from __future__ import annotations

import re
import sys
from collections import deque
from dataclasses import dataclass, field
from typing import Deque, Optional, Pattern, Tuple

try:
    from litellm import completion
except ImportError:  # pragma: no cover - optional dependency
    completion = None  # type: ignore[assignment]

from agent import Agent


@dataclass
class IntelligentCommunicationAgent(Agent):
    """Agent that uses a language model to answer private tells."""

    model: str = "mistral/mistral-tiny"
    system_prompt: str = (
        "Du bist Mistle, ein hilfsbereiter MUD-Bot. "
        "Antworte freundlich und knapp in deutscher Sprache."
    )
    temperature: float = 0.7
    max_output_tokens: int = 120
    fallback_reply: str = "Hallo! Ich bin Mistle und ein Bot."
    tell_pattern: Pattern[str] = field(
        default_factory=lambda: re.compile(
            r"^(?P<player>[^\s]+) teilt (d|D)ir mit: (?P<message>.+)$",
            re.MULTILINE,
        )
    )
    last_output: str = field(default="", init=False)
    pending_replies: Deque[Tuple[str, str]] = field(default_factory=deque, init=False)

    def observe(self, output: str) -> None:
        if not output:
            return
        self.last_output = output
        for match in self.tell_pattern.finditer(output):
            player = match.group("player").strip()
            message = match.group("message").strip()
            if not player:
                continue
            self.pending_replies.append((player, message))
            print(f"[Agent] Received message from {player}: {message}")

    def decide(self) -> Optional[str]:
        if not self.pending_replies:
            return None
        player, message = self.pending_replies.popleft()
        reply_text = self._generate_reply(player, message)
        reply = f"teile {player} mit {reply_text}"
        print(f"[Agent] Replying to {player} with model output")
        return reply

    def _generate_reply(self, player: str, message: str) -> str:
        if completion is None:
            print(
                "[Agent] litellm is not installed; falling back to default reply",
                file=sys.stderr,
            )
            return self.fallback_reply

        try:
            response = completion(
                model=self.model,
                messages=[
                    {"role": "system", "content": self.system_prompt},
                    {
                        "role": "user",
                        "content": (
                            f"Spieler {player} schreibt: {message}\n"
                            "Formuliere eine kurze, freundliche Antwort."
                        ),
                    },
                ],
                temperature=self.temperature,
                max_tokens=self.max_output_tokens,
            )
        except Exception as exc:  # pragma: no cover - network/runtime errors
            print(f"[Agent] Model call failed: {exc}", file=sys.stderr)
            return self.fallback_reply

        try:
            content = response["choices"][0]["message"]["content"].strip()
        except (KeyError, IndexError, TypeError):  # pragma: no cover - defensive
            return self.fallback_reply

        return content or self.fallback_reply