85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
"""种子数据模块。
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启动时把工作区内所有 wov-node-* 子仓库的 manifest 注册为节点，并创建演示
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“视频字幕生成”工作流，方便本地直接体验完整链路。
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"""
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from __future__ import annotations
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from pathlib import Path
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from wov_sdk.models import NodeManifest, WorkflowDefinition, WorkflowEdge, WorkflowNode
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from app.db import Database
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def seed_nodes(db: Database, workspace_root: Path) -> None:
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"""扫描工作区中的节点仓库并注册其 manifest。"""
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for node_dir in sorted(workspace_root.glob("wov-node-*")):
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manifest_path = node_dir / "node.manifest.json"
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# 没有 manifest 的目录不是节点仓库，直接跳过。
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if not manifest_path.is_file():
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continue
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manifest = NodeManifest.load(str(manifest_path))
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# 以目录名作为 repo_dir，确保 NodeManager 能定位到子仓库。
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manifest.repo_dir = node_dir.name
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db.upsert_node(manifest)
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def seed_demo_workflow(db: Database) -> None:
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"""创建演示工作流：提音 -> 转写 -> 翻译 -> ASS。"""
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# 已存在同名工作流时不重复创建，保持幂等。
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if db.get_workflow("demo") is not None:
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return
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definition = WorkflowDefinition(
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name="视频字幕生成",
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version=1,
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nodes=[
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WorkflowNode(
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id="extract",
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node_type="ffmpeg-extract",
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params={"sample_rate": 16000, "channels": 1},
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inputs={"video_uri": "input.video_uri"},
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),
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WorkflowNode(
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id="asr",
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node_type="faster-whisper",
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params={"language": "ja"},
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inputs={"audio_uri": "extract.audio_uri"},
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),
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WorkflowNode(
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id="translate",
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node_type="llm-translate",
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params={"target_language": "zh-CN"},
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inputs={"srt_uri": "asr.srt_uri"},
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),
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WorkflowNode(
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id="ass",
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node_type="srt-to-dual-eye-ass",
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params={"resolution": "3840x1920"},
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inputs={"cn_srt_uri": "translate.cn_srt_uri"},
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),
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],
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edges=[
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WorkflowEdge(from_node="extract", to_node="asr"),
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WorkflowEdge(from_node="asr", to_node="translate"),
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WorkflowEdge(from_node="translate", to_node="ass"),
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],
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entry_inputs={"video_uri": "file"},
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final_outputs={
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"cn_srt": "translate.cn_srt_uri",
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"ass": "ass.ass_uri",
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},
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)
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db.upsert_workflow(
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{
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"id": "demo",
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"name": definition.name,
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"description": "上传视频，自动生成中文字幕和 VR 双眼 ASS。",
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"published": 1,
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"latest_version": 1,
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}
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)
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# 保存第一个版本的 DAG 定义，后续发布流程以版本记录为准。
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db.create_workflow_version("demo", 1, definition.to_dict())
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