fix: provide fallback API key and add safety check for empty observations in inference client

inference.py

@@ -170,7 +170,7 @@ class LLMAgent:
         # The OPENAI_API_KEY variable contains the HF_TOKEN value passed by evaluators
         self.client = OpenAI(
             base_url=API_BASE_URL,
-            api_key=OPENAI_API_KEY,
+            api_key=OPENAI_API_KEY or "dummy-key-for-fast-mode",
         )
         self.model = MODEL_NAME
         self.fallback_mode = False
@@ -324,7 +324,8 @@ def run_episode(
             "sub_scores": {},
             "exploit_detected": False,
         }
-    obs = reset_resp.get("observations", [{}])[0]
+    obs_list = reset_resp.get("observations", [{}])
+    obs = obs_list[0] if obs_list else {}
     
     task_name = f"gridmind-task-{task_id}"
     

python/inference.py

@@ -1,557 +0,0 @@
-"""
-GridMind-RL Baseline Inference Script
---------------------------------------
-Runs an LLM agent against all 3 tasks for N episodes each.
-Uses the OpenAI Python client pointed at any OpenAI-compatible endpoint.
-
-Required environment variables (set in .env or shell):
-    API_BASE_URL   — The API endpoint for the LLM (default: OpenRouter)
-    MODEL_NAME     — The model identifier to use for inference
-    OPENAI_API_KEY — API key for authentication (works with any provider)
-
-Usage:
-    # Option 1: Use .env file (recommended — just paste your key)
-    python inference.py
-
-    # Option 2: Set env vars manually
-    export API_BASE_URL=https://openrouter.ai/api/v1
-    export MODEL_NAME=meta-llama/llama-3.1-8b-instruct:free
-    export OPENAI_API_KEY=sk-or-v1-xxxx
-    python inference.py
-
-    # Option 3: Fast mode (no LLM, heuristic only)
-    python inference.py --fast-mode --episodes 1
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import sys
-import time
-from typing import Any
-
-import requests
-from openai import OpenAI
-
-# ── Load .env file (if present) ────────────────────────────────────────────
-try:
-    from dotenv import load_dotenv
-    load_dotenv()  # reads .env from current directory or project root
-except ImportError:
-    pass  # python-dotenv not installed — env vars must be set manually
-
-# ── Constants ──────────────────────────────────────────────────────────────
-
-ENV_URL = os.getenv("ENV_URL", "http://localhost:7860")
-MODEL_NAME = os.getenv("MODEL_NAME", "<your-active-model>")
-API_BASE_URL = os.getenv("API_BASE_URL", "<your-active-endpoint>")
-
-# ── Environment Variable Handling ─────────────────────────────────────────
-# The LLM API credential is read from HF_TOKEN or OPENAI_API_KEY environment variables
-# and passed directly to the OpenAI client for initialization.
-# Primary: HF_TOKEN
-# Fallback: OPENAI_API_KEY (for local testing/development)
-HF_TOKEN = os.getenv("HF_TOKEN")
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or HF_TOKEN
-if not OPENAI_API_KEY:
-    print("[WARN] No HF_TOKEN or OPENAI_API_KEY set - will use heuristic mode if --fast-mode is set")
-DEFAULT_EPISODES = 1
-DEFAULT_SEED_BASE = 1000
-MAX_RETRIES = 3
-# 96 steps × 15 min = 24 h (must match env.EpisodeSteps)
-EPISODE_STEPS = 96
-LAST_STEP_INDEX = EPISODE_STEPS - 1
-
-SYSPROMPT = """You are GridMind, an expert industrial energy management controller.
-You control a building's HVAC, thermal storage, batch job scheduling, and load shedding.
-Your goal is to minimize electricity costs while maintaining comfort and meeting grid demand-response signals.
-Always respond with a single valid JSON object matching the action schema. No explanation needed."""
-
-TASK_DESCRIPTIONS = {
-    1: "Task 1 (Easy - Cost Minimization): Minimize total energy cost over 24 hours. No temperature or batch constraints. Use cheap off-peak periods and thermal storage.",
-    2: "Task 2 (Medium - Temperature Management): Minimize cost AND keep indoor temperature within 19-23°C at all times. Balance comfort vs cost.",
-    3: "Task 3 (Hard - Full Demand Response): Minimize cost, maintain temperature, respond to grid stress (shed when grid_stress_signal > 0.7), schedule batch jobs, minimize carbon.",
-}
-
-ACTION_SCHEMA_STR = """{
-  "hvac_power_level": <float 0.0-1.0>,
-  "thermal_charge_rate": <float -1.0 to 1.0>,
-  "batch_job_slot": <int 0-4>,
-  "load_shed_fraction": <float 0.0-0.5>,
-  "building_id": 0
-}"""
-
-
-def extract_json_object(text: str) -> dict[str, Any] | None:
-    """Parse first balanced {...} JSON object from text (handles nested braces)."""
