debug: (inference) stuff

inference.py

@@ -1,188 +1,270 @@
-"""
-Inference Script Example
-===================================
-MANDATORY
-- Before submitting, ensure the following variables are defined in your environment configuration:
-    API_BASE_URL   The API endpoint for the LLM.
-    MODEL_NAME     The model identifier to use for inference.
-    HF_TOKEN       Your Hugging Face / API key.
-    LOCAL_IMAGE_NAME The name of the local image to use for the environment if you are using from_docker_image()
-                     method
-
-- Defaults are set only for API_BASE_URL and MODEL_NAME 
-    (and should reflect your active inference setup):
-    API_BASE_URL = os.getenv("API_BASE_URL", "<your-active-endpoint>")
-    MODEL_NAME = os.getenv("MODEL_NAME", "<your-active-model>")
-    
-- The inference script must be named `inference.py` and placed in the root directory of the project
-- Participants must use OpenAI Client for all LLM calls using above variables
-
-STDOUT FORMAT
-- The script must emit exactly three line types to stdout, in this order:
-
-    [START] task=<task_name> env=<benchmark> model=<model_name>
-    [STEP]  step=<n> action=<action_str> reward=<0.00> done=<true|false> error=<msg|null>
-    [END]   success=<true|false> steps=<n> score=<score> rewards=<r1,r2,...,rn>
-
-  Rules:
-    - One [START] line at episode begin.
-    - One [STEP] line per step, immediately after env.step() returns.
-    - One [END] line after env.close(), always emitted (even on exception).
-    - reward and rewards are formatted to 2 decimal places.
-    - done and success are lowercase booleans: true or false.
-    - error is the raw last_action_error string, or null if none.
-    - All fields on a single line with no newlines within a line.
-    - Each tasks should return score in [0, 1]
-
-  Example:
-    [START] task=click-test env=miniwob model=Qwen3-VL-30B
-    [STEP] step=1 action=click('123') reward=0.00 done=false error=null
-    [STEP] step=2 action=fill('456','text') reward=0.00 done=false error=null
-    [STEP] step=3 action=click('789') reward=1.00 done=true error=null
-    [END] success=true steps=3 score=1.00 rewards=0.00,0.00,1.00
-"""
-
 import asyncio
+import json
 import os
-import textwrap
+import sys
+import re
+from pathlib import Path
 from typing import List, Optional
-
+import httpx
+from dotenv import load_dotenv
 from openai import OpenAI
 
-from my_env_v4 import MyEnvV4Action, MyEnvV4Env
-IMAGE_NAME = os.getenv("IMAGE_NAME") # If you are using docker image 
-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("MY_ENV_V4_TASK", "echo")
-BENCHMARK = os.getenv("MY_ENV_V4_BENCHMARK", "my_env_v4")
-MAX_STEPS = 8
-TEMPERATURE = 0.7
-MAX_TOKENS = 150
-SUCCESS_SCORE_THRESHOLD = 0.1  # normalized score in [0, 1]
-
-# Max possible reward: each token contributes 0.1, across all steps
-_MAX_REWARD_PER_STEP = MAX_TOKENS * 0.1
-MAX_TOTAL_REWARD = MAX_STEPS * _MAX_REWARD_PER_STEP
-
-SYSTEM_PROMPT = textwrap.dedent(
-    """
-    You are interacting with a simple echo environment.
-    Each turn you must send a message. The environment will echo it back.
-    Reward is proportional to message length: reward = len(message) * 0.1
-    Your goal is to maximize total reward by sending meaningful, substantive messages.
-    Reply with exactly one message string — no quotes, no prefixes, just the message text.
-    """
-).strip()
+ROOT = Path(__file__).resolve().parent
+REPO_ROOT = ROOT.parent
+
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+load_dotenv()
+
+TASK_DIR = ROOT / "tasks"
+SYSTEM_PROMPT = (ROOT / "system_prompt.txt").read_text().strip()
+
+HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("API_KEY")
+MODEL_NAME = os.getenv("MODEL_NAME", "gpt-4o")
+
+BASE_URL = "https://vishvam10-cloudenv.hf.space"
+
+TASK_ORDER = ["easy-task", "medium-task", "hard-task"]
+
+MAX_PARSE_RETRIES = 3
+MAX_STEPS = 5
+
+client = OpenAI(base_url="https://api.openai.com/v1", api_key=HF_TOKEN)
 
