backend/api/routers/setup/models.py

"""Model catalog, platform detection, and cache introspection.

Extracted from the monolithic ``setup.py`` to keep concerns separate:
- ``KNOWN_MODELS`` loaded from ``config/models.yaml``
- ``GET /models`` endpoint (with 10 s response cache)
- ``GET /setup/recommendations`` device-aware preset endpoint
- ``ModelCatalog`` dependency for use with ``Depends()``
"""
from __future__ import annotations

import logging
import os
import platform as _platform
import sys
import time
from pathlib import Path
from typing import Optional

from fastapi import APIRouter, Depends

logger = logging.getLogger("omnivoice.setup.models")
router = APIRouter()

# ── Model Catalog (loaded from YAML) ──────────────────────────────────────

_YAML_PATH = Path(__file__).resolve().parents[3] / "config" / "models.yaml"


def _load_models_from_yaml() -> list[dict]:
    """Load model catalog from config/models.yaml.

    Falls back to an empty list if the file is missing or unreadable.
    The YAML file is read once at import time — restart to pick up edits.
    """
    try:
        import yaml  # PyYAML is already a transitive dep of huggingface_hub
        with open(_YAML_PATH, "r", encoding="utf-8") as f:
            data = yaml.safe_load(f)
        models = data.get("models", [])
        logger.info("Loaded %d models from %s", len(models), _YAML_PATH)
        return models
    except FileNotFoundError:
        logger.warning("models.yaml not found at %s — using empty catalog", _YAML_PATH)
        return []
    except Exception as e:
        logger.error("Failed to load models.yaml: %s — using empty catalog", e)
        return []


KNOWN_MODELS = _load_models_from_yaml()

# Back-compat tuple view for code that expects (repo_id, label) pairs.
REQUIRED_MODELS = [(m["repo_id"], m["label"]) for m in KNOWN_MODELS if m.get("required")]


# ── Dependency Injection ───────────────────────────────────────────────────
# Use `catalog: ModelCatalog = Depends(get_model_catalog)` in endpoint params
# for testable, mockable access to the model registry.

class ModelCatalog:
    """Injectable service wrapping the model catalog + cache scanner."""

    def __init__(self, models: list[dict] | None = None):
        self.models = models if models is not None else KNOWN_MODELS
        self._by_id = {m["repo_id"]: m for m in self.models}
        self._required = [(m["repo_id"], m["label"]) for m in self.models if m.get("required")]

    def get(self, repo_id: str) -> dict | None:
        return self._by_id.get(repo_id)

    @property
    def required(self) -> list[tuple[str, str]]:
        return self._required

    @property
    def all(self) -> list[dict]:
        return self.models

    def supported_on_host(self, model: dict) -> bool:
        return _model_supported(model)


# Singleton — shared across all requests.
_catalog = ModelCatalog()


def get_model_catalog() -> ModelCatalog:
    """FastAPI dependency — inject with ``Depends(get_model_catalog)``."""
    return _catalog


# ── Platform Detection ─────────────────────────────────────────────────────

def _current_platform_tags() -> list[str]:
    """Return platform tags that the current host supports."""
    tags = [sys.platform]
    arch = _platform.machine()
    tags.append(f"{sys.platform}-{arch}")
    try:
        import torch
        if torch.cuda.is_available():
            tags.append("cuda")
    except Exception:
        pass
    return tags


def _model_supported(model: dict) -> bool:
    """Check if a model is supported on the current platform."""
    plats = model.get("platforms")
    if not plats:
        return True
    return bool(set(plats) & set(_current_platform_tags()))


# ── HF Cache Helpers ───────────────────────────────────────────────────────

def hf_cache_dir() -> str:
    return (
        os.environ.get("HF_HUB_CACHE")
        or os.environ.get("HUGGINGFACE_HUB_CACHE")
        or os.environ.get("HF_HOME")
        or os.path.expanduser("~/.cache/huggingface")
    )


def is_cached(repo_id: str) -> bool:
    """Best-effort check: does HF have this repo in its cache on disk?"""
    try:
        from huggingface_hub import scan_cache_dir
        info = scan_cache_dir()
        for entry in info.repos:
            if entry.repo_id == repo_id and entry.size_on_disk > 0:
                return True
        return False
    except Exception as e:
        logger.debug("scan_cache_dir failed: %s", e)
        return False


# ── Response Cache ─────────────────────────────────────────────────────────
# Simple TTL dict cache to avoid re-scanning the HF cache directory on every
# frontend poll.  Entries expire after ``_CACHE_TTL`` seconds.

_CACHE_TTL = 10.0  # seconds
_cache: dict[str, tuple[float, object]] = {}


def _cached(key: str, ttl: float = _CACHE_TTL):
    """Return cached value if still valid, else None."""
    entry = _cache.get(key)
    if entry and (time.monotonic() - entry[0]) < ttl:
        return entry[1]
    return None


def _set_cache(key: str, value: object) -> None:
    _cache[key] = (time.monotonic(), value)


def invalidate_cache() -> None:
    """Called after install/delete to bust the models cache."""
    _cache.clear()


# ── Endpoints ──────────────────────────────────────────────────────────────

@router.get("/models")
def list_models():
    """Catalogue every known model + its on-disk install state.