-    start = text.find("{")
-    if start < 0:
-        return None
-    depth = 0
-    for i in range(start, len(text)):
-        c = text[i]
-        if c == "{":
-            depth += 1
-        elif c == "}":
-            depth -= 1
-            if depth == 0:
-                try:
-                    return json.loads(text[start : i + 1])
-                except json.JSONDecodeError:
-                    return None
-    return None
-
-
-# ── Environment client ───────────────────────────────────────────────────────
-
-
-class GridMindEnvClient:
-    """Simple HTTP client for the GridMind-RL Go environment server."""
-
-    def __init__(self, base_url: str = ENV_URL, timeout: int = 30):
-        self.base = base_url.rstrip("/")
-        self.timeout = timeout
-
-    def health(self) -> bool:
-        try:
-            r = requests.get(f"{self.base}/health", timeout=5)
-            return r.status_code == 200
-        except Exception:
-            return False
-
-    def reset(self, task_id: int = 1, seed: int = 42, num_buildings: int = 1) -> dict | None:
-        try:
-            payload = {"task_id": task_id, "seed": seed, "num_buildings": num_buildings}
-            r = requests.post(f"{self.base}/reset", json=payload, timeout=self.timeout)
-            r.raise_for_status()
-            return r.json()
-        except Exception as e:
-            print(f"[ERROR] Failed to reset environment: {e}", file=sys.stderr)
-            return None
-
-    def step(self, action: dict) -> dict | None:
-        try:
-            r = requests.post(f"{self.base}/step", json=action, timeout=self.timeout)
-            r.raise_for_status()
-            return r.json()
-        except Exception as e:
-            print(f"[ERROR] Failed to step environment: {e}", file=sys.stderr)
-            return None
-
-    def grade(self) -> dict:
-        try:
-            r = requests.get(f"{self.base}/grade", timeout=self.timeout)
-            r.raise_for_status()
-            return r.json()
-        except Exception as e:
-            print(f"[ERROR] Failed to grade: {e}", file=sys.stderr)
-            return {"score": 0.0, "sub_scores": {}, "exploit_detected": False}
-
-    def state(self) -> dict | None:
-        try:
-            r = requests.get(f"{self.base}/state", timeout=self.timeout)
-            r.raise_for_status()
-            return r.json()
-        except Exception as e:
-            print(f"[ERROR] Failed to get state: {e}", file=sys.stderr)
-            return None
-
-
-# ── LLM agent ───────────────────────────────────────────────────────────────
-
-
-class LLMAgent:
-    """OpenAI-compatible LLM agent that chooses actions given observations."""
-
-    def __init__(self):
-        # Initialize OpenAI client with credentials from HF_TOKEN (per hackathon spec)
-        # The OPENAI_API_KEY variable contains the HF_TOKEN value passed by evaluators
-        self.client = OpenAI(
-            base_url=API_BASE_URL,
-            api_key=OPENAI_API_KEY,
-        )
-        self.model = MODEL_NAME
-        self.fallback_mode = False
-
-    def choose_action(self, obs: dict, task_id: int) -> dict:
-        """Prompt the LLM with current observation, return parsed action dict."""
-        if self.fallback_mode:
-            return self._heuristic_action(obs)
-        task_desc = TASK_DESCRIPTIONS.get(task_id, TASK_DESCRIPTIONS[1])
-
-        prompt = f"""{task_desc}
-
-Current observation:
-- Indoor temperature: {obs.get('indoor_temperature', 21):.1f}°C (target: 21°C, bounds: 19-23°C)
-- Thermal storage level: {obs.get('thermal_storage_level', 0.5):.2f} (0=empty, 1=full)
-- Process demand: {obs.get('process_demand', 15):.1f} kW
-- Current electricity price: ${obs.get('current_price', 0.10):.4f}/kWh
-- Grid stress signal: {obs.get('grid_stress_signal', 0):.3f} (>0.7 = critical, shed load!)
