Oskar Kapala
921a85d30b
Python (ipin_vr/export.py): - generate_bank(): levels 1–2 enumerated exhaustively (120/720 combos), level 3 random with dedup; all deduplicated per-level - run_export(): CLI --per-level / --out / --seed flags per ONDEVICE §4.1 - cli.py routes "export" subcommand before session args (backward-compat) - 14 new tests: format, dedup for all 3 levels, space caps (L1=120, L2=720), seed determinism, file output Unity scaffold (ondevice/Scripts/): - CommandBank.cs: loads commands.json from StreamingAssets (WebRequest on Android) - TtsManager.cs: sherpa-onnx integration with [SHERPA] stubs, StreamingAssets→ persistentDataPath copy, defensive Speak() with onDone callback - SessionController.cs: passthrough flow §6, level switching, busy guard on Next - ondevice/README.md: full manual Editor steps §5 (Unity setup, Meta XR SDK, sherpa-onnx install, scene wiring, font, build, metrics) pytest: 37/37 passed Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
import argparse
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import random
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import sys
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import time
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from .export import run_export
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from .generator import random_command, render
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from .llm import llm_command
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def main() -> None:
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raw = sys.argv[1:]
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if raw and raw[0] == "export":
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run_export(raw[1:])
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return
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_run_session()
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def _run_session() -> None:
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parser = argparse.ArgumentParser(
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description="ipin-vr: generator poleceń do terapii afazji"
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)
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parser.add_argument("--level", type=int, choices=[1, 2, 3], default=1,
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metavar="{1,2,3}", help="poziom trudności (domyślnie: 1)")
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parser.add_argument("--count", type=int, default=5, metavar="N",
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help="liczba poleceń (domyślnie: 5)")
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parser.add_argument("--delay", type=float, default=4.0, metavar="S",
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help="pauza między poleceniami w sekundach (domyślnie: 4.0)")
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parser.add_argument("--voice", metavar="PATH",
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help="ścieżka do modelu Piper .onnx")
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parser.add_argument("--no-audio", action="store_true",
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help="wyłącza TTS")
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parser.add_argument("--llm", action="store_true",
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help="użyj LLM do wyboru poleceń")
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parser.add_argument("--llm-endpoint",
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default="http://localhost:8080/v1/chat/completions",
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metavar="URL", help="endpoint llama.cpp")
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parser.add_argument("--llm-model", default="local", metavar="NAME",
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help="nazwa modelu")
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parser.add_argument("--seed", type=int, metavar="N",
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help="deterministyczny generator losowy")
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args = parser.parse_args(sys.argv[1:])
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if args.seed is not None:
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random.seed(args.seed)
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use_audio = (not args.no_audio) and (args.voice is not None)
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for i in range(1, args.count + 1):
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if args.llm:
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cmd = llm_command(args.level, args.llm_endpoint, args.llm_model)
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else:
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cmd = random_command(args.level)
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text = render(cmd)
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print(f"{i}. {text}")
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if use_audio:
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from .tts import speak
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speak(text, args.voice)
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if i < args.count:
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time.sleep(args.delay)
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