed(1)}MB`
|
||||
}
|
||||
|
||||
function RecognitionBlock({
|
||||
imageAnalysis,
|
||||
isAnalyzing,
|
||||
}: {
|
||||
imageAnalysis: ImageAnalysisResult | null
|
||||
isAnalyzing: boolean
|
||||
}) {
|
||||
const images: ImageProductAnalysis[] = useMemo(() => {
|
||||
return (
|
||||
(imageAnalysis?.images as ImageProductAnalysis[] | undefined) ||
|
||||
(imageAnalysis?.products as ImageProductAnalysis[] | undefined) ||
|
||||
[]
|
||||
)
|
||||
}, [imageAnalysis])
|
||||
|
||||
if (isAnalyzing) {
|
||||
return (
|
||||
<div className="vv-recog">
|
||||
<div className="vv-recog-title">
|
||||
<VVSpinner color="#7c3aed" className="vv-inline-spinner" />
|
||||
识别描述汇览
|
||||
</div>
|
||||
<div className="vv-muted">AI 正在识别画面…</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
if (images.length === 0) return null
|
||||
return (
|
||||
<div className="vv-recog">
|
||||
<div className="vv-recog-title">
|
||||
<CheckCircleFilled style={{ color: "#10b981" }} />
|
||||
识别描述汇览
|
||||
</div>
|
||||
{images.map((p, i) => {
|
||||
const meta = [p.name || "未识别", p.brand, p.category].filter(Boolean)
|
||||
return (
|
||||
<div key={i} className="vv-recog-item vv-recog-md">
|
||||
<div className="vv-recog-line">
|
||||
<span className="vv-recog-k">图片{i + 1}：</span>
|
||||
<span>{meta.join(" · ")}</span>
|
||||
</div>
|
||||
{p.summary_markdown ? (
|
||||
<div
|
||||
className="vv-md-body"
|
||||
dangerouslySetInnerHTML={{ __html: renderMarkdown(p.summary_markdown) }}
|
||||
/>
|
||||
) : (
|
||||
<div className="vv-muted">（暂无叙述描述）</div>
|
||||
)}
|
||||
</div>
|
||||
)
|
||||
})}
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export const Step1UploadPanel: React.FC<Step1UploadPanelProps> = (p) => {
|
||||
return (
|
||||
<div className="vv-section">
|
||||
<div className="vv-step1-head">
|
||||
<span className="vv-step-badge">1</span>
|
||||
<span className="vv-step1-title">STEP 1 上传素材并分析图片</span>
|
||||
</div>
|
||||
<div className="vv-section-body vv-section-body-scroll">
|
||||
<div className="vv-scroll-inner">
|
||||
{/* 图片按钮 */}
|
||||
<div className="vv-action-row">
|
||||
<button className="vv-action-btn" onClick={p.onPickLocalImages}>
|
||||
<UploadOutlined /> 本地上传
|
||||
</button>
|
||||
<button className="vv-action-btn" onClick={p.onPickAssetImages}>
|
||||
<FolderOpenOutlined /> 从素材库选择图片
|
||||
</button>
|
||||
</div>
|
||||
<input
|
||||
ref={p.imgInputRef}
|
||||
type="file"
|
||||
multiple
|
||||
accept="image/*"
|
||||
hidden
|
||||
onChange={(e) => {
|
||||
if (e.target.files) p.onImagesSelected(e.target.files)
|
||||
e.target.value = ""
|
||||
}}
|
||||
/>
|
||||
|
||||
<div className="vv-count-row">
|
||||
<span className="vv-muted">
|
||||
已选 <strong style={{ color: "#111827" }}>{p.images.length}</strong>/9
|
||||
</span>
|
||||
{p.images.length > 0 && (
|
||||
<button className="vv-link-btn" onClick={p.onClearImages}>
|
||||
清空图片
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{p.images.length > 0 && (
|
||||
<div className="vv-file-list">
|
||||
{p.images.map((im, i) => (
|
||||
<div key={im.localId} className="vv-file-item">
|
||||
<div className="vv-file-thumb">
|
||||
<span className="vv-img-badge">{i + 1}</span>
|
||||
<img src={im.ossUrl || im.url} alt={im.name} />
|
||||
</div>
|
||||
<div className="vv-file-info">
|
||||
<div className="vv-file-name" title={im.name}>
|
||||
{im.name}
|
||||
</div>
|
||||
<div className="vv-file-sub">
|
||||
{im.uploading ? (
|
||||
<>
|
||||
<span className="vv-progress-mini-inline" />
|
||||
上传中 {im.pct}%
|
||||
</>
|
||||
) : (
|
||||
<>{fmtSize(im.size)}</>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
<div className="vv-file-actions">
|
||||
<button
|
||||
className="vv-icon-btn"
|
||||
onClick={() => p.onMoveImage(i, -1)}
|
||||
disabled={i === 0 || im.uploading}
|
||||
title="上移"
|
||||
>
|
||||
<ArrowUpOutlined />
|
||||
</button>
|
||||
<button
|
||||
className="vv-icon-btn"
|
||||
onClick={() => p.onMoveImage(i, 1)}
|
||||
disabled={i === p.images.length - 1 || im.uploading}
|
||||
title="下移"
|
||||
>
|
||||
<ArrowDownOutlined />
|
||||
</button>
|
||||
<button
|
||||
className="vv-icon-btn vv-icon-btn-danger"
|
||||
onClick={() => p.onRemoveImage(im.localId)}
|
||||
disabled={!!im.uploading}
|
||||
title="删除"
|
||||
>
|
||||
<CloseOutlined />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 参考视频 */}
|
||||
<div className="vv-subblock">
|
||||
<div className="vv-subblock-head">
|
||||
<span className="vv-subblock-title">参考视频</span>
|
||||
<span className="vv-opt-tag">选填</span>
|
||||
<span className="vv-subblock-hint">最大 200MB · 最长 120 秒</span>
|
||||
</div>
|
||||
<div className="vv-action-row">
|
||||
<button className="vv-action-btn" onClick={p.onPickLocalVideo}>
|
||||
<UploadOutlined /> 本地上传视频
|
||||
</button>
|
||||
</div>
|
||||
<input
|
||||
ref={p.videoInputRef}
|
||||
type="file"
|
||||
accept="video/*"
|
||||
hidden
|
||||
onChange={(e) => {
|
||||
if (e.target.files?.[0]) p.onVideoSelected(e.target.files[0])
|
||||
e.target.value = ""
|
||||
}}
|
||||
/>
|
||||
{p.refVideo && (
|
||||
<div className="vv-ref-video-box">
|
||||
<video
|
||||
src={p.refVideo.ossUrl || p.refVideo.url}
|
||||
controls
|
||||
className="vv-ref-video-el"
|
||||
/>
|
||||
<div className="vv-file-info vv-mt-6">
|
||||
<div className="vv-file-name">{p.refVideo.name}</div>
|
||||
{p.refVideo.uploading && (
|
||||
<div className="vv-progress-bar vv-progress-bar-sm">
|
||||
<div className="vv-progress-fill" style={{ width: `${p.refVideo.pct}%` }} />
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
<button className="vv-link-btn" onClick={p.onRemoveRefVideo}>
|
||||
删除重传
|
||||
</button>
|
||||
{p.refVideo.ossUrl && (
|
||||
<div className="vv-mt-8">
|
||||
<div className="vv-label vv-mb-6">风格借鉴强度</div>
|
||||
<div className="vv-pill-row">
|
||||
{STYLE_STRENGTHS.map((s) => (
|
||||
<button
|
||||
key={s.value}
|
||||
className={`vv-pill ${p.styleStrength === s.value ? "active" : ""}`}
|
||||
onClick={() => p.onStyleStrengthChange(s.value)}
|
||||
>
|
||||
{s.label}
|
||||
</button>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
<VoicePickerSection
|
||||
refAudio={p.refAudio}
|
||||
voicePanel={p.voicePanel}
|
||||
voiceTipsOpen={p.voiceTipsOpen}
|
||||
myVoices={p.myVoices}
|
||||
myVoicesLoading={p.myVoicesLoading}
|
||||
voiceAssets={p.voiceAssets}
|
||||
playingVoiceId={p.playingVoiceId}
|
||||
voiceInputRef={p.voiceInputRef}
|
||||
onOpenCloneModal={p.onOpenCloneModal}
|
||||
onToggleVoicePanel={p.onToggleVoicePanel}
|
||||
onSelectMyVoice={p.onSelectMyVoice}
|
||||
onSelectVoiceAsset={p.onSelectVoiceAsset}
|
||||
onToggleVoicePlay={p.onToggleVoicePlay}
|
||||
onPickLocalAudio={p.onPickLocalAudio}
|
||||
onPickAssetAudio={p.onPickAssetAudio}
|
||||
onVoiceSelected={p.onVoiceSelected}
|
||||
onRemoveRefAudio={p.onRemoveRefAudio}
|
||||
onToggleVoiceTips={p.onToggleVoiceTips}
|
||||
/>
|
||||
|
||||
<RecognitionBlock imageAnalysis={p.imageAnalysis} isAnalyzing={p.isAnalyzing} />
|
||||
</div>
|
||||
|
||||
<button className="vv-btn vv-btn-primary" disabled={!p.canAnalyze} onClick={p.onAnalyze}>
|
||||
{p.analyzeBtnText}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default Step1UploadPanel
|
||||
@@ -1,237 +0,0 @@
|
||||
import React from "react"
|
||||
import { EditOutlined } from "@ant-design/icons"
|
||||
import { Select } from "antd"
|
||||
import { FUSION_LEVELS } from "@/api/viral-video/types"
|
||||
import type { FusionLevel } from "@/api/viral-video/types"
|
||||
import { VVSpinner } from "./VVSpinner"
|
||||
import { ErrorRetryBar } from "./ErrorRetryBar"
|
||||
import { StoryboardCard } from "./StoryboardCard"
|
||||
import type { SbDoc, StoryboardEditor } from "../hooks/useStoryboardEditor"
|
||||
import type { UploadedImage, UIStep, ViralVideoJob } from "../types"
|
||||
import {
|
||||
LANGUAGES,
|
||||
TARGET_CUSTOMERS,
|
||||
PERSONAS,
|
||||
STRUCTURES,
|
||||
PURPOSES,
|
||||
DURATIONS,
|
||||
} from "../ViralVideoPage.constants"
|
||||
import { getJobPhaseMessage } from "@/api/viral-video/types"
|
||||
|
||||
export interface Step2CopyPanelProps {
|
||||
// 启用状态
|
||||
step2Enabled: boolean
|
||||
uiStep: UIStep
|
||||
// 文案参数
|
||||
fusionLevel: FusionLevel
|
||||
userCopy: string
|
||||
industry: string
|
||||
targetCustomer: string
|
||||
persona: string
|
||||
language: string
|
||||
viralStructure: string
|
||||
marketingPurpose: string
|
||||
duration: number
|
||||
// 回调
|
||||
onFusionLevelChange: (v: FusionLevel) => void
|
||||
onUserCopyChange: (v: string) => void
|
||||
onParamChange: (patch: {
|
||||
industry?: string
|
||||
targetCustomer?: string
|
||||
persona?: string
|
||||
language?: string
|
||||
viralStructure?: string
|
||||
marketingPurpose?: string
|
||||
duration?: number
|
||||
}) => void
|
||||
onGenerateCopy: () => void
|
||||
// 分镜/流式预览
|
||||
storyboard: SbDoc | null
|
||||
scriptStreamText: string
|
||||
scriptStreamMessage?: string
|
||||
editor: StoryboardEditor
|
||||
images: UploadedImage[]
|
||||
locked: boolean
|
||||
job: ViralVideoJob | null
|
||||
copyError?: string
|
||||
}
|
||||
|
||||
export const Step2CopyPanel: React.FC<Step2CopyPanelProps> = (p) => {
|
||||
const isGenerating = p.uiStep === "step2_generating"
|
||||
const showGenerateBtn =
|
||||
p.uiStep !== "step2_copy_ready" && p.uiStep !== "step3_generating" && p.uiStep !== "step3_done"
|
||||
|
||||
return (
|
||||
<div className="vv-section">
|
||||
<div className="vv-step-subhead">
|
||||
<span className={`vv-step-badge ${p.step2Enabled ? "" : "vv-step-badge-dim"}`}>2</span>
|
||||
<span className="vv-step-subtitle">STEP 2 生成视频文案</span>
|
||||
{!p.step2Enabled && <span className="vv-step-lock">请先完成 STEP 1</span>}
|
||||
</div>
|
||||
<div className="vv-section-body vv-section-body-scroll">
|
||||
<div className="vv-scroll-inner">
|
||||
{/* 文案融合方式 */}
|
||||
<div className="vv-section-subtitle">文案融合方式</div>
|
||||
<div className="vv-fusion-grid">
|
||||
{FUSION_LEVELS.map((f) => (
|
||||
<button
|
||||
key={f.value}
|
||||
className={`vv-fusion-btn ${p.fusionLevel === f.value ? "active" : ""}`}
|
||||
onClick={() => p.onFusionLevelChange(f.value)}
|
||||
>
|
||||
<strong>{f.label}</strong>
|
||||
{f.desc}
|
||||
</button>
|
||||
))}
|
||||
</div>
|
||||
{(p.fusionLevel === "ai_polish" || p.fusionLevel === "user_primary") && (
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">
|
||||
{p.fusionLevel === "ai_polish" ? "写下你的文案草稿" : "写下你的文案/草稿"}
|
||||
</label>
|
||||
<textarea
|
||||
key={p.fusionLevel}
|
||||
className="vv-textarea"
|
||||
placeholder={
|
||||
p.fusionLevel === "ai_polish"
|
||||
? "请输入你的文案草稿，AI将在此基础上润色优化..."
|
||||
: "请输入你想要的原始文案，AI将严格保留你的表达..."
|
||||
}
|
||||
value={p.userCopy}
|
||||
onChange={(e) => p.onUserCopyChange(e.target.value)}
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 参数（2列网格） */}
|
||||
<div className="vv-form-grid">
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">
|
||||
行业与产品 <span className="vv-label-optional">（选填）</span>
|
||||
</label>
|
||||
<input
|
||||
className="vv-input"
|
||||
placeholder="如：家具店、宠物店、装修公司"
|
||||
value={p.industry}
|
||||
onChange={(e) => p.onParamChange({ industry: e.target.value })}
|
||||
/>
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">目标客户</label>
|
||||
<Select
|
||||
className="vv-select"
|
||||
style={{ width: "100%" }}
|
||||
placeholder="请选择目标客户"
|
||||
value={p.targetCustomer || undefined}
|
||||
onChange={(v) => p.onParamChange({ targetCustomer: v })}
|
||||
options={TARGET_CUSTOMERS.map((i) => ({ value: i, label: i }))}
|
||||
allowClear
|
||||
/>
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">台词语言</label>
|
||||
<Select
|
||||
className="vv-select"
|
||||
style={{ width: "100%" }}
|
||||
value={p.language}
|
||||
onChange={(v) => p.onParamChange({ language: v })}
|
||||
options={LANGUAGES.map((i) => ({ value: i, label: i }))}
|
||||
/>
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">我的人设</label>
|
||||
<Select
|
||||
className="vv-select"
|
||||
style={{ width: "100%" }}
|
||||
placeholder="请选择人设类型"
|
||||
optionFilterProp="children"
|
||||
value={p.persona || undefined}
|
||||
onChange={(v) => p.onParamChange({ persona: v })}
|
||||
options={PERSONAS.map((i) => ({ value: i, label: i }))}
|
||||
/>
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">爆款结构</label>
|
||||
<Select
|
||||
className="vv-select"
|
||||
style={{ width: "100%" }}
|
||||
value={p.viralStructure}
|
||||
onChange={(v) => p.onParamChange({ viralStructure: v })}
|
||||
options={STRUCTURES.map((i) => ({ value: i, label: i }))}
|
||||
/>
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">营销目的</label>
|
||||
<Select
|
||||
className="vv-select"
|
||||
style={{ width: "100%" }}
|
||||
placeholder="请选择营销目的"
|
||||
value={p.marketingPurpose || undefined}
|
||||
onChange={(v) => p.onParamChange({ marketingPurpose: v })}
|
||||
options={PURPOSES.map((i) => ({ value: i, label: i }))}
|
||||
/>
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">文案视频时长</label>
|
||||
<Select
|
||||
className="vv-select"
|
||||
style={{ width: "100%" }}
|
||||
value={p.duration}
|
||||
onChange={(v) => p.onParamChange({ duration: v })}
|
||||
options={DURATIONS.map((n) => ({ value: n, label: `${n}秒` }))}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* 短剧模式提示 */}
|
||||
{p.marketingPurpose && p.marketingPurpose.includes("短剧") && (
|
||||
<div className="vv-shortdrama-hint">
|
||||
💡 短剧模式已选中，对手戏角色将自动分配配角音色
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 文案审核错误（持久显示） */}
|
||||
{p.copyError && <ErrorRetryBar label="文案审核未通过" message={p.copyError} />}
|
||||
|
||||
{/* 分镜结果区 / 流式预览 */}
|
||||
<StoryboardCard
|
||||
uiStep={p.uiStep}
|
||||
scriptStreamText={p.scriptStreamText}
|
||||
scriptStreamMessage={p.scriptStreamMessage}
|
||||
jobPhaseMessage={getJobPhaseMessage(p.job)}
|
||||
storyboard={p.storyboard}
|
||||
editor={p.editor}
|
||||
images={p.images}
|
||||
locked={p.locked}
|
||||
onRegenerateCopy={p.onGenerateCopy}
|
||||
/>
|
||||
</div>
|
||||
|
||||
{showGenerateBtn && (
|
||||
<button
|
||||
className={`vv-btn vv-btn-primary ${!p.step2Enabled ? "vv-btn-disabled-gray" : ""}`}
|
||||
onClick={p.onGenerateCopy}
|
||||
disabled={isGenerating || !p.step2Enabled}
|
||||
title={!p.step2Enabled ? "请先完成图片分析" : ""}
|
||||
>
|
||||
{isGenerating ? (
|
||||
<>
|
||||
<VVSpinner /> AI 生成文案中…
|
||||
</>
|
||||
) : !p.step2Enabled ? (
|
||||
<>
|
||||
<EditOutlined /> 生成文案（请先完成图片分析）
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<EditOutlined /> 生成文案
|
||||
</>
|
||||
)}
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default Step2CopyPanel
|
||||
@@ -1,216 +0,0 @@
|
||||
import React from "react"
|
||||
import {
|
||||
VideoCameraOutlined,
|
||||
SoundOutlined,
|
||||
ReloadOutlined,
|
||||
HistoryOutlined,
|
||||
LoadingOutlined,
|
||||
} from "@ant-design/icons"
|
||||
import { Select } from "antd"
|
||||
import { VVSpinner } from "./VVSpinner"
|
||||
import { ErrorRetryBar } from "./ErrorRetryBar"
|
||||
import type { UIStep, ViralVideoJob, ViralVideoModel } from "../types"
|
||||
import { getJobPhaseMessage } from "@/api/viral-video/types"
|
||||
import { RATIOS, QUALITY_OPTIONS, UPSCALE_OPTIONS } from "../ViralVideoPage.constants"
|
||||
|
||||
export interface Step3VideoPanelProps {
|
||||
step3Enabled: boolean
|
||||
uiStep: UIStep
|
||||
videoRatio: string
|
||||
videoModel: string
|
||||
quality: string
|
||||
upscale: string
|
||||
duration: number
|
||||
estimatedCredits: number | null
|
||||
estimatedCreditsError: boolean
|
||||
isGenerating: boolean
|
||||
outputUrl: string | null
|
||||
videoError?: string
|
||||
creditsReady: boolean
|
||||
job: ViralVideoJob | null
|
||||
videoModels: ViralVideoModel[]
|
||||
locked: boolean
|
||||
// callbacks
|
||||
onRatioChange: (v: string) => void
|
||||
onModelChange: (v: string) => void
|
||||
onQualityChange: (v: string) => void
|
||||
onUpscaleChange: (v: string) => void
|
||||
onGenerate: () => void
|
||||
onReset: () => void
|
||||
onRetry: () => void
|
||||
}
|
||||
|
||||
export const Step3VideoPanel: React.FC<Step3VideoPanelProps> = (p) => {
|
||||
const isDone = p.uiStep === "step3_done"
|
||||
const isGenerating = p.uiStep === "step3_generating"
|
||||
const isFailed = p.uiStep === "failed"
|
||||
|
||||
const curM = p.videoModels.find((m) => m && m.key === p.videoModel)
|
||||
const supportedRes = Array.isArray(curM?.supported_resolutions)
|
||||
? curM!.supported_resolutions
|
||||
: QUALITY_OPTIONS.map((o) => o.v)
|
||||
const qualityPool =
|
||||
supportedRes && supportedRes.length > 0 ? supportedRes : QUALITY_OPTIONS.map((o) => o.v)
|
||||
const qualityOptions = QUALITY_OPTIONS.filter((o) => qualityPool.includes(o.v))
|
||||
|
||||
return (
|
||||
<>
|
||||
<div className="vv-section">
|
||||
<div className="vv-step-subhead vv-step3-head">
|
||||
<div className="vv-step3-head-left">
|
||||
<span className={`vv-step-badge ${p.step3Enabled ? "" : "vv-step-badge-dim"}`}>3</span>
|
||||
<span className="vv-step-subtitle">STEP 3 生成视频</span>
|
||||
</div>
|
||||
{p.step3Enabled && (
|
||||
<button
|
||||
className="vv-btn vv-btn-ghost vv-btn-xs vv-history-btn"
|
||||
onClick={() => {
|
||||
// 历史作品功能待开发，此处保留原行为（message 由父组件触发）
|
||||
}}
|
||||
title="历史作品"
|
||||
>
|
||||
<HistoryOutlined /> 历史作品
|
||||
</button>
|
||||
)}
|
||||
{!p.step3Enabled && <span className="vv-step-lock">请先完成 STEP 2</span>}
|
||||
</div>
|
||||
<div className="vv-step3-divider" />
|
||||
<div className="vv-section-body vv-section-body-scroll vv-step3-config-body">
|
||||
<div className="vv-scroll-inner">
|
||||
<div className="vv-step3-grid">
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">视频比例</label>
|
||||
<Select
|
||||
className="vv-select vv-select-step3"
|
||||
style={{ width: "100%" }}
|
||||
value={p.videoRatio}
|
||||
onChange={p.onRatioChange}
|
||||
disabled={p.locked}
|
||||
options={RATIOS.map((r) => ({ value: r.v, label: r.v }))}
|
||||
/>
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">视频模型</label>
|
||||
<Select
|
||||
className="vv-select vv-select-step3"
|
||||
style={{ width: "100%" }}
|
||||
value={p.videoModel}
|
||||
disabled={p.locked}
|
||||
onChange={(v) => p.onModelChange(v)}
|
||||
options={p.videoModels
|
||||
.filter((m) => m && typeof m === "object" && m.key)
|
||||
.map((m) => ({ value: m.key, label: m.display_name || m.key }))}
|
||||
/>
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">清晰度</label>
|
||||
<Select
|
||||
className="vv-select vv-select-step3"
|
||||
style={{ width: "100%" }}
|
||||
value={p.quality}
|
||||
onChange={p.onQualityChange}
|
||||
disabled={p.locked}
|
||||
options={qualityOptions.map((o) => ({ value: o.v, label: o.label }))}
|
||||
/>
|
||||
</div>
|
||||
<div className="vv-form-row">
|
||||
<label className="vv-label">成片超分</label>
|
||||
<Select
|
||||
className="vv-select vv-select-step3"
|
||||
style={{ width: "100%" }}
|
||||
value={p.upscale}
|
||||
onChange={p.onUpscaleChange}
|
||||
disabled={p.locked}
|
||||
options={UPSCALE_OPTIONS.map((m) => ({ value: m.v, label: m.label }))}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{p.creditsReady && (
|
||||
<div className="vv-credits">
|
||||
<span>
|
||||
<SoundOutlined style={{ marginRight: 4 }} />
|
||||
预计消耗
|
||||
</span>
|
||||
<strong>
|
||||
{p.estimatedCreditsError
|
||||
? "-- 积分"
|
||||
: p.estimatedCredits === null
|
||||
? "… 积分"
|
||||
: `${p.estimatedCredits.toFixed(2)} 积分`}
|
||||
</strong>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 视频预览 */}
|
||||
<div className="vv-section vv-section-preview">
|
||||
<div className="vv-section-body vv-preview-body">
|
||||
<div className="vv-preview">
|
||||
{isDone && p.outputUrl ? (
|
||||
<video src={p.outputUrl} controls />
|
||||
) : isGenerating ? (
|
||||
<div className="vv-preview-placeholder">
|
||||
<div className="ph-icon">
|
||||
<LoadingOutlined className="vv-loading-ic" />
|
||||
</div>
|
||||
<div className="ph-txt">
|
||||
AI 正在生成视频中…
|
||||
<br />
|
||||
预计 1-3 分钟，请勿关闭页面
|
||||
</div>
|
||||
{getJobPhaseMessage(p.job) && (
|
||||
<div className="vv-muted vv-mt-6 vv-fs-12">{getJobPhaseMessage(p.job)}</div>
|
||||
)}
|
||||
</div>
|
||||
) : (
|
||||
<div className="vv-preview-placeholder">
|
||||
<div className="ph-icon">🎬</div>
|
||||
<div className="ph-txt">完成前面步骤后视频将在此处预览</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* 错误条 */}
|
||||
{isFailed && p.videoError && (
|
||||
<ErrorRetryBar label="视频生成失败" message={p.videoError} onRetry={p.onRetry} />
|
||||
)}
|
||||
|
||||
{/* 主按钮 */}
|
||||
{!isDone ? (
|
||||
<button
|
||||
className={`vv-btn vv-btn-primary ${!p.step3Enabled ? "vv-btn-disabled-gray" : ""}`}
|
||||
onClick={p.onGenerate}
|
||||
disabled={isGenerating || !p.step3Enabled}
|
||||
title={!p.step3Enabled ? "请先在 STEP 2 生成文案" : ""}
|
||||
>
|
||||
{isGenerating ? (
|
||||
<>
|
||||
<VVSpinner /> AI 正在生成视频中…
|
||||
</>
|
||||
) : !p.step3Enabled ? (
|
||||
<>
|
||||
<VideoCameraOutlined /> 开始生成视频（请先生成文案）
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<VideoCameraOutlined /> 开始生成视频
|
||||
</>
|
||||
)}
|
||||
</button>
|
||||
) : (
|
||||
p.outputUrl && (
|
||||
<button className="vv-btn vv-btn-ghost vv-btn-block" onClick={p.onReset}>
|
||||
<ReloadOutlined /> 再生成一个
|
||||
</button>
|
||||
)
|
||||
)}
|
||||
</>
|
||||
)
|
||||
}
|
||||
|
||||
export default Step3VideoPanel
|
||||
@@ -1,327 +0,0 @@
|
||||
import React from "react"
|
||||
import { Select } from "antd"
|
||||
import { FileTextOutlined, SoundOutlined, ReloadOutlined } from "@ant-design/icons"
|
||||
import { InlineText } from "./InlineEditTextarea"
|
||||
import { StoryboardShotItem } from "./StoryboardShotItem"
|
||||
import { VVSpinner } from "./VVSpinner"
|
||||
import type { StoryboardEditor, SbDoc } from "../hooks/useStoryboardEditor"
|
||||
import type { UploadedImage, UIStep } from "../types"
|
||||
import { RATIOS } from "../ViralVideoPage.constants"
|
||||
|
||||
export interface StoryboardCardProps {
|
||||
/** 当前 uiStep（仅用于按钮/锁定判定） */
|
||||
uiStep: UIStep
|
||||
/** 当前流式预览文本（step2_generating 时优先展示） */
|
||||
scriptStreamText: string
|
||||
scriptStreamMessage?: string
|
||||
jobPhaseMessage?: string
|
||||
/** 真实 storyboard（已补默认值的 SbDoc） */
|
||||
storyboard: SbDoc | null
|
||||
editor: StoryboardEditor
|
||||
images: UploadedImage[]
|
||||
/** 是否锁定编辑（视频生成中/完成时为 true） */
|
||||
locked: boolean
|
||||
onRegenerateCopy?: () => void
|
||||
}
|
||||
|
||||
import { renderMarkdown } from "../utils/renderMarkdown"
|
||||
|
||||
/** 解析 voiceover_script 中的 [speaker:xxx] 标记，渲染为带颜色的 React 片段 */
|
||||
function renderVoiceoverWithSpeakers(text: string) {
|
||||
if (!text) return null
|
||||
const parts = text.split(/\[speaker:([^\]]+)\]/)
|
||||
if (parts.length === 1) return <span>{text}</span>
|
||||
const elements: React.ReactNode[] = []
|
||||
const MAIN_SPEAKERS = ["店主", "主播", "老板", "我", "主讲人"]
|
||||
for (let i = 1; i < parts.length; i += 2) {
|
||||
const speaker = parts[i]
|
||||
const segText = parts[i + 1] || ""
|
||||
const isMain = MAIN_SPEAKERS.includes(speaker)
|
||||
elements.push(
|
||||
<span key={i}>
|
||||
<strong className={isMain ? "vv-sb-speaker-main" : "vv-sb-speaker-guest"}>{speaker}</strong>
|
||||
<span>{segText}</span>
|
||||
</span>,
|
||||
)
|
||||
}
|
||||
return <>{elements}</>
|
||||
}
|
||||
|
||||
export const StoryboardCard: React.FC<StoryboardCardProps> = ({
|
||||
uiStep,
|
||||
scriptStreamText,
|
||||
scriptStreamMessage,
|
||||
jobPhaseMessage,
|
||||
storyboard,
|
||||
editor,
|
||||
images,
|
||||
locked,
|
||||
onRegenerateCopy,
|
||||
}) => {
|
||||
const {
|
||||
editingField,
|
||||
startEdit,
|
||||
cancelEdit,
|
||||
commitField,
|
||||
updateOverview,
|
||||
updateConstraint,
|
||||
addConstraint,
|
||||
removeConstraint,
|
||||
updateSbField,
|
||||
} = editor
|
||||
|
||||
/* ── 生成中：流式预览 or 骨架 ── */
|
||||
if (uiStep === "step2_generating") {
|
||||
const hasStream = scriptStreamText.trim().length > 0
|
||||
const stageMsg = scriptStreamMessage || jobPhaseMessage || "🎬 AI 正在创作视频分镜脚本..."
