inference.py

#!/usr/bin/env python3
from __future__ import annotations

import argparse
import json
import os
import time
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any

from openai import OpenAI

from support_triage_openenv import Action, SupportTriageEnv

# Mandatory variables requested by organizers.
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"
HF_TOKEN = os.getenv("HF_TOKEN")

BENCHMARK = os.getenv("SUPPORT_TRIAGE_BENCHMARK", "support-triage-openenv")
SUCCESS_SCORE_THRESHOLD = float(os.getenv("SUCCESS_SCORE_THRESHOLD", "0.9"))

SYSTEM_PROMPT = (
    "You are solving customer support ticket triage. "
    "Return exactly one JSON object with keys: "
    "action_type, ticket_id, priority, category, needs_escalation, message."
)

RULE_POLICY: dict[str, list[dict[str, Any]]] = {
    "easy_password_reset": [
        {"action_type": "read_ticket", "ticket_id": "T-1001"},
        {
            "action_type": "classify_ticket",
            "ticket_id": "T-1001",
            "priority": "medium",
            "category": "account",
            "needs_escalation": False,
        },
        {
            "action_type": "draft_reply",
            "message": (
                "We will send a reset link to your email. For security, confirm the request "
                "from your registered email before using the reset link."
            ),
        },
        {"action_type": "resolve_ticket", "ticket_id": "T-1001"},
    ],
    "medium_billing_dispute": [
        {"action_type": "read_ticket", "ticket_id": "T-2001"},
        {"action_type": "read_ticket", "ticket_id": "T-2002"},
        {
            "action_type": "classify_ticket",
            "ticket_id": "T-2001",
            "priority": "high",
            "category": "billing",
            "needs_escalation": False,
        },
        {
            "action_type": "draft_reply",
            "message": (
                "We confirmed a duplicate charge. We are issuing a refund and will share the invoice update. "
                "Refund processing typically takes 3-5 business days."
            ),
        },
        {"action_type": "resolve_ticket", "ticket_id": "T-2001"},
    ],
    "hard_outage_incident": [
        {"action_type": "read_ticket", "ticket_id": "T-3001"},
        {"action_type": "read_ticket", "ticket_id": "T-3002"},
        {"action_type": "read_ticket", "ticket_id": "T-3003"},
        {
            "action_type": "classify_ticket",
            "ticket_id": "T-3001",
            "priority": "urgent",
            "category": "technical",
            "needs_escalation": True,
        },
        {
            "action_type": "draft_reply",
            "message": (
                "We have escalated this incident and are investigating now. "
                "The status page will carry updates while we continue incident response."
            ),
        },
        {"action_type": "resolve_ticket", "ticket_id": "T-3001"},
    ],
    "easy_trial_extension": [
        {"action_type": "read_ticket", "ticket_id": "T-4001"},
        {
            "action_type": "classify_ticket",
            "ticket_id": "T-4001",
            "priority": "low",
            "category": "general",
            "needs_escalation": False,
        },
        {
            "action_type": "draft_reply",
            "message": (
                "We can review a trial extension based on eligibility. "
                "Please check billing settings before the next renewal so the account stays aligned."
            ),
        },
        {"action_type": "resolve_ticket", "ticket_id": "T-4001"},
    ],
    "medium_abuse_phishing": [
        {"action_type": "read_ticket", "ticket_id": "T-5001"},
        {"action_type": "read_ticket", "ticket_id": "T-5002"},
        {
            "action_type": "classify_ticket",
            "ticket_id": "T-5001",
            "priority": "high",
            "category": "abuse",
            "needs_escalation": True,
        },
        {
            "action_type": "draft_reply",
            "message": (
                "We are escalating this phishing report to the abuse team. "
                "Please preserve evidence such as headers while we review blocked indicators and sender details."
            ),
        },
        {"action_type": "resolve_ticket", "ticket_id": "T-5001"},
    ],
    "hard_privacy_deletion": [
        {"action_type": "read_ticket", "ticket_id": "T-6001"},
        {"action_type": "read_ticket", "ticket_id": "T-6002"},
        {"action_type": "read_ticket", "ticket_id": "T-6003"},
        {
            "action_type": "classify_ticket",
            "ticket_id": "T-6001",
            "priority": "high",
            "category": "account",
            "needs_escalation": True,
        },
        {
            "action_type": "draft_reply",
            "message": (
                "We have routed the data deletion request to the privacy team. "
                "Identity verification is required, and completion is normally within 30 days."
            ),
        },
        {"action_type": "resolve_ticket", "ticket_id": "T-6001"},
    ],
}


@dataclass
class EpisodeResult:
    task_id: str
    steps: int
    score: float
    success: bool
    final_reward: float
    rewards: list[float]
    fallback_count: int


