| from openenv.core.env_server import create_app
|
| from server.inventory_env import InventoryEnvironment
|
| from server.grader import grade, compute_baselines
|
| from server.constants import TASKS
|
| from models import InventoryAction, InventoryObservation
|
|
|
| app = create_app(InventoryEnvironment, InventoryAction, InventoryObservation, env_name="quartermaster-env")
|
|
|
|
|
| @app.get("/tasks")
|
| def list_tasks():
|
| """List available tasks with full schemas."""
|
| task_list = []
|
| for name, config in TASKS.items():
|
| demand = {p: list(v) for p, v in config["base_demand"].items()}
|
| task_list.append({
|
| "task_name": name,
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| "seed": config["seed"],
|
| "max_days": config["max_days"],
|
| "initial_cash": config["initial_cash"],
|
| "initial_stock": config["initial_stock"],
|
| "inventory_capacity": config["inventory_capacity"],
|
| "base_demand": demand,
|
| "events": config["events"],
|
| "num_directives": len(config["directives"]),
|
| "num_milestones": len(config["milestones"]),
|
| })
|
| return {"tasks": task_list}
|
|
|
|
|
| @app.post("/grader")
|
| def grader_endpoint(task_name: str, agent_profit: float):
|
| """Return the evaluation score for an episode."""
|
| if task_name not in TASKS:
|
| return {"error": f"Unknown task: {task_name}. Available: {list(TASKS.keys())}"}
|
| floor, ceiling = compute_baselines(task_name)
|
| score = grade(task_name, agent_profit)
|
| return {
|
| "task_name": task_name,
|
| "agent_profit": agent_profit,
|
| "floor": floor,
|
| "ceiling": ceiling,
|
| "score": score,
|
| }
|
|
|
|
|
| @app.get("/baseline")
|
| def baseline_endpoint(task_name: str = "easy"):
|
| """Run baseline inference on a task and return score."""
|
| import subprocess
|
| import os
|
| import re
|
|
|
| if task_name not in TASKS:
|
| return {"error": f"Unknown task: {task_name}. Available: {list(TASKS.keys())}"}
|
|
|
| env = os.environ.copy()
|
| env["TASK_NAME"] = task_name
|
|
|
| try:
|
| result = subprocess.run(
|
| ["python", "inference.py"],
|
| capture_output=True,
|
| text=True,
|
| timeout=1200,
|
| env=env,
|
| )
|
| output = result.stdout
|
|
|
|
|
| score = None
|
| for line in output.splitlines():
|
| if task_name + ":" in line and "profit" in line:
|
| score_match = re.search(r"(\d+\.\d+)\s*\(profit", line)
|
| if score_match:
|
| score = float(score_match.group(1))
|
|
|
| return {
|
| "task_name": task_name,
|
| "score": score,
|
| }
|
| except subprocess.TimeoutExpired:
|
| return {"error": "Inference timed out (20 min limit)"}
|
| except Exception as e:
|
| return {"error": str(e)}
|
|
|
|
|
| def main():
|
| import uvicorn
|
| uvicorn.run(app, host="0.0.0.0", port=8000)
|
|
|
|
|
| if __name__ == "__main__":
|
| main() |
