"""
models.py
─────────────────────────────────────────────────────────────────────────────
Handles LLM inference for the Survival Island AI agent.

Priority chain
--------------
1. NVIDIA API (GLM-4.7 with thinking mode) - DEFAULT
2. Local transformers pipeline   (available on GPU Spaces)
3. HuggingFace Inference API     (works on CPU Spaces, needs HF_TOKEN)
4. Rule-based fallback           (no network / no token)

Environment variables
---------------------
NVIDIA_API_KEY - NVIDIA API key (for GLM-4.7 with thinking)
HF_TOKEN        - HuggingFace API token (required for path 3)
HF_MODEL        - Model repo ID, e.g. "mistralai/Mistral-7B-Instruct-v0.2"
"""

from __future__ import annotations

import json
import logging
import os
import re
import sys
import threading
from typing import Optional

import requests

logger = logging.getLogger(__name__)

# ── Config ────────────────────────────────────────────────────────────────────
NVIDIA_API_KEY: str = os.getenv("NVIDIA_API_KEY") or ""
NVIDIA_BASE_URL: str = "https://integrate.api.nvidia.com/v1"
NVIDIA_MODEL: str = "z-ai/glm4.7"

HF_TOKEN: str = os.getenv("HF_TOKEN", "")
HF_MODEL: str = os.getenv("HF_MODEL", "mistralai/Mistral-7B-Instruct-v0.2")

VALID_ACTIONS: set[str] = {
    "FORAGE", "HUNT", "FISH", "GET_WATER", "SEEK_SHELTER",
    "BUILD_CAMP", "UPGRADE_CAMP", "CRAFT_SPEAR", "CRAFT_BOW",
    "CRAFT_ROD", "CRAFT_BOAT", "EVACUATE", "FIGHT", "FLEE", "WANDER",
}

# ── Singleton ─────────────────────────────────────────────────────────────────
_lock = threading.Lock()
_pipeline = None          # transformers Pipeline object (optional)
_use_local: bool = False  # True once local pipeline loaded successfully


# ── NVIDIA API (GLM-4.7 with thinking mode) ──────────────────────────────
def infer_nvidia(prompt: str) -> dict:
    """Call NVIDIA API with GLM-4.7 model (supports thinking mode)."""
    try:
        from openai import OpenAI
    except ImportError:
        raise RuntimeError("openai package not installed. Run: pip install openai")
    
    client = OpenAI(
        base_url=NVIDIA_BASE_URL,
        api_key=NVIDIA_API_KEY
    )
    
    logger.info("[models] Calling NVIDIA API (GLM-4.7) with thinking mode...")
    
    response = client.chat.completions.create(
        model=NVIDIA_MODEL,
        messages=[{"role": "user", "content": prompt}],
        temperature=0.7,
        top_p=1,
        max_tokens=1024,
        extra_body={"chat_template_kwargs": {"enable_thinking": True, "clear_thinking": False}},
    )
    
    content = response.choices[0].message.content
    reasoning = getattr(response.choices[0].message, "reasoning_content", None)
    
    if reasoning:
        logger.info(f"[models] Thinking: {reasoning[:200]}...")
    
    logger.info(f"[models] Response: {content[:200]}...")
    return _parse_action(content)


# ── Local pipeline (optional - GPU Spaces) ────────────────────────────────────
def _try_load_local() -> bool:
    """Attempts to load the model locally with transformers + torch."""
    global _pipeline, _use_local
    try:
        import torch
        from transformers import pipeline as hf_pipeline

        logger.info(f"[models] Loading {HF_MODEL} locally ...")
        device = 0 if torch.cuda.is_available() else -1
        _pipeline = hf_pipeline(
            "text-generation",
            model=HF_MODEL,
            token=HF_TOKEN or None,
            device=device,
            torch_dtype=torch.float16 if device >= 0 else torch.float32,
            max_new_tokens=80,
        )
        _use_local = True
        logger.info(f"[models] Local pipeline ready (device={device})")
        return True
    except Exception as exc:
        logger.warning(f"[models] Local pipeline skipped: {exc}")
        return False


def get_pipeline():
    """Return the pipeline, initialising on first call."""
    with _lock:
        if _pipeline is None:
            _try_load_local()
    return _pipeline


# ── Output parser ─────────────────────────────────────────────────────────────
def _parse_action(raw: str) -> dict:
    """Extract {"action": ..., "thought": ...} from model output."""
    cleaned = re.sub(r"```(?:json)?|```", "", raw, flags=re.IGNORECASE)
    match = re.search(r"\{[^}]+\}", cleaned, re.DOTALL)
    if not match:
        raise ValueError(f"No JSON object in model output: {raw!r}")
    obj = json.loads(match.group())
    action = str(obj.get("action", "WANDER")).upper()
    if action not in VALID_ACTIONS:
        logger.warning(f"[models] Unknown action '{action}', defaulting to WANDER")
        action = "WANDER"
    return {
        "action": action,
        "thought": str(obj.get("thought", "Processing...")),
    }


# ── Inference paths ───────────────────────────────────────────────────────────
def infer_local(prompt: str) -> dict:
    """Run generation using the locally loaded transformers pipeline."""
    pipe = get_pipeline()
    if pipe is None:
        raise RuntimeError("Local pipeline not available")
    outputs = pipe(
        prompt,
        max_new_tokens=80,
        temperature=0.7,
        do_sample=True,
        return_full_text=False,
    )
    return _parse_action(outputs[0]["generated_text"])


def infer_api(prompt: str) -> dict:
    """Call the HuggingFace Inference API (remote, no GPU needed)."""
    if not HF_TOKEN:
        raise RuntimeError("HF_TOKEN not set - cannot call Inference API")
    url = f"https://api-inference.huggingface.co/models/{HF_MODEL}"
    resp = requests.post(
        url,
        headers={
            "Authorization": f"Bearer {HF_TOKEN}",
            "Content-Type": "application/json",
        },
        json={
            "inputs": prompt,
            "parameters": {
                "max_new_tokens": 80,
                "temperature": 0.7,
                "return_full_text": False,
                "stop": ["\n\n", "</s>", "[INST]"],
            },
        },
        timeout=30,
    )
    resp.raise_for_status()
    data = resp.json()
    raw = (
        data[0]["generated_text"]
        if isinstance(data, list)
        else data.get("generated_text", "")
    )
    return _parse_action(raw)


def run_inference(prompt: str) -> dict:
    """Main entry point used by server/app.py.

    Tries NVIDIA API -> local -> HF API -> rule-based fallback in that order.
    Never raises - always returns a valid {"action", "thought"} dict.
    """
    try:
        logger.info("[models] Trying NVIDIA API inference...")
        return infer_nvidia(prompt)
    except Exception as exc:
        logger.warning(f"[models] NVIDIA API inference failed: {type(exc).__name__}: {exc}")

    if _use_local:
        try:
            logger.info("[models] Trying local inference...")
            return infer_local(prompt)
        except Exception as exc:
            logger.warning(f"[models] Local inference failed: {type(exc).__name__}: {exc}")

    if HF_TOKEN:
        try:
            logger.info("[models] Trying API inference...")
            return infer_api(prompt)
        except Exception as exc:
            logger.warning(f"[models] API inference failed: {type(exc).__name__}: {exc}")

    logger.error("[models] All inference paths failed - returning WANDER")
    return {"action": "WANDER", "thought": "Inference offline. Wandering."}