Class swarmauri_ocr_pytesseract.PytesseractOCR.PytesseractOCR

swarmauri_ocr_pytesseract.PytesseractOCR.PytesseractOCR

PytesseractOCR(**data)

Bases: OCRBase

A model for performing OCR (Optical Character Recognition) using Pytesseract. It can process both local images and image bytes, returning extracted text. Requires Tesseract-OCR to be installed on the system.

Source code in swarmauri_ocr_pytesseract/PytesseractOCR.py

def __init__(self, **data):
    super().__init__(**data)
    pytesseract.pytesseract.tesseract_cmd = self.tesseract_cmd

tesseract_cmd class-attribute instance-attribute

tesseract_cmd = Field(
    default_factory=lambda: get(
        "TESSERACT_CMD",
        "/usr/bin/tesseract"
        if exists("/usr/bin/tesseract")
        else None,
    )
)

type class-attribute instance-attribute

type = 'PytesseractOCR'

language class-attribute instance-attribute

language = Field(default='eng')

config class-attribute instance-attribute

config = Field(default='')

model_config class-attribute instance-attribute

model_config = ConfigDict(protected_namespaces=())

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = None

resource class-attribute instance-attribute

resource = Field(default=OCR.value, frozen=True)

version class-attribute instance-attribute

version = '0.1.0'

allowed_models class-attribute instance-attribute

allowed_models = []

extract_text

extract_text(image, **kwargs)

Extracts text from an image.

PARAMETER	DESCRIPTION
image	Can be a file path, bytes, or PIL Image TYPE: Union[str, bytes, Image]
**kwargs	Additional arguments for OCR processing - language: OCR language (e.g., 'eng', 'fra', etc.) - config: Custom Tesseract configuration string DEFAULT: {}

RETURNS	DESCRIPTION
str	Extracted text as string

Source code in swarmauri_ocr_pytesseract/PytesseractOCR.py

def extract_text(self, image: Union[str, bytes, Image.Image], **kwargs) -> str:
    """
    Extracts text from an image.

    Args:
        image: Can be a file path, bytes, or PIL Image
        **kwargs: Additional arguments for OCR processing
                 - language: OCR language (e.g., 'eng', 'fra', etc.)
                 - config: Custom Tesseract configuration string

    Returns:
        Extracted text as string
    """
    return self._process_image(image, **kwargs)

aextract_text async

aextract_text(image, **kwargs)

Asynchronously extracts text from an image.

Source code in swarmauri_ocr_pytesseract/PytesseractOCR.py

async def aextract_text(
    self, image: Union[str, bytes, Image.Image], **kwargs
) -> str:
    """
    Asynchronously extracts text from an image.
    """
    loop = asyncio.get_event_loop()
    return await loop.run_in_executor(None, self.extract_text, image, **kwargs)

batch

batch(images, **kwargs)

Process multiple images in batch.

PARAMETER	DESCRIPTION
images	List of images (file paths, bytes, or PIL Images) TYPE: List[Union[str, bytes, Image]]
**kwargs	Additional arguments for OCR processing DEFAULT: {}

RETURNS	DESCRIPTION
List[str]	List of extracted texts

Source code in swarmauri_ocr_pytesseract/PytesseractOCR.py

def batch(
    self, images: List[Union[str, bytes, Image.Image]], **kwargs
) -> List[str]:
    """
    Process multiple images in batch.

    Args:
        images: List of images (file paths, bytes, or PIL Images)
        **kwargs: Additional arguments for OCR processing

    Returns:
        List of extracted texts
    """
    results = []
    for image in images:
        text = self.extract_text(image=image, **kwargs)
        results.append(text)
    return results

abatch async

abatch(images, max_concurrent=5, **kwargs)

Asynchronously process multiple images in batch.

PARAMETER	DESCRIPTION
images	List of images (file paths, bytes, or PIL Images) TYPE: List[Union[str, bytes, Image]]
max_concurrent	Maximum number of concurrent operations TYPE: int DEFAULT: 5
**kwargs	Additional arguments for OCR processing DEFAULT: {}

RETURNS	DESCRIPTION
List[str]	List of extracted texts

Source code in swarmauri_ocr_pytesseract/PytesseractOCR.py

async def abatch(
    self,
    images: List[Union[str, bytes, Image.Image]],
    max_concurrent: int = 5,
    **kwargs,
) -> List[str]:
    """
    Asynchronously process multiple images in batch.

