6b0755e1ff
Add a stdlib-only observability site so you can open a browser and watch, step by step: whether it is listening, what it heard, what the brain thought and answered, how long each stage took, and whether anything errored. - wsai/monitor.py: thread-safe telemetry hub (per-turn timed steps, status header, error log) with a pub/sub for live push. - wsai/dashboard.py: stdlib http.server serving a self-contained page plus an SSE (/events) live stream; /api/state snapshot fallback. - Pipeline emits step-by-step turn telemetry (화면 맥락 → 두뇌 → 응답) and listening/running status; optional monitor, so existing paths are untouched. - `python -m wsai --dashboard` starts the site (0.0.0.0:8787, WSAI_DASHBOARD_PORT) and loops the mock voice demo so there is always live activity to watch. - Tests cover turn recording, per-step timing, error marking, and live push. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
89 lines
2.2 KiB
Python
89 lines
2.2 KiB
Python
"""Build a Pipeline from Settings. This is the single place that knows which
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concrete class each config name maps to, so adding a backend = one line here."""
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from __future__ import annotations
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from .config import Settings
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from .monitor import Monitor
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from .pipeline import Pipeline
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def build(settings: Settings, monitor: Monitor | None = None) -> Pipeline:
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pipe = Pipeline(
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source=_source(settings),
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vision=_vision(settings),
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brain=_brain(settings),
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stt=_stt(settings),
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tts=_tts(settings),
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text_channel=_text(settings),
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monitor=monitor,
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)
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if monitor is not None:
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monitor.set_components(
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{
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"source": settings.source or "none",
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"vision": settings.vision or "none",
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"stt": settings.stt or "none",
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"brain": settings.brain,
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"tts": settings.tts or "none",
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"text": settings.text or "none",
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}
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)
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return pipe
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def _source(s: Settings):
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if s.source in (None, "none"):
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return None
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if s.source == "mss":
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from .backends.capture_mss import MSSFrameSource
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return MSSFrameSource(interval=s.capture_interval)
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from .backends.mock import MockFrameSource
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return MockFrameSource(interval=s.capture_interval)
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def _vision(s: Settings):
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if s.vision in (None, "none"):
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return None
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if s.vision == "claude":
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from .backends.claude import ClaudeVision
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return ClaudeVision(model=s.anthropic_model)
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from .backends.mock import MockVision
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return MockVision()
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def _brain(s: Settings):
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if s.brain == "claude":
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from .backends.claude import ClaudeBrain
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return ClaudeBrain(model=s.anthropic_model)
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from .backends.mock import MockBrain
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return MockBrain()
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def _stt(s: Settings):
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if s.stt in (None, "none"):
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return None
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from .backends.mock import MockSTT
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return MockSTT()
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def _tts(s: Settings):
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if s.tts in (None, "none"):
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return None
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from .backends.mock import MockTTS
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return MockTTS()
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def _text(s: Settings):
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if s.text in (None, "none"):
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return None
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raise NotImplementedError("discord text channel backend not implemented yet")
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