 
 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:
+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()
+    action_safe = action.replace("\n", " ").replace("\r", "")[:120]
     print(
-        f"[STEP] step={step} action={action} reward={reward:.2f} done={done_val} error={error_val}",
+        f"[STEP] step={step} action={action_safe} reward={reward:.2f} "
+        f"done={done_val} error={error_val}",
         flush=True,
     )
 
 
-def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None:
+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)
+    print(
+        f"[END] success={str(success).lower()} steps={steps} "
+        f"score={score:.3f} rewards={rewards_str}",
+        flush=True,
+    )
 
 
-def build_user_prompt(step: int, last_echoed: str, last_reward: float, history: List[str]) -> str:
-    history_block = "\n".join(history[-4:]) if history else "None"
-    return textwrap.dedent(
-        f"""
-        Step: {step}
-        Last echoed message: {last_echoed!r}
-        Last reward: {last_reward:.2f}
-        Previous steps:
-        {history_block}
-        Send your next message.
-        """
-    ).strip()
+def load_task(name: str):
+    return json.loads((TASK_DIR / f"{name}.json").read_text())
 
 
-def get_model_message(client: OpenAI, step: int, last_echoed: str, last_reward: float, history: List[str]) -> str:
-    user_prompt = build_user_prompt(step, last_echoed, last_reward, history)
-    try:
-        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,
-            stream=False,
+async def register_pipeline(task: dict):
+    async with httpx.AsyncClient(timeout=30) as http:
+        r = await http.post(
+            f"{BASE_URL}/api/pipelines",
+            json={
+                "pipeline_id": task["pipeline_id"],
+                "broken_pipeline": task["broken_pipeline"],
+                "ideal_pipeline": task["ideal_pipeline"],
+            },
         )
-        text = (completion.choices[0].message.content or "").strip()
-        return text if text else "hello"
-    except Exception as exc:
-        print(f"[DEBUG] Model request failed: {exc}", flush=True)
-        return "hello"
 
+        if r.status_code not in (200, 201, 400):
+            print(
+                f"[PIPELINE REGISTER ERROR] {r.status_code} {r.text}",
+                flush=True,
+            )
 
-async def main() -> None:
-    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
 
-    env = await MyEnvV4Env.from_docker_image(IMAGE_NAME)
+async def env_reset(task_id: str, episode_id: str):
+    async with httpx.AsyncClient(timeout=30) as http:
+        r = await http.post(
+            f"{BASE_URL}/reset",
+            json={"task_id": task_id, "episode_id": episode_id},
+        )
+        r.raise_for_status()
+        return r.json()
+
+
+async def env_step(action: dict):
+    async with httpx.AsyncClient(timeout=30) as http:
+        r = await http.post(
+            f"{BASE_URL}/step",
+            json={"action": action},
+        )
+        r.raise_for_status()
+        return r.json()
+
 
-    history: List[str] = []
-    rewards: List[float] = []
-    steps_taken = 0
-    score = 0.0
-    success = False
+def clean_json(raw: str) -> str:
+    raw = raw.strip()
+    raw = re.sub(r"^```(?:json)?\s*", "", raw)
+    raw = re.sub(r"\s*```$", "", raw)
 
-    log_start(task=TASK_NAME, env=BENCHMARK, model=MODEL_NAME)
+    match = re.search(r"\{.*\}", raw, re.DOTALL)
+    if match:
+        return match.group(0)
 
+    return raw
+
+
+def build_action(raw: str, task_id: str, episode_id: str):
     try:
-        result = await env.reset() # OpenENV.reset()
-        last_echoed = result.observation.echoed_message
-        last_reward = 0.0
+        raw = clean_json(raw)
+        data = json.loads(raw)
 
-        for step in range(1, MAX_STEPS + 1):
-            if result.done:
-                break
+        if isinstance(data, dict) and "action" in data:
+            data = data["action"]
 
-            message = get_model_message(client, step, last_echoed, last_reward, history)
+        if not isinstance(data, dict):
+            return None
 