|
||||
if (!hasStream) {
|
||||
return (
|
||||
<div className="vv-copy-box vv-copy-loading">
|
||||
<VVSpinner />
|
||||
<span className="vv-copy-loading-text">{stageMsg}</span>
|
||||
<span className="vv-copy-loading-hint">预计 10-30 秒，请稍候</span>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
return (
|
||||
<div className="vv-copy-box vv-storyboard vv-streaming-box">
|
||||
<div className="vv-streaming-header">
|
||||
<VVSpinner />
|
||||
<span className="vv-streaming-title">{stageMsg}</span>
|
||||
<span className="vv-streaming-count">{scriptStreamText.length} 字</span>
|
||||
</div>
|
||||
<div className="vv-sb-doc vv-streaming-doc">
|
||||
<pre className="vv-streaming-pre">
|
||||
{scriptStreamText
|
||||
.replace(/<\/?[a-zA-Z_][\w:-]*(?:\s[^>]*)?>/g, "")
|
||||
.replace(/\s{3,}/g, "\n\n")
|
||||
.trim()}
|
||||
<span className="vv-streaming-caret" aria-hidden="true" />
|
||||
</pre>
|
||||
</div>
|
||||
<div className="vv-sb-actions vv-streaming-actions">
|
||||
<span className="vv-streaming-hint">AI 实时输出中，完成后可编辑分镜与口播文案</span>
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
/* ── 没有 storyboard 时的空态 ── */
|
||||
if (!storyboard) {
|
||||
if (uiStep === "step2_copy_ready" || uiStep === "step3_generating" || uiStep === "step3_done") {
|
||||
return (
|
||||
<div className="vv-copy-box vv-copy-loading">
|
||||
<span className="vv-muted">暂无分镜数据</span>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
return null
|
||||
}
|
||||
|
||||
const sb = storyboard
|
||||
const textCls = "vv-sb-field-val"
|
||||
const taCls = "vv-textarea vv-sb-doc-ta vv-sb-inline-edit-ta"
|
||||
|
||||
return (
|
||||
<div className="vv-copy-box vv-storyboard">
|
||||
<div className="vv-sb-doc">
|
||||
{/* 文案预览 markdown（只读） */}
|
||||
{sb.copy_display_markdown && (
|
||||
<div className="vv-copy-preview">
|
||||
<h4 className="vv-sb-h vv-copy-preview-h">
|
||||
<FileTextOutlined style={{ color: "#7c3aed", marginRight: 6 }} />
|
||||
文案预览
|
||||
</h4>
|
||||
<div
|
||||
className="vv-md-body"
|
||||
dangerouslySetInnerHTML={{ __html: renderMarkdown(sb.copy_display_markdown) }}
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 视频总览 */}
|
||||
<h4 className="vv-sb-h">视频总览</h4>
|
||||
<p className="vv-sb-inline-row">
|
||||
<strong className="vv-sb-field-k">整体主题：</strong>
|
||||
<InlineText
|
||||
value={sb.overview.theme}
|
||||
editing={!locked && editingField === "overview.theme"}
|
||||
onStartEdit={() => startEdit("overview.theme")}
|
||||
onCommit={(v) => commitField("overview.theme", v)}
|
||||
onCancel={cancelEdit}
|
||||
textClass={textCls}
|
||||
textareaClass={`${taCls} vv-sb-inline-edit-ta-sm`}
|
||||
disabled={locked}
|
||||
rows={1}
|
||||
/>
|
||||
</p>
|
||||
<p className="vv-sb-inline-row">
|
||||
<strong className="vv-sb-field-k">总时长：</strong>
|
||||
<InlineText
|
||||
value={sb.overview.total_duration}
|
||||
editing={!locked && editingField === "overview.total_duration"}
|
||||
onStartEdit={() => startEdit("overview.total_duration")}
|
||||
onCommit={(v) => commitField("overview.total_duration", v)}
|
||||
onCancel={cancelEdit}
|
||||
textClass={textCls}
|
||||
textareaClass={`${taCls} vv-sb-inline-edit-ta-sm`}
|
||||
disabled={locked}
|
||||
rows={1}
|
||||
/>
|
||||
</p>
|
||||
<p className="vv-sb-inline-row">
|
||||
<strong className="vv-sb-field-k">画幅：</strong>
|
||||
<Select
|
||||
className="vv-select vv-sb-inline-select"
|
||||
style={{ width: 90 }}
|
||||
value={sb.overview.aspect_ratio}
|
||||
disabled={locked}
|
||||
onChange={(v) => updateOverview({ aspect_ratio: v })}
|
||||
options={RATIOS.map((r) => ({ value: r.v, label: r.v }))}
|
||||
variant="borderless"
|
||||
/>
|
||||
</p>
|
||||
|
||||
{/* 场景与光线 */}
|
||||
<h4 className="vv-sb-h">场景与光线</h4>
|
||||
<p className="vv-sb-para">
|
||||
<InlineText
|
||||
value={sb.scene_and_lighting}
|
||||
editing={!locked && editingField === "scene_and_lighting"}
|
||||
onStartEdit={() => startEdit("scene_and_lighting")}
|
||||
onCommit={(v) => commitField("scene_and_lighting", v)}
|
||||
onCancel={cancelEdit}
|
||||
textClass={textCls}
|
||||
textareaClass={taCls}
|
||||
placeholder="描述整体场景氛围、光线方向与色温…"
|
||||
disabled={locked}
|
||||
multiline
|
||||
rows={2}
|
||||
/>
|
||||
</p>
|
||||
|
||||
{/* 逐镜头 */}
|
||||
<h4 className="vv-sb-h">逐秒镜头拆解</h4>
|
||||
<div className="vv-sb-doc-shots">
|
||||
{sb.shots.map((sh, idx) => (
|
||||
<StoryboardShotItem
|
||||
key={idx}
|
||||
index={idx}
|
||||
shot={sh}
|
||||
editor={editor}
|
||||
images={images}
|
||||
locked={locked}
|
||||
/>
|
||||
))}
|
||||
</div>
|
||||
|
||||
{/* 硬性约束 */}
|
||||
<h4 className="vv-sb-h">硬性约束</h4>
|
||||
<div className="vv-sb-tags-block">
|
||||
<div className="vv-sb-taglist">
|
||||
{sb.hard_constraints.map((c, i) => (
|
||||
<span key={i} className="vv-sb-tag vv-sb-tag-hard">
|
||||
<input
|
||||
className="vv-sb-tag-input"
|
||||
value={c}
|
||||
disabled={locked}
|
||||
onChange={(e) => updateConstraint("hard_constraints", i, e.target.value)}
|
||||
/>
|
||||
{!locked && (
|
||||
<button
|
||||
className="vv-sb-tag-del"
|
||||
onClick={() => removeConstraint("hard_constraints", i)}
|
||||
aria-label="删除"
|
||||
>
|
||||
×
|
||||
</button>
|
||||
)}
|
||||
</span>
|
||||
))}
|
||||
{!locked && (
|
||||
<button className="vv-sb-tag-add" onClick={() => addConstraint("hard_constraints")}>
|
||||
+ 添加
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* 负面提示词 */}
|
||||
<h4 className="vv-sb-h">负面提示词</h4>
|
||||
<div className="vv-sb-tags-block">
|
||||
<div className="vv-sb-taglist">
|
||||
{sb.negative_prompts.map((c, i) => (
|
||||
<span key={i} className="vv-sb-tag vv-sb-tag-neg">
|
||||
<input
|
||||
className="vv-sb-tag-input"
|
||||
value={c}
|
||||
disabled={locked}
|
||||
onChange={(e) => updateConstraint("negative_prompts", i, e.target.value)}
|
||||
/>
|
||||
{!locked && (
|
||||
<button
|
||||
className="vv-sb-tag-del"
|
||||
onClick={() => removeConstraint("negative_prompts", i)}
|
||||
aria-label="删除"
|
||||
>
|
||||
×
|
||||
</button>
|
||||
)}
|
||||
</span>
|
||||
))}
|
||||
{!locked && (
|
||||
<button className="vv-sb-tag-add" onClick={() => addConstraint("negative_prompts")}>
|
||||
+ 添加
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* 完整口播稿 */}
|
||||
<p className="vv-sb-field">
|
||||
<strong className="vv-sb-field-k">
|
||||
<SoundOutlined style={{ color: "#7c3aed", marginRight: 4 }} />
|
||||
口播稿：
|
||||
</strong>
|
||||
{editingField === "voiceover_script" ? (
|
||||
<textarea
|
||||
className={taCls}
|
||||
value={sb.voiceover_script}
|
||||
autoFocus
|
||||
onChange={(e) => updateSbField("voiceover_script", e.target.value)}
|
||||
onBlur={() => {
|
||||
// 受控模式：blur 即退出编辑（内容已实时同步）
|
||||
if (editingField === "voiceover_script") cancelEdit()
|
||||
}}
|
||||
onKeyDown={(e) => {
|
||||
if (e.key === "Escape") cancelEdit()
|
||||
}}
|
||||
placeholder="AI 合成配音用的完整口播稿…"
|
||||
rows={4}
|
||||
/>
|
||||
) : (
|
||||
<span
|
||||
className={textCls}
|
||||
onClick={() => {
|
||||
if (!locked) startEdit("voiceover_script")
|
||||
}}
|
||||
style={{ cursor: locked ? "not-allowed" : "text" }}
|
||||
>
|
||||
{renderVoiceoverWithSpeakers(sb.voiceover_script)}
|
||||
</span>
|
||||
)}
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="vv-sb-actions">
|
||||
{uiStep === "step2_copy_ready" && onRegenerateCopy && (
|
||||
<button className="vv-btn vv-btn-ghost" onClick={onRegenerateCopy}>
|
||||
<ReloadOutlined /> 重新生成文案
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default StoryboardCard
|
||||
@@ -1,182 +0,0 @@
|
||||
import React from "react"
|
||||
import { InlineText } from "./InlineEditTextarea"
|
||||
import type { StoryboardEditor, SbShot } from "../hooks/useStoryboardEditor"
|
||||
import type { UploadedImage } from "../types"
|
||||
|
||||
export interface StoryboardShotItemProps {
|
||||
index: number
|
||||
shot: SbShot
|
||||
editor: StoryboardEditor
|
||||
images: UploadedImage[]
|
||||
locked: boolean
|
||||
}
|
||||
|
||||
const MAIN_SPEAKERS = ["店主", "主播", "老板", "我", "主讲人"]
|
||||
|
||||
const SHOT_FIELD_KEY = (idx: number, field: string) => `shot.${idx}.${field}`
|
||||
|
||||
/** 单个镜头行：时间/说话人/景别/场景/动作/BGM/转场/参考图 */
|
||||
export const StoryboardShotItem: React.FC<StoryboardShotItemProps> = ({
|
||||
index,
|
||||
shot,
|
||||
editor,
|
||||
images,
|
||||
locked,
|
||||
}) => {
|
||||
const { editingField, startEdit, cancelEdit, commitField, updateShot } = editor
|
||||
const refImg =
|
||||
typeof shot.reference_image_index === "number" ? images[shot.reference_image_index] : undefined
|
||||
|
||||
const textCls = "vv-sb-field-val"
|
||||
const taCls = "vv-textarea vv-sb-doc-ta vv-sb-inline-edit-ta"
|
||||
|
||||
const isMainSpeaker = shot.speaker ? MAIN_SPEAKERS.includes(shot.speaker) : false
|
||||
|
||||
return (
|
||||
<div className="vv-sb-doc-shot">
|
||||
{/* 时间 */}
|
||||
<InlineText
|
||||
value={shot.time_range}
|
||||
editing={!locked && editingField === SHOT_FIELD_KEY(index, "time_range")}
|
||||
onStartEdit={() => startEdit(SHOT_FIELD_KEY(index, "time_range"))}
|
||||
onCommit={(v) => commitField(SHOT_FIELD_KEY(index, "time_range"), v)}
|
||||
onCancel={cancelEdit}
|
||||
textClass="vv-sb-time-doc"
|
||||
textareaClass={`${taCls} vv-sb-time-ta`}
|
||||
placeholder="0-3秒"
|
||||
disabled={locked}
|
||||
rows={1}
|
||||
/>
|
||||
|
||||
{/* 说话人标签（有 speaker 时展示，纯展示不加编辑） */}
|
||||
{shot.speaker && (
|
||||
<span
|
||||
className={`vv-sb-speaker-tag ${isMainSpeaker ? "vv-sb-speaker-tag-main" : "vv-sb-speaker-tag-guest"}`}
|
||||
>
|
||||
{shot.speaker}
|
||||
</span>
|
||||
)}
|
||||
|
||||
{/* 景别/角度与运镜 */}
|
||||
<p className="vv-sb-field">
|
||||
<strong className="vv-sb-field-k">景别/角度与运镜：</strong>
|
||||
<InlineText
|
||||
value={shot.shot_type_angle_movement}
|
||||
editing={!locked && editingField === SHOT_FIELD_KEY(index, "shot_type_angle_movement")}
|
||||
onStartEdit={() => startEdit(SHOT_FIELD_KEY(index, "shot_type_angle_movement"))}
|
||||
onCommit={(v) => commitField(SHOT_FIELD_KEY(index, "shot_type_angle_movement"), v)}
|
||||
onCancel={cancelEdit}
|
||||
textClass={textCls}
|
||||
textareaClass={taCls}
|
||||
disabled={locked}
|
||||
multiline
|
||||
rows={2}
|
||||
/>
|
||||
</p>
|
||||
|
||||
{/* 场景与对白 */}
|
||||
<p className="vv-sb-field">
|
||||
<strong className="vv-sb-field-k">场景与对白：</strong>
|
||||
<InlineText
|
||||
value={shot.scene_and_dialogue}
|
||||
editing={!locked && editingField === SHOT_FIELD_KEY(index, "scene_and_dialogue")}
|
||||
onStartEdit={() => startEdit(SHOT_FIELD_KEY(index, "scene_and_dialogue"))}
|
||||
onCommit={(v) => commitField(SHOT_FIELD_KEY(index, "scene_and_dialogue"), v)}
|
||||
onCancel={cancelEdit}
|
||||
textClass={textCls}
|
||||
textareaClass={taCls}
|
||||
disabled={locked}
|
||||
multiline
|
||||
rows={2}
|
||||
/>
|
||||
</p>
|
||||
|
||||
{/* 动作与真人细节 */}
|
||||
<p className="vv-sb-field">
|
||||
<strong className="vv-sb-field-k">动作与真人细节：</strong>
|
||||
<InlineText
|
||||
value={shot.action_details}
|
||||
editing={!locked && editingField === SHOT_FIELD_KEY(index, "action_details")}
|
||||
onStartEdit={() => startEdit(SHOT_FIELD_KEY(index, "action_details"))}
|
||||
onCommit={(v) => commitField(SHOT_FIELD_KEY(index, "action_details"), v)}
|
||||
onCancel={cancelEdit}
|
||||
textClass={textCls}
|
||||
textareaClass={taCls}
|
||||
disabled={locked}
|
||||
multiline
|
||||
rows={2}
|
||||
/>
|
||||
</p>
|
||||
|
||||
{/* 音效/BGM */}
|
||||
<p className="vv-sb-field">
|
||||
<strong className="vv-sb-field-k">音效/BGM：</strong>
|
||||
<InlineText
|
||||
value={shot.audio_bgm}
|
||||
editing={!locked && editingField === SHOT_FIELD_KEY(index, "audio_bgm")}
|
||||
onStartEdit={() => startEdit(SHOT_FIELD_KEY(index, "audio_bgm"))}
|
||||
onCommit={(v) => commitField(SHOT_FIELD_KEY(index, "audio_bgm"), v)}
|
||||
onCancel={cancelEdit}
|
||||
textClass={textCls}
|
||||
textareaClass={taCls}
|
||||
disabled={locked}
|
||||
multiline
|
||||
rows={1}
|
||||
/>
|
||||
</p>
|
||||
|
||||
{/* 转场 */}
|
||||
<p className="vv-sb-field">
|
||||
<strong className="vv-sb-field-k">转场：</strong>
|
||||
<InlineText
|
||||
value={shot.transition}
|
||||
editing={!locked && editingField === SHOT_FIELD_KEY(index, "transition")}
|
||||
onStartEdit={() => startEdit(SHOT_FIELD_KEY(index, "transition"))}
|
||||
onCommit={(v) => commitField(SHOT_FIELD_KEY(index, "transition"), v)}
|
||||
onCancel={cancelEdit}
|
||||
textClass={textCls}
|
||||
textareaClass={taCls}
|
||||
disabled={locked}
|
||||
multiline
|
||||
rows={1}
|
||||
/>
|
||||
</p>
|
||||
|
||||
{/* 参考图片 */}
|
||||
<p className="vv-sb-field vv-sb-ref-row">
|
||||
<strong className="vv-sb-field-k">参考图片：</strong>
|
||||
{refImg?.url ? (
|
||||
<span className="vv-sb-ref-badge">
|
||||
<img src={refImg.url} alt={`ref-${index}`} className="vv-sb-ref-thumb" />
|
||||
{!locked && (
|
||||
<button
|
||||
className="vv-sb-ref-x"
|
||||
onClick={() => updateShot(index, { reference_image_index: undefined })}
|
||||
aria-label="移除参考图"
|
||||
>
|
||||
×
|
||||
</button>
|
||||
)}
|
||||
</span>
|
||||
) : !locked && images.length > 0 ? (
|
||||
<span className="vv-sb-ref-pick">
|
||||
{images.map((im, i) => (
|
||||
<button
|
||||
key={im.localId}
|
||||
className="vv-sb-ref-opt"
|
||||
onClick={() => updateShot(index, { reference_image_index: i })}
|
||||
title={`@图片${i + 1}`}
|
||||
>
|
||||
<img src={im.url} alt={`img${i}`} />
|
||||
</button>
|
||||
))}
|
||||
</span>
|
||||
) : (
|
||||
<span className="vv-sb-muted">未指定</span>
|
||||
)}
|
||||
</p>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default StoryboardShotItem
|
||||
@@ -1,26 +0,0 @@
|
||||
import React from "react"
|
||||
|
||||
export interface VVSpinnerProps {
|
||||
size?: number
|
||||
color?: string
|
||||
style?: React.CSSProperties
|
||||
className?: string
|
||||
}
|
||||
|
||||
/**
|
||||
* 统一 spinner 组件：复用 vv-spinner CSS 动画类，避免多处散落的 LoadingOutlined
|
||||
*/
|
||||
export const VVSpinner: React.FC<VVSpinnerProps> = ({ size, color, style, className }) => {
|
||||
return (
|
||||
<span
|
||||
className={`vv-spinner ${className || ""}`}
|
||||
style={{
|
||||
...(size ? { width: size, height: size, borderWidth: Math.max(2, size / 8) } : {}),
|
||||
...(color ? { borderTopColor: color } : {}),
|
||||
...style,
|
||||
}}
|
||||
/>
|
||||
)
|
||||
}
|
||||
|
||||
export default VVSpinner
|
||||
@@ -1,216 +0,0 @@
|
||||
import React from "react"
|
||||
import {
|
||||
UserOutlined,
|
||||
AudioMutedOutlined,
|
||||
PlayCircleOutlined,
|
||||
PauseCircleOutlined,
|
||||
FileTextOutlined,
|
||||
FolderOpenOutlined,
|
||||
UploadOutlined,
|
||||
CaretDownOutlined,
|
||||
CaretRightOutlined,
|
||||
PlusOutlined,
|
||||
} from "@ant-design/icons"
|
||||
import { message } from "antd"
|
||||
import type { RefAudio, VoicePanel } from "../types"
|
||||
import type { VoiceClone } from "@/api/voice-clone"
|
||||
import { getVoiceClonePreview } from "@/api/voice-clone"
|
||||
import type { AssetItem } from "@/api/assets"
|
||||
import { VOICE_TIPS } from "../ViralVideoPage.constants"
|
||||
|
||||
export interface VoicePickerSectionProps {
|
||||
refAudio: RefAudio
|
||||
voicePanel: VoicePanel
|
||||
voiceTipsOpen: boolean
|
||||
myVoices: VoiceClone[]
|
||||
myVoicesLoading: boolean
|
||||
voiceAssets: { id: string; name: string; url: string; desc?: string }[]
|
||||
playingVoiceId: string | null
|
||||
voiceInputRef: React.Ref<HTMLInputElement>
|
||||
onOpenCloneModal: () => void
|
||||
onToggleVoicePanel: (p: VoicePanel) => void
|
||||
onSelectMyVoice: (v: VoiceClone) => void
|
||||
onSelectVoiceAsset: (a: AssetItem) => void
|
||||
onToggleVoicePlay: (v: { id: string; url: string }) => void
|
||||
onPickLocalAudio: () => void
|
||||
onPickAssetAudio: () => void
|
||||
onVoiceSelected: (file: File) => void
|
||||
onRemoveRefAudio: () => void
|
||||
onToggleVoiceTips: () => void
|
||||
}
|
||||
|
||||
export const VoicePickerSection: React.FC<VoicePickerSectionProps> = (p) => {
|
||||
return (
|
||||
<div className="vv-subblock">
|
||||
<div className="vv-subblock-head">
|
||||
<span className="vv-subblock-title">AI 配音</span>
|
||||
<span className="vv-opt-tag">选填</span>
|
||||
<span className="vv-subblock-hint">默认使用我的克隆音色，也可选择内置音色或上传配音</span>
|
||||
</div>
|
||||
|
||||
{/* 我的音色（主路径） */}
|
||||
<div className="vv-voice-section">
|
||||
<div className="vv-voice-section-head">
|
||||
<span className="vv-voice-section-title">
|
||||
<UserOutlined /> 我的音色
|
||||
</span>
|
||||
<button className="vv-link-btn vv-link-btn-sm" onClick={p.onOpenCloneModal}>
|
||||
<PlusOutlined /> 录制新音色
|
||||
</button>
|
||||
</div>
|
||||
{p.myVoicesLoading ? (
|
||||
<div className="vv-muted vv-voice-empty">加载中…</div>
|
||||
) : p.myVoices.length === 0 ? (
|
||||
<div className="vv-voice-empty">
|
||||
<AudioMutedOutlined className="vv-voice-empty-ic" />
|
||||
<div className="vv-voice-empty-text">还没有克隆音色</div>
|
||||
<button className="vv-btn vv-btn-primary vv-btn-sm" onClick={p.onOpenCloneModal}>
|
||||
去录制一个
|
||||
</button>
|
||||
</div>
|
||||
) : (
|
||||
<div className="vv-my-voice-list">
|
||||
{p.myVoices.map((v) => {
|
||||
const selected = p.refAudio?.source === "my_voice" && p.refAudio.id === v.id
|
||||
const playing = p.playingVoiceId === `my-${v.id}`
|
||||
return (
|
||||
<div
|
||||
key={v.id}
|
||||
className={`vv-my-voice-item ${selected ? "selected" : ""}`}
|
||||
onClick={() => p.onSelectMyVoice(v)}
|
||||
>
|
||||
<div className="vv-voice-radio" />
|
||||
<div className="vv-voice-info">
|
||||
<div className="vv-voice-name">{v.name}</div>
|
||||
<div className="vv-voice-desc">
|
||||
AI 克隆声音
|
||||
{v.gender
|
||||
? ` · ${v.gender === "male" ? "男" : v.gender === "female" ? "女" : ""}`
|
||||
: ""}
|
||||
</div>
|
||||
</div>
|
||||
<button
|
||||
className={`vv-voice-play ${playing ? "playing" : ""}`}
|
||||
onClick={(e) => {
|
||||
e.stopPropagation()
|
||||
if (v.sample_url) {
|
||||
p.onToggleVoicePlay({ id: `my-${v.id}`, url: v.sample_url })
|
||||
} else {
|
||||
message.info("正在获取试听音频…")
|
||||
getVoiceClonePreview(v.id)
|
||||
.then((r) => {
|
||||
if (r.audio_url)
|
||||
p.onToggleVoicePlay({ id: `my-${v.id}`, url: r.audio_url })
|
||||
})
|
||||
.catch(() => message.error("试听获取失败"))
|
||||
}
|
||||
}}
|
||||
>
|
||||
{playing ? <PauseCircleOutlined /> : <PlayCircleOutlined />}
|
||||
</button>
|
||||
</div>
|
||||
)
|
||||
})}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* 次要选项 */}
|
||||
<div className="vv-voice-alt-row">
|
||||
<button
|
||||
className={`vv-voice-alt-btn ${p.refAudio?.source === "preset" ? "selected" : ""}`}
|
||||
onClick={() => p.onToggleVoicePanel("preset")}
|
||||
>
|
||||
<FileTextOutlined /> 内置音色
|
||||
</button>
|
||||
<button
|
||||
className={`vv-voice-alt-btn ${p.refAudio?.source === "upload" || p.refAudio?.source === "library" ? "selected" : ""}`}
|
||||
onClick={() => p.onToggleVoicePanel("library")}
|
||||
>
|
||||
<FolderOpenOutlined /> 上传配音
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<input
|
||||
ref={p.voiceInputRef}
|
||||
type="file"
|
||||
accept="audio/*"
|
||||
hidden
|
||||
onChange={(e) => {
|
||||
if (e.target.files?.[0]) p.onVoiceSelected(e.target.files[0])
|
||||
e.target.value = ""
|
||||
}}
|
||||
/>
|
||||
{p.refAudio && (
|
||||
<div className="vv-selected-audio">
|
||||
<span className="vv-mr-6">🔊</span>
|
||||
<span className="vv-flex-1">
|
||||
已选：
|
||||
{p.refAudio.source === "my_voice"
|
||||
? `我的音色 · ${p.refAudio.name}`
|
||||
: p.refAudio.source === "preset"
|
||||
? `内置 · ${p.refAudio.name}`
|
||||
: p.refAudio.name}
|
||||
</span>
|
||||
<button className="vv-link-btn" onClick={p.onRemoveRefAudio}>
|
||||
移除
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
{p.voicePanel === "library" && (
|
||||
<div className="vv-voice-panel">
|
||||
<div className="vv-voice-panel-actions">
|
||||
<button className="vv-link-btn vv-link-btn-sm" onClick={p.onPickLocalAudio}>
|
||||
<UploadOutlined /> 本地上传音频文件
|
||||
</button>
|
||||
<button className="vv-link-btn vv-link-btn-sm" onClick={p.onPickAssetAudio}>
|
||||
<FolderOpenOutlined /> 从素材库选择
|
||||
</button>
|
||||
</div>
|
||||
{p.voiceAssets.length === 0 ? (
|
||||
<div className="vv-muted vv-text-center vv-p-12">暂无已上传的配音文件</div>
|
||||
) : (
|
||||
<div className="vv-voice-list">
|
||||
{p.voiceAssets.map((v) => (
|
||||
<div
|
||||
key={v.id}
|
||||
className={`vv-voice-item ${p.refAudio?.id === v.id && (p.refAudio.source === "library" || p.refAudio.source === "upload") ? "selected" : ""}`}
|
||||
onClick={() =>
|
||||
p.onSelectVoiceAsset({ id: v.id, name: v.name, file_url: v.url } as AssetItem)
|
||||
}
|
||||
>
|
||||
<div className="vv-voice-radio" />
|
||||
<div className="vv-voice-info">
|
||||
<div className="vv-voice-name">{v.name}</div>
|
||||
<div className="vv-voice-desc">{v.desc || ""}</div>
|
||||
</div>
|
||||
{v.url && (
|
||||
<button
|
||||
className={`vv-voice-play ${p.playingVoiceId === v.id ? "playing" : ""}`}
|
||||
onClick={(e) => {
|
||||
e.stopPropagation()
|
||||
p.onToggleVoicePlay(v)
|
||||
}}
|
||||
>
|
||||
{p.playingVoiceId === v.id ? <PauseCircleOutlined /> : <PlayCircleOutlined />}
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div className="vv-tips">
|
||||
<button className="vv-tips-head" onClick={p.onToggleVoiceTips}>
|
||||
{p.voiceTipsOpen ? <CaretDownOutlined /> : <CaretRightOutlined />}
|
||||
支持格式与录音建议
|
||||
</button>
|
||||
{p.voiceTipsOpen && <div className="vv-tips-body">{VOICE_TIPS}</div>}
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default VoicePickerSection
|
||||
@@ -1,140 +0,0 @@
|
||||
import { useCallback, useState } from "react"
|
||||
|
||||
// 最小化的 Storyboard 类型，避免循环依赖（字段名与 api/viral-video/types 对齐）
|
||||
export interface SbShot {
|
||||
time_range: string
|
||||
speaker?: string
|
||||
shot_type_angle_movement: string
|
||||
scene_and_dialogue: string
|
||||
action_details: string
|
||||
audio_bgm: string
|
||||
transition: string
|
||||
reference_image_index?: number
|
||||
}
|
||||
export interface SbOverview {
|
||||
theme: string
|
||||
total_duration: string
|
||||
aspect_ratio: string
|
||||
}
|
||||
export interface SbDoc {
|
||||
overview: SbOverview
|
||||
scene_and_lighting: string
|
||||
shots: SbShot[]
|
||||
hard_constraints: string[]
|
||||
negative_prompts: string[]
|
||||
voiceover_script: string
|
||||
copy_display_markdown: string
|
||||
}
|
||||
|
||||
export type SbUpdater = (patch: (sb: SbDoc) => SbDoc) => void
|
||||
|
||||
export interface StoryboardEditor {
|
||||
editingField: string | null
|
||||
startEdit: (field: string) => void
|
||||
cancelEdit: () => void
|
||||
updateShot: (idx: number, patch: Partial<SbShot>) => void
|
||||
updateOverview: (patch: Partial<SbOverview>) => void
|
||||
updateSbField: <K extends keyof SbDoc>(key: K, value: SbDoc[K]) => void
|
||||
updateConstraint: (list: "hard_constraints" | "negative_prompts", idx: number, v: string) => void
|
||||
addConstraint: (list: "hard_constraints" | "negative_prompts") => void
|
||||
removeConstraint: (list: "hard_constraints" | "negative_prompts", idx: number) => void
|
||||
commitField: (field: string, value: string) => void
|
||||
}
|
||||
|
||||
/**
|
||||
* 分镜脚本编辑器 hook：管理 editingField 状态，以及 storyboard 字段级更新方法。
|
||||
* 父组件传入 updateSb(fn) 即可，fn 接收当前 sb 返回新 sb（immer 风格）。
|
||||
*/
|
||||
export function useStoryboardEditor(updateSb: SbUpdater): StoryboardEditor {
|
||||
const [editingField, setEditingField] = useState<string | null>(null)
|
||||
|
||||
const startEdit = useCallback((field: string) => {
|
||||
setEditingField(field)
|
||||
}, [])
|
||||
|
||||
const cancelEdit = useCallback(() => {
|
||||
setEditingField(null)
|
||||
}, [])
|
||||
|
||||
const updateShot = useCallback(
|
||||
(idx: number, patch: Partial<SbShot>) => {
|
||||
updateSb((sb) => ({
|
||||
...sb,
|
||||
shots: sb.shots.map((s, i) => (i === idx ? { ...s, ...patch } : s)),
|
||||
}))
|
||||
},
|
||||
[updateSb],
|
||||
)
|
||||
|
||||
const updateOverview = useCallback(
|
||||
(patch: Partial<SbOverview>) => {
|
||||
updateSb((sb) => ({ ...sb, overview: { ...sb.overview, ...patch } }))
|
||||
},
|
||||
[updateSb],
|
||||
)
|
||||
|
||||
const updateSbField = useCallback(
|
||||
<K extends keyof SbDoc>(key: K, value: SbDoc[K]) => {
|
||||
updateSb((sb) => ({ ...sb, [key]: value }))
|
||||
},
|
||||
[updateSb],
|
||||
)
|
||||
|
||||
const updateConstraint = useCallback(
|
||||
(list: "hard_constraints" | "negative_prompts", idx: number, v: string) => {
|
||||
updateSb((sb) => {
|
||||
const arr = [...sb[list]]
|
||||
arr[idx] = v
|
||||
return { ...sb, [list]: arr }
|
||||
})
|
||||
},
|
||||
[updateSb],
|
||||
)
|
||||
|
||||
const addConstraint = useCallback(
|
||||
(list: "hard_constraints" | "negative_prompts") => {
|
||||
updateSb((sb) => ({ ...sb, [list]: [...sb[list], ""] }))
|
||||
},
|
||||
[updateSb],
|
||||
)
|
||||
|
||||
const removeConstraint = useCallback(
|
||||
(list: "hard_constraints" | "negative_prompts", idx: number) => {
|
||||
updateSb((sb) => ({ ...sb, [list]: sb[list].filter((_, i) => i !== idx) }))
|
||||
},
|
||||
[updateSb],
|
||||
)
|
||||
|
||||
const commitField = useCallback(
|
||||
(field: string, value: string) => {
|
||||
if (field.startsWith("shot.")) {
|
||||
const parts = field.split(".")