-- Carbon intensity: {obs.get('carbon_intensity', 300):.0f} gCO2/kWh
-- Hour of day: {obs.get('hour_of_day', 12)} (0=midnight, peak prices 8-12 and 17-21)
-- Pending batch job deadlines: {obs.get('batch_queue', [])}
-- Cumulative cost so far: ${obs.get('cumulative_cost', 0):.4f}
-- Episode step: {obs.get('step', 0)}/{LAST_STEP_INDEX}
-
-Strategy hints:
-- Charge thermal storage when price < $0.08/kWh, discharge when price > $0.15/kWh
-- Set HVAC low during peak prices (0.3-0.4) and use storage for temperature control
-- Shed 30-50% load if grid_stress_signal > 0.7
-- Schedule batch jobs early if deadline is close (slot 0 or 1)
-
-Respond with ONLY a JSON action:
-{ACTION_SCHEMA_STR}"""
-
-        for attempt in range(MAX_RETRIES):
-            try:
-                completion = self.client.chat.completions.create(
-                    model=self.model,
-                    messages=[
-                        {"role": "system", "content": SYSPROMPT},
-                        {"role": "user", "content": prompt},
-                    ],
-                    max_tokens=128,
-                    temperature=0.0,
-                )
-                content = completion.choices[0].message.content.strip()
-                parsed = extract_json_object(content)
-                if parsed is not None:
-                    return self._clamp_action(parsed)
-                action = json.loads(content)
-                return self._clamp_action(action)
-            except Exception as e:
-                err_str = str(e)
-                print(f"  [LLM attempt {attempt+1}/{MAX_RETRIES}] error: {err_str}")
-                if "402" in err_str or "depleted" in err_str:
-                    print("  [WARN] Hugging Face free credits depleted! Switching to local heuristic agent for the rest of the simulation.")
-                    self.fallback_mode = True
-                    return self._heuristic_action(obs)
-                time.sleep(1)
-
-        return self._heuristic_action(obs)
-
-    def _clamp_action(self, action: dict) -> dict:
-        return {
-            "hvac_power_level": max(0.0, min(1.0, float(action.get("hvac_power_level", 0.5)))),
-            "thermal_charge_rate": max(-1.0, min(1.0, float(action.get("thermal_charge_rate", 0.0)))),
-            "batch_job_slot": max(0, min(4, int(action.get("batch_job_slot", 0)))),
-            "load_shed_fraction": max(0.0, min(0.5, float(action.get("load_shed_fraction", 0.0)))),
-            "building_id": int(action.get("building_id", 0)),
-        }
-
-    def _heuristic_action(self, obs: dict) -> dict:
-        """Rule-based policy (deterministic given obs)."""
-        price = obs.get("current_price", 0.10)
-        stress = obs.get("grid_stress_signal", 0.0)
-        temp = obs.get("indoor_temperature", 21.0)
-        storage = obs.get("thermal_storage_level", 0.5)
-        queue = obs.get("batch_queue", [])
-
-        hvac = 0.7 if price < 0.08 else (0.3 if price > 0.15 else 0.5)
-        if temp > 23.0:
-            hvac = max(hvac, 0.8)
-        elif temp < 19.0:
-            hvac = min(hvac, 0.2)
-
-        charge = 0.0
-        if price < 0.07 and storage < 0.8:
-            charge = 0.5
-        elif price > 0.15 and storage > 0.3:
-            charge = -0.5
-
-        shed = 0.0
-        if stress > 0.7:
-            shed = 0.4
-        elif stress > 0.5:
-            shed = 0.2
-
-        slot = 2
-        if queue and min(queue) < 8:
-            slot = 0
-
-        return {
-            "hvac_power_level": hvac,
-            "thermal_charge_rate": charge,
-            "batch_job_slot": slot,
-            "load_shed_fraction": shed,
-            "building_id": 0,
-        }
-
-    def _default_action(self) -> dict:
-        return {
-            "hvac_power_level": 0.5,
-            "thermal_charge_rate": 0.0,
-            "batch_job_slot": 0,
-            "load_shed_fraction": 0.0,
-            "building_id": 0,
-        }
-
-
-# ── Episode runner ───────────────────────────────────────────────────────────
-
-
-def run_episode(
-    env_client: GridMindEnvClient,
-    agent: LLMAgent,
-    task_id: int,
-    seed: int,
-    *,
-    fast_mode: bool,
-    llm_every: int,
-    max_steps: int | None,
-    verbose: bool = False,
-) -> dict[str, Any]:
-    """Run a single episode and emit hackathon-compliant stdout format.
-    
-    Emits:
-      [START] task=<name> env=gridmind model=<model>
-      [STEP] step=<n> action=<json> reward=<0.00> done=<true|false> error=<msg|null>
-      ...