-            result = await env.step(MyEnvV4Action(message=message))
-            obs = result.observation
+        service = data.get("service")
+        operation = data.get("operation")
 
-            reward = result.reward or 0.0
-            done = result.done
-            error = None
+        if not service or not operation:
+            return None
 
-            rewards.append(reward)
-            steps_taken = step
-            last_echoed = obs.echoed_message
-            last_reward = reward
+        instance_id = data.get("instance_id")
+
+        if isinstance(service, str) and ":" in service:
+            service, parsed_instance = service.split(":", 1)
+            instance_id = instance_id or parsed_instance
+
+        return {
+            "task_id": task_id,
+            "episode_id": episode_id,
+            "service": service.lower(),
+            "instance_id": instance_id,
+            "operation": operation.strip(),
+            "payload": data.get("payload", {})
+            if isinstance(data.get("payload"), dict)
+            else {},
+            "principal_arn": data.get("principal_arn"),
+            "intent": data.get("intent"),
+        }
+
+    except Exception:
+        return None
 
-            log_step(step=step, action=message, reward=reward, done=done, error=error)
 
-            history.append(f"Step {step}: {message!r} -> reward {reward:+.2f}")
+def call_model(step, state, broken, ideal, history):
+    prompt = f"""
+Return ONLY valid JSON.
 
-            if done:
-                break
+STEP {step}
+STATE {json.dumps(state)[:1000]}
+BROKEN {json.dumps(broken)[:1000]}
+IDEAL {json.dumps(ideal)[:1000]}
+HISTORY {history[-5:]}
+"""
+
+    res = client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=[
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": prompt},
+        ],
+        temperature=0.3,
+        max_tokens=500,
+    )
+
+    return (res.choices[0].message.content or "").strip()
+
+
+async def run_episode(task):
+    task_id = task["pipeline_id"]
+    episode_id = f"{task_id}-episode-1"
+
+    log_start(task=task_id, env="http", model=MODEL_NAME)
 
-        score = sum(rewards) / MAX_TOTAL_REWARD if MAX_TOTAL_REWARD > 0 else 0.0
-        score = min(max(score, 0.0), 1.0)  # clamp to [0, 1]
-        success = score >= SUCCESS_SCORE_THRESHOLD
+    result = await env_reset(task_id, episode_id)
+
+    await env_step(
+        {
+            "task_id": task_id,
+            "episode_id": episode_id,
+            "service": "internal",
+            "instance_id": None,
+            "operation": "InitializeEnvironment",
+            "payload": {},
+        }
+    )
+
+    await register_pipeline(task)
+
+    obs = {}
+    history = []
+    rewards = []
+    step_count = 0
+
+    for step in range(MAX_STEPS):
+        if obs.get("done"):
+            break
+
+        error = None
 
-    finally:
         try:
-            await env.close()
+            raw = call_model(
+                step,
+                obs,
+                task["broken_pipeline"],
+                task["ideal_pipeline"],
+                history,
+            )
+
+            action = build_action(raw, task_id, episode_id)
+
+            if action is None:
+                error = "invalid_action"
+                log_step(step, "INVALID_ACTION", 0.0, False, error)
+                continue
+
+            result = await env_step(action)
+            obs = result["observation"]
+
+            reward = float(obs.get("reward", 0.0))
+            done = bool(obs.get("done", False))
+
+            rewards.append(reward)
+            history.append(raw)
+            step_count += 1
+
+            log_step(
+                step=step_count,
+                action=str(action),
+                reward=reward,
+                done=done,
+                error=None,
+            )
+
         except Exception as e:
-            print(f"[DEBUG] env.close() error (container cleanup): {e}", flush=True)
-        log_end(success=success, steps=steps_taken, score=score, rewards=rewards)
+            error = str(e)
+
+            log_step(step, "ERROR", 0.0, False, error)
+            break
+
+        if obs.get("done"):
+            break
+
+    success = obs.get("done", False)
+    score = sum(rewards) / (len(rewards) or 1)
+
+    log_end(success=success, steps=step_count, score=score, rewards=rewards)
+
+
+async def main():
+    for name in TASK_ORDER:
+        task = load_task(name)
+        await run_episode(task)
 
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(main())