|
||||
const idx = Number(parts[1])
|
||||
const shotField = parts.slice(2).join(".") as keyof SbShot
|
||||
updateShot(idx, { [shotField]: value } as Partial<SbShot>)
|
||||
} else if (field.startsWith("overview.")) {
|
||||
const f = field.split(".")[1] as keyof SbOverview
|
||||
updateOverview({ [f]: value } as Partial<SbOverview>)
|
||||
} else if (field === "scene_and_lighting" || field === "voiceover_script") {
|
||||
updateSbField(field, value)
|
||||
}
|
||||
setEditingField(null)
|
||||
},
|
||||
[updateShot, updateOverview, updateSbField],
|
||||
)
|
||||
|
||||
return {
|
||||
editingField,
|
||||
startEdit,
|
||||
cancelEdit,
|
||||
updateShot,
|
||||
updateOverview,
|
||||
updateSbField,
|
||||
updateConstraint,
|
||||
addConstraint,
|
||||
removeConstraint,
|
||||
commitField,
|
||||
}
|
||||
}
|
||||
|
||||
export default useStoryboardEditor
|
||||
@@ -1,403 +0,0 @@
|
||||
import { useCallback, useEffect } from "react"
|
||||
import axios from "axios"
|
||||
import { message } from "antd"
|
||||
import { isVideoStage, isCopyStage, getJobStage } from "@/api/viral-video/types"
|
||||
import type { UIStep, TabTask } from "../types"
|
||||
import {
|
||||
retryViralVideo,
|
||||
analyzeViralImages,
|
||||
generateViralCopy,
|
||||
confirmViralCopy,
|
||||
} from "@/api/viral-video"
|
||||
import type { CopyResult } from "@/api/viral-video/types"
|
||||
import type { ViralVideoJob } from "@/api/viral-video/types"
|
||||
import { applyPollUpdate } from "../utils/applyPollUpdate"
|
||||
import {
|
||||
useViralVideoMedia,
|
||||
type UseViralVideoMediaOptions,
|
||||
type MediaActions,
|
||||
} from "./useViralVideoMedia"
|
||||
import { resolveModelSwitch } from "../utils/resolveModelSwitch"
|
||||
|
||||
export interface ViralVideoActionsApi extends MediaActions {
|
||||
resetTask: () => void
|
||||
handleAnalyzeImages: () => void
|
||||
handleGenerateCopy: () => void
|
||||
handleGenerateVideo: () => void
|
||||
handleRetry: () => void
|
||||
onPollUpdate: (job: ViralVideoJob) => void
|
||||
onScriptDelta: (ev: { data: { full_text?: string }; progress?: number; message?: string }) => void
|
||||
handleModelChange: (
|
||||
v: string,
|
||||
videoModels: { key: string; supported_resolutions?: string[]; max_duration?: number }[],
|
||||
) => void
|
||||
setTask: (patch: Partial<TabTask> | ((t: TabTask) => TabTask)) => void
|
||||
setAssetPicker: React.Dispatch<
|
||||
React.SetStateAction<{ open: boolean; kind: "image" | "video" | "voice"; multiple: boolean }>
|
||||
>
|
||||
setCloneModalOpen: React.Dispatch<React.SetStateAction<boolean>>
|
||||
setPresetVoicePickerOpen: React.Dispatch<React.SetStateAction<boolean>>
|
||||
refreshVoiceAssets: () => void
|
||||
reloadMyVoices: () => void
|
||||
}
|
||||
|
||||
export interface UseViralVideoHandlersOptions extends Omit<UseViralVideoMediaOptions, "setTask"> {
|
||||
activeId: string
|
||||
setTasks: React.Dispatch<React.SetStateAction<TabTask[]>>
|
||||
videoModels: {
|
||||
key: string
|
||||
supported_resolutions?: string[]
|
||||
max_duration?: number
|
||||
display_name?: string
|
||||
}[]
|
||||
}
|
||||
|
||||
function emptyTask(id: string, title: string): TabTask {
|
||||
return {
|
||||
id,
|
||||
title,
|
||||
images: [],
|
||||
refVideo: null,
|
||||
voicePanel: null,
|
||||
refAudio: null,
|
||||
fusionLevel: "ai_full",
|
||||
styleStrength: "medium",
|
||||
userCopy: "",
|
||||
industry: "",
|
||||
targetCustomer: "",
|
||||
language: "中文(普通话)",
|
||||
viralStructure: "反差破局+亮明观点+还原现状",
|
||||
marketingPurpose: "",
|
||||
duration: 20,
|
||||
persona: "",
|
||||
videoRatio: "9:16",
|
||||
videoModel: "seedance-2.5",
|
||||
quality: "480p",
|
||||
upscale: "default",
|
||||
estimatedCredits: null,
|
||||
estimatedCreditsError: false,
|
||||
uiStep: "step1_upload",
|
||||
imageAnalysis: null,
|
||||
storyboard: null,
|
||||
voiceTipsOpen: false,
|
||||
job: null,
|
||||
jobId: null,
|
||||
playingVoiceId: null,
|
||||
scriptStreamText: "",
|
||||
scriptStreamProgress: 0,
|
||||
scriptStreamMessage: "",
|
||||
}
|
||||
}
|
||||
|
||||
const CLEAR_STREAM = {
|
||||
scriptStreamText: "",
|
||||
scriptStreamProgress: 0,
|
||||
scriptStreamMessage: "",
|
||||
} as const
|
||||
|
||||
/**
|
||||
* 业务 action hook：聚合素材操作 + 三步骤 submit/retry/polling/model switch 等业务动作。
|
||||
*/
|
||||
export function useViralVideoHandlers(opts: UseViralVideoHandlersOptions): ViralVideoActionsApi {
|
||||
const {
|
||||
task,
|
||||
activeId,
|
||||
setTasks,
|
||||
setAssetPicker,
|
||||
setCloneModalOpen,
|
||||
refreshVoiceAssets,
|
||||
reloadMyVoices,
|
||||
setPresetVoicePickerOpen,
|
||||
} = opts
|
||||
|
||||
const setTask = useCallback(
|
||||
(patch: Partial<TabTask> | ((t: TabTask) => TabTask)) => {
|
||||
setTasks((ts) =>
|
||||
ts.map((t) =>
|
||||
t.id === activeId ? (typeof patch === "function" ? patch(t) : { ...t, ...patch }) : t,
|
||||
),
|
||||
)
|
||||
},
|
||||
[activeId, setTasks],
|
||||
)
|
||||
|
||||
const media = useViralVideoMedia({
|
||||
task,
|
||||
setTask,
|
||||
setAssetPicker,
|
||||
setCloneModalOpen,
|
||||
setPresetVoicePickerOpen,
|
||||
refreshVoiceAssets,
|
||||
reloadMyVoices,
|
||||
})
|
||||
|
||||
// 卸载时停掉正在播放的音频
|
||||
useEffect(() => () => media.audioRef.current?.pause(), [media.audioRef])
|
||||
|
||||
const resetTask = () => {
|
||||
media.audioRef.current?.pause()
|
||||
setTasks((ts) => ts.map((t) => (t.id === activeId ? emptyTask(t.id, t.title) : t)))
|
||||
}
|
||||
|
||||
const handleAnalyzeImages = async () => {
|
||||
const readyImgs = task.images.filter((i) => i.ossUrl && !i.uploading)
|
||||
if (readyImgs.length === 0) {
|
||||
message.warning("请至少上传 1 张产品图")
|
||||
return
|
||||
}
|
||||
if (task.images.some((i) => i.uploading)) {
|
||||
message.warning("图片上传中，请稍候")
|
||||
return
|
||||
}
|
||||
if (task.refVideo?.uploading) {
|
||||
message.warning("参考视频上传中，请稍候")
|
||||
return
|
||||
}
|
||||
setTask({
|
||||
uiStep: "step1_analyzing",
|
||||
jobId: null,
|
||||
job: null,
|
||||
analysisError: undefined,
|
||||
copyError: undefined,
|
||||
videoError: undefined,
|
||||
imageAnalysis: undefined,
|
||||
storyboard: undefined,
|
||||
...CLEAR_STREAM,
|
||||
estimatedCredits: null,
|
||||
estimatedCreditsError: false,
|
||||
})
|
||||
try {
|
||||
const job = await analyzeViralImages({
|
||||
images: readyImgs.map((i) => i.ossUrl as string),
|
||||
reference_video_url: task.refVideo?.ossUrl || undefined,
|
||||
style_strength: task.refVideo?.ossUrl ? task.styleStrength : undefined,
|
||||
})
|
||||
setTask({ job, jobId: job.id })
|
||||
message.success("已提交图片分析…")
|
||||
} catch (err: unknown) {
|
||||
message.error(err instanceof Error ? err.message : "分析失败")
|
||||
setTask({
|
||||
uiStep: "failed",
|
||||
analysisError: err instanceof Error ? err.message : "分析失败",
|
||||
copyError: undefined,
|
||||
videoError: undefined,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
const handleGenerateCopy = async () => {
|
||||
if (!task.jobId) {
|
||||
message.warning("请先完成图片分析")
|
||||
return
|
||||
}
|
||||
setTask({
|
||||
uiStep: "step2_generating",
|
||||
copyError: undefined,
|
||||
videoError: undefined,
|
||||
analysisError: undefined,
|
||||
...CLEAR_STREAM,
|
||||
})
|
||||
try {
|
||||
const job = await generateViralCopy(task.jobId, {
|
||||
industry: task.industry || undefined,
|
||||
target_customer: task.targetCustomer || undefined,
|
||||
viral_structure: task.viralStructure || undefined,
|
||||
marketing_purpose: task.marketingPurpose || undefined,
|
||||
duration: task.duration,
|
||||
persona_id: task.persona || undefined,
|
||||
user_copy_text:
|
||||
(task.fusionLevel === "user_primary" || task.fusionLevel === "ai_polish") && task.userCopy
|
||||
? task.userCopy
|
||||
: undefined,
|
||||
fusion_level: task.fusionLevel,
|
||||
style_strength: task.refVideo?.ossUrl ? task.styleStrength : undefined,
|
||||
voice_id:
|
||||
task.refAudio?.source === "preset"
|
||||
? task.refAudio.id
|
||||
: task.refAudio?.source === "my_voice"
|
||||
? task.refAudio.ttsVoiceId || task.refAudio.id
|
||||
: undefined,
|
||||
voice_source: task.refAudio?.source || undefined,
|
||||
video_ratio: task.videoRatio,
|
||||
video_model: task.videoModel,
|
||||
})
|
||||
setTask({ job })
|
||||
} catch (err: unknown) {
|
||||
message.error(err instanceof Error ? err.message : "文案生成失败")
|
||||
setTask({
|
||||
uiStep: "failed",
|
||||
copyError: err instanceof Error ? err.message : "文案生成失败",
|
||||
videoError: undefined,
|
||||
analysisError: undefined,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
const handleGenerateVideo = async () => {
|
||||
setTask({
|
||||
uiStep: "step3_generating",
|
||||
videoError: undefined,
|
||||
copyError: undefined,
|
||||
analysisError: undefined,
|
||||
...CLEAR_STREAM,
|
||||
})
|
||||
try {
|
||||
let job
|
||||
if (task.jobId && task.storyboard && task.storyboard.voiceover_script.trim()) {
|
||||
const edited = task.storyboard.voiceover_script.trim()
|
||||
const originalCopy =
|
||||
(task.job?.copy_result as CopyResult | null | undefined)?.voiceover_script ||
|
||||
task.job?.copy_result?.final_copy ||
|
||||
""
|
||||
job = await confirmViralCopy(task.jobId, {
|
||||
edited_copy: edited && edited !== originalCopy.trim() ? edited : undefined,
|
||||
video_model: task.videoModel,
|
||||
video_resolution: task.quality,
|
||||
video_ratio: task.videoRatio,
|
||||
duration: task.duration,
|
||||
voice_id:
|
||||
task.refAudio?.source === "preset"
|
||||
? task.refAudio.id
|
||||
: task.refAudio?.source === "my_voice"
|
||||
? task.refAudio.ttsVoiceId || task.refAudio.id
|
||||
: undefined,
|
||||
voice_source: task.refAudio?.source || undefined,
|
||||
language: task.language || undefined,
|
||||
})
|
||||
} else {
|
||||
message.warning("请先生成文案后再生成视频")
|
||||
setTask({
|
||||
uiStep: task.storyboard
|
||||
? "step2_copy_ready"
|
||||
: task.imageAnalysis
|
||||
? "step1_done"
|
||||
: "step1_upload",
|
||||
})
|
||||
return
|
||||
}
|
||||
setTask({ job, jobId: job.id })
|
||||
message.success("视频已提交生成，预计 1-3 分钟…")
|
||||
} catch (err: unknown) {
|
||||
if (axios.isAxiosError(err) && err.response?.status === 402) {
|
||||
const detail =
|
||||
(err.response.data as { detail?: string; message?: string; msg?: string } | undefined)
|
||||
?.detail ||
|
||||
(err.response.data as { detail?: string; message?: string; msg?: string } | undefined)
|
||||
?.message ||
|
||||
"积分不足，请充值"
|
||||
message.error(detail)
|
||||
setTask({
|
||||
uiStep: "step2_copy_ready",
|
||||
videoError: detail,
|
||||
copyError: undefined,
|
||||
analysisError: undefined,
|
||||
...CLEAR_STREAM,
|
||||
})
|
||||
return
|
||||
}
|
||||
const msg = err instanceof Error ? err.message : "提交失败"
|
||||
message.error(msg)
|
||||
setTask({ uiStep: "failed", videoError: msg, copyError: undefined, analysisError: undefined })
|
||||
}
|
||||
}
|
||||
|
||||
const handleRetry = async () => {
|
||||
if (task.jobId) {
|
||||
try {
|
||||
const j = await retryViralVideo(task.jobId)
|
||||
const retryStage = getJobStage(j)
|
||||
let retryStep: UIStep = "step1_analyzing"
|
||||
if (isVideoStage(retryStage)) retryStep = "step3_generating"
|
||||
else if (isCopyStage(retryStage)) retryStep = "step2_generating"
|
||||
else if (task.storyboard) retryStep = "step2_copy_ready"
|
||||
else if (task.imageAnalysis) retryStep = "step1_done"
|
||||
setTask({
|
||||
job: j,
|
||||
jobId: j.id,
|
||||
uiStep: retryStep,
|
||||
videoError: undefined,
|
||||
copyError: undefined,
|
||||
analysisError: undefined,
|
||||
imageAnalysis: undefined,
|
||||
storyboard: undefined,
|
||||
...CLEAR_STREAM,
|
||||
estimatedCredits: null,
|
||||
estimatedCreditsError: false,
|
||||
})
|
||||
message.success("已重试")
|
||||
} catch (err: unknown) {
|
||||
message.error(err instanceof Error ? err.message : "重试失败")
|
||||
}
|
||||
} else {
|
||||
setTask({
|
||||
uiStep: task.storyboard ? "step2_copy_ready" : "step1_done",
|
||||
jobId: null,
|
||||
job: null,
|
||||
videoError: undefined,
|
||||
copyError: undefined,
|
||||
analysisError: undefined,
|
||||
imageAnalysis: undefined,
|
||||
storyboard: undefined,
|
||||
...CLEAR_STREAM,
|
||||
estimatedCredits: null,
|
||||
estimatedCreditsError: false,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
const onPollUpdate = useCallback(
|
||||
(job: ViralVideoJob) => {
|
||||
setTask((t) => applyPollUpdate(t, job))
|
||||
},
|
||||
[setTask],
|
||||
)
|
||||
|
||||
const onScriptDelta = useCallback(
|
||||
(ev: { data: { full_text?: string }; progress?: number; message?: string }) => {
|
||||
setTask((t) => ({
|
||||
...t,
|
||||
scriptStreamText: ev.data.full_text || t.scriptStreamText,
|
||||
scriptStreamProgress:
|
||||
typeof ev.progress === "number" ? ev.progress : t.scriptStreamProgress,
|
||||
scriptStreamMessage: ev.message || t.scriptStreamMessage,
|
||||
}))
|
||||
},
|
||||
[setTask],
|
||||
)
|
||||
|
||||
const handleModelChange = (
|
||||
v: string,
|
||||
vms: { key: string; supported_resolutions?: string[]; max_duration?: number }[],
|
||||
) => {
|
||||
const m = vms.find((x) => x && x.key === v)
|
||||
if (!m) {
|
||||
setTask({ videoModel: v })
|
||||
return
|
||||
}
|
||||
const { quality, duration } = resolveModelSwitch(
|
||||
{ quality: task.quality, duration: task.duration },
|
||||
m,
|
||||
)
|
||||
setTask({ videoModel: v, quality, duration })
|
||||
}
|
||||
|
||||
return {
|
||||
...media,
|
||||
resetTask,
|
||||
handleAnalyzeImages,
|
||||
handleGenerateCopy,
|
||||
handleGenerateVideo,
|
||||
handleRetry,
|
||||
onPollUpdate,
|
||||
onScriptDelta,
|
||||
handleModelChange,
|
||||
setTask,
|
||||
setAssetPicker,
|
||||
setCloneModalOpen,
|
||||
setPresetVoicePickerOpen,
|
||||
refreshVoiceAssets,
|
||||
reloadMyVoices,
|
||||
}
|
||||
}
|
||||
|
||||
export default useViralVideoHandlers
|
||||
@@ -1,349 +0,0 @@
|
||||
import { useCallback, useRef } from "react"
|
||||
import { message } from "antd"
|
||||
import { getAssetLibraries, uploadAssetDirect, type AssetItem } from "@/api/assets"
|
||||
import type { UploadedImage, RefVideo, RefAudio, VoicePanel, TabTask } from "../types"
|
||||
import type { VoiceClone } from "@/api/voice-clone"
|
||||
|
||||
export interface UseViralVideoMediaOptions {
|
||||
task: TabTask
|
||||
setTask: (patch: Partial<TabTask> | ((t: TabTask) => TabTask)) => void
|
||||
setAssetPicker: React.Dispatch<
|
||||
React.SetStateAction<{ open: boolean; kind: "image" | "video" | "voice"; multiple: boolean }>
|
||||
>
|
||||
setCloneModalOpen: React.Dispatch<React.SetStateAction<boolean>>
|
||||
setPresetVoicePickerOpen: React.Dispatch<React.SetStateAction<boolean>>
|
||||
refreshVoiceAssets: () => void
|
||||
reloadMyVoices: () => void
|
||||
}
|
||||
|
||||
export interface MediaActions {
|
||||
imgInputRef: React.MutableRefObject<HTMLInputElement | null>
|
||||
videoInputRef: React.MutableRefObject<HTMLInputElement | null>
|
||||
voiceInputRef: React.MutableRefObject<HTMLInputElement | null>
|
||||
audioRef: React.MutableRefObject<HTMLAudioElement | null>
|
||||
appendImagesFromFiles: (files: FileList | File[]) => void
|
||||
appendImagesFromAssets: (assets: AssetItem[]) => void
|
||||
clearImages: () => void
|
||||
removeImg: (localId: string) => void
|
||||
moveImg: (idx: number, dir: -1 | 1) => void
|
||||
handleLocalRefVideo: (file: File) => void
|
||||
removeRefVideo: () => void
|
||||
handleVoiceFile: (file: File) => void
|
||||
onCloneSuccess: (v: VoiceClone | { id?: string; name?: string; voice_id?: string | null }) => void
|
||||
pickVoiceAsset: (a: AssetItem) => void
|
||||
selectMyVoice: (v: VoiceClone) => void
|
||||
toggleVoicePanel: (p: VoicePanel) => void
|
||||
toggleVoice: (v: { id: string; url: string }) => void
|
||||
openAssetPicker: (kind: "image" | "video" | "voice", multiple: boolean) => void
|
||||
}
|
||||
|
||||
/**
|
||||
* 素材上传/音色选择/图片列表/参考视频等操作：独立 hook，保持每个文件 ≤400 行。
|
||||
*/
|
||||
export function useViralVideoMedia(opts: UseViralVideoMediaOptions): MediaActions {
|
||||
const {
|
||||
task,
|
||||
setTask,
|
||||
setAssetPicker,
|
||||
setCloneModalOpen,
|
||||
refreshVoiceAssets,
|
||||
setPresetVoicePickerOpen,
|
||||
} = opts
|
||||
const imgInputRef = useRef<HTMLInputElement>(null)
|
||||
const videoInputRef = useRef<HTMLInputElement>(null)
|
||||
const voiceInputRef = useRef<HTMLInputElement>(null)
|
||||
const audioRef = useRef<HTMLAudioElement | null>(null)
|
||||
|
||||
const uploadFile = useCallback(
|
||||
async (file: File, kind: "image" | "video" | "voice", onProgress?: (pct: number) => void) => {
|
||||
const libs = await getAssetLibraries()
|
||||
const lib = (libs as { kind: string; id: string }[]).find((l) => l.kind === kind) || libs[0]
|
||||
if (!lib) throw new Error("未找到素材库")
|
||||
const res = await uploadAssetDirect({
|
||||
file,
|
||||
library_id: lib.id,
|
||||
onProgress: (p) => onProgress?.(Math.round(p)),
|
||||
})
|
||||
return res.url
|
||||
},
|
||||
[],
|
||||
)
|
||||
|
||||
const appendImagesFromFiles = useCallback(
|
||||
async (files: FileList | File[]) => {
|
||||
const remaining = 9 - task.images.length
|
||||
if (remaining <= 0) {
|
||||
message.warning("最多上传 9 张产品图")
|
||||
return
|
||||
}
|
||||
const arr = Array.from(files)
|
||||
.filter((f) => f.type.startsWith("image/"))
|
||||
.slice(0, remaining)
|
||||
if (arr.length === 0) return
|
||||
const newImgs: UploadedImage[] = arr.map((f) => ({
|
||||
localId: `${Date.now()}_${Math.random().toString(36).slice(2, 7)}`,
|
||||
url: URL.createObjectURL(f),
|
||||
name: f.name,
|
||||
size: f.size,
|
||||
uploading: true,
|
||||
pct: 0,
|
||||
file: f,
|
||||
}))
|
||||
setTask((t) => ({
|
||||
...t,
|
||||
images: [...t.images, ...newImgs.map(({ file: _f, ...rest }) => rest)],
|
||||
}))
|
||||
for (const item of newImgs) {
|
||||
try {
|
||||
const url = await uploadFile(item.file!, "image", (p) => {
|
||||
setTask((t) => ({
|
||||
...t,
|
||||
images: t.images.map((im) => (im.localId === item.localId ? { ...im, pct: p } : im)),
|
||||
}))
|
||||
})
|
||||
setTask((t) => ({
|
||||
...t,
|
||||
images: t.images.map((im) =>
|
||||
im.localId === item.localId
|
||||
? {
|
||||
...im,
|
||||
uploading: false,
|
||||
pct: 100,
|
||||
ossUrl: url,
|
||||
size: item.file!.size || im.size,
|
||||
}
|
||||
: im,
|
||||
),
|
||||
}))
|
||||
} catch {
|
||||
message.error(`${item.name} 上传失败`)
|
||||
setTask((t) => ({ ...t, images: t.images.filter((im) => im.localId !== item.localId) }))
|
||||
}
|
||||
}
|
||||
},
|
||||
[task.images.length, setTask, uploadFile],
|
||||
)
|
||||
|
||||
const appendImagesFromAssets = useCallback(
|
||||
(assets: AssetItem[]) => {
|
||||
const remaining = 9 - task.images.length
|
||||
const picked = assets.slice(0, remaining)
|
||||
if (picked.length === 0) return
|
||||
const newImgs: UploadedImage[] = picked.map((a) => ({
|
||||
localId: `lib_${a.id}_${Math.random().toString(36).slice(2, 6)}`,
|
||||
url: a.thumbnail_url || a.file_url || "",
|
||||
name: a.name || "未命名",
|
||||
size: a.file_size || undefined,
|
||||
uploading: false,
|
||||
pct: 100,
|
||||
ossUrl: a.file_url || undefined,
|
||||
}))
|
||||
setTask((t) => ({ ...t, images: [...t.images, ...newImgs] }))
|
||||
message.success(`已添加 ${picked.length} 张图片`)
|
||||
},
|
||||
[task.images.length, setTask],
|
||||
)
|
||||
|
||||
const clearImages = () => {
|
||||
setTask((t) => {
|
||||
t.images.forEach((im) => {
|
||||
if (im.url?.startsWith("blob:")) URL.revokeObjectURL(im.url)
|
||||
})
|
||||
return {
|
||||
...t,
|
||||
images: [],
|
||||
imageAnalysis: null,
|
||||
uiStep: "step1_upload",
|
||||
storyboard: null,
|
||||
job: null,
|
||||
jobId: null,
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
const removeImg = (localId: string) => {
|
||||
setTask((t) => {
|
||||
const im = t.images.find((i) => i.localId === localId)
|
||||
if (im?.url?.startsWith("blob:")) URL.revokeObjectURL(im.url)
|
||||
const newImgs = t.images.filter((i) => i.localId !== localId)
|
||||
return {
|
||||
...t,
|
||||
images: newImgs,
|
||||
imageAnalysis: newImgs.length === 0 ? null : t.imageAnalysis,
|
||||
storyboard: newImgs.length === 0 ? null : t.storyboard,
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
const moveImg = (idx: number, dir: -1 | 1) => {
|
||||
setTask((t) => {
|
||||
const imgs = [...t.images]
|
||||
const target = idx + dir
|
||||
if (target < 0 || target >= imgs.length) return t
|
||||
;[imgs[idx], imgs[target]] = [imgs[target], imgs[idx]]
|
||||
return { ...t, images: imgs }
|
||||
})
|
||||
}
|
||||
|
||||
const handleLocalRefVideo = useCallback(
|
||||
async (file: File) => {
|
||||
if (file.size > 200 * 1024 * 1024) {
|
||||
message.warning("视频不能超过 200MB")
|
||||
return
|
||||
}
|
||||
const localUrl = URL.createObjectURL(file)
|
||||
setTask({
|
||||
refVideo: {
|
||||
source: "local",
|
||||
url: localUrl,
|
||||
name: file.name,
|
||||
uploading: true,
|
||||
pct: 0,
|
||||
file,
|
||||
} as RefVideo,
|
||||
})
|
||||
try {
|
||||
const url = await uploadFile(file, "video", (p) => {
|
||||
setTask((t) => (t.refVideo ? { ...t, refVideo: { ...t.refVideo, pct: p } } : t))
|
||||
})
|
||||
setTask((t) =>
|
||||
t.refVideo
|
||||
? { ...t, refVideo: { ...t.refVideo, uploading: false, pct: 100, ossUrl: url } }
|
||||
: t,
|
||||
)
|
||||
} catch {
|
||||
message.error("参考视频上传失败")
|
||||
if (localUrl.startsWith("blob:")) URL.revokeObjectURL(localUrl)
|
||||
setTask({ refVideo: null })
|
||||
}
|
||||
},
|
||||
[setTask, uploadFile],
|
||||
)
|
||||
|
||||
const removeRefVideo = () => {
|
||||
setTask((t) => {
|
||||
if (t.refVideo?.url.startsWith("blob:")) URL.revokeObjectURL(t.refVideo.url)
|
||||
return { ...t, refVideo: null }
|
||||
})
|
||||
}
|
||||
|
||||
const handleVoiceFile = useCallback(
|
||||
async (file: File) => {
|
||||
if (file.size > 10 * 1024 * 1024) {
|
||||
message.warning("音频不能超过 10MB")
|
||||
return
|
||||
}
|
||||
const localUrl = URL.createObjectURL(file)
|
||||
try {
|
||||
const url = await uploadFile(file, "voice")
|
||||
setTask({
|
||||
refAudio: { source: "upload", name: file.name, url, id: url } as RefAudio,
|
||||
voicePanel: null,
|
||||
})
|
||||
message.success("音频已上传")
|
||||
refreshVoiceAssets()
|
||||
} catch {
|
||||
message.error("音频上传失败")
|
||||
URL.revokeObjectURL(localUrl)
|
||||
}
|
||||
},
|
||||
[setTask, uploadFile, refreshVoiceAssets],
|
||||
)
|
||||
|
||||
const onCloneSuccess = (
|
||||
v: VoiceClone | { id?: string; name?: string; voice_id?: string | null },
|
||||
) => {
|
||||
setCloneModalOpen(false)
|
||||
opts.reloadMyVoices()
|
||||
const vid = "voice_id" in v ? v.voice_id : undefined
|
||||
if (v?.id) {
|
||||
setTask({
|
||||
refAudio: {
|
||||
source: "my_voice",
|
||||
id: v.id,
|
||||
name: v.name || "我录制的音色",
|
||||
ttsVoiceId: vid || undefined,
|
||||
} as RefAudio,
|
||||
voicePanel: null,
|
||||
})
|
||||
refreshVoiceAssets()
|
||||
message.success("音色克隆完成，已自动选中")
|
||||
}
|
||||
}
|
||||
|
||||
const pickVoiceAsset = (asset: AssetItem) => {
|
||||
setTask({
|
||||
refAudio: {
|
||||
source: "library",
|
||||
id: asset.id,
|
||||
name: asset.name,
|
||||
url: asset.file_url || "",
|
||||
} as RefAudio,
|
||||
voicePanel: null,
|
||||
})
|
||||
}
|
||||
|
||||
const selectMyVoice = (v: VoiceClone) => {
|
||||
setTask({
|
||||
refAudio: {
|
||||
source: "my_voice",
|
||||
id: v.id,
|
||||
name: v.name,
|
||||
ttsVoiceId: v.voice_id || undefined,
|
||||
} as RefAudio,
|
||||
voicePanel: null,
|
||||
})
|
||||
}
|
||||
|
||||
const toggleVoicePanel = (p: VoicePanel) => {
|
||||
if (p === "preset") {
|
||||
setPresetVoicePickerOpen(true)
|
||||
return
|
||||
}
|
||||
setTask({ voicePanel: task.voicePanel === p ? null : p })
|
||||
}
|
||||
|
||||
const openAssetPicker = (kind: "image" | "video" | "voice", multiple: boolean) => {
|
||||
setAssetPicker({ open: true, kind, multiple })
|
||||
}
|
||||
|
||||
const toggleVoice = (v: { id: string; url: string }) => {
|
||||
if (!v.url) return
|
||||
if (task.playingVoiceId === v.id) {
|
||||
audioRef.current?.pause()
|
||||
audioRef.current = null
|
||||
setTask({ playingVoiceId: null })
|
||||
return
|
||||
}
|
||||
audioRef.current?.pause()
|
||||
const a = new Audio(v.url)
|
||||
a.dataset.playingId = v.id
|
||||
