# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

"""
Model Management API routes
"""

import os
import sys
from pathlib import Path
from fastapi import APIRouter, Body, Depends, HTTPException, Query
from typing import List, Optional
from loggers import get_logger

import re as _re

_VALID_REPO_ID = _re.compile(r"^[A-Za-z0-9._-]+/[A-Za-z0-9._-]+$")


def _is_valid_repo_id(repo_id: str) -> bool:
    return bool(_VALID_REPO_ID.fullmatch(repo_id))


# Add backend directory to path
backend_path = Path(__file__).parent.parent.parent
if str(backend_path) not in sys.path:
    sys.path.insert(0, str(backend_path))

from auth.authentication import get_current_subject

# Import backend functions
try:
    from utils.models import (
        scan_trained_loras,
        scan_exported_models,
        load_model_defaults,
        get_base_model_from_lora,
        is_vision_model,
        is_embedding_model,
        scan_checkpoints,
        list_gguf_variants,
        ModelConfig,
    )
    from utils.models.model_config import (
        _pick_best_gguf,
        _extract_quant_label,
        is_audio_input_type,
    )
    from core.inference import get_inference_backend
    from utils.paths import (
        outputs_root,
        exports_root,
        resolve_output_dir,
        resolve_export_dir,
    )
except ImportError:
    # Fallback: try to import from parent directory
    parent_backend = backend_path.parent / "backend"
    if str(parent_backend) not in sys.path:
        sys.path.insert(0, str(parent_backend))
    from utils.models import (
        scan_trained_loras,
        scan_exported_models,
        load_model_defaults,
        get_base_model_from_lora,
        is_vision_model,
        is_embedding_model,
        scan_checkpoints,
        list_gguf_variants,
        ModelConfig,
    )
    from utils.models.model_config import (
        _pick_best_gguf,
        _extract_quant_label,
        is_audio_input_type,
    )
    from core.inference import get_inference_backend
    from utils.paths import (
        outputs_root,
        exports_root,
        resolve_output_dir,
        resolve_export_dir,
    )

from models import (
    CheckpointInfo,
    CheckpointListResponse,
    LocalModelInfo,
    LocalModelListResponse,
    ModelCheckpoints,
    ModelDetails,
    LoRAScanResponse,
    LoRAInfo,
    ModelListResponse,
)
from models.models import GgufVariantDetail, GgufVariantsResponse, ModelType
from models.responses import (
    LoRABaseModelResponse,
    VisionCheckResponse,
    EmbeddingCheckResponse,
)

router = APIRouter()
logger = get_logger(__name__)


def derive_model_type(
    is_vision: bool, audio_type: Optional[str], is_embedding: bool = False
) -> ModelType:
    """Collapse individual capability flags into a single model modality string."""
    if is_embedding:
        return "embeddings"
    if audio_type is not None:
        return "audio"
    if is_vision:
        return "vision"
    return "text"


def _resolve_hf_cache_dir() -> Path:
    """Resolve local HF cache root used by hub downloads."""
    try:
        from huggingface_hub.constants import HF_HUB_CACHE

        return Path(HF_HUB_CACHE)
    except Exception:
        return Path.home() / ".cache" / "huggingface" / "hub"


def _scan_models_dir(models_dir: Path) -> List[LocalModelInfo]:
    if not models_dir.exists() or not models_dir.is_dir():
        return []

    found: List[LocalModelInfo] = []
    for child in models_dir.iterdir():
        if not child.is_dir():
            continue
        has_model_files = (
            (child / "config.json").exists()
            or (child / "adapter_config.json").exists()
            or any(child.glob("*.safetensors"))
            or any(child.glob("*.bin"))
            or any(child.glob("*.gguf"))
        )
        if not has_model_files:
            continue
        try:
            updated_at = child.stat().st_mtime
        except OSError:
            updated_at = None
        found.append(
            LocalModelInfo(
                id = str(child),
                display_name = child.name,
                path = str(child),
                source = "models_dir",
                updated_at = updated_at,
            ),
        )
    # Also scan for standalone .gguf files directly in the models directory
    for gguf_file in models_dir.glob("*.gguf"):
        if gguf_file.is_file():
            try:
                updated_at = gguf_file.stat().st_mtime
            except OSError:
                updated_at = None
            found.append(
                LocalModelInfo(
                    id = str(gguf_file),
                    display_name = gguf_file.stem,
                    path = str(gguf_file),
                    source = "models_dir",
                    updated_at = updated_at,
                ),
            )

    return found


def _scan_hf_cache(cache_dir: Path) -> List[LocalModelInfo]:
    if not cache_dir.exists() or not cache_dir.is_dir():
        return []

    found: List[LocalModelInfo] = []
    for repo_dir in cache_dir.glob("models--*"):
        if not repo_dir.is_dir():
            continue

        repo_name = repo_dir.name[len("models--") :]
        if not repo_name:
            continue
        model_id = repo_name.replace("--", "/")

        try:
            updated_at = repo_dir.stat().st_mtime
        except OSError:
            updated_at = None

        found.append(
            LocalModelInfo(
                id = model_id,
                model_id = model_id,
                display_name = model_id.split("/")[-1],
                path = str(repo_dir),
                source = "hf_cache",
                updated_at = updated_at,
            ),
        )
    return found


def _scan_lmstudio_dir(lm_dir: Path) -> List[LocalModelInfo]:
    """Scan an LM Studio models directory for model files.