    Args:
        images: List of images (file paths, bytes, or PIL Images)
        max_concurrent: Maximum number of concurrent operations
        **kwargs: Additional arguments for OCR processing

    Returns:
        List of extracted texts
    """
    semaphore = asyncio.Semaphore(max_concurrent)

    async def process_image(image):
        async with semaphore:
            return await self.aextract_text(image=image, **kwargs)

    tasks = [process_image(image) for image in images]
    return await asyncio.gather(*tasks)

get_supported_languages

get_supported_languages()

Returns a list of supported languages by executing 'tesseract --list-langs' command.

RETURNS	DESCRIPTION
List[str]	List[str]: List of available language codes (e.g., ['eng', 'osd'])

RAISES	DESCRIPTION
Exception	If the command execution fails or returns unexpected output

Source code in swarmauri_ocr_pytesseract/PytesseractOCR.py

def get_supported_languages(self) -> List[str]:
    """
    Returns a list of supported languages by executing 'tesseract --list-langs' command.

    Returns:
        List[str]: List of available language codes (e.g., ['eng', 'osd'])

    Raises:
        Exception: If the command execution fails or returns unexpected output
    """
    try:
        # Execute tesseract command to list languages
        import subprocess

        result = subprocess.run(
            [self.tesseract_cmd, "--list-langs"],
            capture_output=True,
            text=True,
            check=True,
        )

        # Parse the output
        output_lines = result.stdout.strip().split("\n")

        # Skip the first line which is the directory info
        # and filter out empty lines
        languages = [lang.strip() for lang in output_lines[1:] if lang.strip()]

        return languages

    except subprocess.CalledProcessError as e:
        raise Exception(f"Failed to get language list from Tesseract: {e.stderr}")
    except Exception as e:
        raise Exception(f"Error getting supported languages: {str(e)}")

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger

add_allowed_model

add_allowed_model(model)

Add a new model to the list of allowed models.

RAISES	DESCRIPTION
ValueError	If the model is already in the allowed models list.

Source code in swarmauri_base/ocrs/OCRBase.py

def add_allowed_model(self, model: str) -> None:
    """
    Add a new model to the list of allowed models.

    Raises:
        ValueError: If the model is already in the allowed models list.
    """
    if model in self.allowed_models:
        raise ValueError(f"Model '{model}' is already allowed.")
    self.allowed_models.append(model)

remove_allowed_model

remove_allowed_model(model)

Remove a model from the list of allowed models.

RAISES	DESCRIPTION
ValueError	If the model is not in the allowed models list.

Source code in swarmauri_base/ocrs/OCRBase.py

def remove_allowed_model(self, model: str) -> None:
    """
    Remove a model from the list of allowed models.

    Raises:
        ValueError: If the model is not in the allowed models list.
    """
    if model not in self.allowed_models:
        raise ValueError(f"Model '{model}' is not in the allowed models list.")
    self.allowed_models.remove(model)

predict abstractmethod

predict(*args, **kwargs)

Source code in swarmauri_base/ocrs/OCRBase.py

@abstractmethod
def predict(self, *args, **kwargs):
    raise NotImplementedError("predict() not implemented in subclass yet.")

apredict abstractmethod async

apredict(*args, **kwargs)

Source code in swarmauri_base/ocrs/OCRBase.py

@abstractmethod
async def apredict(self, *args, **kwargs):
    raise NotImplementedError("apredict() not implemented in subclass yet.")

stream abstractmethod

stream(*args, **kwargs)

Source code in swarmauri_base/ocrs/OCRBase.py

@abstractmethod
def stream(self, *args, **kwargs):
    raise NotImplementedError("stream() not implemented in subclass yet.")

astream abstractmethod async

astream(*args, **kwargs)

Source code in swarmauri_base/ocrs/OCRBase.py

@abstractmethod
async def astream(self, *args, **kwargs):
    raise NotImplementedError("astream() not implemented in subclass yet.")