a.onended = () => setTask({ playingVoiceId: null })
|
||||
a.play().catch(() => {})
|
||||
audioRef.current = a
|
||||
setTask({ playingVoiceId: v.id })
|
||||
}
|
||||
|
||||
return {
|
||||
imgInputRef,
|
||||
videoInputRef,
|
||||
voiceInputRef,
|
||||
audioRef,
|
||||
appendImagesFromFiles,
|
||||
appendImagesFromAssets,
|
||||
clearImages,
|
||||
removeImg,
|
||||
moveImg,
|
||||
handleLocalRefVideo,
|
||||
removeRefVideo,
|
||||
handleVoiceFile,
|
||||
onCloneSuccess,
|
||||
pickVoiceAsset,
|
||||
selectMyVoice,
|
||||
toggleVoicePanel,
|
||||
toggleVoice,
|
||||
openAssetPicker,
|
||||
}
|
||||
}
|
||||
|
||||
export default useViralVideoMedia
|
||||
@@ -1,11 +1,6 @@
|
||||
import { useCallback, useEffect, useRef } from "react"
|
||||
import { getViralVideoJob } from "@/api/viral-video"
|
||||
import {
|
||||
isAnalysisStage,
|
||||
getJobStage,
|
||||
type ViralVideoJob,
|
||||
type ViralVideoStatus,
|
||||
} from "@/api/viral-video/types"
|
||||
import { isAnalysisStage, type ViralVideoJob, type ViralVideoStatus } from "@/api/viral-video/types"
|
||||
|
||||
const TERMINAL: ViralVideoStatus[] = ["completed", "failed", "cancelled"]
|
||||
|
||||
@@ -16,9 +11,8 @@ export interface UseViralVideoPollingOptions {
|
||||
|
||||
/**
|
||||
* 爆款视频任务 HTTP 轮询 hook。
|
||||
* - onUpdate 通过 ref 持有，避免 useCallback 依赖变化导致重启轮询。
|
||||
* - 任务进入终态（completed/failed/cancelled）后自动停止。
|
||||
* - 连续失败 20 次（约 2 分钟）后熔断。
|
||||
* 负责持续拉取任务状态并回调给上层；上层负责根据状态/阶段切换 UI 文案。
|
||||
* 任务进入终态（completed/failed/cancelled）后自动停止。
|
||||
*/
|
||||
export function useViralVideoPolling(
|
||||
jobId: string | null | undefined,
|
||||
@@ -29,9 +23,6 @@ export function useViralVideoPolling(
|
||||
const timerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
|
||||
const stoppedRef = useRef(false)
|
||||
const failCountRef = useRef(0)
|
||||
// ref 持有最新 onUpdate，避免依赖变化导致 pollOnce 重建、轮询被重启
|
||||
const onUpdateRef = useRef(onUpdate)
|
||||
onUpdateRef.current = onUpdate
|
||||
|
||||
const stop = useCallback(() => {
|
||||
stoppedRef.current = true
|
||||
@@ -46,30 +37,26 @@ export function useViralVideoPolling(
|
||||
try {
|
||||
const job = await getViralVideoJob(id)
|
||||
failCountRef.current = 0
|
||||
onUpdateRef.current(job)
|
||||
onUpdate(job)
|
||||
if (TERMINAL.includes(job.status)) {
|
||||
stop()
|
||||
return
|
||||
}
|
||||
if (stoppedRef.current) return
|
||||
const stage = getJobStage(job)
|
||||
const inRender = stage === "rendering"
|
||||
const isAnalyzing = isAnalysisStage(stage)
|
||||
// 视频渲染阶段（Seedance 多段视频生成较慢）拉长轮询间隔
|
||||
const inRender = job.progress_stage === "rendering"
|
||||
// 分析阶段走默认间隔即可
|
||||
const isAnalyzing = isAnalysisStage(job.progress_stage)
|
||||
const nextDelay = inRender ? 3000 : isAnalyzing ? 2000 : intervalMs
|
||||
timerRef.current = setTimeout(() => pollOnce(id), nextDelay)
|
||||
} catch (_err) {
|
||||
failCountRef.current += 1
|
||||
if (stoppedRef.current) return
|
||||
if (failCountRef.current >= 20) {
|
||||
// 轮询连续失败 20 次，停止轮询
|
||||
stop()
|
||||
return
|
||||
}
|
||||
const delay = Math.min(intervalMs * 2 ** Math.min(failCountRef.current, 3), 10000)
|
||||
timerRef.current = setTimeout(() => pollOnce(id), delay)
|
||||
}
|
||||
},
|
||||
[intervalMs, stop],
|
||||
[intervalMs, onUpdate, stop],
|
||||
)
|
||||
|
||||
useEffect(() => {
|
||||
|
||||
@@ -1,151 +0,0 @@
|
||||
import { useCallback, useEffect, useRef } from "react"
|
||||
|
||||
export interface ViralVideoDeltaEvent {
|
||||
type: "viral_video:script_delta"
|
||||
job_id: string
|
||||
stage: string
|
||||
progress: number
|
||||
message?: string
|
||||
data: {
|
||||
delta: string
|
||||
full_text: string
|
||||
text_length: number
|
||||
}
|
||||
}
|
||||
|
||||
export interface ViralVideoWSError {
|
||||
message: string
|
||||
}
|
||||
|
||||
export interface ViralVideoTerminalEvent {
|
||||
type: "viral_video:completed" | "viral_video:failed" | "viral_video:copy_generated"
|
||||
job_id: string
|
||||
}
|
||||
|
||||
export interface UseViralVideoWSOptions {
|
||||
/** delta 事件回调（script_delta 推送时触发） */
|
||||
onDelta?: (ev: ViralVideoDeltaEvent) => void
|
||||
/** 连接关闭/失败回调 */
|
||||
onError?: (err: ViralVideoWSError) => void
|
||||
/** 连接建立回调 */
|
||||
onOpen?: () => void
|
||||
/** P2-6: 收到终态事件（copy_generated/completed/failed）时回调，上层应立即主动拉取一次最新 job，减少终态延迟 */
|
||||
onTerminal?: (ev: ViralVideoTerminalEvent) => void
|
||||
}
|
||||
|
||||
const WS_SCHEME =
|
||||
typeof window !== "undefined" && window.location.protocol === "https:" ? "wss:" : "ws:"
|
||||
|
||||
function buildWsUrl(jobId: string): string {
|
||||
const token = localStorage.getItem("access_token") || ""
|
||||
const host = window.location.host
|
||||
return `${WS_SCHEME}//${host}/api/v1/viral-video/ws/${jobId}?token=${encodeURIComponent(token)}`
|
||||
}
|
||||
|
||||
/**
|
||||
* 爆款视频 WebSocket 订阅 hook。
|
||||
* 仅在 jobId 非空时建立连接；组件卸载/ jobId 变更时自动关闭。
|
||||
* 失败静默：后端已说明流式异常会自动 fallback 到 HTTP 轮询的 copy_generated 终态，
|
||||
* 前端不需要中断主流程，连接失败时只记录不影响轮询继续推进 UI。
|
||||
*/
|
||||
export function useViralVideoWS(
|
||||
jobId: string | null | undefined,
|
||||
{ onDelta, onError, onOpen, onTerminal }: UseViralVideoWSOptions = {},
|
||||
) {
|
||||
const wsRef = useRef<WebSocket | null>(null)
|
||||
const manualCloseRef = useRef(false)
|
||||
const onDeltaRef = useRef(onDelta)
|
||||
const onErrorRef = useRef(onError)
|
||||
const onOpenRef = useRef(onOpen)
|
||||
const onTerminalRef = useRef<UseViralVideoWSOptions["onTerminal"]>()
|
||||
onDeltaRef.current = onDelta
|
||||
onErrorRef.current = onError
|
||||
onOpenRef.current = onOpen
|
||||
onTerminalRef.current = onTerminal
|
||||
|
||||
const close = useCallback(() => {
|
||||
manualCloseRef.current = true
|
||||
if (wsRef.current) {
|
||||
try {
|
||||
wsRef.current.close()
|
||||
} catch {
|
||||
/* noop */
|
||||
}
|
||||
wsRef.current = null
|
||||
}
|
||||
}, [])
|
||||
|
||||
useEffect(() => {
|
||||
if (!jobId) {
|
||||
close()
|
||||
return
|
||||
}
|
||||
manualCloseRef.current = false
|
||||
let closed = false
|
||||
let retryTimer: ReturnType<typeof setTimeout> | null = null
|
||||
let startTimer: ReturnType<typeof setTimeout> | null = null
|
||||
let retries = 0
|
||||
const MAX_RETRIES = 3
|
||||
|
||||
const connect = () => {
|
||||
if (closed) return
|
||||
try {
|
||||
const ws = new WebSocket(buildWsUrl(jobId))
|
||||
wsRef.current = ws
|
||||
|
||||
ws.onopen = () => {
|
||||
if (closed) return
|
||||
retries = 0
|
||||
onOpenRef.current?.()
|
||||
}
|
||||
|
||||
ws.onmessage = (ev) => {
|
||||
try {
|
||||
const payload = JSON.parse(ev.data)
|
||||
if (payload?.type === "viral_video:script_delta" && payload.data) {
|
||||
onDeltaRef.current?.(payload as ViralVideoDeltaEvent)
|
||||
} else if (
|
||||
payload?.type === "viral_video:completed" ||
|
||||
payload?.type === "viral_video:failed" ||
|
||||
payload?.type === "viral_video:copy_generated"
|
||||
) {
|
||||
// P2-6: 收到终态事件时主动通知上层立即拉取，降低终态延迟（不用等下一个轮询周期）
|
||||
onTerminalRef.current?.(payload as ViralVideoTerminalEvent)
|
||||
}
|
||||
// 心跳/其他事件忽略
|
||||
} catch {
|
||||
// 心跳/非 JSON 消息忽略
|
||||
}
|
||||
}
|
||||
|
||||
ws.onerror = () => {
|
||||
onErrorRef.current?.({ message: "websocket error" })
|
||||
}
|
||||
|
||||
ws.onclose = () => {
|
||||
wsRef.current = null
|
||||
if (closed || manualCloseRef.current) return
|
||||
if (retries < MAX_RETRIES) {
|
||||
retries += 1
|
||||
const delay = 500 * 2 ** (retries - 1)
|
||||
retryTimer = setTimeout(connect, delay)
|
||||
}
|
||||
}
|
||||
} catch (e) {
|
||||
onErrorRef.current?.({ message: (e as Error).message })
|
||||
}
|
||||
}
|
||||
|
||||
// 延迟 100ms 再连，给后端 job 初始化留一点时间
|
||||
startTimer = setTimeout(connect, 100)
|
||||
|
||||
return () => {
|
||||
closed = true
|
||||
if (startTimer) clearTimeout(startTimer)
|
||||
if (retryTimer) clearTimeout(retryTimer)
|
||||
close()
|
||||
}
|
||||
}, [jobId, close])
|
||||
|
||||
return { close }
|
||||
}
|
||||
@@ -1,132 +0,0 @@
|
||||
import type {
|
||||
FusionLevel,
|
||||
StyleStrength,
|
||||
ViralVideoJob,
|
||||
ImageAnalysisResult,
|
||||
CopyResult,
|
||||
ShotScript,
|
||||
ViralVideoModel,
|
||||
} from "@/api/viral-video/types"
|
||||
|
||||
/* ─────────── UI 阶段（三步骤独立交互） ─────────── */
|
||||
export type UIStep =
|
||||
| "step1_upload" // 初始：上传素材
|
||||
| "step1_analyzing" // 图片分析中
|
||||
| "step1_done" // 图片分析完成
|
||||
| "step2_generating" // 文案生成中
|
||||
| "step2_copy_ready" // 文案已生成，等待确认
|
||||
| "step3_generating" // 视频生成中
|
||||
| "step3_done" // 视频完成
|
||||
| "failed" // 失败
|
||||
|
||||
export const TERMINAL_UI_STEPS: UIStep[] = ["step3_done", "failed"]
|
||||
|
||||
export type UploadedImage = {
|
||||
localId: string
|
||||
url: string
|
||||
name: string
|
||||
size: number | undefined
|
||||
uploading: boolean
|
||||
pct: number
|
||||
ossUrl?: string
|
||||
file?: File
|
||||
}
|
||||
|
||||
export type RefVideo = {
|
||||
source: "local"
|
||||
url: string
|
||||
name: string
|
||||
uploading: boolean
|
||||
pct: number
|
||||
ossUrl?: string
|
||||
file?: File
|
||||
} | null
|
||||
|
||||
export type RefAudio = {
|
||||
source: "preset" | "library" | "upload" | "clone" | "my_voice"
|
||||
id?: string
|
||||
name: string
|
||||
url?: string
|
||||
/** 克隆音色的 CosyVoice voice_id（source=my_voice 时使用） */
|
||||
ttsVoiceId?: string
|
||||
} | null
|
||||
|
||||
export type VoicePanel = "my_voices" | "preset" | "upload" | "library" | "record" | null
|
||||
|
||||
// ── 分镜脚本数据模型（新后端 copy_result 结构） ──
|
||||
export interface StoryboardShot {
|
||||
time_range: string
|
||||
speaker?: string
|
||||
shot_type_angle_movement: string
|
||||
scene_and_dialogue: string
|
||||
action_details: string
|
||||
audio_bgm: string
|
||||
transition: string
|
||||
reference_image_index?: number
|
||||
}
|
||||
export interface Storyboard {
|
||||
overview: { theme: string; total_duration: string; aspect_ratio: string }
|
||||
scene_and_lighting: string
|
||||
shots: StoryboardShot[]
|
||||
hard_constraints: string[]
|
||||
negative_prompts: string[]
|
||||
voiceover_script: string
|
||||
/** v3: 用户端展示用 markdown 文案（由提示词控制排版） */
|
||||
copy_display_markdown: string
|
||||
}
|
||||
|
||||
export type TabTask = {
|
||||
id: string
|
||||
title: string
|
||||
// 素材
|
||||
images: UploadedImage[]
|
||||
refVideo: RefVideo
|
||||
voicePanel: VoicePanel
|
||||
refAudio: RefAudio
|
||||
// STEP2 参数
|
||||
fusionLevel: FusionLevel
|
||||
styleStrength: StyleStrength
|
||||
userCopy: string
|
||||
industry: string
|
||||
targetCustomer: string
|
||||
persona: string
|
||||
language: string
|
||||
viralStructure: string
|
||||
marketingPurpose: string
|
||||
duration: number
|
||||
// STEP3 设置
|
||||
videoRatio: string
|
||||
videoModel: string
|
||||
quality: string
|
||||
upscale: string
|
||||
// 积分预估
|
||||
estimatedCredits: number | null
|
||||
estimatedCreditsError: boolean
|
||||
// UI 状态
|
||||
uiStep: UIStep
|
||||
imageAnalysis: ImageAnalysisResult | null
|
||||
storyboard: Storyboard | null
|
||||
voiceTipsOpen: boolean
|
||||
copyError?: string
|
||||
videoError?: string
|
||||
analysisError?: string
|
||||
// 运行态（后端 job）
|
||||
job: ViralVideoJob | null
|
||||
jobId: string | null
|
||||
playingVoiceId: string | null
|
||||
// 分镜脚本流式预览
|
||||
scriptStreamText: string
|
||||
scriptStreamProgress: number
|
||||
scriptStreamMessage: string
|
||||
}
|
||||
|
||||
// re-export api types for convenience
|
||||
export type {
|
||||
FusionLevel,
|
||||
StyleStrength,
|
||||
ViralVideoJob,
|
||||
ImageAnalysisResult,
|
||||
CopyResult,
|
||||
ShotScript,
|
||||
ViralVideoModel,
|
||||
}
|
||||
@@ -1,99 +0,0 @@
|
||||
import { message } from "antd"
|
||||
import {
|
||||
isVideoStage,
|
||||
isCopyStage,
|
||||
isImageAnalysisStage,
|
||||
getJobStage,
|
||||
type ViralVideoJob,
|
||||
} from "@/api/viral-video/types"
|
||||
import type { UIStep, TabTask } from "../types"
|
||||
import { TERMINAL_UI_STEPS } from "../types"
|
||||
import { copyResultToStoryboard } from "./copyResultToStoryboard"
|
||||
import { classifyJobError, cleanErrorMessage } from "./errorClassification"
|
||||
|
||||
/**
|
||||
* 根据 job 状态计算下一个 TabTask 快照：单调性守卫 + 阶段推进 + 错误分类。
|
||||
* 返回 null 表示丢弃此次更新（终态守卫命中，使用旧状态即可）。
|
||||
*/
|
||||
export function applyPollUpdate(t: TabTask, job: ViralVideoJob): TabTask {
|
||||
if (
|
||||
TERMINAL_UI_STEPS.includes(t.uiStep) &&
|
||||
job.status !== "completed" &&
|
||||
job.status !== "failed"
|
||||
) {
|
||||
return t
|
||||
}
|
||||
let nextStep: UIStep = t.uiStep
|
||||
let copyError = t.copyError
|
||||
let videoError = t.videoError
|
||||
let analysisError = t.analysisError
|
||||
const err = job.error_message || job.error_msg
|
||||
const nextImageAnalysis = job.image_analysis || t.imageAnalysis
|
||||
const nextCopy = job.copy_result
|
||||
const nextStoryboard = nextCopy ? copyResultToStoryboard(nextCopy) || t.storyboard : t.storyboard
|
||||
const stage = getJobStage(job)
|
||||
|
||||
switch (job.status) {
|
||||
case "image_analyzed":
|
||||
nextStep = "step1_done"
|
||||
copyError = undefined
|
||||
break
|
||||
case "copy_generated":
|
||||
nextStep = "step2_copy_ready"
|
||||
copyError = undefined
|
||||
break
|
||||
case "running":
|
||||
if (isVideoStage(stage)) nextStep = "step3_generating"
|
||||
else if (isCopyStage(stage)) nextStep = "step2_generating"
|
||||
else if (isImageAnalysisStage(stage)) nextStep = "step1_analyzing"
|
||||
copyError = undefined
|
||||
videoError = undefined
|
||||
break
|
||||
case "completed":
|
||||
nextStep = "step3_done"
|
||||
copyError = undefined
|
||||
videoError = undefined
|
||||
break
|
||||
case "failed": {
|
||||
if (stage === "review" && err && (err.includes("合规") || err.includes("审核未通过"))) {
|
||||
nextStep = "step2_copy_ready"
|
||||
copyError = err
|
||||
videoError = undefined
|
||||
analysisError = undefined
|
||||
message.warning(err)
|
||||
break
|
||||
}
|
||||
nextStep = "failed"
|
||||
const cleaned = cleanErrorMessage(err || "")
|
||||
const kind = classifyJobError(cleaned, stage)
|
||||
if (kind === "video") {
|
||||
videoError = cleaned || "视频生成失败"
|
||||
copyError = undefined
|
||||
analysisError = undefined
|
||||
} else if (kind === "image") {
|
||||
analysisError = cleaned || "图片分析失败"
|
||||
copyError = undefined
|
||||
videoError = undefined
|
||||
} else {
|
||||
copyError = cleaned || "文案生成失败"
|
||||
videoError = undefined
|
||||
analysisError = undefined
|
||||
}
|
||||
break
|
||||
}
|
||||
case "pending":
|
||||
break
|
||||
}
|
||||
return {
|
||||
...t,
|
||||
job,
|
||||
uiStep: nextStep,
|
||||
imageAnalysis: nextImageAnalysis,
|
||||
storyboard: nextStoryboard,
|
||||
copyError,
|
||||
videoError,
|
||||
analysisError,
|
||||
}
|
||||
}
|
||||
|
||||
export default applyPollUpdate
|
||||
@@ -1,71 +0,0 @@
|
||||
import type { Storyboard } from "../types"
|
||||
import type { CopyResult, ShotScript } from "@/api/viral-video/types"
|
||||
|
||||
export function copyResultToStoryboard(cr: CopyResult | null | undefined): Storyboard | null {
|
||||
if (!cr) return null
|
||||
if (Array.isArray(cr.shots) && cr.overview) {
|
||||
return {
|
||||
overview: {
|
||||
theme: cr.overview.theme || cr.title || "",
|
||||
total_duration: (() => {
|
||||
const d = cr.overview.total_duration
|
||||
if (!d) return ""
|
||||
return String(d).endsWith("s") ? String(d) : `${d}s`
|
||||
})(),
|
||||
aspect_ratio: cr.overview.aspect_ratio || "9:16",
|
||||
},
|
||||
scene_and_lighting: cr.scene_and_lighting || "",
|
||||
shots: ((cr.shots ?? []) as ShotScript[]).map((s) => ({
|
||||
time_range: s.time_range || "",
|
||||
speaker: s.speaker || "",
|
||||
shot_type_angle_movement: s.shot_type_angle_movement || "",
|
||||
scene_and_dialogue: s.scene_and_dialogue || "",
|
||||
action_details: s.action_details || "",
|
||||
audio_bgm: s.audio_bgm || "",
|
||||
transition: s.transition || "",
|
||||
reference_image_index:
|
||||
typeof s.reference_image_index === "number" ? s.reference_image_index : undefined,
|
||||
})),
|
||||
hard_constraints: Array.isArray(cr.hard_constraints) ? cr.hard_constraints : [],
|
||||
negative_prompts: Array.isArray(cr.negative_prompts) ? cr.negative_prompts : [],
|
||||
voiceover_script: cr.voiceover_script || cr.final_copy || cr.suggested_copy || "",
|
||||
copy_display_markdown: cr.copy_display_markdown || "",
|
||||
}
|
||||
}
|
||||
const finalCopy = cr.final_copy || cr.suggested_copy || ""
|
||||
const title = cr.title || ""
|
||||
return {
|
||||
overview: { theme: title || "AI 分镜脚本", total_duration: "", aspect_ratio: "9:16" },
|
||||
scene_and_lighting: "",
|
||||
shots:
|
||||
cr.scenes && cr.scenes.length
|
||||
? cr.scenes.map((sc, i: number) => ({
|
||||
time_range: sc.duration
|
||||
? `${i * sc.duration}-${(i + 1) * sc.duration}s`
|
||||
: `镜头${i + 1}`,
|
||||
shot_type_angle_movement: sc.shot || "",
|
||||
scene_and_dialogue: sc.narration || "",
|
||||
action_details: "",
|
||||
audio_bgm: "",
|
||||
transition: "",
|
||||
}))
|
||||
: finalCopy
|
||||
? [
|
||||
{
|
||||
time_range: "0-15s",
|
||||
shot_type_angle_movement: "",
|
||||
scene_and_dialogue: finalCopy,
|
||||
action_details: "",
|
||||
audio_bgm: "",
|
||||
transition: "",
|
||||
},
|
||||
]
|
||||
: [],
|
||||
hard_constraints: [],
|
||||
negative_prompts: [],
|
||||
voiceover_script: finalCopy,
|
||||
copy_display_markdown: cr.copy_display_markdown || "",
|
||||
}
|
||||
}
|
||||
|
||||
export default copyResultToStoryboard
|
||||
@@ -1,55 +0,0 @@
|
||||
import {
|
||||
isCopyStage,
|
||||
isImageAnalysisStage,
|
||||
isVideoStage,
|
||||
getJobStage,
|
||||
} from "@/api/viral-video/types"
|
||||
import type { ViralVideoJob, ViralVideoStage } from "@/api/viral-video/types"
|
||||
|
||||
/**
|
||||
* 统一的视频/文案/图片阶段错误分类。
|
||||
* 优先按 stage 字段判定；stage 丢失时基于错误消息关键词兜底。
|
||||
*/
|
||||
export type JobErrorKind = "image" | "copy" | "video"
|
||||
|
||||
const VIDEO_ERR_RE =
|
||||
/wan 3\.0|wan-?3|seedance|视频生成|视频下载|视频渲染|tts|配音|render|musetalk|lip.?sync/i
|
||||
const IMAGE_ERR_RE = /vlm|图片分析|vision|商品识别|ocr|image analysis|图片识别|图像分析/i
|
||||
|
||||
export function classifyJobError(err: string, stage?: string | null): JobErrorKind {
|
||||
const msg = err || ""
|
||||
if (stage) {
|
||||
const s = stage as ViralVideoStage
|
||||
if (isVideoStage(s)) return "video"
|
||||
if (isImageAnalysisStage(s)) return "image"
|
||||
if (isCopyStage(s)) return "copy"
|
||||
}
|
||||
if (VIDEO_ERR_RE.test(msg)) return "video"
|
||||
if (IMAGE_ERR_RE.test(msg)) return "image"
|
||||
return "copy"
|
||||
}
|
||||
|
||||
/**
|
||||
* 去掉后端错误消息中常见的"XX失败："前缀，避免前端 UI 重复前缀。
|
||||
*/
|
||||
const ERROR_PREFIX_RE =
|
||||
/^(视频生成失败|文案生成失败|图片分析失败|Wan 3\.0 视频生成失败|Seedance[0-9. ]*视频生成失败)[：:]\s*/g
|
||||
|
||||
export function cleanErrorMessage(err: string): string {
|
||||
if (!err) return ""
|
||||
return err.replace(ERROR_PREFIX_RE, "").trim()
|
||||
}
|
||||
|
||||
/**
|
||||
* 基于 job 统一提取错误分类和清理后的消息；无错误返回 null。
|
||||
*/
|
||||
export function extractJobError(
|
||||
job: ViralVideoJob | null | undefined,
|
||||
): { kind: JobErrorKind; msg: string } | null {
|
||||
if (!job) return null
|
||||
const raw = job.error_message || job.error_msg
|
||||
if (!raw) return null
|
||||
const stage = getJobStage(job)
|
||||
const kind = classifyJobError(raw, stage)
|
||||
return { kind, msg: cleanErrorMessage(raw) }
|
||||
}
|
||||
@@ -1,70 +0,0 @@
|
||||
/**
|
||||
* 极简 markdown 渲染器：仅支持粗体、斜体、标题、列表、换行。
|
||||
* 不依赖 marked 包（node_modules 中暂未安装），满足 viral-video 文案预览的
|
||||
* 简单排版需求。
|
||||
*/
|
||||
function escapeHtml(s: string): string {
|
||||
return s
|
||||
.replace(/&/g, "&")
|
||||
.replace(/</g, "<")
|
||||
.replace(/>/g, ">")
|
||||
.replace(/"/g, """)
|
||||
}
|
||||
|
||||
function inline(md: string): string {
|
||||
let s = escapeHtml(md)
|
||||
// bold **xxx**
|
||||
s = s.replace(/\*\*([^*]+)\*\*/g, "<strong>$1</strong>")
|
||||
// italic *xxx*
|
||||
s = s.replace(/(^|[^*])\*([^*\n]+)\*/g, "$1<em>$2</em>")
|
||||
// inline code `xxx`
|
||||
s = s.replace(/`([^`]+)`/g, "<code>$1</code>")
|
||||
return s
|
||||
}
|
||||
|
||||
export function renderMarkdown(md: string): string {
|
||||
if (!md) return ""
|
||||
const lines = md.split(/\r?\n/)
|
||||
const out: string[] = []
|
||||
let inList = false
|
||||
for (const raw of lines) {
|
||||
const line = raw.trimEnd()
|
||||
if (!line.trim()) {
|
||||
if (inList) {
|
||||
out.push("</ul>")
|
||||
inList = false
|
||||
}
|
||||
continue
|
||||
}
|
||||
// headings
|
||||
const h = line.match(/^(#{1,4})\s+(.*)$/)
|
||||
if (h) {
|
||||
if (inList) {
|
||||
out.push("</ul>")
|
||||
inList = false
|
||||
}
|
||||
const lvl = h[1].length
|
||||
out.push(`<h${lvl}>${inline(h[2])}</h${lvl}>`)
|
||||
continue
|
||||
}
|
||||
// unordered list
|
||||
const li = line.match(/^\s*[-*]\s+(.*)$/)
|
||||
if (li) {
|
||||
if (!inList) {
|
||||
out.push("<ul>")
|
||||
inList = true
|
||||
}
|
||||
out.push(`<li>${inline(li[1])}</li>`)
|
||||
continue
|
||||
}
|
||||
if (inList) {
|
||||
out.push("</ul>")
|
||||
inList = false
|
||||
}
|
||||
out.push(`<p>${inline(line)}</p>`)
|
||||
}
|
||||
if (inList) out.push("</ul>")
|
||||
return out.join("\n")
|
||||
}
|
||||
|
||||
export default renderMarkdown
|
||||
@@ -1,30 +0,0 @@
|
||||
import { RESOLUTION_ORDER } from "../ViralVideoPage.constants"
|
||||
|
||||
/**
|
||||
* resolveModelSwitch 所需的最小模型信息（结构性类型，兼容 ViralVideoModel）。
|
||||
*/
|
||||
export interface ModelSwitchInfo {
|
||||
supported_resolutions?: string[]
|
||||
max_duration?: number
|
||||
}
|
||||
|
||||
/**
|
||||
* 切换视频模型时，联动修正分辨率/时长：新模型不支持当前分辨率则回退到最低支持，
|
||||
* 当前时长超过新模型 max_duration 则截断到 max_duration。
|
||||
*/
|
||||
export function resolveModelSwitch(
|
||||
cur: { quality: string; duration: number },
|
||||
m: ModelSwitchInfo,
|
||||
): { quality: string; duration: number } {
|
||||
const mRes = Array.isArray(m.supported_resolutions)
|
||||
? m.supported_resolutions
|
||||
: ["480p", "720p", "1080p"]
|
||||
const sortedRes = mRes.slice().sort((a, b) => {
|
||||
const ra = RESOLUTION_ORDER.indexOf(a)
|
||||
const rb = RESOLUTION_ORDER.indexOf(b)
|
||||
return (rb === -1 ? -1 : rb) - (ra === -1 ? -1 : ra)
|
||||
})
|
||||
const newRes = mRes.includes(cur.quality) ? cur.quality : sortedRes[0] || "480p"
|
||||
const newDur = Math.min(cur.duration, typeof m.max_duration === "number" ? m.max_duration : 15)
|
||||
return { quality: newRes, duration: newDur }
|
||||
}
|
||||
@@ -116,6 +116,31 @@ const appChildren: RouteObject[] = [
|
||||
path: "profile",
|
||||
lazy: lazyRoute(() => import("@/pages/profile/Settings")),
|
||||
},
|
||||
{
|
||||
path: "admin",
|
||||
children: [
|
||||
{
|
||||
index: true,
|
||||
lazy: lazyRoute(() => import("@/pages/admin/AdminComingSoon")),
|
||||
},
|
||||
{
|
||||
path: "users",
|
||||
lazy: lazyRoute(() => import("@/pages/admin/AdminComingSoon")),
|
||||
},
|
||||
{
|
||||
path: "analytics",
|
||||
lazy: lazyRoute(() => import("@/pages/admin/AdminComingSoon")),
|
||||
},
|
||||
{
|
||||
path: "monitor",
|
||||
lazy: lazyRoute(() => import("@/pages/admin/AdminComingSoon")),
|
||||
},
|
||||
{
|
||||
path: "logs",
|
||||
lazy: lazyRoute(() => import("@/pages/admin/AdminComingSoon")),
|
||||
},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
export const appRoutes: RouteObject = {
|
||||
|
||||
@@ -1,6 +1,8 @@
|
||||
import { describe, expect, it, vi, beforeEach, afterEach } from "vitest"
|
||||
import {
|
||||
generateViralVideo,
|
||||
getViralVideoJob,
|
||||
confirmViralVideoIntent,
|
||||
retryViralVideo,
|
||||
getViralVideoHistory,
|
||||
getViralStyleTemplates,
|
||||
@@ -74,10 +76,10 @@ describe("viral-video constants & stage helpers", () => {
|
||||
})
|
||||
|
||||
it("isCopyStage", () => {
|
||||
expect(isCopyStage("intent_parsing")).toBe(true)
|
||||
expect(isCopyStage("script_generation")).toBe(true)
|
||||
expect(isCopyStage("review")).toBe(true)
|
||||
expect(isCopyStage("tts")).toBe(false)
|
||||
expect(isCopyStage("image_analysis")).toBe(false)
|
||||
})
|
||||
|
||||
it("isAnalysisStage is union", () => {
|
||||
@@ -99,6 +101,14 @@ describe("viral-video API wrappers", () => {
|
||||
mockPost.mockResolvedValue({ data: {} })
|
||||
})
|
||||
|
||||
it("generateViralVideo", async () => {
|
||||
