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Providers API

culicidaelab.providers

Data provider implementations for accessing datasets and models.

This package contains classes that implement the BaseProvider interface to interact with various data sources like Hugging Face, Kaggle, etc. Each provider module offers specific logic for downloading datasets and model weights.

Available Classes
  • HuggingFaceProvider: A provider for interacting with the Hugging Face Hub.
__all__ = ['HuggingFaceProvider'] module-attribute
HuggingFaceProvider

Provider for downloading and managing HuggingFace datasets and models.

This class interfaces with the Hugging Face Hub to fetch dataset metadata, download full datasets or specific splits, and download model weights. It uses the core settings object for path resolution and API key access.

Attributes:

Name Type Description
provider_name str

The name of the provider, "huggingface".
settings Settings

The main Settings object for the library.
dataset_url str

The base URL for fetching Hugging Face dataset metadata.
api_key str | None

The Hugging Face API key, if provided.
Source code in culicidaelab/providers/huggingface_provider.py 
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class HuggingFaceProvider(BaseProvider):
    """Provider for downloading and managing HuggingFace datasets and models.

    This class interfaces with the Hugging Face Hub to fetch dataset metadata,
    download full datasets or specific splits, and download model weights. It uses
    the core settings object for path resolution and API key access.

    Attributes:
        provider_name (str): The name of the provider, "huggingface".
        settings (Settings): The main Settings object for the library.
        dataset_url (str): The base URL for fetching Hugging Face dataset metadata.
        api_key (str | None): The Hugging Face API key, if provided.
    """

    def __init__(self, settings: Settings, dataset_url: str, **kwargs: Any) -> None:
        """Initializes the HuggingFace provider.

        This constructor is called by the `ProviderService`, which injects the
        global `settings` object and unpacks the specific provider's configuration
        (e.g., `dataset_url`) as keyword arguments.

        Args:
            settings (Settings): The main Settings object for the library.
            dataset_url (str): The base URL for fetching Hugging Face dataset metadata.
            **kwargs (Any): Catches other config parameters (e.g., `api_key`).
        """
        super().__init__()
        self.provider_name = "huggingface"
        self.settings = settings
        self.dataset_url = dataset_url
        self.api_key: str | None = kwargs.get("api_key") or self.settings.get_api_key(
            self.provider_name,
        )

    def download_dataset(
        self,
        dataset_name: str,
        save_dir: str | None = None,
        split: str | None = None,
        *args: Any,
        **kwargs: Any,
    ) -> Path:
        """Downloads a dataset from HuggingFace.

        Args:
            dataset_name (str): Name of the dataset to download (e.g., "segmentation").
            save_dir (str | None, optional): Directory to save the dataset.
                Defaults to None, using the path from settings.
            split (str | None, optional): Dataset split to download (e.g., "train").
                Defaults to None.
            *args (Any): Additional positional arguments to pass to `load_dataset`.
            **kwargs (Any): Additional keyword arguments to pass to `load_dataset`.

        Returns:
            Path: The path to the downloaded dataset.

        Raises:
            ValueError: If the configuration is missing the `repository` ID.
            RuntimeError: If the download fails.
        """
        save_path = self.settings.get_dataset_path(dataset_name)
        if save_dir:
            save_path = Path(save_dir)
        dataset_config = self.settings.get_config(f"datasets.{dataset_name}")

        repo_id = dataset_config.repository
        if not repo_id:
            raise ValueError(
                f"Configuration for dataset '{dataset_name}' is missing the 'repository' (repository ID).",
            )

        try:
            if self.api_key:
                dataset = load_dataset(repo_id, split=split, token=self.api_key, **kwargs)
            else:
                dataset = load_dataset(repo_id, split=split, **kwargs)

            if split:
                save_path = save_path / split

            if save_path.exists() and save_path.is_dir():
                print(f"Existing dataset found at '{save_path}'. Removing it for a clean download.")
                shutil.rmtree(save_path)

            dataset.save_to_disk(str(save_path))
            if isinstance(dataset, Dataset):
                dataset.cleanup_cache_files()

            return save_path
        except Exception as e:
            raise RuntimeError(f"Failed to download dataset {repo_id}: {str(e)}") from e

    def download_model_weights(self, model_type: str, *args: Any, **kwargs: Any) -> Path:
        """Downloads and caches model weights from the HuggingFace Hub.