    LM Studio uses a ``publisher/model-name`` folder structure containing
    GGUF files, or standalone GGUF files at the top level.
    """
    if not lm_dir.exists() or not lm_dir.is_dir():
        return []

    found: List[LocalModelInfo] = []
    for child in lm_dir.iterdir():
        if not child.is_dir():
            if child.suffix == ".gguf" and child.is_file():
                try:
                    updated_at = child.stat().st_mtime
                except OSError:
                    updated_at = None
                found.append(
                    LocalModelInfo(
                        id = str(child),
                        display_name = child.stem,
                        path = str(child),
                        source = "lmstudio",
                        updated_at = updated_at,
                    ),
                )
            continue

        # child is a publisher directory — scan its sub-directories
        for model_dir in child.iterdir():
            if model_dir.is_dir():
                has_model = (
                    any(model_dir.glob("*.gguf"))
                    or (model_dir / "config.json").exists()
                    or any(model_dir.glob("*.safetensors"))
                )
                if not has_model:
                    continue
                model_id = f"{child.name}/{model_dir.name}"
                try:
                    updated_at = model_dir.stat().st_mtime
                except OSError:
                    updated_at = None
                found.append(
                    LocalModelInfo(
                        id = str(model_dir),
                        model_id = model_id,
                        display_name = model_dir.name,
                        path = str(model_dir),
                        source = "lmstudio",
                        updated_at = updated_at,
                    ),
                )
            elif model_dir.suffix == ".gguf" and model_dir.is_file():
                try:
                    updated_at = model_dir.stat().st_mtime
                except OSError:
                    updated_at = None
                found.append(
                    LocalModelInfo(
                        id = str(model_dir),
                        model_id = f"{child.name}/{model_dir.stem}",
                        display_name = model_dir.stem,
                        path = str(model_dir),
                        source = "lmstudio",
                        updated_at = updated_at,
                    ),
                )
    return found


@router.get("/local", response_model = LocalModelListResponse)
async def list_local_models(
    models_dir: str = Query(
        default = "./models", description = "Directory to scan for local model folders"
    ),
    current_subject: str = Depends(get_current_subject),
):
    """
    List local model candidates from custom models dir, HF cache,
    legacy Unsloth HF cache, and LM Studio directories.
    """
    from utils.paths import (
        legacy_hf_cache_dir,
        hf_default_cache_dir,
        lmstudio_model_dirs,
    )

    # Resolve all scan directories up front.
    hf_cache_dir = _resolve_hf_cache_dir()
    legacy_hf = legacy_hf_cache_dir()
    hf_default = hf_default_cache_dir()
    lm_dirs = lmstudio_model_dirs()

    # Validate models_dir against an allowlist of trusted directories.
    # Only the trusted Path objects are used for filesystem access -- the
    # user-supplied string is only used for matching, never for path construction.
    allowed_roots: list[Path] = [Path("./models").resolve(), hf_cache_dir]
    if legacy_hf.is_dir():
        allowed_roots.append(legacy_hf)
    if hf_default.is_dir():
        allowed_roots.append(hf_default)
    try:
        from utils.paths import studio_root, outputs_root

        allowed_roots.extend([studio_root(), outputs_root()])
    except Exception:
        pass

    requested = os.path.realpath(os.path.expanduser(models_dir))
    models_root = None
    for root in allowed_roots:
        root_str = os.path.realpath(str(root))
        if requested == root_str or requested.startswith(root_str + os.sep):
            models_root = root  # Use the trusted root, not the user-supplied path
            break
    if models_root is None:
        raise HTTPException(
            status_code = 403,
            detail = "Directory not allowed",
        )

    try:
        local_models = _scan_models_dir(models_root) + _scan_hf_cache(hf_cache_dir)

        # Scan legacy Unsloth HF cache for backward compatibility
        if legacy_hf.is_dir() and legacy_hf.resolve() != hf_cache_dir.resolve():
            local_models += _scan_hf_cache(legacy_hf)

        # Scan HF system default cache (may differ when env vars are overridden)
        if (
            hf_default.is_dir()
            and hf_default.resolve() != hf_cache_dir.resolve()
            and hf_default.resolve() != legacy_hf.resolve()
        ):
            local_models += _scan_hf_cache(hf_default)