mockPost.mockResolvedValue({ data: { id: "j1" } })
|
||||
const r = generateViralVideo({ images: ["img1"] } as never)
|
||||
vi.runAllTimersAsync()
|
||||
expect(await r).toEqual({ id: "j1" })
|
||||
expect(mockPost).toHaveBeenCalledWith("/viral-video/generate", { images: ["img1"] })
|
||||
})
|
||||
|
||||
it("getViralVideoJob", async () => {
|
||||
mockGet.mockResolvedValue({ data: { id: "j2" } })
|
||||
const r = getViralVideoJob("j2")
|
||||
@@ -107,6 +117,16 @@ describe("viral-video API wrappers", () => {
|
||||
expect(mockGet).toHaveBeenCalledWith("/viral-video/j2")
|
||||
})
|
||||
|
||||
it("confirmViralVideoIntent", async () => {
|
||||
mockPost.mockResolvedValue({ data: { id: "j3" } })
|
||||
const r = confirmViralVideoIntent("j3", { confirmed_copy: "hi" })
|
||||
vi.runAllTimersAsync()
|
||||
await r
|
||||
expect(mockPost).toHaveBeenCalledWith("/viral-video/j3/confirm-intent", {
|
||||
confirmed_copy: "hi",
|
||||
})
|
||||
})
|
||||
|
||||
it("retryViralVideo", async () => {
|
||||
mockPost.mockResolvedValue({ data: { id: "j4" } })
|
||||
await retryViralVideo("j4")
|
||||
|
||||
@@ -0,0 +1,32 @@
|
||||
import { describe, expect, it, vi } from "vitest"
|
||||
import { render, screen } from "@testing-library/react"
|
||||
import { MemoryRouter } from "react-router-dom"
|
||||
import AdminComingSoon from "@/pages/admin/AdminComingSoon"
|
||||
|
||||
vi.mock("react-router-dom", async () => {
|
||||
const actual = await vi.importActual("react-router-dom")
|
||||
return {
|
||||
...actual,
|
||||
useNavigate: () => vi.fn(),
|
||||
}
|
||||
})
|
||||
|
||||
describe("AdminComingSoon Page", () => {
|
||||
it("should render without crashing", () => {
|
||||
render(
|
||||
<MemoryRouter>
|
||||
<AdminComingSoon />
|
||||
</MemoryRouter>,
|
||||
)
|
||||
expect(screen.getByText("Admin 后台暂未开放")).toBeTruthy()
|
||||
})
|
||||
|
||||
it("should render back button", () => {
|
||||
render(
|
||||
<MemoryRouter>
|
||||
<AdminComingSoon />
|
||||
</MemoryRouter>,
|
||||
)
|
||||
expect(screen.getByText("返回首页")).toBeTruthy()
|
||||
})
|
||||
})
|
||||
@@ -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,
|
||||
|
||||
+1040
-1753
File diff suppressed because it is too large
Load Diff
@@ -1,95 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 prompt 解析：优先读后台 viral_video_prompt_templates（prompt_type='image_analysis'
|
||||
且 is_active=true），30s TTL 热加载；DB 无有效记录/异常时，fallback 到 prompts.py 的
|
||||
image_analysis v8 默认 system/user。
|
||||
|
||||
规则：
|
||||
- DB 有 is_active=true 的 image_analysis 记录：system 原样用 DB.system_prompt
|
||||
（自带完整输出格式，不追加任何硬编码 schema），user 用 DB.user_prompt_template
|
||||
渲染（填入 image_url / ocr_text）；
|
||||
- DB 无记录/异常：system/user 用 prompts.py 里的 v8 默认模板。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _default_template() -> dict:
|
||||
# 延迟导入：避免模块加载时拉起整个 packages 依赖链（也便于旧 Python 收集测试）
|
||||
from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
|
||||
|
||||
for item in DEFAULT_TEMPLATES:
|
||||
if item["prompt_type"] == "image_analysis":
|
||||
return item
|
||||
raise RuntimeError("image_analysis 默认模板缺失")
|
||||
|
||||
|
||||
_cache_lock = threading.Lock()
|
||||
_cache: dict[str, tuple[float, tuple[str, str]]] = {}
|
||||
_CACHE_TTL = 30.0
|
||||
|
||||
|
||||
def _load_db_template():
|
||||
"""查 DB is_active=true 的 image_analysis 记录；不可达/无记录返回 None。"""
|
||||
try:
|
||||
from packages.application.viral_video.prompt_loader import _load_from_db
|
||||
|
||||
return _load_from_db("image_analysis")
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("[vision.v2] 查询DB image_analysis prompt失败: %s", e)
|
||||
return None
|
||||
|
||||
|
||||
def _render_user(user_tpl: str, image_url: str, ocr_text: str) -> str:
|
||||
try:
|
||||
return user_tpl.format(image_url=image_url, ocr_text=ocr_text or "无")
|
||||
except Exception: # noqa: BLE001
|
||||
return user_tpl
|
||||
|
||||
|
||||
def _resolve(kind: str, image_url: str = "", ocr_text: 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:
|
||||
sys_prompt, usr_prompt = hit[1]
|
||||
return sys_prompt, _render_user(usr_prompt, image_url, ocr_text)
|
||||
|
||||
default = _default_template()
|
||||
sys_prompt = default["system_prompt"]
|
||||
usr_prompt = default["user_prompt_template"]
|
||||
|
||||
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_usr = getattr(tpl, "user_prompt_template", "") or usr_prompt
|
||||
usr_prompt = db_usr or usr_prompt
|
||||
logger.info(
|
||||
"[vision.v2] 使用DB image_analysis prompt version=%s",
|
||||
getattr(tpl, "version", "?"),
|
||||
)
|
||||
|
||||
with _cache_lock:
|
||||
_cache[cache_key] = (now, (sys_prompt, usr_prompt))
|
||||
return sys_prompt, _render_user(usr_prompt, image_url, ocr_text)
|
||||
|
||||
|
||||
def resolve_fast_prompt(image_url: str = "", ocr_text: str = "") -> tuple[str, str]:
|
||||
return _resolve("fast", image_url, ocr_text)
|
||||
|
||||
|
||||
def resolve_pro_prompt(image_url: str = "", ocr_text: str = "") -> tuple[str, str]:
|
||||
return _resolve("pro", image_url, ocr_text)
|
||||
|
||||
|
||||
def invalidate_cache() -> None:
|
||||
with _cache_lock:
|
||||
_cache.clear()
|
||||
@@ -0,0 +1,114 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 VLM 多后端 provider 切换：ark（火山方舟/豆包，默认）/ dashscope（阿里云百炼/qwen）。
|
||||
|
||||
通过环境变量 VISION_V2_PROVIDER 切换，默认 dashscope（对比测试用）。
|
||||
各后端的 fast（小模型 JSON-only）和 pro（大模型兜底）模型配置如下：
|
||||
- ark: doubao-seed-2-1-lite-260915 / doubao-seed-2-1-pro-260915
|
||||
- dashscope: qwen3.8-flash / qwen3.7-plus
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 默认 dashscope 做对比测试（灵应10-05指示）
|
||||
PROVIDER = os.environ.get("VISION_V2_PROVIDER", "dashscope").lower()
|
||||
|
||||
# fast 模型（JSON-only，强约束输出）
|
||||
_FAST_MODEL_MAP = {
|
||||
"ark": "doubao-seed-2-1-lite-260915",
|
||||
"dashscope": "qwen3.8-flash",
|
||||
}
|
||||
# pro 模型（兜底）
|
||||
_PRO_MODEL_MAP = {
|
||||
"ark": "doubao-seed-2-1-pro-260915",
|
||||
"dashscope": "qwen3.7-plus",
|
||||
}
|
||||
# base URL
|
||||
_BASE_URL_MAP = {
|
||||
"ark": "https://ark.cn-beijing.volces.com/api/v3",
|
||||
"dashscope": "https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
}
|
||||
# API key 环境变量
|
||||
_API_KEY_ENVS = {
|
||||
"ark": "DOUBAO_API_KEY",
|
||||
"dashscope": "DASHSCOPE_API_KEY",
|
||||
}
|
||||
# 关 thinking 参数（各后端不一样）
|
||||
_THINKING_PARAMS = {
|
||||
# 豆包 Seed 2.x 用 thinking={type:"disabled"}，不要和 reasoning_effort 同时传
|
||||
"ark": {"thinking": {"type": "disabled"}},
|
||||
# 百炼 qwen3 用 enable_thinking:false（顶层字段，不是 OpenAI 标准）
|
||||
"dashscope": {"enable_thinking": False},
|
||||
}
|
||||
# fast/pro 超时
|
||||
_FAST_TIMEOUT_MAP = {"ark": 8, "dashscope": 8}
|
||||
_PRO_TIMEOUT_MAP = {"ark": 45, "dashscope": 20}
|
||||
|
||||
|
||||
def get_provider() -> str:
|
||||
return PROVIDER
|
||||
|
||||
|
||||
def fast_model() -> str:
|
||||
return _FAST_MODEL_MAP.get(PROVIDER, _FAST_MODEL_MAP["ark"])
|
||||
|
||||
|
||||
def pro_model() -> str:
|
||||
return _PRO_MODEL_MAP.get(PROVIDER, _PRO_MODEL_MAP["ark"])
|
||||
|
||||
|
||||
def base_url() -> str:
|
||||
return _BASE_URL_MAP.get(PROVIDER, _BASE_URL_MAP["ark"])
|
||||
|
||||
|
||||
def api_key() -> str | None:
|
||||
"""返回当前 provider 的 API key；ark 从 shared_settings 读，dashscope 从环境变量读。"""
|
||||
if PROVIDER == "ark":
|
||||
try:
|
||||
from packages.shared import get_shared_settings
|
||||
|
||||
return get_shared_settings().doubao_api_key
|
||||
except Exception:
|
||||
return os.environ.get("DOUBAO_API_KEY")
|
||||
return os.environ.get(_API_KEY_ENVS.get(PROVIDER, "DASHSCOPE_API_KEY"))
|
||||
|
||||
|
||||
def thinking_params() -> dict[str, Any]:
|
||||
return dict(_THINKING_PARAMS.get(PROVIDER, {}))
|
||||
|
||||
|
||||
def is_dashscope() -> bool:
|
||||
return PROVIDER == "dashscope"
|
||||
|
||||
|
||||
def fast_timeout_default() -> int:
|
||||
return _FAST_TIMEOUT_MAP.get(PROVIDER, 8)
|
||||
|
||||
|
||||
def pro_timeout_default() -> int:
|
||||
return _PRO_TIMEOUT_MAP.get(PROVIDER, 45)
|
||||
|
||||
|
||||
def is_400_thinking_error(body_text: str) -> bool:
|
||||
"""400 响应是否是因为 thinking 参数不被支持（触发降级重试）。"""
|
||||
b = body_text.lower()
|
||||
if PROVIDER == "ark":
|
||||
return "thinking" in b or "reasoning" in b
|
||||
if PROVIDER == "dashscope":
|
||||
return "enable_thinking" in b or "thinking" in b
|
||||
return False
|
||||
|
||||
|
||||
def pop_thinking_param(payload: dict[str, Any]) -> None:
|
||||
"""从 payload 里移除 thinking 相关参数（降级重试用）。"""
|
||||
if PROVIDER == "ark":
|
||||
payload.pop("thinking", None)
|
||||
payload.pop("reasoning_effort", None)
|
||||
elif PROVIDER == "dashscope":
|
||||
payload.pop("enable_thinking", None)
|
||||
payload.pop("thinking", None)
|
||||
@@ -1,106 +1,307 @@
|
||||
"""V2 结果组装（v8 叙述优先，大幅精简）。
|
||||
# -*- coding: utf-8 -*-
|
||||
"""把 fast_json VLM 输出 + OCR 文本组装为与旧 _normalize() 完全一致的 dict。
|
||||
|
||||
设计原则：VLM 直接输出最终给用户看的 summary_markdown，assembler 只负责
|
||||
- 解析 fast JSON（兼容顶层 {"images":[...]} 与 {"products":[...]} 两种键）；
|
||||
- 补齐 5 个必备字段（type/name/brand/has_person/summary_markdown）；
|
||||
- summary_markdown 缺失（异常）时才拼一句最基础的兜底文字。
|
||||
|
||||
正常情况下不改写、不“润色”VLM 输出，不做 brand 多级兜底，不处理任何
|
||||
colors/material/key_features 等细分字段。
|
||||
目标：下游（信任链t2i/intent_parsing/script_generation）零改动。
|
||||
必出字段：name, brand, category, appearance, packaging, text_on_package,
|
||||
key_features, scene, mood, portrait_prompt, summary, _source
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_VALID_TYPES = ("store", "product", "person", "scene")
|
||||
# ---------- portrait_prompt 模板 ----------
|
||||
# 目标：60-100 字的人物穿搭描述，用于 Seedream 纯文生图。要求具体、风格化、视觉细节丰富。
|
||||
# 旧 VLM 输出格式参考："一位25岁左右的亚洲女性，身穿白色V领短袖T恤，黑色高腰阔腿裤，
|
||||
# 搭配银色项链，长发披肩，表情自信，街拍风格，阳光明媚的城市街头"
|
||||
|
||||
|
||||
def _coerce_bool(value: Any) -> bool:
|
||||
if isinstance(value, bool):
|
||||
return value
|
||||
if isinstance(value, (int, float)):
|
||||
return value != 0
|
||||
if isinstance(value, str):
|
||||
return value.strip().lower() in ("true", "1", "yes", "是")
|
||||
return False
|
||||
def _join_parts(*parts: str | None) -> str:
|
||||
return "".join(p for p in parts if p)
|
||||
|
||||
|
||||
def _basic_markdown(image: dict[str, Any]) -> str:
|
||||
"""异常兜底：VLM 没给 summary_markdown 时只拼一句基础文字。"""
|
||||
name = (image.get("name") or "").strip() or "未识别"
|
||||
brand = (image.get("brand") or "").strip()
|
||||
typ = image.get("type") or "scene"
|
||||
label = f"{brand}{name}" if brand and brand not in name else (brand or name)
|
||||
if typ == "store":
|
||||
return f"这是{label}的门店场景，画面细节识别不完整。"
|
||||
if typ == "person":
|
||||
return f"画面中的人物与{label}相关，细节识别不完整。"
|
||||
if typ == "product":
|
||||
return f"这是{label}的商品图片，具体外观细节识别不完整。"
|
||||
return f"画面内容为{label}，细节识别不完整。"
|
||||
_AGE_PREFIX = {
|
||||
"青年": "年轻",
|
||||
"中年": "中年",
|
||||
"老年": "老年",
|
||||
}
|
||||
# gender 后缀
|
||||
_GENDER_WORD = {"男": "男性", "女": "女性"}
|
||||
|
||||
|
||||
def _normalize_image(raw: Any, idx: int) -> dict[str, Any]:
|
||||
"""把一条 VLM 输出归一化为 5 字段 dict。"""
|
||||
if not isinstance(raw, dict):
|
||||
raw = {}
|
||||
def _person_subject(fj: dict[str, Any]) -> str:
|
||||
"""人物主语：年轻女性 / 中年男性 / 少女 / 小男孩 / 人物 等。"""
|
||||
gender = fj.get("gender") or ""
|
||||
age = fj.get("age_range") or ""
|
||||
gw = _GENDER_WORD.get(gender, "")
|
||||
if age == "儿童":
|
||||
if gender == "女":
|
||||
return "小女孩"
|
||||
if gender == "男":
|
||||
return "小男孩"
|
||||
return "儿童"
|
||||
if age == "青少年":
|
||||
if gender == "女":
|
||||
return "少女"
|
||||
if gender == "男":
|
||||
return "少年"
|
||||
return "青少年"
|
||||
prefix = _AGE_PREFIX.get(age, "")
|
||||
if gw:
|
||||
return f"{prefix}{gw}" if prefix else gw
|
||||
return f"{prefix}人物" if prefix else "人物"
|
||||
|
||||
typ = str(raw.get("type") or "").strip().lower()
|
||||
if typ not in _VALID_TYPES:
|
||||
typ = "scene"
|
||||
|
||||
name = str(raw.get("name") or "").strip() or "未识别"
|
||||
brand = str(raw.get("brand") or "").strip()
|
||||
has_person = _coerce_bool(raw.get("has_person"))
|
||||
if typ == "person" and not has_person:
|
||||
# type=person 通常意味着主体是人，保持一致（仅异常补全）
|
||||
has_person = True
|
||||
def _build_wear_sentence(fj: dict[str, Any]) -> str:
|
||||
"""穿搭段：上装+下装/连衣裙，带颜色+材质+图案。"""
|
||||
upper = fj.get("upper_wear") or ""
|
||||
upper_color = fj.get("upper_color") or ""
|
||||
lower = fj.get("lower_wear") or ""
|
||||
lower_color = fj.get("lower_color") or ""
|
||||
dress_color = fj.get("dress_color") or ""
|
||||
material = fj.get("material") or ""
|
||||
pattern = fj.get("pattern") or ""
|
||||
|
||||
summary = raw.get("summary_markdown")
|
||||
summary = summary.strip() if isinstance(summary, str) else ""
|
||||
is_dress = ("连衣裙" in upper) or ("裙" in upper and not lower)
|
||||
if is_dress:
|
||||
c = dress_color or upper_color
|
||||
wear = f"{c}{upper}" if c else upper
|
||||
if material and material not in wear:
|
||||
wear = f"{material}{wear}"
|
||||
if pattern and pattern not in wear and pattern != "纯色":
|
||||
wear += f"，{pattern}图案"
|
||||
return f"身穿{wear}"
|
||||
|
||||
image: dict[str, Any] = {
|
||||
"type": typ,
|
||||
parts: list[str] = []
|
||||
if upper:
|
||||
up = f"{upper_color}{upper}" if upper_color else upper
|
||||
if material and material not in up:
|
||||
up = f"{material}{up}"
|
||||
if pattern and pattern != "纯色" and pattern not in up:
|
||||
up += f"（{pattern}）"
|
||||
parts.append(f"上身{up}" if up else "")
|
||||
if lower:
|
||||
lo = f"{lower_color}{lower}" if lower_color else lower
|
||||
parts.append(f"下身{lo}" if lo else "")
|
||||
return "，".join(p for p in parts if p)
|
||||
|
||||
|
||||
def _build_portrait_prompt(fj: dict[str, Any]) -> str:
|
||||
"""组装最终 portrait_prompt（目标 60-100 字，用于 Seedream 纯文生图）。"""
|
||||
if not fj.get("has_person"):
|
||||
# 非人像：用商品+场景+mood 拼一段
|
||||
name = fj.get("product_name") or "商品"
|
||||
brand = fj.get("brand") or ""
|
||||
colors = fj.get("colors") or []
|
||||
style = fj.get("style") or ""
|
||||
scene = fj.get("scene") or ""
|
||||
mood = fj.get("mood") or ""
|
||||
pieces = []
|
||||
if brand:
|
||||
pieces.append(brand)
|
||||
pieces.append(name)
|
||||
if colors:
|
||||
pieces.append("、".join(colors[:3]) + "配色")
|
||||
if style:
|
||||
pieces.append(style + "风格")
|
||||
if mood:
|
||||
pieces.append(mood + "氛围")
|
||||
if scene and scene not in ("通用",):
|
||||
pieces.append(scene + "场景")
|
||||
pieces.append("产品特写")
|
||||
prompt = "，".join(p for p in pieces if p)
|
||||
return prompt if len(prompt) >= 10 else "产品展示图，特写镜头"
|
||||
|
||||
subject = _person_subject(fj)
|
||||
wear = _build_wear_sentence(fj)
|
||||
|
||||
accessories = fj.get("accessories") or []
|
||||
if isinstance(accessories, str):
|
||||
accessories = [accessories]
|
||||
acc_str = ""
|
||||
if accessories:
|
||||
acc_str = "，佩戴" + "、".join(str(a) for a in accessories if a)
|
||||
|
||||
hairstyle = fj.get("hairstyle") or ""
|
||||
expression = fj.get("expression") or ""
|
||||
pose = fj.get("pose") or ""
|
||||
style = fj.get("style") or ""
|
||||
scene = fj.get("scene") or ""
|
||||
mood = fj.get("mood") or ""
|
||||
|
||||
detail_parts: list[str] = []
|
||||
if hairstyle:
|
||||
detail_parts.append(hairstyle)
|
||||
if expression and expression not in ("自然", "平静"):
|
||||
detail_parts.append(f"神情{expression}")
|
||||
if pose and pose not in ("站立",):
|
||||
detail_parts.append(pose)
|
||||
|
||||
style_parts: list[str] = []
|
||||
if style:
|
||||
style_parts.append(style)
|
||||
if mood:
|
||||
style_parts.append(mood)
|
||||
if scene and scene not in ("通用",):
|
||||
style_parts.append(scene)
|
||||
|
||||
pieces = [f"一位{subject}"]
|
||||
if wear:
|
||||
pieces.append(wear)
|
||||
if acc_str:
|
||||
pieces.append(acc_str.lstrip("，"))
|
||||
if detail_parts:
|
||||
pieces.append("，".join(detail_parts))
|
||||
if style_parts:
|
||||
# 风格词之间不用逗号，用空格紧凑
|
||||
pieces.append("".join(style_parts) + "风格")
|
||||
else:
|
||||
pieces.append("人像写真")
|
||||
|
||||
full = "，".join(p for p in pieces if p)
|
||||
# 过短补充镜头词
|
||||
if len(full) < 40:
|
||||
full += "，自然光线下人像特写，画面清晰"
|
||||
# 过长截断
|
||||
if len(full) > 120:
|
||||
full = full[:120].rstrip("，") + "。"
|
||||
return full
|
||||
|
||||
|
||||
# ---------- 商品字段 ----------
|
||||
|
||||
|
||||
def _infer_name(fj: dict[str, Any], ocr_texts: list[str]) -> str:
|
||||
pname = fj.get("product_name")
|
||||
if pname and pname != "未识别":
|
||||
return str(pname)
|
||||
# 人物图 → name 用穿搭主件
|
||||
if fj.get("has_person"):
|
||||
up = fj.get("upper_wear") or ""
|
||||
if "连衣裙" in up:
|
||||
return up
|
||||
return up or "人物穿搭"
|
||||
if ocr_texts:
|
||||
# 商品名可能是 OCR 最长的一行（品牌/产品名）
|
||||
return max(ocr_texts, key=len)
|
||||
return "未识别"
|
||||
|
||||
|
||||
def _infer_brand(fj: dict[str, Any], ocr_texts: list[str]) -> str:
|
||||
brand = fj.get("brand")
|
||||
if brand:
|
||||
return str(brand)
|
||||
# OCR 里短的、纯字母/汉字短串可能是 brand
|
||||
for t in ocr_texts:
|
||||
if 1 < len(t) <= 12:
|
||||
return t
|
||||
return "无法判断"
|
||||
|
||||
|
||||
def _infer_category(fj: dict[str, Any]) -> str:
|
||||
cat = fj.get("category")
|
||||
if cat:
|
||||
return str(cat)
|
||||
if fj.get("has_person"):
|
||||
return "服饰"
|
||||
return "非产品图"
|
||||
|
||||
|
||||
def _build_appearance(fj: dict[str, Any]) -> str:
|
||||
"""外观描述：颜色+款式+材质+图案 拼成一段。"""
|
||||
parts: list[str] = []
|
||||
for key, _label in [
|
||||
("upper_color", "主色"),
|
||||
("upper_wear", "款式"),
|
||||
("material", "材质"),
|
||||
("pattern", "图案"),
|
||||
]:
|
||||
v = fj.get(key)
|
||||
if v and v not in ("无法判断", "未知", "纯色"):
|
||||
parts.append(str(v))
|
||||
if not parts:
|
||||
if fj.get("has_person"):
|
||||
return "人像穿搭整体造型"
|
||||
return "无法判断"
|
||||
return "、".join(parts)
|
||||
|
||||
|
||||
def _build_key_features(fj: dict[str, Any], ocr_texts: list[str]) -> list[str]:
|
||||
feats: list[str] = []
|
||||
for key in (
|
||||
"upper_wear",
|
||||
"lower_wear",
|
||||
"upper_color",
|
||||
"lower_color",
|
||||
"dress_color",
|
||||
"material",
|
||||
"pattern",
|
||||
"style",
|
||||
"accessories",
|
||||
):
|
||||
v = fj.get(key)
|
||||
if not v:
|
||||
continue
|
||||
if isinstance(v, list):
|
||||
feats.extend(str(x) for x in v if x)
|
||||
elif isinstance(v, str) and v not in ("无法判断", "未知", "纯色"):
|
||||
feats.append(v)
|
||||
if ocr_texts:
|
||||
feats.append(f"画面文字: {'/'.join(ocr_texts[:3])}")
|
||||
# 去重
|
||||
out: list[str] = []
|
||||
seen: set[str] = set()
|
||||
for f in feats:
|
||||
f = f.strip()
|
||||
if f and f not in seen and len(f) <= 30:
|
||||
seen.add(f)
|
||||
out.append(f)
|
||||
return out[:6] if out else ["无法判断"]
|
||||
|
||||
|
||||
def assemble_result(
|
||||
idx: int,
|
||||
fast_json: dict[str, Any] | None,
|
||||
ocr_texts: list[str],
|
||||
) -> dict[str, Any]:
|
||||
"""把 fast_json 结果 + OCR 文本组装成下游兼容的 product dict。"""
|
||||
fj = fast_json or {}
|
||||
ocr_texts = ocr_texts or []
|
||||
|
||||
portrait_prompt = _build_portrait_prompt(fj)
|
||||
name = _infer_name(fj, ocr_texts)
|
||||
brand = _infer_brand(fj, ocr_texts)
|
||||
category = _infer_category(fj)
|
||||
appearance = _build_appearance(fj)
|
||||
key_features = _build_key_features(fj, ocr_texts)
|
||||
scene = fj.get("scene") or "通用"
|
||||
mood = fj.get("mood") or ""
|
||||
packaging = "无法判断" # 包装细节专用API无，保留占位
|
||||
text_on_package = ocr_texts[:8]
|
||||
summary = _build_summary(fj, name, brand, category)
|
||||
|
||||
return {
|
||||
"name": name,
|
||||
"brand": brand,
|
||||
"has_person": has_person,
|
||||
"summary_markdown": summary,
|
||||
"category": category,
|
||||
"appearance": appearance,
|
||||
"packaging": packaging,
|
||||
"text_on_package": text_on_package,
|
||||
"key_features": key_features,
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": portrait_prompt,
|
||||
"summary": summary,
|
||||
"_source": "v2_fast_json",
|
||||
}
|
||||
if not summary:
|
||||
image["summary_markdown"] = _basic_markdown(image)
|
||||
image["_source"] = "summary_missing"
|
||||
logger.info("[assembler] 图片 #%s 缺少 summary_markdown，使用基础兜底", idx)
|
||||
return image
|
||||
|
||||
|
||||
def _extract_items(fast_json: Any) -> list[Any]:
|
||||
"""从 fast JSON 中取出图片条目：优先 images，兼容 products。"""
|
||||
if not isinstance(fast_json, dict):
|
||||
return []
|
||||
items = fast_json.get("images")
|
||||
if not isinstance(items, list):
|
||||
items = fast_json.get("products")
|
||||
return items if isinstance(items, list) else []
|
||||
|
||||
|
||||
def assemble_result(idx: int, fast_json: Any, ocr_texts: list[str] | None = None) -> dict[str, Any]:
|
||||
"""组装单张图片分析结果。
|
||||
|
||||
每次调用对应一张图片；fast_json 形如 {"images": [{...}]}（v8）。
|
||||
返回单条 image dict（5 字段，必要时带 _source）。
|
||||
"""
|
||||
items = _extract_items(fast_json)
|
||||
if items:
|
||||
image = _normalize_image(items[0], idx)
|
||||
else:
|
||||
# 极端异常：fast 无任何可用条目，OCR 文字可作为名称线索
|
||||
ocr_hint = ""
|
||||
if ocr_texts:
|
||||
ocr_hint = "、".join(t for t in ocr_texts if t)[:40]
|
||||
image = _normalize_image({"name": ocr_hint or "未识别", "summary_markdown": ""}, idx)
|
||||
image["_source"] = "empty_fast_json"
|
||||
return image
|
||||
def _build_summary(fj: dict, name: str, brand: str, category: str) -> str:
|
||||
if fj.get("has_person"):
|
||||
up = fj.get("upper_wear") or "穿搭"
|
||||
style = fj.get("style") or ""
|
||||
base = f"{style}{up}" if style and style not in up else up
|
||||
return base
|
||||
if brand != "无法判断" and name != brand:
|
||||
return f"{brand} {name}"
|
||||
return name
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 图片分析主路径（v8 叙述优先）：每图并行 OCR（火山 MediaKit，未配置自动跳过）
|
||||
+ fast VLM 强约束 JSON；失败时单次 pro VLM 兜底。
|
||||
"""V2 图片分析主路径：每图并行 OCR（火山专用API，未配置时自动跳过）+ lite JSON VLM，失败时单次 pro VLM 兜底。
|
||||
|
||||
架构：
|
||||
- 单图 2 路并行（OCR + fast VLM），外层 N 图全并发（workers=8）；
|
||||
- 兜底单次 pro VLM，无竞速/复杂重试；
|
||||
- 输出统一为 5 字段 image dict（type/name/brand/has_person/summary_markdown）。
|
||||
支持双后端（环境变量 VISION_V2_PROVIDER=ark|dashscope，默认 dashscope）。
|
||||
设计原则：
|
||||
- 主力路径简洁：单图2路并行，外层N图全并发
|
||||
- 兜底简单：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
|
||||
@@ -16,28 +16,40 @@ import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from typing import Any
|
||||
|
||||
from . import assembler, ocr_volc, vlm_fallback, vlm_fast_json
|
||||
from . import _provider, assembler, ocr_volc, vlm_fallback, vlm_fast_json
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 可通过环境变量调参（默认值随 provider 变化）
|
||||
_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"))
|
||||
_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "8"))
|
||||
_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", str(_provider.fast_timeout_default())))
|
||||
_OCR_TIMEOUT = float(os.environ.get("VISION_V2_OCR_TIMEOUT", "6"))
|
||||
_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "45"))
|
||||
_PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", str(_provider.pro_timeout_default())))
|
||||
|
||||
_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] | None) -> bool:
|
||||
if not isinstance(r, dict):
|
||||
return False
|
||||
return bool((r.get("summary_markdown") or "").strip())
|
||||
|
||||
|
||||
def _basic_failure(ocr_result: list[str], fast_elapsed: float, source: str) -> dict[str, Any]:
|
||||
image = assembler.assemble_result(-1, {}, ocr_result)
|
||||
image["_source"] = source
|
||||
image["_fast_elapsed"] = round(fast_elapsed, 2)
|
||||
return image
|
||||
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]:
|
||||
@@ -45,45 +57,60 @@ def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
|
||||
|
||||
fj_result: dict[str, Any] | None = None
|
||||
ocr_result: list[str] = []
|
||||
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: # noqa: BLE001
|
||||
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)
|
||||
with ThreadPoolExecutor(max_workers=2) as pool:
|
||||
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)
|
||||
|
||||
fast_elapsed = time.time() - t0
|
||||
|
||||
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", idx, fast_elapsed)
|
||||
logger.info(
|
||||
"[vision.v2] 图片 #%d fast命中 provider=%s elapsed=%.2fs pp=%s",
|
||||
idx,
|
||||
_provider.get_provider(),
|
||||
fast_elapsed,
|
||||
(assembled.get("portrait_prompt") or "")[:40],
|
||||
)
|
||||
return assembled
|
||||
|
||||
pro_result = vlm_fallback.call_pro_vlm(img_url, idx, ocr_hint=ocr_result, timeout=_PRO_TIMEOUT)
|
||||
if _is_usable(pro_result):
