#!/usr/bin/env python3
"""
Email Triage OpenEnv - Baseline Inference Script

Runs a language model agent against all 3 email triage tasks and logs
results in the required OpenEnv [START]/[STEP]/[END] format.

Environment variables:
  MODEL_NAME     : Model identifier (default: google/gemma-4-31B-it)
  API_BASE_URL   : LLM API endpoint (default: https://router.huggingface.co/hf-inference/models/google/gemma-4-31B-it/v1)
  HF_TOKEN       : HuggingFace API token (fallback: OPENAI_API_KEY)

Usage:
  export HF_TOKEN="hf_..."
  python inference.py
"""

from __future__ import annotations

import logging
import os
import sys
import time
from typing import Dict, List, Optional

from openai import OpenAI

from src.environment import EmailTriageEnv, TASK_CONFIG
from src.grader import grade_task_basic, grade_task_medium, grade_task_hard
from src.models import Action, Observation
from src.utils import format_action_for_log, parse_action_string

# ── Configuration ────────────────────────────────────────────────────────────

API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/hf-inference/models/google/gemma-4-31B-it/v1")
MODEL_NAME = os.getenv("MODEL_NAME", "google/gemma-4-31B-it")
API_KEY = os.getenv("HF_TOKEN") or os.getenv("OPENAI_API_KEY") or ""
TEMPERATURE = 0.3  # low temperature for consistency
MAX_RETRIES = 2

logging.basicConfig(
    level=logging.WARNING,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
    stream=sys.stderr,
)
logger = logging.getLogger(__name__)

# Task → grader mapping
GRADERS = {
    "basic_triage": grade_task_basic,
    "multi_folder_triage": grade_task_medium,
    "advanced_triage_with_urgency": grade_task_hard,
}


# ── Logging (OpenEnv format → stdout) ────────────────────────────────────────

def log_start(task_id: str, model: str) -> None:
    print(f"[START] task={task_id} env=email-triage model={model}", flush=True)


def log_step(
    step: int,
    action_str: str,
    reward: float,
    done: bool,
    error: Optional[str],
) -> None:
    error_part = f'"{error}"' if error else "null"
    print(
        f"[STEP] step={step} action={action_str} "
        f"reward={reward:.2f} done={str(done).lower()} error={error_part}",
        flush=True,
    )


def log_end(
    task_id: str,
    success: bool,
    steps: int,
    score: float,
    rewards: List[float],
) -> None:
    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
    print(
        f"[END] task={task_id} success={str(success).lower()} "
        f"steps={steps} score={score:.2f} rewards={rewards_str}",
        flush=True,
    )


# ── Agent ────────────────────────────────────────────────────────────────────

class EmailTriageAgent:
    """Baseline LLM agent for email triage."""

    def __init__(self, model_name: str, api_base: str, api_key: str) -> None:
        self.client = OpenAI(api_key=api_key, base_url=api_base)
        self.model_name = model_name

    def get_action(
        self,
        observation: Observation,
        step_num: int,
        max_steps: int,
    ) -> str:
        """Query the LLM to decide which action to take.

        Returns the raw action string from the model.
        """
        inbox_lines = []
        for e in observation.inbox_emails[:10]:
            vip_tag = " [VIP]" if e.is_vip_sender else ""
            prio_tag = " [URGENT]" if e.priority_flag else ""
            inbox_lines.append(
                f"  ID={e.id} | From: {e.sender} | "
                f"Subject: {e.subject[:60]}{vip_tag}{prio_tag}"
            )
        inbox_text = "\n".join(inbox_lines) if inbox_lines else "  (empty)"

        prompt = f"""You are an email triage assistant. Sort each email into the correct folder.

INBOX ({len(observation.inbox_emails)} emails):
{inbox_text}

FOLDERS: {', '.join(observation.available_folders)}

RULES:
- Work emails (@acmecorp.com, project updates, code reviews) -> work
- Invoices, expenses, budgets, purchase orders -> finance
- Calendar invites, meeting notes, RSVPs -> meetings
- Phishing, marketing spam, suspicious domains (.ru, .xyz, .biz) -> spam
- Newsletters, notifications, automated reports -> archive
- VIP/urgent emails should be prioritized and classified correctly

