inference.py

"""
PreferenceLab Baseline Inference Script
Mandatory stdout format: [START], [STEP], [END]

Environment variables:
    API_BASE_URL  — LLM API endpoint (required, with default)
    MODEL_NAME    — Model identifier (required, with default)
    HF_TOKEN      — Hugging Face API key (no default — injected by HF Spaces)
    ENV_BASE_URL  — PreferenceLab Space URL (optional, defaults to localhost)

Usage:
    python inference.py
    HF_TOKEN=hf_xxx MODEL_NAME=meta-llama/Llama-3.1-8B-Instruct python inference.py
"""

import os
import json

from openai import OpenAI

# ── Mandatory env vars ─────────────────────────────────────────
API_BASE_URL = os.getenv("API_BASE_URL", "/static-proxy?url=https%3A%2F%2Frouter.huggingface.co%2Fv1%26quot%3B%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L23">
MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct")
HF_TOKEN     = os.getenv("HF_TOKEN")
ENV_BASE_URL = os.getenv("ENV_BASE_URL", "http://localhost:8000")

client = OpenAI(api_key=HF_TOKEN, base_url=API_BASE_URL)

from server.environment import PreferenceLabEnvironment


# ── Mandatory log functions ────────────────────────────────────

def log_start(task: str, env: str, model: str):
    """Stdout START log — required structured format."""
    print(f"[START] task={task} env={env} model={model}", flush=True)


def log_step(step: int, action: str, reward: float, done: bool, error=None):
    """Stdout STEP log — required structured format."""
    err = error if error else "null"
    print(
        f"[STEP] step={step} action={action} reward={reward:.2f} "
        f"done={str(done).lower()} error={err}",
        flush=True,
    )


def log_end(success: bool, steps: int, score: float, rewards: list[float]):
    """Stdout END log — required structured format."""
    r_str = ",".join(f"{r:.2f}" for r in rewards)
    print(
        f"[END] success={str(success).lower()} steps={steps} "
        f"score={score:.2f} rewards={r_str}",
        flush=True,
    )


# ── LLM call ──────────────────────────────────────────────────

def call_llm(system: str, user: str) -> str:
    """Call the LLM via OpenAI-compatible client. Returns raw text."""
    try:
        resp = client.chat.completions.create(
            model=MODEL_NAME,
            messages=[
                {"role": "system", "content": system},
                {"role": "user",   "content": user},
            ],
            max_tokens=100,
            temperature=0.0,  # deterministic for reproducibility
        )
        return resp.choices[0].message.content.strip()
    except Exception as e:
        print(f"  [LLM ERROR] {e}", flush=True)
        return ""


def parse_json(text: str, fallback: dict) -> dict:
    """Extract and parse the first JSON object found in text using robust decoding."""
    try:
        decoder = json.JSONDecoder()
        # Find first '{' to start decoding directly
        start = text.find("{")
        if start >= 0:
            obj, _ = decoder.raw_decode(text[start:])
            return obj
    except json.JSONDecodeError:
        pass
    except Exception:
        pass
    return fallback


# ── Task runners ───────────────────────────────────────────────

SYSTEMS = {
    "pairwise": (
        'You are an expert RLHF annotator. Think step by step before answering.\n'
        'Example: Prompt: "What is 2+2?" A: "4" B: "Five" → {"choice":"A"} because A is factually correct.\n'
        'Reply ONLY with valid JSON: {"choice":"A"} or {"choice":"B"} or {"choice":"tie"}.'
    ),
    "likert": (
        'You are an expert RLHF annotator. Think step by step.\n'
        'Rate helpfulness (does it answer?), honesty (is it true?), '
        'harmlessness (is it safe?), instruction_following (does it follow exactly?).\n'
        'Reply ONLY with JSON: {"helpfulness":4,"honesty":5,"harmlessness":5,"instruction_following":4}'
    ),
    "consistency": (
        'You are an expert RLHF annotator. Think step by step.\n'
        'Rank responses by: accuracy first, then completeness, then clarity.\n'
        'Example: If C is most accurate and D is vague → {"ranking":["C","A","B","D"]}\n'
        'Reply ONLY with JSON: {"ranking":["B","A","C","D"]}'
    ),
}


def run_task(env, task_type: str, task_name: str) -> float:
    """
    Run a full episode for the given task type.

    Args:
        env:        PreferenceLabEnvironment instance.
        task_type:  'pairwise' | 'likert' | 'consistency'
        task_name:  Human-readable name for the [START] log.