|
||||
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)
|
||||
return _basic_failure(ocr_result, fast_elapsed, "v2_all_failed")
|
||||
logger.warning(
|
||||
"[vision.v2] 图片 #%d 全路径失败 provider=%s elapsed=%.2fs", idx, _provider.get_provider(), 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]]:
|
||||
@@ -93,9 +120,10 @@ def analyze_images_v2(img_urls: list[str]) -> list[dict[str, Any]]:
|
||||
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",
|
||||
"[vision.v2] 开始图片分析 n=%d workers=%d provider=%s fast_timeout=%.0fs pro_timeout=%.0fs",
|
||||
len(img_urls),
|
||||
workers,
|
||||
_provider.get_provider(),
|
||||
_FAST_TIMEOUT,
|
||||
_PRO_TIMEOUT,
|
||||
)
|
||||
@@ -106,19 +134,14 @@ def analyze_images_v2(img_urls: list[str]) -> list[dict[str, Any]]:
|
||||
idx = future_to_idx[fut]
|
||||
try:
|
||||
results[idx] = fut.result()
|
||||
except Exception as e: # noqa: BLE001
|
||||
except Exception as e:
|
||||
logger.warning("[vision.v2] 图片 #%d future异常: %s", idx, e, exc_info=True)
|
||||
results[idx] = assembler.assemble_result(idx, {}, [])
|
||||
results[idx]["_source"] = "v2_future_exception" # type: ignore[index]
|
||||
r = dict(_FALLBACK_RESULT)
|
||||
r["_source"] = "v2_future_exception"
|
||||
results[idx] = r
|
||||
|
||||
elapsed = time.time() - t0
|
||||
succ = sum(1 for r in results if _is_usable(r))
|
||||
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] # type: ignore[misc]
|
||||
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 +1,353 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 兜底路径：vision client（fallback 变体）单图调用，走 v8 叙述优先 prompt。
|
||||
"""VLM pro 兜底：fast 路径失败时的单次调用，支持双后端。
|
||||
|
||||
fast 超时/非 JSON/为空时单次调用；输出统一走 assembler.assemble_result 组装，
|
||||
与 fast 路径同为 5 字段 image dict。
|
||||
- ark（火山/豆包）：通过 ai_client 走 doubao-seed-2-1-pro，保留 XML+JSON 双解析和 prompt_loader
|
||||
- dashscope（百炼/qwen）：直接 httpx 走 qwen3.7-plus，用精简 JSON-only prompt 提升速度
|
||||
|
||||
设计原则：简单、直接、无竞速、无复杂重试。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
from . import _prompt, assembler
|
||||
from . import _provider
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DEFAULT_TIMEOUT = 45
|
||||
DEFAULT_TIMEOUT = _provider.pro_timeout_default()
|
||||
DEFAULT_MAX_TOKENS = 800
|
||||
|
||||
# DashScope 兜底用的精简 JSON-only prompt（比 prompt_loader 模板短很多，减少延迟）
|
||||
_DS_PRO_SYSTEM = (
|
||||
"你是图片分析助手。仔细观察图片，严格按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'
|
||||
' "outfit": "人物穿搭描述，60字以内（例：白色T恤+牛仔裤）",\n'
|
||||
' "hair": "发型",\n'
|
||||
' "pose": "姿态",\n'
|
||||
' "expression": "表情",\n'
|
||||
' "scene": "场景",\n'
|
||||
' "mood": "氛围",\n'
|
||||
' "has_product": true/false,\n'
|
||||
' "category": "类目",\n'
|
||||
' "product_name": "产品名称",\n'
|
||||
' "brand": "品牌",\n'
|
||||
' "key_features": ["特征数组"]\n'
|
||||
"}"
|
||||
)
|
||||
_DS_PRO_USER = "分析这张图片，返回符合schema的JSON。"
|
||||
|
||||
|
||||
def _strip_code_fence(s: str) -> str:
|
||||
s = s.strip()
|
||||
if s.startswith("```"):
|
||||
lines = s.split("\n")
|
||||
if lines and lines[0].startswith("```"):
|
||||
lines = lines[1:]
|
||||
if lines and lines[-1].strip().startswith("```"):
|
||||
lines = lines[:-1]
|
||||
s = "\n".join(lines).strip()
|
||||
return s
|
||||
|
||||
|
||||
def _xml_text(tag: str, xml: str) -> str:
|
||||
m = re.search(rf"<{tag}[^>]*>(.*?)</{tag}>", xml, re.S)
|
||||
return (m.group(1) if m else "").strip()
|
||||
|
||||
|
||||
def _xml_attr(tag: str, attr: str, xml: str) -> str:
|
||||
m = re.search(rf"<{tag}[^>]*\b{attr}\s*=\s*[\"']([^\"']*)[\"']", xml)
|
||||
return (m.group(1) if m else "").strip()
|
||||
|
||||
|
||||
def _xml_to_product(raw: str, idx: int) -> dict[str, Any]:
|
||||
"""解析 ARK pro 返回的 XML 格式。"""
|
||||
scene = _xml_text("scene", raw) or "通用"
|
||||
mood = _xml_text("mood", raw) or ""
|
||||
|
||||
portrait_prompt = "无人像"
|
||||
p_has = _xml_attr("people", "has_person", raw)
|
||||
if p_has and p_has.lower() != "false":
|
||||
gender = _xml_attr("people", "gender", raw) or ""
|
||||
age = _xml_attr("people", "age_range", raw) or ""
|
||||
outfit = _xml_attr("people", "outfit", raw) or ""
|
||||
hair = _xml_attr("people", "hair", raw) or "自然发型"
|
||||
pose = _xml_attr("people", "pose", raw) or ""
|
||||
expr = _xml_attr("people", "expression", raw) or "自然"
|
||||
parts: list[str] = []
|
||||
if gender:
|
||||
parts.append(gender + ("性" if not gender.endswith("性") else ""))
|
||||
if age:
|
||||
parts.append(age)
|
||||
parts.append("人物")
|
||||
parts.append(hair)
|
||||
if outfit:
|
||||
parts.append(f"身着{outfit}")
|
||||
if pose:
|
||||
parts.append(f"姿态{pose}")
|
||||
parts.append(f"表情{expr}")
|
||||
portrait_prompt = "，".join(parts)
|
||||
|
||||
m = re.search(r"<product[^>]*>(.*?)</product>", raw, re.S)
|
||||
if m:
|
||||
name = _xml_attr("product", "name", raw) or "未识别"
|
||||
brand = _xml_attr("product", "brand", raw) or "无法判断"
|
||||
category = _xml_attr("product", "category", raw) or "无法判断"
|
||||
appearance = _xml_attr("product", "appearance", raw) or "无法判断"
|
||||
packaging = _xml_attr("product", "packaging", raw) or "无法判断"
|
||||
feat = _xml_attr("product", "features", raw) or ""
|
||||
feat_list = [x.strip() for x in re.split(r"[,，;；]", feat) if x.strip()] if feat else ["无法判断"]
|
||||
top_text = _xml_attr("product", "text_on_package", raw) or ""
|
||||
text_list = [x.strip() for x in re.split(r"[,，;；]", top_text) if x.strip()] if top_text else []
|
||||
summary = _xml_attr("product", "summary", raw) or f"{brand} {name}"
|
||||
return {
|
||||
"name": name,
|
||||
"brand": brand,
|
||||
"category": category,
|
||||
"appearance": appearance,
|
||||
"packaging": packaging,
|
||||
"text_on_package": text_list,
|
||||
"key_features": feat_list,
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": portrait_prompt,
|
||||
"summary": summary,
|
||||
"_source": "vlm_pro_xml",
|
||||
}
|
||||
if portrait_prompt != "无人像":
|
||||
return {
|
||||
"name": "未识别",
|
||||
"brand": "无法判断",
|
||||
"category": "无法判断",
|
||||
"appearance": "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": [],
|
||||
"key_features": ["无法判断"],
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": portrait_prompt,
|
||||
"summary": "未识别",
|
||||
"_source": "vlm_pro_no_product",
|
||||
}
|
||||
return {
|
||||
"name": "未识别",
|
||||
"brand": "无法判断",
|
||||
"category": "无法判断",
|
||||
"appearance": "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": [],
|
||||
"key_features": ["无法判断"],
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": "无人像",
|
||||
"summary": "未识别",
|
||||
"_source": "vlm_pro_no_tag",
|
||||
}
|
||||
|
||||
|
||||
def _assemble_pp_from_json(obj: dict[str, Any]) -> str:
|
||||
"""从 DashScope pro 返回的 JSON 组装 portrait_prompt。"""
|
||||
if not obj.get("has_person", False):
|
||||
return "无人像"
|
||||
parts: list[str] = []
|
||||
gender = obj.get("gender")
|
||||
age = obj.get("age_range")
|
||||
if gender:
|
||||
parts.append(gender + ("性" if not gender.endswith("性") else ""))
|
||||
if age:
|
||||
parts.append(age)
|
||||
parts.append("人物")
|
||||
hair = obj.get("hair")
|
||||
if hair:
|
||||
parts.append(hair)
|
||||
outfit = obj.get("outfit")
|
||||
if outfit:
|
||||
parts.append(f"身着{outfit}")
|
||||
pose = obj.get("pose")
|
||||
if pose:
|
||||
parts.append(f"姿态{pose}")
|
||||
expr = obj.get("expression")
|
||||
if expr:
|
||||
parts.append(f"表情{expr}")
|
||||
return "，".join(parts) if parts else "无人像"
|
||||
|
||||
|
||||
def _call_dashscope_pro(img_url: str, idx: int, timeout: int) -> dict[str, Any] | None:
|
||||
"""DashScope qwen3.7-plus 兜底，直接 httpx 发精简 JSON prompt。"""
|
||||
import httpx
|
||||
|
||||
t0 = time.time()
|
||||
api_key = _provider.api_key()
|
||||
if not api_key:
|
||||
logger.warning("[vision.vlm] dashscope api_key 未配置")
|
||||
return None
|
||||
url = f"{_provider.base_url().rstrip('/')}/chat/completions"
|
||||
payload: dict[str, Any] = {
|
||||
"model": _provider.pro_model(),
|
||||
"messages": [
|
||||
{"role": "system", "content": _DS_PRO_SYSTEM},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": _DS_PRO_USER},
|
||||
],
|
||||
},
|
||||
],
|
||||
"temperature": 0.3,
|
||||
"max_tokens": DEFAULT_MAX_TOKENS,
|
||||
"stream": False,
|
||||
}
|
||||
payload.update(_provider.thinking_params())
|
||||
try:
|
||||
r = httpx.post(
|
||||
url,
|
||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||||
json=payload,
|
||||
timeout=timeout,
|
||||
)
|
||||
elapsed = time.time() - t0
|
||||
if r.status_code != 200:
|
||||
logger.warning(
|
||||
"[vision.vlm] dashscope pro HTTP %d elapsed=%.1fs body=%s", r.status_code, elapsed, r.text[:200]
|
||||
)
|
||||
return None
|
||||
data = r.json()
|
||||
raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
|
||||
if not raw:
|
||||
logger.warning("[vision.vlm] dashscope pro 返回空 elapsed=%.1fs", elapsed)
|
||||
return None
|
||||
usage = data.get("usage") or {}
|
||||
logger.info(
|
||||
"[vision.vlm] dashscope pro 完成 idx=%d elapsed=%.1fs in=%d out=%d",
|
||||
idx,
|
||||
elapsed,
|
||||
usage.get("prompt_tokens", 0),
|
||||
usage.get("completion_tokens", 0),
|
||||
)
|
||||
text = _strip_code_fence(raw)
|
||||
l, r_pos = text.find("{"), text.rfind("}")
|
||||
if l < 0 or r_pos <= l:
|
||||
logger.warning("[vision.vlm] dashscope pro 无JSON elapsed=%.1fs head=%s", elapsed, raw[:200])
|
||||
return None
|
||||
obj = json.loads(text[l : r_pos + 1])
|
||||
if not isinstance(obj, dict):
|
||||
return None
|
||||
scene = obj.get("scene") or "通用"
|
||||
mood = obj.get("mood") or ""
|
||||
pp = _assemble_pp_from_json(obj)
|
||||
name = obj.get("product_name") or "未识别"
|
||||
brand = obj.get("brand") or "无法判断"
|
||||
category = obj.get("category") or ("非产品图" if obj.get("has_person") else "无法判断")
|
||||
return {
|
||||
"name": name,
|
||||
"brand": brand,
|
||||
"category": category,
|
||||
"appearance": obj.get("outfit") or "无法判断",
|
||||
"packaging": "无法判断",
|
||||
"text_on_package": [],
|
||||
"key_features": obj.get("key_features") or ["无法判断"],
|
||||
"scene": scene,
|
||||
"mood": mood,
|
||||
"portrait_prompt": pp,
|
||||
"summary": f"{brand} {name}" if name != "未识别" else "未识别",
|
||||
"_source": "vlm_pro_dashscope_json",
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning("[vision.vlm] dashscope pro 异常 idx=%d elapsed=%.1fs err=%s", idx, time.time() - t0, e)
|
||||
return None
|
||||
|
||||
|
||||
def _call_ark_pro(img_url: str, idx: int, model: str | None, timeout: int) -> dict[str, Any] | None:
|
||||
"""ARK 豆包 pro 兜底，保留 ai_client + prompt_loader + XML/JSON 双解析。"""
|
||||
t0 = time.time()
|
||||
try:
|
||||
from packages.application.viral_video.prompt_loader import (
|
||||
get_template,
|
||||
render_system_prompt,
|
||||
render_user_prompt,
|
||||
)
|
||||
from packages.shared.ai_client import get_doubao_client
|
||||
except ImportError as e:
|
||||
logger.warning("[vision.vlm] 导入失败: %s", e)
|
||||
return None
|
||||
try:
|
||||
template = get_template("image_analysis")
|
||||
system = render_system_prompt(template)
|
||||
user = render_user_prompt(template, image_count=1, industry="通用", image_urls=f"第1张：{img_url}")
|
||||
except Exception as e:
|
||||
logger.warning("[vision.vlm] 模板加载失败: %s", e)
|
||||
return None
|
||||
client = get_doubao_client()
|
||||
if not client.is_available:
|
||||
return None
|
||||
use_model = model or _provider.pro_model()
|
||||
_orig_retries = client.max_retries
|
||||
client.max_retries = 0
|
||||
try:
|
||||
raw = client.vision_completion(
|
||||
messages=[{"role": "system", "content": system}, {"role": "user", "content": user}],
|
||||
images=[img_url],
|
||||
temperature=0.3,
|
||||
max_tokens=DEFAULT_MAX_TOKENS,
|
||||
timeout=timeout,
|
||||
model=use_model,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning("[vision.vlm] ark pro 调用失败 idx=%d elapsed=%.1fs err=%s", idx, time.time() - t0, e)
|
||||
client.max_retries = _orig_retries
|
||||
return None
|
||||
client.max_retries = _orig_retries
|
||||
elapsed = time.time() - t0
|
||||
if not raw:
|
||||
logger.warning("[vision.vlm] ark pro 返回空 elapsed=%.1fs", elapsed)
|
||||
return None
|
||||
text = _strip_code_fence(raw)
|
||||
l, r_pos = text.find("{"), text.rfind("}")
|
||||
if l >= 0 and r_pos > l:
|
||||
try:
|
||||
obj = json.loads(text[l : r_pos + 1])
|
||||
if isinstance(obj, dict):
|
||||
logger.info("[vision.vlm] ark pro JSON 完成 idx=%d elapsed=%.1fs", idx, elapsed)
|
||||
return {
|
||||
"name": obj.get("name") or "未识别",
|
||||
"brand": obj.get("brand") or "无法判断",
|
||||
"category": obj.get("category") or "无法判断",
|
||||
"appearance": obj.get("appearance") or "无法判断",
|
||||
"packaging": obj.get("packaging") or "无法判断",
|
||||
"text_on_package": obj.get("text_on_package") or [],
|
||||
"key_features": obj.get("key_features") or obj.get("features") or ["无法判断"],
|
||||
"scene": obj.get("scene") or "通用",
|
||||
"mood": obj.get("mood") or "",
|
||||
"portrait_prompt": obj.get("portrait_prompt") or "无人像",
|
||||
"summary": obj.get("summary") or f"{obj.get('brand','')} {obj.get('name','')}",
|
||||
"_source": "vlm_pro_json",
|
||||
}
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
try:
|
||||
return _xml_to_product(text, idx)
|
||||
except Exception as e:
|
||||
logger.warning("[vision.vlm] ark pro 解析失败 idx=%d elapsed=%.1fs err=%s head=%s", idx, elapsed, e, raw[:200])
|
||||
return None
|
||||
|
||||
|
||||
def call_pro_vlm(
|
||||
img_url: str,
|
||||
idx: int,
|
||||
*,
|
||||
ocr_hint: list[str] | None = None,
|
||||
timeout: int = _DEFAULT_TIMEOUT,
|
||||
max_tokens: int | None = None,
|
||||
model: str | None = None,
|
||||
timeout: int = DEFAULT_TIMEOUT,
|
||||
) -> dict[str, Any] | None:
|
||||
t0 = time.time()
|
||||
ocr_text = "、".join(t for t in (ocr_hint or []) if t)[:200]
|
||||
|
||||
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: # noqa: BLE001
|
||||
logger.warning("[vision.v2] ai_router 获取失败: %s", e)
|
||||
return None
|
||||
|
||||
system_prompt, user_prompt = _prompt.resolve_pro_prompt(img_url, ocr_text)
|
||||
|
||||
messages: list[dict[str, Any]] = [
|
||||
{"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,
|
||||
"temperature": 0.3,
|
||||
"timeout": timeout,
|
||||
"enable_thinking": False,
|
||||
"response_format": {"type": "json_object"},
|
||||
"max_tokens": max_tokens if max_tokens is not None else 4000,
|
||||
}
|
||||
|
||||
from .json_utils import extract_json_object
|
||||
|
||||
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 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:
|
||||
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 截断JSON(partial) elapsed=%.1fs", elapsed)
|
||||
|
||||
result = assembler.assemble_result(idx, obj, ocr_hint or [])
|
||||
result["_source"] = "vlm_pro"
|
||||
result["_fallback_used"] = True
|
||||
logger.info("[vision.v2] pro 完成 model=%s elapsed=%.1fs", client.model, elapsed)
|
||||
return result
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning(
|
||||
"[vision.v2] pro 异常 elapsed=%.1fs err=%s",
|
||||
time.time() - t0,
|
||||
e,
|
||||
exc_info=True,
|
||||
)
|
||||
return None
|
||||
"""单次调用 pro VLM，解析后返回 product dict；失败返回 None。"""
|
||||
if _provider.is_dashscope():
|
||||
return _call_dashscope_pro(img_url, idx, timeout)
|
||||
return _call_ark_pro(img_url, idx, model, timeout)
|
||||
|
||||
@@ -1,110 +1,202 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""V2 快速路径：vision client（默认 image_analysis 能力）强约束 JSON-only 调用。
|
||||
"""V2 快速路径 JSON-only VLM 调用。
|
||||
|
||||
要点：
|
||||
- 通过 ai_router.get_vision_client() 获取 client；
|
||||
- enable_thinking=False 关闭推理链，response_format=json_object 强约束 JSON；
|
||||
- system/user prompt 优先读后台模板（v8 叙述优先），DB 不可用时用 prompts.py 默认；
|
||||
- temperature=0.1（稳定输出 JSON）；两次尝试（第二次去 json_object 约束）。
|
||||
支持双后端切换（通过环境变量 VISION_V2_PROVIDER=ark|dashscope）：
|
||||
- ark（火山方舟/豆包）：doubao-seed-2-1-lite-260915，thinking={type:"disabled"}
|
||||
- dashscope（阿里云百炼/qwen，默认对比测试）：qwen3.8-flash，enable_thinking=false
|
||||
|
||||
设计要点：
|
||||
- 直接用 httpx 发最小 payload，不走 ai_client 包装
|
||||
- 关闭 thinking/推理链（reasoning_tokens 是延迟主因）
|
||||
- system prompt 极致精简，只给字段 schema 和强约束
|
||||
- max_tokens=350、temperature=0.1（稳定输出 JSON）
|
||||
- 单次调用不重试（失败由外层走 pro 兜底）
|
||||
- 期望返回纯 JSON object（无 ```json 包裹、无解释文字）
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
from . import _prompt
|
||||
from . import _provider
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DEFAULT_TIMEOUT = 20
|
||||
# 极简 system prompt：只给字段定义 + 硬性输出要求
|
||||
_FAST_SYSTEM = (
|
||||
"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'
|
||||
"}"
|
||||
)
|
||||
|
||||
_FAST_USER = "识别这张图片的人物穿搭与主体信息，只返回JSON对象。"
|
||||
|
||||
DEFAULT_TIMEOUT = _provider.fast_timeout_default()
|
||||
DEFAULT_MAX_TOKENS = 350
|
||||
|
||||
|
||||
def _strip_code_fence(s: str) -> str:
|
||||
s = s.strip()
|
||||
if s.startswith("```"):
|
||||
lines = s.split("\n")
|
||||
if lines and lines[0].startswith("```"):
|
||||
lines = lines[1:]
|
||||
if lines and lines[-1].strip().startswith("```"):
|
||||
lines = lines[:-1]
|
||||
s = "\n".join(lines).strip()
|
||||
return s
|
||||
|
||||
|
||||
def call_fast_json(
|
||||
img_url: str,
|
||||
*,
|
||||
timeout: int = _DEFAULT_TIMEOUT,
|
||||
max_tokens: int | None = None,
|
||||
model: str | None = None,
|
||||
timeout: int = DEFAULT_TIMEOUT,
|
||||
max_tokens: int = DEFAULT_MAX_TOKENS,
|
||||
) -> dict[str, Any] | None:
|
||||
"""调用 fast VLM 返回结构化 dict；失败/非 JSON 返回 None。"""
|
||||
t0 = time.time()
|
||||
import httpx
|
||||
|
||||
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")
|
||||
api_key = _provider.api_key()
|
||||
base_url = _provider.base_url().rstrip("/")
|
||||
if not api_key:
|
||||
logger.warning("[vision.v2] provider=%s api_key 未配置，跳过 fast_json", _provider.get_provider())
|
||||
return None
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.warning("[vision.v2] ai_router 获取失败: %s", e)
|
||||
return None
|
||||
|
||||
system_prompt, user_prompt = _prompt.resolve_fast_prompt(img_url, "")
|
||||
|
||||
messages: list[dict[str, Any]] = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": user_prompt},
|
||||
use_model = model or _provider.fast_model()
|
||||
url = f"{base_url}/chat/completions"
|
||||
payload: dict[str, Any] = {
|
||||
"model": use_model,
|
||||
"messages": [
|
||||
{"role": "system", "content": _FAST_SYSTEM},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": img_url}},
|
||||
{"type": "text", "text": _FAST_USER},
|
||||
],
|
||||
},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
try:
|
||||
call_kwargs: dict[str, Any] = {
|
||||
"messages": messages,
|
||||
"images": None,
|
||||
"temperature": 0.1,
|
||||
"timeout": timeout,
|
||||
"enable_thinking": False,
|
||||
"response_format": {"type": "json_object"},
|
||||
"max_tokens": max_tokens,
|
||||
"stream": False,
|
||||
}
|
||||
if max_tokens is not None:
|
||||
call_kwargs["max_tokens"] = max_tokens
|
||||
|
||||
from .json_utils import extract_json_object
|
||||
|
||||
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 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:
|
||||
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])
|
||||
# 按 provider 设置关 thinking 参数
|
||||
payload.update(_provider.thinking_params())
|
||||
|
||||
resp = httpx.post(
|
||||
url,
|
||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||||
json=payload,
|
||||
timeout=timeout,
|
||||
)
|
||||
elapsed = time.time() - t0
|
||||
if obj is None:
|
||||
logger.warning("[vision.v2] fast_json 两次均未得到JSON elapsed=%.1fs", elapsed)
|
||||
if resp.status_code == 400:
|
||||
logger.warning(
|
||||
"[vision.v2] fast_json HTTP 400 provider=%s elapsed=%.1fs body=%s",
|
||||
_provider.get_provider(),
|
||||
elapsed,
|
||||
resp.text[:200],
|
||||
)
|
||||
if _provider.is_400_thinking_error(resp.text[:500]):
|
||||
_provider.pop_thinking_param(payload)
|
||||
resp = httpx.post(
|
||||
url,
|
||||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||||
json=payload,
|
||||
timeout=timeout,
|
||||
)
|
||||
elapsed = time.time() - t0
|
||||
if resp.status_code != 200:
|
||||
logger.warning("[vision.v2] fast_json 降级重试 HTTP %d elapsed=%.1fs", resp.status_code, elapsed)
|
||||
return None
|
||||
else:
|
||||
return None
|
||||
elif resp.status_code != 200:
|
||||
logger.warning(
|
||||
"[vision.v2] fast_json HTTP %d provider=%s elapsed=%.1fs body=%s",
|
||||
resp.status_code,
|
||||
_provider.get_provider(),
|
||||
elapsed,
|
||||
resp.text[:200],
|
||||
)
|
||||
return None
|
||||
if obj.get("_partial"):
|
||||
logger.warning("[vision.v2] fast_json 截断JSON(partial) elapsed=%.1fs", elapsed)
|
||||
data = resp.json()
|
||||
raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
|
||||
if raw is None:
|
||||
logger.warning("[vision.v2] fast_json 返回 None elapsed=%.1fs", elapsed)
|
||||
return None
|
||||
usage = data.get("usage") or {}
|
||||
reasoning_tokens = usage.get("reasoning_tokens", 0)
|
||||
ctd = usage.get("completion_tokens_details") or {}
|
||||
if not reasoning_tokens:
|
||||
reasoning_tokens = ctd.get("reasoning_tokens", 0)
|
||||
logger.info(
|
||||
"[vision.v2] fast_json 完成 model=%s elapsed=%.1fs type=%s",
|
||||
client.model,
|
||||
"[vision.v2] fast_json 完成 provider=%s model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
|
||||
_provider.get_provider(),
|
||||
use_model,
|
||||
elapsed,
|
||||
obj.get("type"),
|
||||
usage.get("prompt_tokens", 0),
|
||||
usage.get("completion_tokens", 0),
|
||||
reasoning_tokens,
|
||||
)
|
||||
text = _strip_code_fence(raw)
|
||||
l = text.find("{")
|
||||
r = text.rfind("}")
|
||||
if l >= 0 and r > l:
|
||||
text = text[l : r + 1]
|
||||
try:
|
||||
obj = json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
logger.warning("[vision.v2] fast_json JSON 解析失败 elapsed=%.1fs head=%s", elapsed, raw[:200])
|
||||
return None
|
||||
if not isinstance(obj, dict):
|
||||
logger.warning("[vision.v2] fast_json 非 dict: %s", type(obj))
|
||||
return None
|
||||
logger.info(
|
||||
"[vision.v2] fast_json 完成 provider=%s model=%s elapsed=%.1fs has_person=%s has_product=%s category=%s",
|
||||
_provider.get_provider(),
|
||||
use_model,
|
||||
elapsed,
|
||||
obj.get("has_person"),
|
||||
obj.get("has_product"),
|
||||
obj.get("category"),
|
||||
)
|
||||
return obj
|
||||
except Exception as e: # noqa: BLE001
|
||||
except Exception as e:
|
||||
elapsed = time.time() - t0
|
||||
logger.warning(
|
||||
"[vision.v2] fast_json 异常 elapsed=%.1fs err=%s",
|
||||
time.time() - t0,
|
||||
"[vision.v2] fast_json 异常 provider=%s elapsed=%.1fs err=%s",
|
||||
_provider.get_provider(),
|
||||
elapsed,
|
||||
e,
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
@@ -252,9 +252,6 @@ DOUBAO_TIMEOUT=${DOUBAO_TIMEOUT}
|
||||
# 最大重试次数
|
||||
DOUBAO_MAX_RETRIES=${DOUBAO_MAX_RETRIES}
|
||||
|
||||
# Ditto 情绪分析 LLM 调用超时（秒）
|
||||
DITTO_EMOTION_TIMEOUT=${DITTO_EMOTION_TIMEOUT}
|
||||
|
||||
# 视觉模型（支持图片/视频理解的模型，model name 格式）
|
||||
DOUBAO_VISION_MODEL=${DOUBAO_VISION_MODEL}
|
||||
|
||||
@@ -314,10 +311,3 @@ GPU_ENCODE_CRF=23
|
||||
GPU_ENCODE_FALLBACK_CPU=true
|
||||
GPU_ENCODE_MEZZANINE_TRANSPORT=oss
|
||||
GPU_ENCODE_OSS_TMP_PREFIX=tmp/gpu-mezzanine/
|
||||
|
||||
# ==================== Ditto 蚂蚁数字人口型 ====================
|
||||
# 注意：这些值必须写死在模板里（不是 CI Secret），否则每次 CI 重新渲染 .env 都会被丢弃，
|
||||
# 导致 staging 发版后 Ditto 口型服务静默降级到 GPU/MediaKit（P0 防复发）。
|
||||
USE_DITTO_LIPSYNC=true
|
||||
DITTO_API_BASE_URL=http://100.76.80.23:8000
|
||||
DITTO_DEFAULT_VIDEO_URL=https://xiaoxia-autocut.oss-cn-hangzhou.aliyuncs.com/uploads/default_avatar.mp4
|
||||
|
||||
@@ -40,9 +40,7 @@ server {
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
proxy_set_header Upgrade $http_upgrade;
|
||||
proxy_set_header Connection "upgrade";
|
||||
proxy_read_timeout 3600s;
|
||||
proxy_read_timeout 300s;
|
||||
proxy_send_timeout 300s;
|
||||
proxy_request_buffering off;
|
||||
}
|
||||
|
||||
@@ -39,9 +39,7 @@ server {
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
proxy_set_header Upgrade $http_upgrade;
|
||||
proxy_set_header Connection "upgrade";
|
||||
proxy_read_timeout 3600s;
|
||||
proxy_read_timeout 300s;
|
||||
proxy_send_timeout 300s;
|
||||
proxy_request_buffering off;
|
||||
}
|
||||
|
||||
@@ -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)
|
||||
@@ -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."""