        # Scan LM Studio directories
        for lm_dir in lm_dirs:
            local_models += _scan_lmstudio_dir(lm_dir)

        deduped: dict[str, LocalModelInfo] = {}
        for model in local_models:
            if model.id not in deduped:
                deduped[model.id] = model

        models = sorted(
            deduped.values(),
            key = lambda item: (item.updated_at or 0),
            reverse = True,
        )

        return LocalModelListResponse(
            models_dir = str(models_root),
            hf_cache_dir = str(hf_cache_dir),
            lmstudio_dirs = [str(d) for d in lm_dirs],
            models = models,
        )
    except Exception as e:
        logger.error(f"Error listing local models: {e}", exc_info = True)
        raise HTTPException(
            status_code = 500,
            detail = f"Failed to list local models: {str(e)}",
        )


@router.get("/list")
async def list_models(
    current_subject: str = Depends(get_current_subject),
):
    """
    List available models (default models and loaded models).

    This endpoint returns the default models and any currently loaded models.
    """
    try:
        inference_backend = get_inference_backend()

        # Get default models
        default_models = inference_backend.default_models

        # Get loaded models
        loaded_models = []
        for model_name, model_data in inference_backend.models.items():
            _is_vision = model_data.get("is_vision", False)
            _audio_type = model_data.get("audio_type")
            model_info = ModelDetails(
                id = model_name,
                name = model_name.split("/")[-1] if "/" in model_name else model_name,
                is_vision = _is_vision,
                is_lora = model_data.get("is_lora", False),
                is_audio = model_data.get("is_audio", False),
                audio_type = _audio_type,
                has_audio_input = model_data.get("has_audio_input", False),
                model_type = derive_model_type(_is_vision, _audio_type),
            )
            loaded_models.append(model_info)

        # Include active GGUF model (loaded via llama-server)
        from routes.inference import get_llama_cpp_backend

        llama_backend = get_llama_cpp_backend()
        if llama_backend.is_loaded and llama_backend.model_identifier:
            loaded_models.append(
                ModelDetails(
                    id = llama_backend.model_identifier,
                    name = llama_backend.model_identifier.split("/")[-1],
                    is_gguf = True,
                    is_vision = llama_backend.is_vision,
                    is_audio = getattr(llama_backend, "_is_audio", False),
                    audio_type = getattr(llama_backend, "_audio_type", None),
                )
            )

        # Combine default and loaded models
        all_models = []
        seen_ids = set()

        # Add default models
        for model_id in default_models:
            if model_id not in seen_ids:
                model_info = ModelDetails(
                    id = model_id,
                    name = model_id.split("/")[-1] if "/" in model_id else model_id,
                    is_gguf = model_id.upper().endswith("-GGUF"),
                )
                all_models.append(model_info)
                seen_ids.add(model_id)

        # Add loaded models
        for model_info in loaded_models:
            if model_info.id not in seen_ids:
                all_models.append(model_info)
                seen_ids.add(model_info.id)

        return ModelListResponse(models = all_models, default_models = default_models)

    except Exception as e:
        logger.error(f"Error listing models: {e}", exc_info = True)
        raise HTTPException(status_code = 500, detail = f"Failed to list models: {str(e)}")


def _get_max_position_embeddings(config) -> Optional[int]:
    """Extract max_position_embeddings from a model config, checking text_config fallback."""
    if hasattr(config, "max_position_embeddings"):
        return config.max_position_embeddings
    if hasattr(config, "text_config") and hasattr(
        config.text_config, "max_position_embeddings"
    ):
        return config.text_config.max_position_embeddings
    return None


def _get_model_size_bytes(
    model_name: str, hf_token: Optional[str] = None
) -> Optional[int]:
    """Get total size of model weight files from HF Hub."""
    try:
        from huggingface_hub import HfApi

        api = HfApi(token = hf_token)
        info = api.repo_info(model_name, repo_type = "model", token = hf_token)
        if not info.siblings:
            return None

        weight_exts = (".safetensors", ".bin", ".pt", ".pth", ".gguf")
        total = 0
        for sibling in info.siblings:
            if sibling.rfilename and any(
                sibling.rfilename.endswith(ext) for ext in weight_exts
            ):
                if sibling.size is not None:
                    total += sibling.size

        return total if total > 0 else None
    except Exception as e:
        logger.warning(f"Could not get model size for {model_name}: {e}")
        return None


@router.get("/config/{model_name:path}")
async def get_model_config(
    model_name: str,
    hf_token: Optional[str] = Query(None),
    current_subject: str = Depends(get_current_subject),
):
    """
    Get configuration for a specific model.