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: str | None) -> 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)


def _extract_json(text: str) -> str:
    text = text.strip()
    start = text.find("{")
    end = text.rfind("}")
    if start == -1 or end == -1 or end <= start:
        raise ValueError("No JSON object found in model response")
    return text[start : end + 1]


def heuristic_action(task_id: str, step_idx: int) -> Action:
    plan = RULE_POLICY[task_id]
    idx = min(step_idx, len(plan) - 1)
    return Action.model_validate(plan[idx])


def llm_action(client: OpenAI, observation: dict[str, Any], state: dict[str, Any]) -> Action:
    prompt = json.dumps(
        {
            "instruction": "Pick the best next single action to maximize final task score.",
            "observation": observation,
            "state": state,
        },
        ensure_ascii=True,
    )
    completion = client.chat.completions.create(
        model=MODEL_NAME,
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": prompt},
        ],
        temperature=0,
        max_tokens=220,
        stream=False,
    )
    text = (completion.choices[0].message.content or "").strip()
    payload = json.loads(_extract_json(text))
    return Action.model_validate(payload)


def action_to_str(action: Action) -> str:
    if action.action_type == "read_ticket":
        return f"read_ticket({action.ticket_id})"
    if action.action_type == "classify_ticket":
        return (
            f"classify_ticket({action.ticket_id},{action.priority},{action.category},"
            f"{str(bool(action.needs_escalation)).lower()})"
        )
    if action.action_type == "draft_reply":
        length = len((action.message or "").strip())
        return f"draft_reply(len={length})"
    if action.action_type == "resolve_ticket":
        return f"resolve_ticket({action.ticket_id})"
    return action.action_type


def run_episode(
    env: SupportTriageEnv,
    task_id: str,
    mode: str,
    client: OpenAI | None,
    started_at: float,
    runtime_limit_seconds: int,
) -> EpisodeResult:
    obs = env.reset(task_id)
    done = False
    info: dict[str, Any] = {}
    rewards: list[float] = []
    steps_taken = 0
    fallback_count = 0
    success = False
    score = 0.0
    final_reward = 0.0

    log_start(task=task_id, env=BENCHMARK, model=MODEL_NAME)

    while not done:
        if time.monotonic() - started_at > runtime_limit_seconds:
            raise TimeoutError(f"Runtime exceeded {runtime_limit_seconds}s")

        step_idx = env.state()["step_count"]

        if mode == "heuristic":
            action = heuristic_action(task_id, step_idx)
        else:
            assert client is not None
            try:
                action = llm_action(client, obs.model_dump(), env.state())
            except Exception:
                fallback_count += 1
                action = heuristic_action(task_id, step_idx)

        step_error: str | None = None
        try:
            obs, reward, done, info = env.step(action)
            reward_value = float(reward.value)
        except Exception as exc:
            step_error = str(exc)
            reward_value = 0.0
            done = True

        steps_taken = step_idx + 1
        rewards.append(reward_value)
        final_reward = reward_value

        log_step(
            step=steps_taken,
            action=action_to_str(action),
            reward=reward_value,
            done=done,
            error=step_error,
        )

        if done:
            break

    score = max(0.0, min(1.0, float(info.get("grader_score", 0.0))))
    success = score >= SUCCESS_SCORE_THRESHOLD
    log_end(success=success, steps=steps_taken, score=score, rewards=rewards)

    return EpisodeResult(
        task_id=task_id,
        steps=steps_taken,
        score=round(score, 4),
        success=success,
        final_reward=round(final_reward, 4),
        rewards=[round(r, 4) for r in rewards],
        fallback_count=fallback_count,
    )


def main() -> None:
    parser = argparse.ArgumentParser(description="Submission inference script.")
    parser.add_argument("--mode", choices=["openai", "heuristic"], default="openai")
    parser.add_argument("--output", default="scores/inference_scores.json")
    parser.add_argument("--runtime-limit-seconds", type=int, default=1200)
    parser.add_argument("--task-id", default="", help="Optional single task id; default runs all tasks")
    args = parser.parse_args()

    if args.mode == "openai" and not HF_TOKEN:
        raise RuntimeError("HF_TOKEN is required for --mode openai")

    env = SupportTriageEnv()
    task_ids = [args.task_id] if args.task_id else env.task_ids

    client = None
    if args.mode == "openai":
        client = OpenAI(base_url=API_BASE_URL, api_key=HF_TOKEN)

    started_at = time.monotonic()
    episodes: list[EpisodeResult] = []
    for task_id in task_ids:
        if task_id not in env.task_ids:
            raise ValueError(f"Unknown task_id '{task_id}'")
        episodes.append(
            run_episode(
                env=env,
                task_id=task_id,
                mode=args.mode,
                client=client,
                started_at=started_at,
                runtime_limit_seconds=args.runtime_limit_seconds,
            )
        )

    summary = {
        "mode": args.mode,
        "api_base_url": API_BASE_URL,
        "model_name": MODEL_NAME,
        "avg_score": round(sum(e.score for e in episodes) / len(episodes), 4),
        "avg_final_reward": round(sum(e.final_reward for e in episodes) / len(episodes), 4),
        "total_steps": int(sum(e.steps for e in episodes)),
        "episodes": [asdict(e) for e in episodes],
    }

    output_path = Path(args.output)
    output_path.parent.mkdir(parents=True, exist_ok=True)
    output_path.write_text(json.dumps(summary, indent=2), encoding="utf-8")


if __name__ == "__main__":
    main()