    Uses a 10 s response cache to avoid repeated ``scan_cache_dir()`` disk
    walks when the frontend polls.
    """
    cached_response = _cached("models")
    if cached_response is not None:
        return cached_response

    cached_by_repo: dict[str, dict] = {}
    try:
        from huggingface_hub import scan_cache_dir
        info = scan_cache_dir()
        for entry in info.repos:
            cached_by_repo[entry.repo_id] = {
                "size_on_disk": entry.size_on_disk,
                "last_accessed": entry.last_accessed,
                "nb_files": entry.nb_files,
            }
    except Exception as e:
        logger.warning("scan_cache_dir failed: %s", e)

    out = []
    for m in KNOWN_MODELS:
        cached = cached_by_repo.get(m["repo_id"])
        out.append({
            **m,
            "installed": cached is not None and cached["size_on_disk"] > 0,
            "size_on_disk_bytes": cached["size_on_disk"] if cached else 0,
            "nb_files": cached["nb_files"] if cached else 0,
            "supported": _model_supported(m),
        })
    response = {
        "models": out,
        "total_installed_bytes": sum(m["size_on_disk_bytes"] for m in out),
        "hf_cache_dir": hf_cache_dir(),
        "platform_tags": _current_platform_tags(),
    }
    _set_cache("models", response)
    return response


@router.get("/setup/recommendations")
def recommendations():
    """Return a curated model preset for the caller's device + architecture."""
    is_mac_arm = sys.platform == "darwin" and _platform.machine() == "arm64"
    is_mac_intel = sys.platform == "darwin" and _platform.machine() == "x86_64"
    is_linux = sys.platform.startswith("linux")
    is_windows = sys.platform == "win32"

    has_cuda = False
    try:
        import torch
        has_cuda = bool(torch.cuda.is_available())
    except Exception:
        pass

    # Device label — used as the card title.
    if is_mac_arm:
        device_label = f"Apple Silicon ({_platform.machine()})"
    elif is_mac_intel:
        device_label = "macOS Intel (x86_64)"
    elif is_windows:
        device_label = "Windows x64" + (" + CUDA" if has_cuda else "")
    elif is_linux:
        device_label = "Linux x64" + (" + CUDA" if has_cuda else "")
    else:
        device_label = f"{sys.platform} / {_platform.machine()}"

    # Pick the preset for this device.
    if is_mac_arm:
        recommended_ids = [
            "k2-fsa/OmniVoice",
            "Systran/faster-whisper-large-v3",
            "mlx-community/whisper-large-v3-mlx",
            "mlx-community/whisper-large-v3-turbo",
            "mlx-community/Kokoro-82M-bf16",
            "KittenML/kitten-tts-mini-0.8",
        ]
        rationale = (
            "Apple Silicon gets the full stack: OmniVoice for multilingual clone + "
            "WhisperX (faster-whisper weights) for cross-platform ASR + MLX-Whisper "
            "for the Apple-optimised speedup + Whisper Turbo (5× faster) for live "
            "dictation + Kokoro (mlx-audio) for fast local English + KittenTTS as "
            "a CPU-realtime backup."
        )
    else:
        recommended_ids = [
            "k2-fsa/OmniVoice",
            "Systran/faster-whisper-large-v3",
            "KittenML/kitten-tts-mini-0.8",
        ]
        if has_cuda:
            recommended_ids.append("openai/whisper-large-v3")
            rationale = (
                "Cross-platform stack + pytorch-whisper as a CUDA-accelerated "
                "ASR fallback. MLX / mlx-audio are Apple-Silicon-only and don't "
                "apply here."
            )
        else:
            rationale = (
                "Cross-platform stack: OmniVoice (multilingual clone) + WhisperX "
                "(faster-whisper ASR) + KittenTTS (English turbo, CPU-realtime). "
                "Clean install, every model runs on CPU."
            )

    known_by_id = {m["repo_id"]: m for m in KNOWN_MODELS}
    cached_ids: set[str] = set()
    try:
        from huggingface_hub import scan_cache_dir
        info = scan_cache_dir()
        cached_ids = {
            entry.repo_id for entry in info.repos if entry.size_on_disk > 0
        }
    except Exception:
        pass

    entries = []
    for rid in recommended_ids:
        meta = known_by_id.get(rid, {})
        entries.append({
            "repo_id": rid,
            "label": meta.get("label", rid),
            "role": meta.get("role", ""),
            "size_gb": meta.get("size_gb", 0),
            "required": bool(meta.get("required", False)),
            "note": meta.get("note"),
            "installed": rid in cached_ids,
        })

    to_download_gb = sum(e["size_gb"] for e in entries if not e["installed"])
    all_installed = all(e["installed"] for e in entries)

    return {
        "device": {
            "os": sys.platform,
            "arch": _platform.machine(),
            "is_mac_arm": is_mac_arm,
            "is_mac_intel": is_mac_intel,
            "is_linux": is_linux,
            "is_windows": is_windows,
            "has_cuda": has_cuda,
            "label": device_label,
        },
        "rationale": rationale,
        "models": entries,
        "download_gb_remaining": round(to_download_gb, 2),
        "total_gb": round(sum(e["size_gb"] for e in entries), 2),
        "all_installed": all_installed,
    }