update inference

inference.py

@@ -1,11 +1,248 @@
-def parse_action(text):
-   parts = text.split()
+"""
+Factory Environment Inference Script
+===================================
+Follows OpenEnv evaluation format strictly.
+"""
 
-   if parts[0] == "assign_job":
-       return Action(
-           action_type="assign_job",
-           job_id=parts[1],
-           machine_id=parts[2],
-       )
+import asyncio
+import os
+import textwrap
+from typing import List, Optional
 
-   return Action(action_type="wait")
+from openai import OpenAI
+
+from factory_env.env import FactoryEnv
+from factory_env.models import Action
+
+# =========================
+# ENV VARIABLES (MANDATORY)
+# =========================
+API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY")
+API_BASE_URL = os.getenv("API_BASE_URL") or "https://router.huggingface.co/v1"
+MODEL_NAME = os.getenv("MODEL_NAME") or "Qwen/Qwen2.5-72B-Instruct"
+
+TASK_NAME = os.getenv("FACTORY_TASK", "easy")
+BENCHMARK = "factory_env"
+
+MAX_STEPS = 20
+TEMPERATURE = 0.2
+MAX_TOKENS = 100
+SUCCESS_SCORE_THRESHOLD = 0.5
+
+# =========================
+# PROMPTS
+# =========================
+SYSTEM_PROMPT = textwrap.dedent(
+    """
+    You are controlling a factory scheduling system.
+
+    Your goal:
+    - Assign jobs to machines efficiently
+    - Minimize idle machines
+    - Finish all jobs as fast as possible
+
+    Available actions:
+    1. assign_job <job_id> <machine_id>
+    2. wait
+
+    Rules:
+    - Only assign jobs that exist
+    - Only assign to idle machines
+    - One action per step
+
+    Respond ONLY with the action string.
+    Example:
+    assign_job J1 M1
+    """
+).strip()
+
+
+# =========================
+# LOGGING FUNCTIONS (STRICT FORMAT)
+# =========================
+def log_start(task: str, env: str, model: str) -> None:
+    print(f"[START] task={task} env={env} model={model}", flush=True)
+
+
+def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str]) -> None:
+    error_val = error if error else "null"
+    done_val = str(done).lower()
+    print(
+        f"[STEP] step={step} action={action} reward={reward:.2f} done={done_val} error={error_val}",
+        flush=True,
+    )
+
+
+def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None:
+    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
+    print(
+        f"[END] success={str(success).lower()} steps={steps} score={score:.3f} rewards={rewards_str}",
+        flush=True,
+    )
+
+
+# =========================
+# PROMPT BUILDER
+# =========================
+def build_user_prompt(step, obs, last_reward):
+    machines_str = "\n".join(
+        [f"{m.id}: {m.status} (job={m.current_job})" for m in obs.machines]
+    )
+
+    jobs_str = "\n".join(
+        [f"{j.id}: remaining={j.remaining_time}, deadline={j.deadline}" for j in obs.pending_jobs]
+    ) or "None"
+
+    return textwrap.dedent(
+        f"""
+        Step: {step}
+
+        Current Time: {obs.time}
+
+        Machines:
+        {machines_str}
+
+        Pending Jobs:
+        {jobs_str}
+
+        Last reward: {last_reward:.2f}
+
+        What action do you take?
+        """
+    ).strip()
+
+
+# =========================
+# LLM CALL
+# =========================
+def get_model_action(client: OpenAI, step, obs, last_reward) -> str:
+    try:
+        user_prompt = build_user_prompt(step, obs, last_reward)
+
+        completion = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": user_prompt},
+            ],
+            temperature=TEMPERATURE,
+            max_tokens=MAX_TOKENS,
+        )
+
+        text = (completion.choices[0].message.content or "").strip()
+        return text if text else "wait"
+
+    except Exception as e:
+        print(f"[DEBUG] LLM error: {e}", flush=True)
+        return "wait"
+
+
+# =========================
+# ACTION PARSER
+# =========================
+def parse_action(text: str) -> Action:
+    try:
+        parts = text.strip().split()
+
+        if parts[0] == "assign_job" and len(parts) == 3:
+            return Action(
+                action_type="assign_job",
+                job_id=parts[1],
+                machine_id=parts[2],
+            )
+
+        elif parts[0] == "wait":
+            return Action(action_type="wait")
+
+    except Exception:
+        pass
+
+    # fallback safe action
+    return Action(action_type="wait")
+
+
+# =========================
+# SIMPLE HEURISTIC FALLBACK
+# =========================
+def heuristic_action(obs) -> Action:
+    for job in obs.pending_jobs:
+        for machine in obs.machines:
+            if machine.status == "idle":
+                return Action(
+                    action_type="assign_job",
+                    job_id=job.id,
+                    machine_id=machine.id,
+                )
+    return Action(action_type="wait")
+
+
+# =========================
+# MAIN LOOP
+# =========================
+async def main():
+    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
+
+    env = FactoryEnv(task=TASK_NAME)
+
+    rewards: List[float] = []
+    steps_taken = 0
+    score = 0.0
+    success = False
+
+    log_start(task=TASK_NAME, env=BENCHMARK, model=MODEL_NAME)
+
+    try:
+        result = await env.reset()
+        obs = result.observation
+        last_reward = 0.0
+
+        for step in range(1, MAX_STEPS + 1):
+            if result.done:
+                break
+
+            # LLM decision
+            action_text = get_model_action(client, step, obs, last_reward)
+
+            # Parse action
+            action = parse_action(action_text)
+
+            # Fallback if invalid
+            if action.action_type == "wait" and len(obs.pending_jobs) > 0:
+                action = heuristic_action(obs)
+                action_text = "heuristic_assign"
+
+            # Step env
+            result = await env.step(action)
+
+            obs = result.observation
+            reward = result.reward or 0.0
+            done = result.done
+            error = None
+
+            rewards.append(reward)
+            steps_taken = step
+            last_reward = reward
+
+            log_step(step, action_text, reward, done, error)
+
+            if done:
+                break
+
+        # Normalize score
+        if rewards:
+            score = sum(rewards) / len(rewards)
+            score = max(0.0, min(1.0, score))
+
+        success = score >= SUCCESS_SCORE_THRESHOLD
+
+    finally:
+        try:
+            await env.close()
+        except Exception as e:
+            print(f"[DEBUG] env.close error: {e}", flush=True)
+
+        log_end(success, steps_taken, score, rewards)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())