    This endpoint wraps the backend load_model_defaults function.
    """
    try:
        from utils.models.model_config import is_local_path

        if not is_local_path(model_name):
            model_name = model_name.lower()

        logger.info(f"Getting model config for: {model_name}")
        from utils.models.model_config import detect_audio_type

        # Load model defaults from backend
        config_dict = load_model_defaults(model_name)

        # Detect model capabilities (pass HF token for gated models)
        is_vision = is_vision_model(model_name)
        is_embedding = is_embedding_model(model_name, hf_token = hf_token)
        audio_type = detect_audio_type(model_name, hf_token = hf_token)

        # Check if it's a LoRA adapter
        is_lora = False
        base_model = None
        max_position_embeddings = None
        try:
            model_config = ModelConfig.from_identifier(model_name)
            is_lora = model_config.is_lora
            base_model = model_config.base_model if is_lora else None
            max_position_embeddings = _get_max_position_embeddings(model_config)
        except Exception:
            pass

        # Fallback: try AutoConfig directly if not found yet
        if max_position_embeddings is None:
            try:
                from transformers import AutoConfig as _AutoConfig

                _trust = model_name.lower().startswith("unsloth/")
                _ac = _AutoConfig.from_pretrained(
                    model_name, trust_remote_code = _trust, token = hf_token
                )
                max_position_embeddings = _get_max_position_embeddings(_ac)
            except Exception:
                pass

        logger.info(
            f"Model config result for {model_name}: is_vision={is_vision}, is_embedding={is_embedding}, audio_type={audio_type}, is_lora={is_lora}, max_position_embeddings={max_position_embeddings}"
        )
        return ModelDetails(
            id = model_name,
            model_name = model_name,
            config = config_dict,
            is_vision = is_vision,
            is_embedding = is_embedding,
            is_lora = is_lora,
            is_audio = audio_type is not None,
            audio_type = audio_type,
            has_audio_input = is_audio_input_type(audio_type),
            model_type = derive_model_type(is_vision, audio_type, is_embedding),
            base_model = base_model,
            max_position_embeddings = max_position_embeddings,
            model_size_bytes = _get_model_size_bytes(model_name, hf_token),
        )

    except Exception as e:
        logger.error(f"Error getting model config: {e}", exc_info = True)
        raise HTTPException(
            status_code = 500, detail = f"Failed to get model config: {str(e)}"
        )


@router.get("/loras")
async def scan_loras(
    outputs_dir: str = Query(
        default = str(outputs_root()), description = "Directory to scan for LoRA adapters"
    ),
    exports_dir: str = Query(
        default = str(exports_root()), description = "Directory to scan for exported models"
    ),
    current_subject: str = Depends(get_current_subject),
):
    """
    Scan for trained LoRA adapters and exported models.

    Returns both training outputs (from outputs_dir) and exported models
    (from exports_dir) in a single list, distinguished by source field.
    """
    try:
        resolved_outputs_dir = str(resolve_output_dir(outputs_dir))
        resolved_exports_dir = str(resolve_export_dir(exports_dir))
        lora_list = []

        # Scan training outputs
        trained_loras = scan_trained_loras(outputs_dir = resolved_outputs_dir)
        for display_name, adapter_path in trained_loras:
            base_model = get_base_model_from_lora(adapter_path)
            lora_list.append(
                LoRAInfo(
                    display_name = display_name,
                    adapter_path = adapter_path,
                    base_model = base_model,
                    source = "training",
                )
            )

        # Scan exported models (merged, LoRA, base — skips GGUF)
        exported = scan_exported_models(exports_dir = resolved_exports_dir)
        for display_name, model_path, export_type, base_model in exported:
            lora_list.append(
                LoRAInfo(
                    display_name = display_name,
                    adapter_path = model_path,
                    base_model = base_model,
                    source = "exported",
                    export_type = export_type,
                )
            )

        return LoRAScanResponse(loras = lora_list, outputs_dir = resolved_outputs_dir)

    except Exception as e:
        logger.error(f"Error scanning LoRAs: {e}", exc_info = True)
        raise HTTPException(
            status_code = 500, detail = f"Failed to scan LoRA adapters: {str(e)}"
        )


@router.get("/loras/{lora_path:path}/base-model", response_model = LoRABaseModelResponse)
async def get_lora_base_model(
    lora_path: str,
    current_subject: str = Depends(get_current_subject),
):
    """
    Get the base model for a LoRA adapter.

    This endpoint wraps the backend get_base_model_from_lora function.
    """
    try:
        base_model = get_base_model_from_lora(lora_path)

        if base_model is None:
            raise HTTPException(
                status_code = 404,
                detail = f"Could not determine base model for LoRA: {lora_path}",
            )

        return LoRABaseModelResponse(
            lora_path = lora_path,
            base_model = base_model,
        )

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error getting LoRA base model: {e}", exc_info = True)
        raise HTTPException(
            status_code = 500, detail = f"Failed to get base model: {str(e)}"
        )


@router.get("/check-vision/{model_name:path}", response_model = VisionCheckResponse)
async def check_vision_model(
    model_name: str,
    current_subject: str = Depends(get_current_subject),
):
    """
    Check if a model is a vision model.