Step {step_num}/{max_steps}. Respond with ONLY the action, e.g.: move(0, work)
"""

        for attempt in range(MAX_RETRIES + 1):
            try:
                response = self.client.chat.completions.create(
                    model=self.model_name,
                    messages=[{"role": "user", "content": prompt}],
                    max_tokens=60,
                    temperature=TEMPERATURE,
                    timeout=30,
                )
                return response.choices[0].message.content.strip()
            except Exception as e:
                logger.warning(
                    "LLM call failed (attempt %d/%d): %s",
                    attempt + 1, MAX_RETRIES + 1, e,
                )
                if attempt < MAX_RETRIES:
                    time.sleep(2 ** attempt)

        # Fallback: move first email to work
        if observation.inbox_emails:
            return f"move({observation.inbox_emails[0].id}, work)"
        return "move(0, work)"


def _parse_or_fallback(
    raw_action: str, observation: Observation
) -> Action:
    """Parse the LLM's raw output into an Action, with a safe fallback."""
    action = parse_action_string(raw_action)
    if action is not None:
        return action

    # Fallback: move first available email to work
    logger.warning("Failed to parse action: '%s'", raw_action)
    if observation.inbox_emails:
        return Action(
            action_type="move",
            email_id=observation.inbox_emails[0].id,
            target_folder="work",
        )
    return Action(action_type="move", email_id=0, target_folder="work")


# ── Task Runner ──────────────────────────────────────────────────────────────

def run_task(task_id: str, agent: EmailTriageAgent, seed: int = 42) -> Dict:
    """Run the agent on a single task and log results.

    Returns a summary dict with score, steps, success status.
    """
    env = EmailTriageEnv(task_id=task_id, seed=seed)
    obs = env.reset()
    max_steps = env.max_steps

    log_start(task_id, MODEL_NAME)

    rewards: List[float] = []
    steps_taken = 0
    error_msg: Optional[str] = None

    try:
        for step_num in range(1, max_steps + 1):
            if obs.done:
                break

            raw_action = agent.get_action(obs, step_num, max_steps)
            action = _parse_or_fallback(raw_action, obs)
            action_str = format_action_for_log(action)

            try:
                obs, reward, done, info = env.step(action)
                reward_val = reward.value
                step_error = None
            except Exception as e:
                reward_val = 0.0
                done = False
                step_error = str(e)
                logger.error("Step error: %s", e)

            rewards.append(reward_val)
            steps_taken = step_num

            log_step(
                step=step_num,
                action_str=action_str,
                reward=reward_val,
                done=done,
                error=step_error,
            )

            if done:
                break

    except Exception as e:
        error_msg = str(e)
        logger.error("Task %s failed: %s", task_id, error_msg)

    # Compute final score using the appropriate grader
    grader = GRADERS.get(task_id)
    if grader and env.history:
        score = grader(env.history)
    elif rewards:
        score = sum(rewards) / len(rewards)
    else:
        score = 0.0

    score = max(0.0, min(1.0, score))
    success = score >= TASK_CONFIG[task_id].get("success_threshold", 0.7)

    log_end(
        task_id=task_id,
        success=success,
        steps=steps_taken,
        score=score,
        rewards=rewards,
    )

    return {
        "task_id": task_id,
        "score": score,
        "steps": steps_taken,
        "rewards": rewards,
        "success": success,
        "error": error_msg,
    }


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

def main() -> None:
    """Run baseline inference on all 3 tasks."""
    if not API_KEY:
        logger.error(
            "No API key found. Set HF_TOKEN or OPENAI_API_KEY environment variable."
        )
        sys.exit(1)

    agent = EmailTriageAgent(
        model_name=MODEL_NAME,
        api_base=API_BASE_URL,
        api_key=API_KEY,
    )

    tasks = [
        "basic_triage",
        "multi_folder_triage",
        "advanced_triage_with_urgency",
    ]

    results = []
    for task_id in tasks:
        result = run_task(task_id, agent, seed=42)
        results.append(result)

    # Summary to stderr (not stdout, to keep stdout clean for log parsing)
    print("\n" + "=" * 60, file=sys.stderr)
    print("BASELINE INFERENCE SUMMARY", file=sys.stderr)
    print("=" * 60, file=sys.stderr)
    for r in results:
        status = "PASS" if r["success"] else "FAIL"
        print(
            f"  {r['task_id']:40s} score={r['score']:.2f}  [{status}]",
            file=sys.stderr,
        )
    avg = sum(r["score"] for r in results) / len(results) if results else 0.0
    print(f"\n  Average Score: {avg:.2f}", file=sys.stderr)
    print("=" * 60, file=sys.stderr)


if __name__ == "__main__":
    main()