from __future__ import annotations

import shutil
import subprocess
from pathlib import Path

from ...config import settings


def parse_nvidia_smi_csv(output: str) -> list[dict]:
    gpus: list[dict] = []
    for line in output.splitlines():
        if not line.strip():
            continue
        parts = [part.strip() for part in line.split(",")]
        if len(parts) < 7:
            continue
        index, name, total, used, free, util, temp = parts[:7]
        try:
            gpus.append(
                {
                    "index": int(index),
                    "name": name,
                    "memory_total_mb": int(total),
                    "memory_used_mb": int(used),
                    "memory_free_mb": int(free),
                    "utilization_gpu_percent": int(util),
                    "temperature_c": int(temp),
                }
            )
        except ValueError:
            continue
    return gpus


def get_gpus() -> dict:
    cmd = [
        "nvidia-smi",
        "--query-gpu=index,name,memory.total,memory.used,memory.free,utilization.gpu,temperature.gpu",
        "--format=csv,noheader,nounits",
    ]
    try:
        result = subprocess.run(cmd, capture_output=True, text=True, check=True)
        return {"available": True, "gpus": parse_nvidia_smi_csv(result.stdout)}
    except Exception as exc:
        return {"available": False, "gpus": [], "error": str(exc)}


def get_conda_envs() -> dict:
    try:
        result = subprocess.run(["conda", "env", "list"], capture_output=True, text=True, check=True)
    except Exception as exc:
        return {"available": False, "envs": [], "error": str(exc)}
    envs = []
    for line in result.stdout.splitlines():
        raw = line.strip()
        if not raw or raw.startswith("#"):
            continue
        marker = "*" in raw.split()
        parts = raw.replace("*", " ").split()
        if len(parts) >= 2:
            envs.append({"name": parts[0], "path": parts[-1], "active": marker})
    return {"available": True, "envs": envs, "task_default": settings.task_conda_env, "mmseg_default": settings.mmseg_conda_env}


def disk_usage() -> dict:
    usage = shutil.disk_usage(settings.source_root)
    return {
        "path": str(settings.source_root),
        "total": usage.total,
        "used": usage.used,
        "free": usage.free,
    }


def scan_results() -> list[dict]:
    roots = [
        settings.source_root / "DataSet_Public_outputs",
        settings.source_root / "BestMode_Predict_Results_DataSet_Public",
        settings.source_root / "Hardisk",
        settings.source_root / "Seg_All_In_One_Analysis",
    ]
    exts = {".csv", ".png", ".jpg", ".jpeg", ".svg", ".log", ".pth", ".pt"}
    results: list[dict] = []
    for root in roots:
        if not root.exists():
            continue
        for path in root.rglob("*"):
            if path.is_file() and path.suffix.lower() in exts:
                try:
                    stat = path.stat()
                    results.append(
                        {
                            "name": path.name,
                            "path": str(path.resolve()),
                            "relative_path": str(path.resolve().relative_to(settings.source_root)),
                            "size": stat.st_size,
                            "modified": stat.st_mtime,
                            "kind": path.suffix.lower().lstrip("."),
                        }
                    )
                except OSError:
                    continue
    results.sort(key=lambda item: item["modified"], reverse=True)
    return results[:1000]