    This endpoint wraps the backend is_vision_model function.
    """
    try:
        logger.info(f"Checking if vision model: {model_name}")
        is_vision = is_vision_model(model_name)

        logger.info(f"Vision check result for {model_name}: is_vision={is_vision}")
        return VisionCheckResponse(
            model_name = model_name,
            is_vision = is_vision,
        )

    except Exception as e:
        logger.error(f"Error checking vision model: {e}", exc_info = True)
        raise HTTPException(
            status_code = 500, detail = f"Failed to check vision model: {str(e)}"
        )


@router.get("/check-embedding/{model_name:path}", response_model = EmbeddingCheckResponse)
async def check_embedding_model(
    model_name: str,
    hf_token: Optional[str] = Query(None),
    current_subject: str = Depends(get_current_subject),
):
    """
    Check if a model is an embedding model.

    This endpoint wraps the backend is_embedding_model function.
    """
    try:
        logger.info(f"Checking if embedding model: {model_name}")
        is_embedding = is_embedding_model(model_name, hf_token = hf_token)

        logger.info(
            f"Embedding check result for {model_name}: is_embedding={is_embedding}"
        )
        return EmbeddingCheckResponse(
            model_name = model_name,
            is_embedding = is_embedding,
        )

    except Exception as e:
        logger.error(f"Error checking embedding model: {e}", exc_info = True)
        raise HTTPException(
            status_code = 500, detail = f"Failed to check embedding model: {str(e)}"
        )


@router.get("/gguf-variants", response_model = GgufVariantsResponse)
async def get_gguf_variants(
    repo_id: str = Query(
        ..., description = "HuggingFace repo ID (e.g. 'unsloth/gemma-3-4b-it-GGUF')"
    ),
    hf_token: Optional[str] = Query(
        None, description = "HuggingFace token for private repos"
    ),
    current_subject: str = Depends(get_current_subject),
):
    """
    List available GGUF quantization variants for a HuggingFace repo
    or a local directory (e.g. LM Studio model folder).

    Returns all available quantization variants (Q4_K_M, Q8_0, BF16, etc.)
    with file sizes, whether the model supports vision, and the recommended
    default variant.
    """
    try:
        from utils.models.model_config import is_local_path, list_local_gguf_variants

        # Local directory path (e.g. LM Studio models) — scan filesystem
        if is_local_path(repo_id):
            variants, has_vision = list_local_gguf_variants(repo_id)

            filenames = [v.filename for v in variants]
            best = _pick_best_gguf(filenames)
            default_variant = _extract_quant_label(best) if best else None

            return GgufVariantsResponse(
                repo_id = repo_id,
                variants = [
                    GgufVariantDetail(
                        filename = v.filename,
                        quant = v.quant,
                        size_bytes = v.size_bytes,
                        downloaded = True,  # all local variants are downloaded
                    )
                    for v in variants
                ],
                has_vision = has_vision,
                default_variant = default_variant,
            )

        # Remote HuggingFace repo — query HF API
        variants, has_vision = list_gguf_variants(repo_id, hf_token = hf_token)

        # Determine default variant
        filenames = [v.filename for v in variants]
        best = _pick_best_gguf(filenames)
        default_variant = _extract_quant_label(best) if best else None

        # Check which variants are fully downloaded in the HF cache.
        # For split GGUFs, ALL shards must be present -- sum cached bytes
        # per variant and compare against the expected total.
        # HF cache dir uses the exact case from the repo_id at download time,
        # which may differ from the canonical HF repo_id, so do a
        # case-insensitive match.
        cached_bytes_by_quant: dict[str, int] = {}
        try:
            from huggingface_hub import constants as hf_constants

            # Sanitize repo_id: must be "owner/name" with safe chars only
            if not _is_valid_repo_id(repo_id):
                raise ValueError(f"Invalid repo_id format: {repo_id}")

            cache_dir = Path(hf_constants.HF_HUB_CACHE)
            target = f"models--{repo_id.replace('/', '--')}".lower()
            for entry in cache_dir.iterdir():
                if entry.name.lower() == target:
                    snapshots = entry / "snapshots"
                    if snapshots.is_dir():
                        for snap in snapshots.iterdir():
                            for f in snap.rglob("*.gguf"):
                                q = _extract_quant_label(f.name)
                                cached_bytes_by_quant[q] = (
                                    cached_bytes_by_quant.get(q, 0) + f.stat().st_size
                                )
                    break
        except Exception:
            pass

        def _is_fully_downloaded(variant) -> bool:
            cached = cached_bytes_by_quant.get(variant.quant, 0)
            if cached == 0 or variant.size_bytes == 0:
                return False
            # Allow small rounding tolerance (symlinks vs real sizes)
            return cached >= variant.size_bytes * 0.99

        return GgufVariantsResponse(
            repo_id = repo_id,
            variants = [
                GgufVariantDetail(
                    filename = v.filename,
                    quant = v.quant,
                    size_bytes = v.size_bytes,
                    downloaded = _is_fully_downloaded(v),
                )
                for v in variants
            ],
            has_vision = has_vision,
            default_variant = default_variant,
        )

    except Exception as e:
        logger.error(f"Error listing GGUF variants for '{repo_id}': {e}", exc_info = True)
        raise HTTPException(
            status_code = 500,
            detail = f"Failed to list GGUF variants: {str(e)}",
        )