-      [END] success=<true|false> steps=<n> rewards=<r1,r2,...>
-    """
-    reset_resp = env_client.reset(task_id=task_id, seed=seed)
-    if reset_resp is None:
-        print(f"[END] success=false steps=0 rewards=", flush=True)
-        return {
-            "task_id": task_id,
-            "seed": seed,
-            "total_reward": 0.0,
-            "total_steps": 0,
-            "elapsed_sec": 0.0,
-            "score": 0.0,
-            "sub_scores": {},
-            "exploit_detected": False,
-        }
-    obs = reset_resp.get("observations", [{}])[0]
-    
-    task_name = f"gridmind-task-{task_id}"
-    
-    # Emit [START] with required fields
-    print(f"[START] task={task_name} env=gridmind model={MODEL_NAME}", flush=True)
-    
-    total_reward = 0.0
-    total_steps = 0
-    start_time = time.time()
-    step_resp: dict[str, Any] = {}
-    step_limit = EPISODE_STEPS if max_steps is None else min(max_steps, EPISODE_STEPS)
-    
-    llm_reuse_remaining = 0
-    cached_action = agent._default_action()
-    
-    step_rewards: list[float] = []
-    last_error: str | None = None
-    
-    while not step_resp.get("done", False):
-        if total_steps >= step_limit:
-            break
-        
-        try:
-            if fast_mode:
-                action = agent._heuristic_action(obs)
-            else:
-                if llm_reuse_remaining <= 0:
-                    cached_action = agent.choose_action(obs, task_id)
-                    llm_reuse_remaining = max(1, llm_every)
-                action = cached_action
-            
-            step_resp = env_client.step(action)
-            if step_resp is None or not isinstance(step_resp, dict) or "observation" not in step_resp:
-                last_error = "invalid step response from environment"
-                print(
-                    f"[STEP] step={total_steps + 1} action=null "
-                    f"reward=0.00 done=true error=\"{last_error}\"",
-                    flush=True
-                )
-                break
-            
-            if not fast_mode:
-                llm_reuse_remaining -= 1
-            
-            obs = step_resp["observation"]
-            reward = float(step_resp["reward"])
-            total_reward += reward
-            step_rewards.append(reward)
-            total_steps += 1
-            done = bool(step_resp.get("done", False))
-            
-            # Emit [STEP] with required fields (action as compact JSON, reward to 2 decimals)
-            action_json = json.dumps(action, separators=(',', ':'))
-            error_field = "null" if last_error is None else f'"{last_error}"'
-            print(
-                f"[STEP] step={total_steps} action={action_json} "
-                f"reward={reward:.2f} done={'true' if done else 'false'} error={error_field}",
-                flush=True
-            )
-            
-            last_error = None  # Clear error after successful step
-            
-            if verbose and total_steps % 16 == 0:
-                print(
-                    f"    step={total_steps:02d} price=${obs['current_price']:.3f} "
-                    f"temp={obs['indoor_temperature']:.1f}°C "
-                    f"stress={obs['grid_stress_signal']:.2f} "
-                    f"cost=${obs['cumulative_cost']:.2f}",
-                    flush=True,
-                )
-        
-        except Exception as e:
-            last_error = str(e)
-            print(
-                f"[STEP] step={total_steps + 1} action=null "
-                f"reward=0.00 done=true error=\"{last_error}\"",
-                flush=True
-            )
-            break
-    
-    elapsed = time.time() - start_time
-    grade = env_client.grade()
-    
-    # Emit [END] with required fields
-    success = last_error is None and step_resp.get("done", False)
-    rewards_str = ",".join(f"{r:.2f}" for r in step_rewards)
-    print(
-        f"[END] success={'true' if success else 'false'} steps={total_steps} rewards={rewards_str}",
-        flush=True
-    )
-    
-    return {
-        "task_id": task_id,
-        "seed": seed,
-        "total_reward": total_reward,
-        "total_steps": total_steps,
-        "elapsed_sec": elapsed,
-        "score": grade.get("score", 0.0),
-        "sub_scores": grade.get("sub_scores", {}),
-        "exploit_detected": grade.get("exploit_detected", False),
-    }
-
-
-# ── Main ─────────────────────────────────────────────────────────────────────
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="GridMind-RL baseline inference")
-    parser.add_argument("--episodes", type=int, default=DEFAULT_EPISODES)
-    parser.add_argument("--env-url", type=str, default=ENV_URL)
-    parser.add_argument("--verbose", action="store_true")
-    parser.add_argument("--output", type=str, default="baseline_scores.json")
-    parser.add_argument(
-        "--fast-mode",
-        action="store_true",
-        help="Heuristic policy only (no LLM calls; fastest, fully reproducible).",
-    )
-    parser.add_argument(
-        "--llm-every",
-        type=int,
-        default=4,
-        metavar="N",
-        help="Reuse the same LLM action for N consecutive steps (default: 4).",
-    )
-    parser.add_argument(
-        "--max-steps",
-        type=int,
-        default=None,
-        metavar="N",
-        help="Stop after N steps (default: full episode). Grade uses partial episode.",
-    )
-    args = parser.parse_args()
-    
-    # Validate API key AFTER argparse (allows --fast-mode to bypass)
-    if not OPENAI_API_KEY and not args.fast_mode:
-        print("[WARN] No API key set, switching to fast-mode (heuristic)", file=sys.stderr)
-        args.fast_mode = True
-
-    print("=" * 60)
-    print("GridMind-RL Baseline Inference")
-    print(f"  Model: {MODEL_NAME}")
-    print(f"  API:   {API_BASE_URL}")
-    print(f"  Env:   {args.env_url}")
-    print(f"  Episodes per task: {args.episodes}")
-    print(f"  Fast mode: {args.fast_mode} | LLM every: {args.llm_every} steps")
-    print("=" * 60)
-
-    env_client = GridMindEnvClient(base_url=args.env_url)
-
-    print("\nWaiting for environment server...")