        Checks if the weights exist locally. If not, it downloads them
        from the repository specified in the configuration and saves them
        to the appropriate directory.

        Args:
            model_type (str): The type of model ('detector', 'segmenter', or 'classifier').
            *args (Any): Additional positional arguments (unused).
            **kwargs (Any): Additional keyword arguments (unused).

        Returns:
            Path: The path to the model weights file.

        Raises:
            ValueError: If the model type is not found in config or if `repository_id`
                or `filename` are missing.
            RuntimeError: If the download fails for any reason.
            NotADirectoryError: If the destination directory could not be created.
        """
        local_path = self.settings.get_model_weights_path(model_type).resolve()

        if local_path.exists():
            if local_path.is_symlink():
                try:
                    real_path = local_path.resolve(strict=True)
                    print(f"Symlink found at {local_path}, resolved to real file: {real_path}")
                    return real_path
                except FileNotFoundError:
                    print(f"Warning: Broken symlink found at {local_path}. It will be removed.")
                    local_path.unlink()
            else:
                print(f"Weights file found at: {local_path}")
                return local_path

        print(f"Model weights for '{model_type}' not found. Attempting to download...")

        predictor_config = self.settings.get_config(f"predictors.{model_type}")
        repo_id = predictor_config.repository_id
        filename = predictor_config.filename

        if not repo_id or not filename:
            raise ValueError(
                f"Cannot download weights for '{model_type}'. "
                f"Configuration is missing 'repository_id' or 'filename'. "
                f"Please place the file manually at: {local_path}",
            )

        try:
            dest_dir = local_path.parent.resolve()
            print(f"Ensuring destination directory exists: {dest_dir}")

            dest_dir.mkdir(parents=True, exist_ok=True)
            if not dest_dir.is_dir():
                raise NotADirectoryError(f"Failed to create directory: {dest_dir}")

            downloaded_path_str = hf_hub_download(
                repo_id=repo_id,
                filename=filename,
                cache_dir=self.settings.cache_dir / "huggingface",
                local_dir=str(local_path.parent),
            )
            print(f"Downloaded weights to: {downloaded_path_str}")

            return local_path

        except Exception as e:
            if local_path.exists():
                local_path.unlink()

            dir_status = "exists" if dest_dir.exists() else "missing"
            dir_type = "directory" if dest_dir.is_dir() else "not-a-directory"
            raise RuntimeError(
                f"Failed to download weights for '{model_type}' to {local_path}. "
                f"Directory status: {dir_status} ({dir_type}). Error: {e}",
            ) from e

    def get_dataset_metadata(self, dataset_name: str) -> dict[str, Any]:
        """Gets metadata for a specific dataset from HuggingFace.

        Args:
            dataset_name (str): The name of the dataset to get metadata for.

        Returns:
            dict[str, Any]: The dataset metadata as a dictionary.

        Raises:
            requests.RequestException: If the HTTP request fails.
        """
        url = self.dataset_url.format(dataset_name=dataset_name)
        headers = {"Authorization": f"Bearer {self.api_key}"} if self.api_key else {}

        try:
            response = requests.get(url, headers=headers, timeout=10.0)
            response.raise_for_status()
            return cast(dict[str, Any], response.json())
        except requests.RequestException as e:
            raise requests.RequestException(
                f"Failed to fetch dataset metadata for {dataset_name}: {str(e)}",
            ) from e

    def get_provider_name(self) -> str:
        """Returns the provider's name.

        Returns:
            str: The name of the provider, "huggingface".
        """
        return self.provider_name

    def load_dataset(self, dataset_path: str | Path, split: str | None = None, **kwargs: Any) -> Any:
        """Loads a dataset from disk.

        This method attempts to load a dataset from the specified path. If a `split`
        name is provided and a corresponding subdirectory exists, it will load
        the split from that subdirectory. Otherwise, it loads the entire dataset
        from the base path.