@router.get("/gguf-download-progress")
async def get_gguf_download_progress(
    repo_id: str = Query(..., description = "HuggingFace repo ID"),
    variant: str = Query("", description = "Quantization variant (e.g. UD-TQ1_0)"),
    expected_bytes: int = Query(0, description = "Expected total download size in bytes"),
    current_subject: str = Depends(get_current_subject),
):
    """Return download progress by checking cached GGUF files for a specific variant.

    Tracks completed shard downloads in snapshots and in-progress downloads
    in the blobs directory (incomplete files).
    """
    try:
        if not _is_valid_repo_id(repo_id):
            return {
                "downloaded_bytes": 0,
                "expected_bytes": expected_bytes,
                "progress": 0,
            }

        from huggingface_hub import constants as hf_constants

        cache_dir = Path(hf_constants.HF_HUB_CACHE)
        target = f"models--{repo_id.replace('/', '--')}".lower()
        variant_lower = variant.lower().replace("-", "").replace("_", "")
        downloaded_bytes = 0
        in_progress_bytes = 0
        for entry in cache_dir.iterdir():
            if entry.name.lower() == target:
                # Count completed .gguf files matching this variant in snapshots
                for f in entry.rglob("*.gguf"):
                    fname = f.name.lower().replace("-", "").replace("_", "")
                    if not variant_lower or variant_lower in fname:
                        downloaded_bytes += f.stat().st_size
                # Check blobs for in-progress downloads (.incomplete files)
                blobs_dir = entry / "blobs"
                if blobs_dir.is_dir():
                    for f in blobs_dir.iterdir():
                        if f.is_file() and f.name.endswith(".incomplete"):
                            in_progress_bytes += f.stat().st_size
                break

        total_progress_bytes = downloaded_bytes + in_progress_bytes
        progress = (
            min(total_progress_bytes / expected_bytes, 0.99)
            if expected_bytes > 0
            else 0
        )
        # Only report 1.0 when all bytes are in completed files (not in-progress)
        if expected_bytes > 0 and downloaded_bytes >= expected_bytes:
            progress = 1.0
        return {
            "downloaded_bytes": total_progress_bytes,
            "expected_bytes": expected_bytes,
            "progress": round(progress, 3),
        }
    except Exception:
        return {"downloaded_bytes": 0, "expected_bytes": expected_bytes, "progress": 0}


@router.get("/download-progress")
async def get_download_progress(
    repo_id: str = Query(..., description = "HuggingFace repo ID"),
    current_subject: str = Depends(get_current_subject),
):
    """Return download progress for any HuggingFace model repo.

    Checks the local HF cache for completed blobs and in-progress
    (.incomplete) downloads. Uses the HF API to determine the expected
    total size on the first call, then caches it for subsequent polls.
    """
    _empty = {"downloaded_bytes": 0, "expected_bytes": 0, "progress": 0}
    try:
        if not _is_valid_repo_id(repo_id):
            return _empty

        from huggingface_hub import constants as hf_constants

        cache_dir = Path(hf_constants.HF_HUB_CACHE)
        target = f"models--{repo_id.replace('/', '--')}".lower()
        completed_bytes = 0
        in_progress_bytes = 0

        for entry in cache_dir.iterdir():
            if entry.name.lower() != target:
                continue
            blobs_dir = entry / "blobs"
            if not blobs_dir.is_dir():
                break
            for f in blobs_dir.iterdir():
                if not f.is_file():
                    continue
                if f.name.endswith(".incomplete"):
                    in_progress_bytes += f.stat().st_size
                else:
                    completed_bytes += f.stat().st_size
            break

        downloaded_bytes = completed_bytes + in_progress_bytes
        if downloaded_bytes == 0:
            return _empty

        # Get expected size from HF API (cached per repo_id)
        expected_bytes = _get_repo_size_cached(repo_id)
        if expected_bytes <= 0:
            # Cannot determine total; report bytes only, no percentage
            return {
                "downloaded_bytes": downloaded_bytes,
                "expected_bytes": 0,
                "progress": 0,
            }

        # Use 95% threshold for completion (blob deduplication can make
        # completed_bytes differ slightly from expected_bytes).
        # Do NOT use "no .incomplete files" as a completion signal --
        # HF downloads files sequentially, so between files there are
        # no .incomplete files even though the download is far from done.
        if completed_bytes >= expected_bytes * 0.95:
            progress = 1.0
        else:
            progress = min(downloaded_bytes / expected_bytes, 0.99)
        return {
            "downloaded_bytes": downloaded_bytes,
            "expected_bytes": expected_bytes,
            "progress": round(progress, 3),
        }
    except Exception as e:
        logger.warning(f"Error checking download progress for {repo_id}: {e}")
        return _empty


