import os
from pathlib import Path


block_cipher = None

project_dir = Path(globals().get("SPECPATH", os.getcwd())).resolve()


def _data_entry(relative_path: str, target_dir: str | None = None):
    src = project_dir / relative_path
    if not src.exists():
        return None
    return (str(src), target_dir or relative_path)


datas = []
for relative_path, target_dir in [
    ("config", "config"),
    ("data/bdpm", "data/bdpm"),
    ("data/finess", "data/finess"),
    ("data/insee", "data/insee"),
    ("models/camembert-bio-deid/onnx", "models/camembert-bio-deid/onnx"),
    ("detectors", "detectors"),
    ("scripts", "scripts"),
    ("assets", "assets"),
]:
    entry = _data_entry(relative_path, target_dir)
    if entry is not None:
        datas.append(entry)

# Fichiers directs sous data/ requis par le core.
for relative_path in [
    "data/stopwords_manuels.txt",
    "data/villes_blacklist.txt",
    "data/dpi_labels_blacklist.txt",
    "data/companion_blacklist.txt",
]:
    entry = _data_entry(relative_path, "data")
    if entry is not None:
        datas.append(entry)

onnxtr_cache_dir = Path(os.environ.get("ONNXTR_CACHE_DIR", Path.home() / ".cache" / "onnxtr"))
required_onnxtr_weights = [
    "db_resnet50-69ba0015.onnx",
    "crnn_vgg16_bn-743599aa.onnx",
]
missing_onnxtr_weights = []
for filename in required_onnxtr_weights:
    src = onnxtr_cache_dir / "models" / filename
    if src.exists():
        datas.append((str(src), "models/onnxtr/models"))
    else:
        missing_onnxtr_weights.append(str(src))
if missing_onnxtr_weights:
    raise FileNotFoundError(
        "Poids OCR OnnxTR manquants pour le build frozen : "
        + ", ".join(missing_onnxtr_weights)
        + ". Précharger OnnxTR (lancer une OCR une fois) ou définir ONNXTR_CACHE_DIR avant PyInstaller."
    )


hiddenimports = [
    "Pseudonymisation_Gui_V5",
    "anonymizer_core_refactored_onnx",
    "admin_rules",
    "config_defaults",
    "profile_defaults",
    "gui_batch_paths",
    "manual_masking",
    "pdf_mask_designer",
    "format_converter",
    "ner_manager_onnx",
    "camembert_ner_manager",
    "eds_pseudo_manager",
    "gliner_manager",
    "vlm_manager",
    "build_info",
    "doctr",
    "doctr.io",
    "doctr.models",
    "doctr.models.detection",
    "doctr.models.recognition",
    # OCR OnnxTR (remplace docTR — ONNX Runtime, sans torch)
    "onnxtr",
    "onnxtr.io",
    "onnxtr.models",
    "onnxtr.models.detection",
    "onnxtr.models.recognition",
    "onnxtr.utils",
    "onnxtr.utils.data",
    "cv2",
    "torchvision",
    "edsnlp",
    "edsnlp.pipes",
    "edsnlp.pipes.ner",
    "edsnlp.pipes.ner.pseudo",
    "spacy",
    "spacy.lang.fr",
    "gliner",
    "onnxruntime",
    "transformers",
    "tokenizers",
    "torch",
    "pdfplumber",
    "fitz",
    "PIL",
    "yaml",
    "loguru",
    "regex",
    "optimum",
    "optimum.onnxruntime",
    "optimum.pipelines",
    "optimum.modeling_base",
    "optimum.exporters.onnx",
]


a = Analysis(
    [str(project_dir / "launcher.py")],
    pathex=[str(project_dir)],
    datas=datas,
    hiddenimports=hiddenimports,
    cipher=block_cipher,
    noarchive=False,
)
pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)

splash = Splash(
    str(project_dir / "assets" / "splash.png"),
    binaries=a.binaries,
    datas=a.datas,
    text_pos=(60, 195),
    text_size=10,
    text_color="white",
    minify_script=True,
    always_on_top=False,
)

exe = EXE(
    pyz,
    a.scripts,
    splash,
    splash.binaries,
    a.binaries,
    a.zipfiles,
    a.datas,
    [],
    name="Anonymisation",
    debug=False,
    strip=False,
    upx=False,
    console=False,
    icon=str(project_dir / "assets" / "icons" / "app.ico"),
)