|
||||
@@ -961,7 +947,6 @@ class ViralVideoJobModel(Base):
|
||||
# v1.5 音频/视频参数
|
||||
voice_id = Column(String(200), nullable=False, default="")
|
||||
voice_source = Column(String(20), nullable=False, default="")
|
||||
language = Column(String(20), nullable=False, default="zh-CN")
|
||||
video_ratio = Column(String(10), nullable=False, default="9:16")
|
||||
video_model = Column(String(100), nullable=False, default="")
|
||||
# 结果与状态
|
||||
@@ -1020,20 +1005,3 @@ class ViralVideoPromptTemplateModel(Base):
|
||||
is_active = Column(Boolean, nullable=False, default=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))
|
||||
|
||||
|
||||
class SystemSettingModel(Base):
|
||||
"""系统配置表 ORM 模型（#2246：后台可配置项，表已手工存在于 staging）."""
|
||||
|
||||
__tablename__ = "system_settings"
|
||||
|
||||
id = Column(String(36), primary_key=True)
|
||||
setting_key = Column(String(100), nullable=False, unique=True)
|
||||
setting_value = Column(Text, nullable=True)
|
||||
setting_type = Column(String(20), nullable=False)
|
||||
description = Column(String(255), nullable=False, default="", server_default="")
|
||||
is_public = Column(Boolean, nullable=False, default=False, server_default="false")
|
||||
updated_by = Column(String(36), nullable=True)
|
||||
category = Column(String(50), nullable=False, default="general", server_default="general")
|
||||
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))
|
||||
|
||||
@@ -1,65 +0,0 @@
|
||||
"""system_settings 表 SQLAlchemy Repository — #2246."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from packages.adapters.sqlalchemy_impl.models import SystemSettingModel
|
||||
from packages.domain.system_setting import SystemSetting
|
||||
|
||||
|
||||
class SQLAlchemySystemSettingRepository:
|
||||
def __init__(self, session: Session):
|
||||
self.session = session
|
||||
|
||||
def get_by_key(self, setting_key: str) -> SystemSetting | None:
|
||||
model = self.session.query(SystemSettingModel).filter(SystemSettingModel.setting_key == setting_key).first()
|
||||
if model is None:
|
||||
return None
|
||||
return self._to_domain(model)
|
||||
|
||||
def list_all(self, category: str | None = None) -> list[SystemSetting]:
|
||||
query = self.session.query(SystemSettingModel)
|
||||
if category is not None:
|
||||
query = query.filter(SystemSettingModel.category == category)
|
||||
return [self._to_domain(m) for m in query.all()]
|
||||
|
||||
def upsert(self, setting: SystemSetting) -> SystemSetting:
|
||||
model = (
|
||||
self.session.query(SystemSettingModel).filter(SystemSettingModel.setting_key == setting.setting_key).first()
|
||||
)
|
||||
if model is None:
|
||||
model = SystemSettingModel(id=setting.id)
|
||||
self.session.add(model)
|
||||
model.setting_key = setting.setting_key
|
||||
model.setting_value = setting.setting_value
|
||||
model.setting_type = setting.setting_type
|
||||
model.description = setting.description
|
||||
model.is_public = setting.is_public
|
||||
model.category = setting.category
|
||||
model.updated_by = setting.updated_by
|
||||
self.session.commit()
|
||||
return setting
|
||||
|
||||
def delete_by_key(self, setting_key: str) -> bool:
|
||||
model = self.session.query(SystemSettingModel).filter(SystemSettingModel.setting_key == setting_key).first()
|
||||
if model is None:
|
||||
return False
|
||||
self.session.delete(model)
|
||||
self.session.commit()
|
||||
return True
|
||||
|
||||
@staticmethod
|
||||
def _to_domain(model: SystemSettingModel) -> SystemSetting:
|
||||
return SystemSetting(
|
||||
id=model.id,
|
||||
setting_key=model.setting_key,
|
||||
setting_value=model.setting_value,
|
||||
setting_type=model.setting_type,
|
||||
description=model.description or "",
|
||||
is_public=bool(model.is_public),
|
||||
category=model.category or "general",
|
||||
updated_by=model.updated_by,
|
||||
created_at=model.created_at,
|
||||
updated_at=model.updated_at,
|
||||
)
|
||||
@@ -49,13 +49,13 @@ def _to_domain(model: ViralVideoJobModel) -> ViralVideoJob:
|
||||
style_template_id=getattr(model, "style_template_id", "") or "",
|
||||
voice_id=getattr(model, "voice_id", "") or "",
|
||||
voice_source=getattr(model, "voice_source", "") or "",
|
||||
language=getattr(model, "language", "zh-CN") or "zh-CN",
|
||||
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 "",
|
||||
@@ -102,13 +102,13 @@ class SQLAlchemyViralVideoJobRepository:
|
||||
style_template_id=job.style_template_id,
|
||||
voice_id=job.voice_id,
|
||||
voice_source=job.voice_source,
|
||||
language=getattr(job, "language", "zh-CN") or "zh-CN",
|
||||
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,
|
||||
@@ -137,6 +137,7 @@ class SQLAlchemyViralVideoJobRepository:
|
||||
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 ""
|
||||
@@ -168,7 +169,6 @@ class SQLAlchemyViralVideoJobRepository:
|
||||
model.style_template_id = job.style_template_id
|
||||
model.voice_id = job.voice_id or ""
|
||||
model.voice_source = job.voice_source or ""
|
||||
model.language = getattr(job, "language", "zh-CN") or "zh-CN"
|
||||
model.video_ratio = job.video_ratio or "9:16"
|
||||
model.video_model = job.video_model or ""
|
||||
model.updated_at = datetime.now(timezone.utc)
|
||||
@@ -196,7 +196,9 @@ class SQLAlchemyViralVideoJobRepository:
|
||||
self.session.query(ViralVideoJobModel)
|
||||
.filter(
|
||||
ViralVideoJobModel.user_id == user_id,
|
||||
ViralVideoJobModel.status.in_(["pending", "running", "image_analyzed", "copy_generated"]),
|
||||
ViralVideoJobModel.status.in_(
|
||||
["pending", "running", "wait_user_confirm", "image_analyzed", "copy_generated"]
|
||||
),
|
||||
)
|
||||
.count()
|
||||
)
|
||||
|
||||
@@ -3,24 +3,10 @@
|
||||
使用 bcrypt 安全存储密码
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
from typing import Optional
|
||||
|
||||
import bcrypt
|
||||
|
||||
# bcrypt 只对前 72 字节有效，且 bcrypt>=4.1 会对超长输入直接抛 ValueError。
|
||||
# 超长密码先做一次 SHA-256（定长 hex），再交给 bcrypt，
|
||||
# 既绕过长度限制又保持对超长不同密码的区分度。
|
||||
_BCRYPT_MAX_BYTES = 72
|
||||
|
||||
|
||||
def _prepare_password_bytes(password: str) -> bytes:
|
||||
raw = password.encode("utf-8")
|
||||
if len(raw) > _BCRYPT_MAX_BYTES:
|
||||
return hashlib.sha256(raw).hexdigest().encode("utf-8")
|
||||
return raw
|
||||
|
||||
|
||||
from packages.domain.auth.password_hasher import PasswordHasherPort, PasswordValidatorPort
|
||||
|
||||
|
||||
@@ -56,8 +42,8 @@ class PasswordHasher(PasswordHasherPort):
|
||||
if not password:
|
||||
raise ValueError("Password cannot be empty")
|
||||
|
||||
# bcrypt 需要 bytes（超长密码先 SHA-256 以兼容 72 字节限制）
|
||||
password_bytes = _prepare_password_bytes(password)
|
||||
# bcrypt 需要 bytes
|
||||
password_bytes = password.encode("utf-8")
|
||||
|
||||
# 生成 salt 并哈希
|
||||
salt = bcrypt.gensalt(rounds=self.rounds)
|
||||
@@ -81,7 +67,7 @@ class PasswordHasher(PasswordHasherPort):
|
||||
return False
|
||||
|
||||
try:
|
||||
password_bytes = _prepare_password_bytes(password)
|
||||
password_bytes = password.encode("utf-8")
|
||||
hashed_bytes = hashed_password.encode("utf-8")
|
||||
|
||||
return bcrypt.checkpw(password_bytes, hashed_bytes)
|
||||
|
||||
@@ -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）
|
||||
|
||||
@@ -1,403 +0,0 @@
|
||||
"""Ditto LLM 情绪分析服务 — #2076 后续：根据文案生成 emo_timeline.
|
||||
|
||||
职责：
|
||||
1. 分句：先按 。！？； 句末标点切分；整段无句末标点且超长时按逗号等语义停顿二次切分
|
||||
2. 调 DoubaoClient.chat_completion 分析每句表情（emo: 0-7 八种全部开放, intensity: 0-1）
|
||||
3. 结果 LRU 缓存（文案 hash → 情绪列表）
|
||||
4. LLM 失败/超时/格式错 → 返回空列表（降级中性表情，不阻塞生成）
|
||||
5. TTS 完成后按字数比例或 sentence_timings 对齐成秒级 timeline
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from functools import lru_cache
|
||||
from pathlib import Path
|
||||
from typing import Any, Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ── 表情常量 ─────────────────────────────────────────────────────
|
||||
EMO_ANGER = 0
|
||||
EMO_DISGUST = 1
|
||||
EMO_FEAR = 2
|
||||
EMO_HAPPY = 3
|
||||
EMO_NEUTRAL = 4
|
||||
EMO_SAD = 5
|
||||
EMO_SURPRISE = 6
|
||||
EMO_CONTEMPT = 7
|
||||
ALLOWED_EMOS = set(range(8)) # 8 种表情全部开放（0-7）；超出范围的非法值仍会被降级为中性
|
||||
|
||||
# ── 分句正则 ─────────────────────────────────────────────────────
|
||||
_SENT_SPLIT_RE = re.compile(r"(?<=[。！？；!?;])\s*")
|
||||
# 二次切分：逗号/分号/顿号/冒号等语义停顿（lookbehind 保留标点在前句末尾）
|
||||
_CLAUSE_SPLIT_RE = re.compile(r"(?<=[，,、：:])")
|
||||
# 整段无句末标点且超过该长度时才做逗号二次切分
|
||||
_SINGLE_SENTENCE_THRESHOLD = 30
|
||||
# 二次切分后过短片段（<10 字）向后并入相邻句，确保每段至少 10 字、语义相对完整
|
||||
# （如"心中有爱，眼里有光，"合并为一段）；避免长文案被切成十几个碎段导致
|
||||
# LLM 处理的句子过多而超时，且过碎片段语义不完整、情绪分析意义不大
|
||||
_MIN_CLAUSE_LEN = 10
|
||||
|
||||
# ── 默认 prompt 模板文件路径 ──────────────────────────────────────
|
||||
_DEFAULT_PROMPT_PATH = Path(__file__).parent / "prompts" / "ditto_emotion.txt"
|
||||
|
||||
|
||||
def _load_default_prompt() -> str:
|
||||
try:
|
||||
return _DEFAULT_PROMPT_PATH.read_text(encoding="utf-8").strip()
|
||||
except Exception:
|
||||
# 文件不存在时用极简兜底
|
||||
return (
|
||||
"分析文案每句话表情，输出JSON数组："
|
||||
'[{"text":"句子","emo":4,"intensity":0.2}]，emo:0-7八种表情全部开放，'
|
||||
"至少30%句子用非中性表情。\n【文案】\n{文案}"
|
||||
)
|
||||
|
||||
|
||||
# ── 数据结构 ─────────────────────────────────────────────────────
|
||||
class EmotionSegment:
|
||||
"""单句情绪结果（LLM 输出的原始结构）."""
|
||||
|
||||
__slots__ = ("text", "emo", "intensity")
|
||||
|
||||
def __init__(self, text: str, emo: int, intensity: float):
|
||||
self.text = text
|
||||
self.emo = emo
|
||||
self.intensity = intensity
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
return {"text": self.text, "emo": self.emo, "intensity": self.intensity}
|
||||
|
||||
|
||||
class EmotionTimelineEntry:
|
||||
"""对齐到音频时间轴后的情绪片段（传给 Ditto）."""
|
||||
|
||||
__slots__ = ("start", "end", "emo", "intensity")
|
||||
|
||||
def __init__(self, start: float, end: float, emo: int, intensity: float):
|
||||
self.start = round(start, 2)
|
||||
self.end = round(end, 2)
|
||||
self.emo = emo
|
||||
self.intensity = round(intensity, 2)
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
return {
|
||||
"start": self.start,
|
||||
"end": self.end,
|
||||
"emo": self.emo,
|
||||
"intensity": self.intensity,
|
||||
}
|
||||
|
||||
|
||||
# ── 分句 ─────────────────────────────────────────────────────────
|
||||
def _split_by_clauses(sentence: str) -> list[str]:
|
||||
"""按逗号/分号等语义停顿二次切分；过短片段（<10 字）向后并入相邻句，避免切太碎.
|
||||
|
||||
每段至少 10 字、尽量控制在 10-50 字左右（50 字为软上限，无更细停顿点时保持
|
||||
语义完整不硬切）。例："心中有爱，眼里有光，"合并为一段而非各自成段。
|
||||
"""
|
||||
raw = [c for c in _CLAUSE_SPLIT_RE.split(sentence) if c and c.strip()]
|
||||
if len(raw) <= 1:
|
||||
return [sentence]
|
||||
merged: list[str] = []
|
||||
buf = ""
|
||||
for clause in raw:
|
||||
if not buf:
|
||||
buf = clause
|
||||
continue
|
||||
if len(buf) < _MIN_CLAUSE_LEN:
|
||||
buf += clause
|
||||
continue
|
||||
merged.append(buf)
|
||||
buf = clause
|
||||
if buf:
|
||||
if merged and len(buf) < _MIN_CLAUSE_LEN:
|
||||
merged[-1] += buf
|
||||
else:
|
||||
merged.append(buf)
|
||||
return [m.strip() for m in merged if m.strip()]
|
||||
|
||||
|
||||
def split_sentences(text: str) -> list[str]:
|
||||
"""按中文句末标点切分，过滤空串.
|
||||
|
||||
Bug1 修复：若按句末标点切分后只有 1 段且文本超过 30 字（文案全是逗号、
|
||||
结尾无标点），再按逗号/分号等语义停顿做二次切分，避免整段只返回 1 个
|
||||
segment 导致 LLM 只能对整段打一个表情。
|
||||
"""
|
||||
if not text:
|
||||
return []
|
||||
parts = [p.strip() for p in _SENT_SPLIT_RE.split(text.strip()) if p and p.strip()]
|
||||
if len(parts) <= 1 and parts and len(parts[0]) > _SINGLE_SENTENCE_THRESHOLD:
|
||||
parts = _split_by_clauses(parts[0])
|
||||
return parts
|
||||
|
||||
|
||||
# ── 解析 LLM 返回的 JSON ─────────────────────────────────────────
|
||||
def _parse_emotion_json(raw: str) -> list[EmotionSegment]:
|
||||
"""解析 LLM 返回，容错处理：
|
||||
- 去掉 markdown 代码块包裹
|
||||
- 只取第一个 JSON 数组
|
||||
- 逐行校验 emo/intensity 合法性，过滤无效项
|
||||
"""
|
||||
if not raw:
|
||||
return []
|
||||
text = raw.strip()
|
||||
# 去掉 ```json ... ``` 包裹
|
||||
if text.startswith("```"):
|
||||
text = re.sub(r"^```(?:json)?\s*", "", text)
|
||||
text = re.sub(r"\s*```$", "", text)
|
||||
# 找第一个 [ 到最后一个 ]
|
||||
lb = text.find("[")
|
||||
rb = text.rfind("]")
|
||||
if lb == -1 or rb == -1 or rb <= lb:
|
||||
return []
|
||||
try:
|
||||
data = json.loads(text[lb : rb + 1])
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
return []
|
||||
if not isinstance(data, list):
|
||||
return []
|
||||
|
||||
results: list[EmotionSegment] = []
|
||||
for item in data:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
try:
|
||||
emo = int(item.get("emo", EMO_NEUTRAL))
|
||||
intensity = float(item.get("intensity", 0.2))
|
||||
except (TypeError, ValueError):
|
||||
continue
|
||||
if emo not in ALLOWED_EMOS:
|
||||
emo = EMO_NEUTRAL
|
||||
intensity = max(0.05, min(1.0, intensity))
|
||||
sent_text = str(item.get("text", "")).strip()
|
||||
if not sent_text:
|
||||
continue
|
||||
results.append(EmotionSegment(text=sent_text, emo=emo, intensity=intensity))
|
||||
return results
|
||||
|
||||
|
||||
# ── 时间对齐（按字数比例）────────────────────────────────────────
|
||||
def align_timeline_by_length(
|
||||
segments: list[EmotionSegment],
|
||||
audio_duration: float,
|
||||
) -> list[EmotionTimelineEntry]:
|
||||
"""按各句字数占总字数比例分配 audio_duration 时长."""
|
||||
if not segments or audio_duration <= 0:
|
||||
return []
|
||||
total_chars = sum(len(s.text) for s in segments)
|
||||
if total_chars <= 0:
|
||||
return []
|
||||
entries: list[EmotionTimelineEntry] = []
|
||||
pos = 0.0
|
||||
for i, seg in enumerate(segments):
|
||||
if i == len(segments) - 1:
|
||||
end = audio_duration # 最后一段到结尾，避免浮点误差
|
||||
else:
|
||||
end = pos + (len(seg.text) / total_chars) * audio_duration
|
||||
if end > pos:
|
||||
entries.append(
|
||||
EmotionTimelineEntry(
|
||||
start=pos,
|
||||
end=end,
|
||||
emo=seg.emo,
|
||||
intensity=seg.intensity,
|
||||
)
|
||||
)
|
||||
pos = end
|
||||
return entries
|
||||
|
||||
|
||||
def align_timeline_by_timings(
|
||||
segments: list[EmotionSegment],
|
||||
sentence_timings: list[dict[str, Any]],
|
||||
audio_duration: float,
|
||||
) -> list[EmotionTimelineEntry]:
|
||||
"""使用 TTS sentence_timings 精确对齐（优先方案）.
|
||||
|
||||
sentence_timings 格式：[{"start":0.0,"end":1.2,"text":"句子"}, ...]
|
||||
按句序匹配 segments 和 timings，长度不一致时回退到按字数比例。
|
||||
"""
|
||||
if not sentence_timings or len(sentence_timings) != len(segments):
|
||||
return align_timeline_by_length(segments, audio_duration)
|
||||
entries: list[EmotionTimelineEntry] = []
|
||||
for seg, timing in zip(segments, sentence_timings, strict=False):
|
||||
try:
|
||||
start = float(timing.get("start", 0))
|
||||
end = float(timing.get("end", 0))
|
||||
except (TypeError, ValueError):
|
||||
return align_timeline_by_length(segments, audio_duration)
|
||||
if end <= start:
|
||||
continue
|
||||
entries.append(
|
||||
EmotionTimelineEntry(
|
||||
start=start,
|
||||
end=end,
|
||||
emo=seg.emo,
|
||||
intensity=seg.intensity,
|
||||
)
|
||||
)
|
||||
return entries
|
||||
|
||||
|
||||
# ── LLM 结果合理性校验（提示词遵循度排查）─────────────────────────
|
||||
# 感叹号或明显情绪关键词出现时，LLM 不应返回全中性/低强度
|
||||
_EMO_SIGNAL_RE = re.compile(r"[！!]|是不是|有没有|居然|竟然|只要|太坑|千万别|免费|赶紧|推荐|惊艳")
|
||||
|
||||
|
||||
def _warn_if_suspicious_all_neutral(text: str, segments: list[EmotionSegment]) -> None:
|
||||
"""文案含明显情绪信号但 LLM 返回全部 emo=4 且 intensity<=0.1 时打 warning 日志."""
|
||||
if not segments:
|
||||
return
|
||||
all_flat = all(s.emo == EMO_NEUTRAL and s.intensity <= 0.1 for s in segments)
|
||||
if all_flat and _EMO_SIGNAL_RE.search(text):
|
||||
logger.warning(
|
||||
"[ditto_emotion] 文案含情绪信号(感叹号/关键词)但 LLM 返回全中性/低强度，"
|
||||
"疑似提示词遵循度差: text=%.80s, segments=%d",
|
||||
text,
|
||||
len(segments),
|
||||
)
|
||||
|
||||
|
||||
# ── LLM 情绪分析服务 ─────────────────────────────────────────────
|
||||
class DittoEmotionService:
|
||||
"""Ditto 情绪分析服务（带 LRU 缓存）."""
|
||||
|
||||
def __init__(self, settings=None):
|
||||
from packages.config import get_api_settings
|
||||
|
||||
self.settings = settings or get_api_settings()
|
||||
self._client = None
|
||||
self._client_api_key: str | None = None
|
||||
|
||||
def _cfg(self, key: str) -> Any:
|
||||
"""优先读后台 system_config，未配置则回退到 settings（env 默认）."""
|
||||
try:
|
||||
from packages.application.system_config_service import get_config
|
||||
|
||||
return get_config(key, getattr(self.settings, key, None))
|
||||
except Exception:
|
||||
return getattr(self.settings, key, None)
|
||||
|
||||
@property
|
||||
def enabled(self) -> bool:
|
||||
return bool(self._cfg("ditto_emotion_enabled"))
|
||||
|
||||
def _get_prompt_template(self) -> str:
|
||||
"""优先用配置（环境变量），否则读文件."""
|
||||
cfg_prompt = self._cfg("ditto_emotion_prompt") or ""
|
||||
if cfg_prompt.strip():
|
||||
return cfg_prompt.strip()
|
||||
return _load_default_prompt()
|
||||
|
||||
def _cache_key(self, text: str) -> str:
|
||||
return hashlib.md5(text.strip().encode("utf-8")).hexdigest()
|
||||
|
||||
def _get_llm_client(self):
|
||||
"""创建/复用 DoubaoClient；API Key 优先读后台配置（DB），fallback 环境变量。
|
||||
|
||||
后台可能随时改 doubao_api_key，每次调用前对比 key，变化则重建 client。
|
||||
"""
|
||||
api_key = (self._cfg("doubao_api_key") or "").strip()
|
||||
if self._client is None or self._client_api_key != api_key:
|
||||
from packages.shared.ai_client import DoubaoClient
|
||||
|
||||
self._client = DoubaoClient(api_key=api_key)
|
||||
self._client_api_key = api_key
|
||||
return self._client
|
||||
|
||||
def _call_llm(self, text: str) -> list[EmotionSegment]:
|
||||
"""调 LLM 分析情绪，失败返回空列表."""
|
||||
template = self._get_prompt_template()
|
||||
prompt = template.replace("{文案}", text)
|
||||
messages = [{"role": "user", "content": prompt}]
|
||||
model = self._cfg("ditto_emotion_model") or None
|
||||
temperature = self._cfg("ditto_emotion_temperature")
|
||||
timeout = getattr(self.settings, "ditto_emotion_timeout", 10)
|
||||
max_tokens = getattr(self.settings, "ditto_emotion_max_tokens", 1024)
|
||||
try:
|
||||
client = self._get_llm_client()
|
||||
result = client.chat_completion(
|
||||
messages=messages,
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
timeout=timeout,
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.warning("[ditto_emotion] LLM 调用异常: %s", exc)
|
||||
return []
|
||||
if not result:
|
||||
return []
|
||||
segments = _parse_emotion_json(result)
|
||||
if not segments:
|
||||
logger.warning("[ditto_emotion] LLM 返回解析失败: %s", result[:200])
|
||||
else:
|
||||
_warn_if_suspicious_all_neutral(text, segments)
|
||||
return segments
|
||||
|
||||
def analyze(self, text: str) -> list[EmotionSegment]:
|
||||
"""分析文案情绪（带缓存），失败返回空列表."""