_repo_size_cache: dict[str, int] = {}


def _get_repo_size_cached(repo_id: str) -> int:
    if repo_id in _repo_size_cache:
        return _repo_size_cache[repo_id]
    try:
        from huggingface_hub import model_info as hf_model_info

        info = hf_model_info(repo_id, token = None, files_metadata = True)
        total = sum(s.size for s in info.siblings if s.size)
        _repo_size_cache[repo_id] = total
        return total
    except Exception as e:
        logger.warning(f"Failed to get repo size for {repo_id}: {e}")
        return 0


def _all_hf_cache_scans():
    """Return scan_cache_dir results for the active, legacy, and default HF caches."""
    from huggingface_hub import scan_cache_dir
    from utils.paths import legacy_hf_cache_dir, hf_default_cache_dir

    scans = [scan_cache_dir()]
    seen: set[str] = set()
    try:
        # Resolve the active cache dir so we can dedup
        from huggingface_hub.constants import HF_HUB_CACHE

        seen.add(str(Path(HF_HUB_CACHE).resolve()))
    except Exception:
        pass

    for extra_fn in (legacy_hf_cache_dir, hf_default_cache_dir):
        extra = extra_fn()
        if extra.is_dir() and str(extra.resolve()) not in seen:
            seen.add(str(extra.resolve()))
            try:
                scans.append(scan_cache_dir(cache_dir = str(extra)))
            except Exception as exc:
                logger.warning("Could not scan HF cache %s: %s", extra, exc)
    return scans


@router.get("/cached-gguf")
async def list_cached_gguf(
    current_subject: str = Depends(get_current_subject),
):
    """List GGUF repos downloaded to HF cache, legacy Unsloth cache, and HF default cache."""
    try:
        cache_scans = _all_hf_cache_scans()

        seen_lower: dict[str, dict] = {}
        for hf_cache in cache_scans:
            for repo_info in hf_cache.repos:
                if repo_info.repo_type != "model":
                    continue
                repo_id = repo_info.repo_id
                if not repo_id.upper().endswith("-GGUF"):
                    continue
                total_size = 0
                has_gguf = False
                for revision in repo_info.revisions:
                    for f in revision.files:
                        if f.file_name.endswith(".gguf"):
                            has_gguf = True
                            total_size += f.size_on_disk
                if not has_gguf:
                    continue
                key = repo_id.lower()
                existing = seen_lower.get(key)
                if existing is None or total_size > existing["size_bytes"]:
                    seen_lower[key] = {
                        "repo_id": repo_id,
                        "size_bytes": total_size,
                        "cache_path": str(repo_info.repo_path),
                    }
        cached = sorted(seen_lower.values(), key = lambda c: c["repo_id"])
        return {"cached": cached}
    except Exception as e:
        logger.error(f"Error listing cached GGUF repos: {e}", exc_info = True)
        return {"cached": []}


@router.get("/cached-models")
async def list_cached_models(
    current_subject: str = Depends(get_current_subject),
):
    """List non-GGUF model repos downloaded to HF cache, legacy Unsloth cache, and HF default cache."""
    _WEIGHT_EXTENSIONS = (".safetensors", ".bin")

    try:
        cache_scans = _all_hf_cache_scans()

        seen_lower: dict[str, dict] = {}
        for hf_cache in cache_scans:
            for repo_info in hf_cache.repos:
                if repo_info.repo_type != "model":
                    continue
                repo_id = repo_info.repo_id
                if repo_id.upper().endswith("-GGUF"):
                    continue
                total_size = sum(
                    f.size_on_disk for rev in repo_info.revisions for f in rev.files
                )
                if total_size == 0:
                    continue
                has_weights = any(
                    f.file_name.endswith(_WEIGHT_EXTENSIONS)
                    for rev in repo_info.revisions
                    for f in rev.files
                )
                if not has_weights:
                    continue
                key = repo_id.lower()
                existing = seen_lower.get(key)
                if existing is None or total_size > existing["size_bytes"]:
                    seen_lower[key] = {
                        "repo_id": repo_id,
                        "size_bytes": total_size,
                    }
        cached = sorted(seen_lower.values(), key = lambda c: c["repo_id"])
        return {"cached": cached}
    except Exception as e:
        logger.error(f"Error listing cached models: {e}", exc_info = True)
        return {"cached": []}


@router.delete("/delete-cached")
async def delete_cached_model(
    repo_id: str = Body(...),
    variant: Optional[str] = Body(None),
    current_subject: str = Depends(get_current_subject),
):
    """Delete a cached model repo (or a specific GGUF variant) from the HF cache.