        Args:
            dataset_path (str | Path): The local path to the dataset,
                typically returned by `download_dataset`.
            split (str | None, optional): The specific split to load (e.g., "train",
                "validation", "test"). If a subdirectory with this name
                exists, it will be loaded. Defaults to None.
            **kwargs: Additional keyword arguments to pass to the
                `datasets.load_from_disk` function.

        Returns:
            Any: The loaded dataset, typically a `datasets.Dataset` or
                `datasets.DatasetDict` object.
        """
        path_to_load = Path(dataset_path)
        if split and (path_to_load / split).exists():
            return load_from_disk(str(path_to_load / split), **kwargs)
        return load_from_disk(str(dataset_path), **kwargs)
provider_name = 'huggingface' instance-attribute
settings = settings instance-attribute
dataset_url = dataset_url instance-attribute
api_key: str | None = kwargs.get('api_key') or self.settings.get_api_key(self.provider_name) instance-attribute
__init__(settings: Settings, dataset_url: str, **kwargs: Any) -> None

Initializes the HuggingFace provider.

This constructor is called by the ProviderService, which injects the global settings object and unpacks the specific provider's configuration (e.g., dataset_url) as keyword arguments.

Parameters:

Name Type Description Default
settings Settings

The main Settings object for the library.

 required
dataset_url str

The base URL for fetching Hugging Face dataset metadata.

 required
**kwargs Any

Catches other config parameters (e.g., api_key).

 {}
Source code in culicidaelab/providers/huggingface_provider.py 
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def __init__(self, settings: Settings, dataset_url: str, **kwargs: Any) -> None:
    """Initializes the HuggingFace provider.

    This constructor is called by the `ProviderService`, which injects the
    global `settings` object and unpacks the specific provider's configuration
    (e.g., `dataset_url`) as keyword arguments.

    Args:
        settings (Settings): The main Settings object for the library.
        dataset_url (str): The base URL for fetching Hugging Face dataset metadata.
        **kwargs (Any): Catches other config parameters (e.g., `api_key`).
    """
    super().__init__()
    self.provider_name = "huggingface"
    self.settings = settings
    self.dataset_url = dataset_url
    self.api_key: str | None = kwargs.get("api_key") or self.settings.get_api_key(
        self.provider_name,
    )
download_dataset(dataset_name: str, save_dir: str | None = None, split: str | None = None, *args: Any, **kwargs: Any) -> Path

Downloads a dataset from HuggingFace.

Parameters:

Name Type Description Default
dataset_name str

Name of the dataset to download (e.g., "segmentation").

 required
save_dir str | None

Directory to save the dataset. Defaults to None, using the path from settings.

 None
split str | None

Dataset split to download (e.g., "train"). Defaults to None.

 None
*args Any

Additional positional arguments to pass to load_dataset.

 ()
**kwargs Any

Additional keyword arguments to pass to load_dataset.

 {}

Returns:

Name Type Description
Path Path

The path to the downloaded dataset.

Raises:

Type Description
ValueError

If the configuration is missing the repository ID.
RuntimeError

If the download fails.
Source code in culicidaelab/providers/huggingface_provider.py 
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def download_dataset(
    self,
    dataset_name: str,
    save_dir: str | None = None,
    split: str | None = None,
    *args: Any,
    **kwargs: Any,
) -> Path:
    """Downloads a dataset from HuggingFace.

    Args:
        dataset_name (str): Name of the dataset to download (e.g., "segmentation").
        save_dir (str | None, optional): Directory to save the dataset.
            Defaults to None, using the path from settings.
        split (str | None, optional): Dataset split to download (e.g., "train").
            Defaults to None.
        *args (Any): Additional positional arguments to pass to `load_dataset`.
        **kwargs (Any): Additional keyword arguments to pass to `load_dataset`.

    Returns:
        Path: The path to the downloaded dataset.

