66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
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import argparse
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import random
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from pathlib import Path
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from dotenv_loader import load_dotenv_into_environ
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from task_activities import WORKBENCH_ACTIVITIES
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# --------------------------------
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# Example workflow
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# --------------------------------
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"activity",
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nargs="?",
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help="Activity key to run (defaults to random).",
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)
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args = parser.parse_args()
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# Load `.env` from this folder into `os.environ` before importing OpenAI client code.
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load_dotenv_into_environ(Path(__file__).resolve().parent / ".env", override=False)
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from llm_task_generator import generate_task_spec_from_activity
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from llm_task_validator import ALLOWED_PREDICATES, ALLOWED_SKILLS, validate_task_spec
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from llm_fsm_multi_env import (
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TaskSpec,
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LLMFSMCompilerBatched,
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run_compiled_task_multi_env,
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)
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# 1) select activity
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if args.activity is not None:
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if args.activity not in WORKBENCH_ACTIVITIES:
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available = ", ".join(sorted(WORKBENCH_ACTIVITIES.keys()))
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raise SystemExit(f"Unknown activity `{args.activity}`. Available: {available}")
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activity_key = args.activity
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else:
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activity_key = random.choice(list(WORKBENCH_ACTIVITIES.keys()))
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activity_def = WORKBENCH_ACTIVITIES[activity_key]
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# 2) generate task under this activity
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task_dict = generate_task_spec_from_activity(
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activity_key,
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activity_def,
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allowed_skills=list(ALLOWED_SKILLS.keys()),
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allowed_predicates=sorted(ALLOWED_PREDICATES),
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)
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validate_task_spec(task_dict)
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# 3) convert dict → TaskSpec dataclass
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task_spec = TaskSpec.from_dict(task_dict)
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# 4) compile & run
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compiler = LLMFSMCompilerBatched()
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compiled = compiler.compile(env=batched_env, task_spec=task_spec)
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# result = run_compiled_task_multi_env(compiled)
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# print("Result:", result)
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if __name__ == "__main__":
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main()
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