    Returns:
        Average episode reward (float).
    """
    import sys
    sys.path.insert(0, ".")
    from models import PairwiseAction, LikertAction, ConsistencyAction

    log_start(task=task_name, env="preference_lab", model=MODEL_NAME)

    obs     = env.reset(seed=42, task_type=task_type)
    rewards: list[float] = []
    steps   = 0
    success = False

    for step in range(1, 6):
        try:
            # ── Build action from LLM output ─────────────────
            if task_type == "pairwise":
                user = (
                    f"Prompt: {obs.prompt}\n\n"
                    f"Response A:\n{obs.response_a}\n\n"
                    f"Response B:\n{obs.response_b}"
                )
                out    = parse_json(call_llm(SYSTEMS["pairwise"], user), {"choice": "A"})
                choice = out.get("choice", "A")
                if choice not in ("A", "B", "tie", "skip"):
                    choice = "A"
                action     = PairwiseAction(choice=choice)
                action_str = f"choice={choice}"

            elif task_type == "likert":
                user = (
                    f"Prompt: {obs.prompt}\n\n"
                    f"Response:\n{obs.response}"
                )
                out = parse_json(
                    call_llm(SYSTEMS["likert"], user),
                    {"helpfulness": 3, "honesty": 3, "harmlessness": 4, "instruction_following": 3},
                )
                def c(v): return max(1, min(5, int(out.get(v, 3))))  # clamp 1-5
                action = LikertAction(
                    helpfulness=c("helpfulness"),
                    honesty=c("honesty"),
                    harmlessness=c("harmlessness"),
                    instruction_following=c("instruction_following"),
                )
                action_str = (
                    f"h={c('helpfulness')},ho={c('honesty')},"
                    f"ha={c('harmlessness')},i={c('instruction_following')}"
                )

            else:  # consistency
                user = (
                    f"Prompt: {obs.prompt}\n\n"
                    f"A: {obs.response_a}\n"
                    f"B: {obs.response_b}\n"
                    f"C: {obs.response_c}\n"
                    f"D: {obs.response_d}"
                )
                out     = parse_json(call_llm(SYSTEMS["consistency"], user), {"ranking": ["A", "B", "C", "D"]})
                ranking = out.get("ranking", ["A", "B", "C", "D"])
                if not isinstance(ranking, list) or len(ranking) != 4:
                    ranking = ["A", "B", "C", "D"]
                action     = ConsistencyAction(ranking=ranking)
                action_str = ">".join(ranking)

            # ── Step the environment (returns Observation) ────
            obs    = env.step(action)
            reward = obs.reward
            done   = obs.done

        except Exception as e:
            reward = 0.0
            done   = True
            log_step(step=step, action="error", reward=reward, done=done, error=str(e))
            break

        rewards.append(reward)
        steps = step
        log_step(step=step, action=action_str, reward=reward, done=done)

        if done:
            break

    score   = sum(rewards) / max(len(rewards), 1)
    success = score > 0.0
    log_end(success=success, steps=steps, score=score, rewards=rewards)
    return score


# ── Main ───────────────────────────────────────────────────────

def main():
    if not HF_TOKEN:
        raise SystemExit("HF_TOKEN is required to run baseline inference.")

    print("=" * 60, flush=True)
    print("PreferenceLab Baseline Inference", flush=True)
    print(f"Model:    {MODEL_NAME}", flush=True)
    print(f"API URL:  {API_BASE_URL}", flush=True)
    print(f"Env URL:  {ENV_BASE_URL}", flush=True)
    print("=" * 60, flush=True)

    env = PreferenceLabEnvironment()

    scores = []
    scores.append(run_task(env, "pairwise",     "pairwise-ranking"))
    scores.append(run_task(env, "likert",        "likert-scoring"))
    scores.append(run_task(env, "consistency",   "consistency-ranking"))

    if scores:
        print(f"\nOverall avg: {sum(scores) / len(scores):.2f}", flush=True)

    if len(scores) >= 3:
        print("\n=== CURRICULUM LEARNING DEMO ===")
        print(f"Task 1 Pairwise (Easy):         {scores[0]:.2f}")
        print(f"Task 2 Likert (Medium):         {scores[1]:.2f}")  
        print(f"Task 3 Consistency (Hard):      {scores[2]:.2f}")
        print(f"Difficulty progression: {scores[0]:.2f} → {scores[1]:.2f} → {scores[2]:.2f}")


if __name__ == "__main__":
    main()