|
||||
if not self.enabled or not text or not text.strip():
|
||||
return []
|
||||
key = self._cache_key(text)
|
||||
return _cached_analyze(self, key, text)
|
||||
|
||||
def build_timeline(
|
||||
self,
|
||||
text: str,
|
||||
audio_duration: float,
|
||||
sentence_timings: Optional[list[dict[str, Any]]] = None,
|
||||
) -> str:
|
||||
"""完整流程：分句→LLM分析→时间对齐→序列化为JSON字符串.
|
||||
|
||||
返回: JSON 字符串（可直接传 Ditto emo_timeline 参数）；空字符串表示降级中性。
|
||||
"""
|
||||
segments = self.analyze(text)
|
||||
if not segments:
|
||||
return ""
|
||||
if sentence_timings:
|
||||
entries = align_timeline_by_timings(segments, sentence_timings, audio_duration)
|
||||
else:
|
||||
entries = align_timeline_by_length(segments, audio_duration)
|
||||
if not entries:
|
||||
return ""
|
||||
return json.dumps([e.to_dict() for e in entries], ensure_ascii=False)
|
||||
|
||||
|
||||
# ── 模块级 LRU 缓存实例 ─────────────────────────────────────────
|
||||
# 每个 service 实例共享缓存（按 cache_key 区分）
|
||||
@lru_cache(maxsize=512)
|
||||
def _cached_analyze(service: DittoEmotionService, cache_key: str, text: str) -> list[EmotionSegment]:
|
||||
"""LRU 缓存包装：cache_key 由文案 hash 生成，maxsize 从配置读."""
|
||||
# 注意：service 参数仅用于传递调用，缓存由 cache_key 驱动
|
||||
segments = service._call_llm(text)
|
||||
# 如果 LLM 返回空（比如分句数量不匹配），尝试直接对预分句结果分析
|
||||
if not segments:
|
||||
pre_splits = split_sentences(text)
|
||||
if len(pre_splits) >= 1:
|
||||
# 用预分句结果兜底：全中性低强度
|
||||
segments = [EmotionSegment(text=s, emo=EMO_NEUTRAL, intensity=0.1) for s in pre_splits]
|
||||
return segments
|
||||
|
||||
|
||||
_singleton: Optional[DittoEmotionService] = None
|
||||
|
||||
|
||||
def get_ditto_emotion_service() -> DittoEmotionService:
|
||||
global _singleton
|
||||
if _singleton is None:
|
||||
_singleton = DittoEmotionService()
|
||||
return _singleton
|
||||
|
||||
|
||||
def reset_ditto_emotion_service() -> None:
|
||||
"""#2246：后台配置变更后重置单例并清空 LLM 结果 LRU 缓存."""
|
||||
global _singleton
|
||||
_singleton = None
|
||||
_cached_analyze.cache_clear()
|
||||
@@ -1,308 +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（人物驱动视频 URL，用户上传优先；未传时用 default_video_url 兜底）
|
||||
+ audio_url（TTS 音频 URL） + script（文案原文）
|
||||
+ emo_timeline（可选，LLM 情绪时间线 JSON 字符串）
|
||||
- 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/网络不可达视为失败
|
||||
- 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
|
||||
- video_url 时长对齐（视频<音频时循环延长）在 Celery 任务层用 ffmpeg 预处理
|
||||
- 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)
|
||||
try:
|
||||
from packages.application.system_config_service import get_config
|
||||
|
||||
self.blend_frames = int(get_config("ditto_blend_frames", s.ditto_blend_frames))
|
||||
except Exception:
|
||||
self.blend_frames = int(s.ditto_blend_frames)
|
||||
|
||||
@property
|
||||
def is_configured(self) -> bool:
|
||||
"""配置是否完整（base_url 必填 + 默认模板视频兜底 URL 有值）。
|
||||
|
||||
注意：即使 is_configured=True，实际生成时优先使用用户上传的 video_url；
|
||||
default_video_url 仅作为用户未上传视频时的兜底。
|
||||
"""
|
||||
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: Optional[int] = None,
|
||||
emo_timeline: str = "",
|
||||
) -> 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 = " "
|
||||
|
||||
_blend = blend_frames if blend_frames is not None else self.blend_frames
|
||||
payload = {
|
||||
"video_url": driver_url,
|
||||
"audio_url": audio_url,
|
||||
"script": script,
|
||||
"emo_global": emo_global,
|
||||
"use_script_emo": use_script_emo,
|
||||
"blend_frames": _blend,
|
||||
}
|
||||
if emo_timeline:
|
||||
payload["emo_timeline"] = emo_timeline
|
||||
url = f"{self.base_url}/generate"
|
||||
|
||||
last_exc: Optional[Exception] = None
|
||||
for attempt in range(self.max_retries + 1):
|
||||
try:
|
||||
start = time.monotonic()
|
||||
# 精细化超时：connect=10s（网络不通快速失败），read=120s（最长音频~45s按RTF=2.8推算）
|
||||
_timeout = httpx.Timeout(connect=10.0, read=self.timeout, write=30.0, pool=10.0)
|
||||
with httpx.Client(timeout=_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.ConnectError, httpx.NetworkError, ConnectionError, OSError) as exc:
|
||||
# 网络不通/连接被拒（如 GPU 断网/Tailscale 掉线），不重试，直接快速回退
|
||||
logger.warning("[ditto] 网络不可达 attempt=%d err=%s", attempt + 1, exc)
|
||||
raise DittoError(
|
||||
f"Ditto 网络不可达: {exc}",
|
||||
code="NetworkUnreachable",
|
||||
) from exc
|
||||
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（read={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,
|
||||
emo_timeline: str = "",
|
||||
blend_frames: Optional[int] = 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,
|
||||
emo_timeline=emo_timeline,
|
||||
blend_frames=blend_frames,
|
||||
)
|
||||
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
|
||||
|
||||
|
||||
def reset_ditto_client() -> None:
|
||||
"""#2246：后台配置变更后重置 DittoClient 单例."""
|
||||
global _ditto_client_singleton
|
||||
_ditto_client_singleton = None
|
||||
@@ -1,42 +0,0 @@
|
||||
你是一个数字人短视频表情导演。为每句话分配一个最贴切的表情，让数字人表情丰富、有情绪感染力，像真人说话一样有起伏变化。
|
||||
|
||||
【8种表情】
|
||||
0=愤怒：怒斥、骂坑、强烈不满、控诉
|
||||
1=厌恶：嫌弃、吐槽劣质、鄙视套路
|
||||
2=害怕：紧张、警告、危险提示、悬念
|
||||
3=开心：夸赞、推荐、好消息、福利、号召
|
||||
4=中性：陈述事实、客观描述、数据、过渡衔接
|
||||
5=伤心：共情痛点、遗憾、难过、踩坑经历
|
||||
6=惊讶：惊喜、超预期、居然、竟然、只要、数字冲击
|
||||
7=轻蔑：嘲讽、冷笑、拆穿套路、不屑
|
||||
|
||||
【分句说明】
|
||||
- 输入文案已按句末标点预分句；若某句内部仍有明显语义停顿（逗号、分号），你应根据语义进一步拆分成更细的句子分别打分
|
||||
- 若输入没有句末标点，要自己根据语义在逗号处分句
|
||||
|
||||
【强度】0.1几乎无变化 0.3轻微自然 0.5明显情绪 0.7强烈 0.9爆发
|
||||
感叹号结尾0.4-0.7，句号0.2-0.4，问号0.3-0.6。
|
||||
|
||||
【示例1】
|
||||
文案："家人们！你们是不是经常买东西踩坑？花了大价钱买回来的东西根本没法用！但是今天这款产品真的惊艳到我了！"
|
||||
输出：
|
||||
[{"text":"家人们！","emo":6,"intensity":0.5},{"text":"你们是不是经常买东西踩坑？","emo":5,"intensity":0.3},{"text":"花了大价钱买回来的东西根本没法用！","emo":0,"intensity":0.5},{"text":"但是今天这款产品真的惊艳到我了！","emo":3,"intensity":0.6}]
|
||||
|
||||
【示例2】
|
||||
文案："做公益不一定是一个人去做惊天动地的大事，而是一群人扎扎实实的干好身边的小事，心中有爱，眼里有光。"
|
||||
输出：
|
||||
[{"text":"做公益不一定是一个人去做惊天动地的大事，","emo":4,"intensity":0.2},{"text":"而是一群人扎扎实实的干好身边的小事，","emo":4,"intensity":0.2},{"text":"心中有爱，","emo":3,"intensity":0.4},{"text":"眼里有光。","emo":3,"intensity":0.5}]
|
||||
|
||||
【关键要求】
|
||||
- 每句话都必须独立判断情绪，至少有30%的句子使用非中性(4)表情！感叹号结尾的句子intensity不得低于0.4！
|
||||
- 严禁全部句子都返回中性(4)！严禁全部强度都是0.1！
|
||||
- 情绪要有变化：痛点段伤心或愤怒→转折→卖点开心→福利惊讶→号召开心
|
||||
- 语义优先：根据每句话的实际语义选择表情，不要固定开头结尾情绪
|
||||
- 8种表情全部开放，该愤怒就愤怒(0)，该厌恶就厌恶(1)，该害怕就害怕(2)，该轻蔑就轻蔑(7)
|
||||
- 开头第一句就根据内容定情绪，不要强行用中性开场
|
||||
|
||||
【输出】严格JSON数组，只输出JSON，不要其他文字。
|
||||
[{"text":"原文","emo":3,"intensity":0.5}]
|
||||
|
||||
【文案】
|
||||
{文案}
|
||||
@@ -1,189 +0,0 @@
|
||||
"""系统配置应用服务 — #2246.
|
||||
|
||||
- get_config(key, default) / set_config(...) / list_configs(category)
|
||||
- 首次访问时从 DB 加载并进程内缓存；set_config 后失效缓存
|
||||
- set_config 成功后重置 Ditto 情绪服务与 Ditto 客户端单例（含 LRU 缓存），
|
||||
保证后台改动立即生效；worker 不直接 HTTP 读配置，统一通过本模块。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import threading
|
||||
from typing import Any
|
||||
|
||||
from packages.adapters.sqlalchemy_impl import session as db_session
|
||||
from packages.adapters.sqlalchemy_impl.system_setting_repository import (
|
||||
SQLAlchemySystemSettingRepository,
|
||||
)
|
||||
from packages.domain.system_setting import (
|
||||
SystemSetting,
|
||||
infer_setting_type,
|
||||
serialize_setting_value,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SystemConfigService:
|
||||
"""进程内缓存的系统配置服务."""
|
||||
|
||||
def __init__(self, session_factory=None):
|
||||
self._session_factory = session_factory
|
||||
self._cache: dict[str, Any] | None = None
|
||||
self._lock = threading.Lock()
|
||||
|
||||
# ── 会话 ─────────────────────────────────────────────────────
|
||||
def _get_session_factory(self):
|
||||
# 必须运行时读取模块属性：模块导入时 SessionLocal 还是 None，
|
||||
# initialize_database() 之后才被赋值，import 时绑定会拿到旧值。
|
||||
factory = self._session_factory or db_session.SessionLocal
|
||||
if factory is None:
|
||||
raise RuntimeError("数据库会话工厂未初始化")
|
||||
return factory
|
||||
|
||||
# ── 缓存 ─────────────────────────────────────────────────────
|
||||
def _load_cache(self) -> dict[str, Any]:
|
||||
with self._lock:
|
||||
if self._cache is not None:
|
||||
return self._cache
|
||||
cache: dict[str, Any] = {}
|
||||
factory = self._get_session_factory()
|
||||
session = factory()
|
||||
try:
|
||||
repo = SQLAlchemySystemSettingRepository(session)
|
||||
for setting in repo.list_all():
|
||||
try:
|
||||
cache[setting.setting_key] = setting.get_typed_value()
|
||||
except Exception as exc: # 损坏配置不阻塞启动
|
||||
logger.warning(
|
||||
"[system_config] 跳过损坏配置 %s: %s",
|
||||
setting.setting_key,
|
||||
exc,
|
||||
)
|
||||
finally:
|
||||
session.close()
|
||||
self._cache = cache
|
||||
return cache
|
||||
|
||||
def reload(self) -> dict[str, Any]:
|
||||
"""强制重新从 DB 加载配置，返回新缓存."""
|
||||
with self._lock:
|
||||
self._cache = None
|
||||
return self._load_cache()
|
||||
|
||||
def invalidate(self) -> None:
|
||||
with self._lock:
|
||||
self._cache = None
|
||||
|
||||
# ── 读 ───────────────────────────────────────────────────────
|
||||
def get_config(self, key: str, default: Any = None) -> Any:
|
||||
cache = self._load_cache()
|
||||
return cache.get(key, default)
|
||||
|
||||
def list_configs(self, category: str | None = None) -> list[SystemSetting]:
|
||||
factory = self._get_session_factory()
|
||||
session = factory()
|
||||
try:
|
||||
return SQLAlchemySystemSettingRepository(session).list_all(category)
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
# ── 写 ───────────────────────────────────────────────────────
|
||||
def set_config(
|
||||
self,
|
||||
key: str,
|
||||
value: Any,
|
||||
setting_type: str | None = None,
|
||||
updated_by: str | None = None,
|
||||
*,
|
||||
description: str | None = None,
|
||||
category: str = "general",
|
||||
is_public: bool = False,
|
||||
) -> Any:
|
||||
st = setting_type or infer_setting_type(value)
|
||||
serialized = serialize_setting_value(value, st)
|
||||
|
||||
factory = self._get_session_factory()
|
||||
session = factory()
|
||||
try:
|
||||
repo = SQLAlchemySystemSettingRepository(session)
|
||||
existing = repo.get_by_key(key)
|
||||
if existing is not None:
|
||||
existing.setting_value = serialized
|
||||
existing.setting_type = st
|
||||
if updated_by is not None:
|
||||
existing.updated_by = updated_by
|
||||
if description is not None:
|
||||
existing.description = description
|
||||
setting = existing
|
||||
else:
|
||||
setting = SystemSetting(
|
||||
setting_key=key,
|
||||
setting_value=serialized,
|
||||
setting_type=st,
|
||||
description=description or "",
|
||||
is_public=is_public,
|
||||
category=category,
|
||||
updated_by=updated_by,
|
||||
)
|
||||
repo.upsert(setting)
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
self.invalidate()
|
||||
self._reset_ditto_singletons(key)
|
||||
return value
|
||||
|
||||
def delete_config(self, key: str) -> bool:
|
||||
factory = self._get_session_factory()
|
||||
session = factory()
|
||||
try:
|
||||
deleted = SQLAlchemySystemSettingRepository(session).delete_by_key(key)
|
||||
finally:
|
||||
session.close()
|
||||
if deleted:
|
||||
self.invalidate()
|
||||
self._reset_ditto_singletons(key)
|
||||
return deleted
|
||||
|
||||
# ── Ditto 联动 ───────────────────────────────────────────────
|
||||
@staticmethod
|
||||
def _reset_ditto_singletons(key: str) -> None:
|
||||
try:
|
||||
from packages.application import ditto_emotion_service as emo_mod
|
||||
from packages.application import ditto_service as ditto_mod
|
||||
|
||||
emo_mod.reset_ditto_emotion_service()
|
||||
ditto_mod.reset_ditto_client()
|
||||
logger.info("[system_config] %s 更新，已重置 Ditto 单例", key)
|
||||
except Exception as exc: # 联动失败不影响配置落库
|
||||
logger.warning("[system_config] 重置 Ditto 单例失败: %s", exc)
|
||||
|
||||
|
||||
_service_singleton: SystemConfigService | None = None
|
||||
|
||||
|
||||
def get_system_config_service() -> SystemConfigService:
|
||||
global _service_singleton
|
||||
if _service_singleton is None:
|
||||
_service_singleton = SystemConfigService()
|
||||
return _service_singleton
|
||||
|
||||
|
||||
def get_config(key: str, default: Any = None) -> Any:
|
||||
"""便捷读取：优先 DB 配置，未配置时返回 default（调用方传 env 值兜底）."""
|
||||
try:
|
||||
return get_system_config_service().get_config(key, default)
|
||||
except Exception as exc:
|
||||
logger.warning("[system_config] 读取 %s 失败，使用默认值: %s", key, exc)
|
||||
return default
|
||||
|
||||
|
||||
def set_config(
|
||||
key: str,
|
||||
value: Any,
|
||||
setting_type: str | None = None,
|
||||
updated_by: str | None = None,
|
||||
) -> Any:
|
||||
return get_system_config_service().set_config(key, value, setting_type=setting_type, updated_by=updated_by)
|
||||
@@ -1,86 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""爆款视频时长-字数-镜头数统一规范。
|
||||
|
||||
prompt 模板与代码校验共用同一份数据，避免不一致。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
# 时长规格：key=duration(秒)，value=对应约束
|
||||
DURATION_SPEC: dict[int, dict[str, int]] = {
|
||||
5: {"min_chars": 12, "max_chars": 20, "min_shots": 1, "max_shots": 2},
|
||||
10: {"min_chars": 25, "max_chars": 40, "min_shots": 2, "max_shots": 3},
|
||||
15: {"min_chars": 38, "max_chars": 60, "min_shots": 3, "max_shots": 4},
|
||||
20: {"min_chars": 50, "max_chars": 80, "min_shots": 4, "max_shots": 5},
|
||||
30: {"min_chars": 75, "max_chars": 120, "min_shots": 6, "max_shots": 8},
|
||||
}
|
||||
|
||||
|
||||
def get_duration_spec(dur: int) -> dict[str, int]:
|
||||
"""获取时长规格；clamp 到 [5,30] 并选取最近档位。
|
||||
|
||||
注意：dur=0/falsey 时默认取 15 秒档位（兜底）；dur 为正数时按实际值 clamp。
|
||||
"""
|
||||
if dur is None:
|
||||
dur = 15
|
||||
try:
|
||||
d = int(dur)
|
||||
except (TypeError, ValueError):
|
||||
d = 15
|
||||
if d <= 0:
|
||||
d = 15
|
||||
clamped = max(5, min(30, d))
|
||||
nearest = min(DURATION_SPEC.keys(), key=lambda k: abs(k - clamped))
|
||||
return DURATION_SPEC[nearest]
|
||||
|
||||
|
||||
def build_duration_constraint_text(dur: int) -> str:
|
||||
"""生成注入到 LLM user prompt 的字数/镜头数约束文本。"""
|
||||
spec = get_duration_spec(dur)
|
||||
return (
|
||||
f"视频时长{dur}秒。口播总字数严格控制在{spec['min_chars']}~{spec['max_chars']}字之间"
|
||||
f"（中文正常口播语速约2.5~4字/秒，{dur}秒={dur * 4}字为硬上限，宁可少写也不要多写，"
|
||||
f"超长会导致TTS配音超出视频时长而失败）。"
|
||||
f"分镜镜头数{spec['min_shots']}~{spec['max_shots']}个，每个镜头的time_range首尾相接，"
|
||||
f"不得跳秒或重叠，每个镜头时长≥2秒。"
|
||||
)
|
||||
|
||||
|
||||
def get_expected_shot_count(dur: int) -> tuple[int, int]:
|
||||
"""返回 (min_shots, max_shots)，供代码校验使用。"""
|
||||
spec = get_duration_spec(dur)
|
||||
return spec["min_shots"], spec["max_shots"]
|
||||
|
||||
|
||||
# ── Seedance 生成默认约束（worker/API 共用，避免硬编码漂移） ──────────────
|
||||
DEFAULT_HARD_CONSTRAINTS: list[str] = [
|
||||
"无字幕、无水印、无任何自动生成文字、无 logo",
|
||||
"严格还原参考图片中的真实场景、门店环境、商品陈列、人物外貌服装特征，不得凭空生成与参考图无关的人物、场景或物品",
|
||||
"同一人物全程保持一致的五官、发型、服装、身材，不得换脸或变形",
|
||||
"口播语音必须在指定时长内自然念完，语速自然，口型与语音同步",
|
||||
"画面流畅无闪烁、无多余肢体、无扭曲变形、无穿模",
|
||||
"色彩自然、曝光正确、电影级质感、高清细节",
|
||||
]
|
||||
|
||||
DEFAULT_NEGATIVE_PROMPTS: list[str] = [
|
||||
"字幕",
|
||||
"自动字幕",
|
||||
"水印",
|
||||
"logo",
|
||||
"图标",
|
||||
"错误文字",
|
||||
"乱码文字",
|
||||
"男女声错配",
|
||||
"中途换声",
|
||||
"五官崩坏",
|
||||
"脸部变形",
|
||||
"多余手指",
|
||||
"肢体扭曲",
|
||||
"闪烁",
|
||||
"画面抖动",
|
||||
"模糊",
|
||||
"低分辨率",
|
||||
]
|
||||
|
||||
# API _build_copy_result 兼容老数据的兜底（比 worker 的短，仅用于老字段拼装）
|
||||
API_FALLBACK_HARD_CONSTRAINTS: list[str] = ["无字幕", "无水印", "人物一致性"]
|
||||
API_FALLBACK_NEGATIVE_PROMPTS: list[str] = ["字幕", "水印", "错误文字", "五官崩坏"]
|
||||
@@ -1,12 +1,427 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""爆款视频旧 5 步编排器（已废弃）。
|
||||
"""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。
|
||||
|
||||
PR1 (refactor/viral-video-backend-p0-deadcode-and-consistency)：CopyGenerator 整体删除。
|
||||
- 图片分析走 worker_app/tasks/vision 下的 V2 fast_path（analyze_images_v2）；
|
||||
- 文案/分镜/审核走 worker_app/tasks/viral_video.py 的 _step_script_generation / _step_review；
|
||||
- 旧的 intent_parsing / copy_fusion 两步早已在叙述优先重构中删除，不再有调用点。
|
||||
|
||||
本文件保留为空壳，避免误导入；任何新代码请不要从这里 import。
|
||||
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)
|
||||
|
||||
@@ -1,115 +0,0 @@
|
||||
"""爆款视频参数指导（persona/viral_structure/marketing_purpose/target_customer/language）。
|
||||
|
||||
PR3 P2(h): 把原来 worker 里 130 行 if/elif 硬编码 mapping 迁到 DB 表
|
||||
`viral_video_param_guides`，提供 30 秒 TTL 缓存读取。DB miss 时返回 None，
|
||||
由调用方兜底到代码默认值（保证 worker 冷启动/DB 不可用时不阻断流程）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import threading
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
import sqlalchemy as sa
|
||||
|
||||
from packages.adapters.sqlalchemy_impl import session as _session_mod
|
||||
|
||||
CACHE_TTL_SECONDS = 30.0
|
||||
|
||||
VALID_GUIDE_TYPES = frozenset({"persona", "structure", "marketing_purpose", "target_customer", "language"})
|
||||
|
||||
_lock = threading.Lock()
|
||||
# key = f"{guide_type}:{guide_key}", value = (expire_monotonic_ts, text|None)
|
||||
_cache: dict[str, tuple[float, Optional[str]]] = {}
|
||||
|
||||
_lazy_session = None
|
||||
|
||||
|
||||
def _get_session():
|
||||
"""优先复用 worker 的 SessionLocal；否则懒建（API 场景）。"""
|
||||
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://") and "psycopg" not in url:
|
||||
url = url.replace("postgresql://", "postgresql+psycopg://", 1)
|
||||
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 _cache_key(guide_type: str, guide_key: str) -> str:
|
||||
return f"{guide_type}:{guide_key}"
|
||||
|
||||
|
||||
def _load_from_db(guide_type: str, guide_key: str) -> Optional[str]:
|
||||
session = None
|
||||
try:
|
||||
session = _get_session()
|
||||
if session is None:
|
||||
return None
|
||||
sql = sa.text("""
|
||||
SELECT guide_text
|
||||
FROM viral_video_param_guides
|
||||
WHERE guide_type = :gt AND guide_key = :gk
|
||||
""")
|
||||
row = session.execute(sql, {"gt": guide_type, "gk": guide_key}).first()
|
||||
if row is None:
|
||||
return None
|
||||
return row[0] if row[0] is not None else None
|
||||
except Exception: # noqa: BLE001 - 表不存在/DB 不可用静默返回 None
|
||||
return None
|
||||
finally:
|
||||
if session is not None:
|
||||
try:
|
||||
session.close()
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
|
||||
|
||||
def get_param_guide(guide_type: str, guide_key: str, *, force_refresh: bool = False) -> Optional[str]:
|
||||
"""读参数指导文本，30 秒 TTL；DB 无数据返回 None，调用方自行兜底。"""
|
||||
if guide_type not in VALID_GUIDE_TYPES:
|
||||
return None
|
||||
gk = (guide_key or "").strip()
|
||||
if not gk:
|
||||
return None
|
||||
key = _cache_key(guide_type, gk)
|
||||
now = time.monotonic()
|
||||
with _lock:
|
||||
if not force_refresh:
|
||||
cached = _cache.get(key)
|
||||
if cached and now < cached[0]:
|
||||
return cached[1]
|
||||
|
||||
text = _load_from_db(guide_type, gk)
|
||||
with _lock:
|
||||
_cache[key] = (now + CACHE_TTL_SECONDS, text)
|
||||
return text
|
||||
|
||||
|
||||
def invalidate() -> None:
|
||||
"""清空缓存（测试用）。"""
|
||||
with _lock:
|
||||
_cache.clear()
|
||||
|
||||
|
||||
__all__ = [
|
||||
"CACHE_TTL_SECONDS",
|
||||
"VALID_GUIDE_TYPES",
|
||||
"get_param_guide",
|
||||
"invalidate",
|
||||
]
|
||||
@@ -1,12 +1,10 @@
|
||||
"""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；
|
||||
首次回落会打 warning（PR3 P2(f)），避免静默掩盖配置问题，又不刷屏。
|
||||
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 logging
|
||||
import threading
|
||||
import time
|
||||
from dataclasses import dataclass
|
||||
@@ -17,27 +15,9 @@ import sqlalchemy as sa
|
||||
from packages.adapters.sqlalchemy_impl import session as _session_mod
|
||||
from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
CACHE_TTL_SECONDS = 30.0
|
||||
|
||||
_VALID_TYPES = {"image_analysis", "storyboard", "review"}
|
||||
|
||||
# PR3 P2(f): 回落 warning 去重 — key -> 上次打 warning 的 monotonic 时间戳，
|
||||
# 同一 key 在 _WARN_COOLDOWN_SECONDS 内不再重复告警。
|
||||
_WARN_COOLDOWN_SECONDS = 300.0 # 5 分钟
|
||||
_warned: dict[str, float] = {}
|
||||
_warn_lock = threading.Lock()
|
||||
|
||||
|
||||
def _warn_once(key: str, template_type: str, e: BaseException | str | None = None) -> None:
|
||||
now = time.monotonic()
|
||||
with _warn_lock:
|
||||
last = _warned.get(key, 0.0)
|
||||
if now - last < _WARN_COOLDOWN_SECONDS:
|
||||
return
|
||||
_warned[key] = now
|
||||
logger.warning("viral_video prompt template %s fallback to default: %s", template_type, e)
|
||||
_VALID_TYPES = {"image_analysis", "intent_parsing", "copy_fusion", "storyboard", "review"}
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -84,15 +64,12 @@ def _get_session():
|
||||
from packages.config import get_shared_settings
|
||||
|
||||
url = str(get_shared_settings().database_url)
|
||||
except Exception as e: # noqa: BLE001
|
||||
_warn_once("session_config", "db_config", e)
|
||||
except Exception: # noqa: BLE001
|
||||
return None
|
||||
if not url:
|
||||
_warn_once("session_url", "db_url", "database_url empty")
|
||||
return None
|
||||
url = url.replace("postgresql+asyncpg://", "postgresql+psycopg://")
|
||||
if url.startswith("postgresql://") and "psycopg" not in url:
|
||||
url = url.replace("postgresql://", "postgresql+psycopg://", 1)
|
||||
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
|
||||
|
||||
@@ -127,15 +104,14 @@ def _load_from_db(prompt_type: str) -> Optional[PromptTemplate]:
|
||||
example_output=row[5] or "",
|
||||
is_active=bool(row[6]),
|
||||
)
|
||||
except Exception as e: # noqa: BLE001 - 表不存在/DB 不可用时静默回落
|
||||
_warn_once(f"load_db:{prompt_type}", prompt_type, e)
|
||||
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 as e: # noqa: BLE001
|
||||
_warn_once(f"session_close:{prompt_type}", prompt_type, e)
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
|
||||
|
||||
def get_template(prompt_type: str, *, force_refresh: bool = False) -> Optional[PromptTemplate]:
|
||||
@@ -150,8 +126,6 @@ def get_template(prompt_type: str, *, force_refresh: bool = False) -> Optional[P
|
||||
return cached[1]
|
||||
|
||||
template = _load_from_db(prompt_type) or _fallback(prompt_type)
|
||||
if template is None:
|
||||
_warn_once(f"no_template:{prompt_type}", prompt_type, "no template in db or code defaults")
|
||||
if template is not None:
|
||||
with _lock:
|
||||
_cache[prompt_type] = (now, template)
|
||||
@@ -162,8 +136,6 @@ def invalidate() -> None:
|
||||
"""清空缓存（测试用）。"""
|
||||
with _lock:
|
||||
_cache.clear()
|
||||
with _warn_lock:
|
||||
_warned.clear()
|
||||
|
||||
|
||||
class _SafeDict(dict):
|
||||
@@ -174,8 +146,7 @@ class _SafeDict(dict):
|
||||
def _safe_format(text: str, kwargs: dict) -> str:
|
||||
try:
|
||||
return text.format_map(_SafeDict(kwargs))
|
||||
except Exception as e: # noqa: BLE001
|
||||
_warn_once("safe_format", "format", e)
|
||||
except Exception: # noqa: BLE001
|
||||
return text
|
||||
|
||||
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user