    When *variant* is provided, only the GGUF files matching that quant label
    are removed (e.g. ``UD-Q4_K_XL``).  Otherwise the entire repo is deleted.
    Refuses if the model is currently loaded for inference.
    """
    if not _is_valid_repo_id(repo_id):
        raise HTTPException(status_code = 400, detail = "Invalid repo_id format")

    # Check if model is currently loaded
    try:
        from routes.inference import get_llama_cpp_backend

        llama_backend = get_llama_cpp_backend()
        if llama_backend.is_loaded and llama_backend.model_identifier:
            loaded_id = llama_backend.model_identifier.lower()
            if loaded_id == repo_id.lower() or loaded_id.startswith(repo_id.lower()):
                raise HTTPException(
                    status_code = 400,
                    detail = "Unload the model before deleting",
                )
    except HTTPException:
        raise
    except Exception:
        pass

    try:
        inference_backend = get_inference_backend()
        if inference_backend.active_model_name:
            active = inference_backend.active_model_name.lower()
            if active == repo_id.lower() or active.startswith(repo_id.lower()):
                raise HTTPException(
                    status_code = 400,
                    detail = "Unload the model before deleting",
                )
    except HTTPException:
        raise
    except Exception:
        pass

    try:
        cache_scans = _all_hf_cache_scans()

        target_repo = None
        for hf_cache in cache_scans:
            for repo_info in hf_cache.repos:
                if repo_info.repo_type != "model":
                    continue
                if repo_info.repo_id.lower() == repo_id.lower():
                    target_repo = repo_info
                    break
            if target_repo is not None:
                break

        if target_repo is None:
            raise HTTPException(status_code = 404, detail = "Model not found in cache")

        # ── Per-variant GGUF deletion ────────────────────────────
        if variant:
            deleted_bytes = 0
            deleted_count = 0
            for rev in target_repo.revisions:
                for f in rev.files:
                    if not f.file_name.endswith(".gguf"):
                        continue
                    quant = _extract_quant_label(f.file_name)
                    if quant.lower() != variant.lower():
                        continue
                    # Delete the blob (actual data) and the snapshot symlink
                    try:
                        blob = Path(f.blob_path)
                        snap = Path(f.file_path)
                        size = blob.stat().st_size if blob.exists() else 0
                        if snap.exists() or snap.is_symlink():
                            snap.unlink()
                        if blob.exists():
                            blob.unlink()
                        deleted_bytes += size
                        deleted_count += 1
                    except Exception as e:
                        logger.warning(f"Failed to delete {f.file_name}: {e}")

            if deleted_count == 0:
                raise HTTPException(
                    status_code = 404,
                    detail = f"Variant {variant} not found in cache for {repo_id}",
                )

            freed_mb = deleted_bytes / (1024 * 1024)
            logger.info(
                f"Deleted {deleted_count} file(s) for {repo_id} variant {variant}: "
                f"{freed_mb:.1f} MB freed"
            )
            return {"status": "deleted", "repo_id": repo_id, "variant": variant}

        # ── Full repo deletion ───────────────────────────────────
        revision_hashes = [rev.commit_hash for rev in target_repo.revisions]
        if not revision_hashes:
            raise HTTPException(status_code = 404, detail = "No revisions found for model")

        delete_strategy = hf_cache.delete_revisions(*revision_hashes)
        logger.info(
            f"Deleting cached model {repo_id}: "
            f"{delete_strategy.expected_freed_size_str} will be freed"
        )
        delete_strategy.execute()

        return {"status": "deleted", "repo_id": repo_id}

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error deleting cached model {repo_id}: {e}", exc_info = True)
        raise HTTPException(
            status_code = 500,
            detail = f"Failed to delete cached model: {str(e)}",
        )


@router.get("/checkpoints", response_model = CheckpointListResponse)
async def list_checkpoints(
    outputs_dir: str = Query(
        default = str(outputs_root()),
        description = "Directory to scan for checkpoints",
    ),
    current_subject: str = Depends(get_current_subject),
):
    """
    List available checkpoints in the outputs directory.

    Scans the outputs folder for training runs and their checkpoints.
    """
    try:
        resolved_outputs_dir = str(resolve_output_dir(outputs_dir))
        raw_models = scan_checkpoints(outputs_dir = resolved_outputs_dir)

        models = [
            ModelCheckpoints(
                name = model_name,
                checkpoints = [
                    CheckpointInfo(display_name = display_name, path = path, loss = loss)
                    for display_name, path, loss in checkpoints
                ],
                base_model = metadata.get("base_model"),
                peft_type = metadata.get("peft_type"),
                lora_rank = metadata.get("lora_rank"),
                is_quantized = metadata.get("is_quantized", False),
            )
            for model_name, checkpoints, metadata in raw_models
        ]

        return CheckpointListResponse(
            outputs_dir = resolved_outputs_dir,
            models = models,
        )
    except Exception as e:
        logger.error(f"Error listing checkpoints: {e}", exc_info = True)
        raise HTTPException(
            status_code = 500,
            detail = f"Failed to list checkpoints: {str(e)}",
        )