    Raises:
        ValueError: If the configuration is missing the `repository` ID.
        RuntimeError: If the download fails.
    """
    save_path = self.settings.get_dataset_path(dataset_name)
    if save_dir:
        save_path = Path(save_dir)
    dataset_config = self.settings.get_config(f"datasets.{dataset_name}")

    repo_id = dataset_config.repository
    if not repo_id:
        raise ValueError(
            f"Configuration for dataset '{dataset_name}' is missing the 'repository' (repository ID).",
        )

    try:
        if self.api_key:
            dataset = load_dataset(repo_id, split=split, token=self.api_key, **kwargs)
        else:
            dataset = load_dataset(repo_id, split=split, **kwargs)

        if split:
            save_path = save_path / split

        if save_path.exists() and save_path.is_dir():
            print(f"Existing dataset found at '{save_path}'. Removing it for a clean download.")
            shutil.rmtree(save_path)

        dataset.save_to_disk(str(save_path))
        if isinstance(dataset, Dataset):
            dataset.cleanup_cache_files()

        return save_path
    except Exception as e:
        raise RuntimeError(f"Failed to download dataset {repo_id}: {str(e)}") from e
download_model_weights(model_type: str, *args: Any, **kwargs: Any) -> Path

Downloads and caches model weights from the HuggingFace Hub.

Checks if the weights exist locally. If not, it downloads them from the repository specified in the configuration and saves them to the appropriate directory.

Parameters:

Name Type Description Default
model_type str

The type of model ('detector', 'segmenter', or 'classifier').

 required
*args Any

Additional positional arguments (unused).

 ()
**kwargs Any

Additional keyword arguments (unused).

 {}

Returns:

Name Type Description
Path Path

The path to the model weights file.

Raises:

Type Description
ValueError

If the model type is not found in config or if repository_id or filename are missing.
RuntimeError

If the download fails for any reason.
NotADirectoryError

If the destination directory could not be created.
Source code in culicidaelab/providers/huggingface_provider.py 
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def download_model_weights(self, model_type: str, *args: Any, **kwargs: Any) -> Path:
    """Downloads and caches model weights from the HuggingFace Hub.

    Checks if the weights exist locally. If not, it downloads them
    from the repository specified in the configuration and saves them
    to the appropriate directory.

    Args:
        model_type (str): The type of model ('detector', 'segmenter', or 'classifier').
        *args (Any): Additional positional arguments (unused).
        **kwargs (Any): Additional keyword arguments (unused).

    Returns:
        Path: The path to the model weights file.

    Raises:
        ValueError: If the model type is not found in config or if `repository_id`
            or `filename` are missing.
        RuntimeError: If the download fails for any reason.
        NotADirectoryError: If the destination directory could not be created.
    """
    local_path = self.settings.get_model_weights_path(model_type).resolve()

    if local_path.exists():
        if local_path.is_symlink():
            try:
                real_path = local_path.resolve(strict=True)
                print(f"Symlink found at {local_path}, resolved to real file: {real_path}")
                return real_path
            except FileNotFoundError:
                print(f"Warning: Broken symlink found at {local_path}. It will be removed.")
                local_path.unlink()
        else:
            print(f"Weights file found at: {local_path}")
            return local_path

    print(f"Model weights for '{model_type}' not found. Attempting to download...")

    predictor_config = self.settings.get_config(f"predictors.{model_type}")
    repo_id = predictor_config.repository_id
    filename = predictor_config.filename

    if not repo_id or not filename:
        raise ValueError(
            f"Cannot download weights for '{model_type}'. "
            f"Configuration is missing 'repository_id' or 'filename'. "
            f"Please place the file manually at: {local_path}",
        )

    try:
        dest_dir = local_path.parent.resolve()
        print(f"Ensuring destination directory exists: {dest_dir}")

        dest_dir.mkdir(parents=True, exist_ok=True)
        if not dest_dir.is_dir():
            raise NotADirectoryError(f"Failed to create directory: {dest_dir}")

        downloaded_path_str = hf_hub_download(
            repo_id=repo_id,
            filename=filename,
            cache_dir=self.settings.cache_dir / "huggingface",
            local_dir=str(local_path.parent),
        )
        print(f"Downloaded weights to: {downloaded_path_str}")

        return local_path

    except Exception as e:
        if local_path.exists():
            local_path.unlink()

        dir_status = "exists" if dest_dir.exists() else "missing"
        dir_type = "directory" if dest_dir.is_dir() else "not-a-directory"
        raise RuntimeError(
            f"Failed to download weights for '{model_type}' to {local_path}. "
            f"Directory status: {dir_status} ({dir_type}). Error: {e}",
        ) from e
get_dataset_metadata(dataset_name: str) -> dict[str, Any]