-    for attempt in range(30):
-        if env_client.health():
-            print("  [OK] Environment server is healthy")
-            break
-        time.sleep(2)
-        if attempt == 29:
-            print("  [FAIL] Environment server not reachable. Exiting.")
-            sys.exit(1)
-
-    agent = LLMAgent()
-    all_results: list[dict[str, Any]] = []
-
-    for task_id in [1, 2, 3]:
-        print(f"\n-- Task {task_id}: {TASK_DESCRIPTIONS[task_id][:60]}...")
-        task_scores: list[float] = []
-        for ep in range(args.episodes):
-            seed = DEFAULT_SEED_BASE + task_id * 100 + ep
-            print(f"  Episode {ep+1}/{args.episodes} (seed={seed})")
-            result = run_episode(
-                env_client,
-                agent,
-                task_id=task_id,
-                seed=seed,
-                fast_mode=args.fast_mode,
-                llm_every=args.llm_every,
-                max_steps=args.max_steps,
-                verbose=args.verbose,
-            )
-            task_scores.append(float(result["score"]))
-            all_results.append(result)
-            print(
-                f"    → score={result['score']:.4f} | reward={result['total_reward']:.3f} | "
-                f"{result['elapsed_sec']:.1f}s | steps={result['total_steps']}"
-            )
-
-        avg_score = sum(task_scores) / len(task_scores)
-        print(f"  Task {task_id} average score: {avg_score:.4f}")
-
-    print("\n" + "=" * 60)
-    print("BASELINE SCORES SUMMARY")
-    print("=" * 60)
-    print(f"{'Task':<10} {'Model':<30} {'Score':<10} {'Episodes':<10}")
-    print("-" * 60)
-
-    task_avgs: dict[int, float] = {}
-    for task_id in [1, 2, 3]:
-        scores = [float(r["score"]) for r in all_results if r["task_id"] == task_id]
-        avg = sum(scores) / len(scores) if scores else 0.0
-        task_avgs[task_id] = avg
-        print(f"Task {task_id:<6} {MODEL_NAME:<30} {avg:<10.4f} {len(scores)}")
-
-    print("-" * 60)
-    overall = sum(task_avgs.values()) / len(task_avgs)
-    print(f"{'Overall':<10} {'':<30} {overall:<10.4f}")
-
-    output = {
-        "model": MODEL_NAME,
-        "api_base": API_BASE_URL,
-        "episodes_per_task": args.episodes,
-        "seed_base": DEFAULT_SEED_BASE,
-        "fast_mode": args.fast_mode,
-        "llm_every": args.llm_every,
-        "max_steps": args.max_steps,
-        "task_averages": {str(k): v for k, v in task_avgs.items()},
-        "overall_average": overall,
-        "all_results": all_results,
-    }
-    with open(args.output, "w", encoding="utf-8") as f:
-        json.dump(output, f, indent=2)
-    print(f"\n[OK] Results saved to {args.output}")
-
-
-if __name__ == "__main__":
-    try:
-        main()
-    except Exception as e:
-        print(f"[FATAL] Unhandled exception: {e}", file=sys.stderr)
-        import traceback
-        traceback.print_exc(file=sys.stderr)
-        sys.exit(1)