Gets metadata for a specific dataset from HuggingFace.

Parameters:

Name Type Description Default
dataset_name str

The name of the dataset to get metadata for.

 required

Returns:

Type Description
dict[str, Any]

dict[str, Any]: The dataset metadata as a dictionary.

Raises:

Type Description
RequestException

If the HTTP request fails.
Source code in culicidaelab/providers/huggingface_provider.py 
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def get_dataset_metadata(self, dataset_name: str) -> dict[str, Any]:
    """Gets metadata for a specific dataset from HuggingFace.

    Args:
        dataset_name (str): The name of the dataset to get metadata for.

    Returns:
        dict[str, Any]: The dataset metadata as a dictionary.

    Raises:
        requests.RequestException: If the HTTP request fails.
    """
    url = self.dataset_url.format(dataset_name=dataset_name)
    headers = {"Authorization": f"Bearer {self.api_key}"} if self.api_key else {}

    try:
        response = requests.get(url, headers=headers, timeout=10.0)
        response.raise_for_status()
        return cast(dict[str, Any], response.json())
    except requests.RequestException as e:
        raise requests.RequestException(
            f"Failed to fetch dataset metadata for {dataset_name}: {str(e)}",
        ) from e
get_provider_name() -> str

Returns the provider's name.

Returns:

Name Type Description
str str

The name of the provider, "huggingface".
Source code in culicidaelab/providers/huggingface_provider.py 
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def get_provider_name(self) -> str:
    """Returns the provider's name.

    Returns:
        str: The name of the provider, "huggingface".
    """
    return self.provider_name
load_dataset(dataset_path: str | Path, split: str | None = None, **kwargs: Any) -> Any

Loads a dataset from disk.

This method attempts to load a dataset from the specified path. If a split name is provided and a corresponding subdirectory exists, it will load the split from that subdirectory. Otherwise, it loads the entire dataset from the base path.

Parameters:

Name Type Description Default
dataset_path str | Path

The local path to the dataset, typically returned by download_dataset.

 required
split str | None

The specific split to load (e.g., "train", "validation", "test"). If a subdirectory with this name exists, it will be loaded. Defaults to None.

 None
**kwargs Any

Additional keyword arguments to pass to the datasets.load_from_disk function.

 {}

Returns:

Name Type Description
Any Any

The loaded dataset, typically a datasets.Dataset or datasets.DatasetDict object.
Source code in culicidaelab/providers/huggingface_provider.py 
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def load_dataset(self, dataset_path: str | Path, split: str | None = None, **kwargs: Any) -> Any:
    """Loads a dataset from disk.

    This method attempts to load a dataset from the specified path. If a `split`
    name is provided and a corresponding subdirectory exists, it will load
    the split from that subdirectory. Otherwise, it loads the entire dataset
    from the base path.

    Args:
        dataset_path (str | Path): The local path to the dataset,
            typically returned by `download_dataset`.
        split (str | None, optional): The specific split to load (e.g., "train",
            "validation", "test"). If a subdirectory with this name
            exists, it will be loaded. Defaults to None.
        **kwargs: Additional keyword arguments to pass to the
            `datasets.load_from_disk` function.

    Returns:
        Any: The loaded dataset, typically a `datasets.Dataset` or
            `datasets.DatasetDict` object.
    """
    path_to_load = Path(dataset_path)
    if split and (path_to_load / split).exists():
        return load_from_disk(str(path_to_load / split), **kwargs)
    return load_from_disk(str(dataset_path), **kwargs)

selection:

members: true