Generated V1

Sample Code (sync):

from mindee import Client, PredictResponse, product

# Init a new client
mindee_client = Client(api_key="my-api-key")

# Add the corresponding endpoint (document). Set the account_name to "mindee" if you are using OTS.
my_endpoint = mindee_client.create_endpoint(
    account_name="my-account",
    endpoint_name="my-endpoint",
    version="my-version"
)

# Load a file from disk
input_doc = mindee_client.source_from_path("/path/to/the/file.ext")

# Parse the file.
# The endpoint must be specified since it cannot be determined from the class.
result: PredictResponse = mindee_client.parse(
    product.GeneratedV1,
    input_doc,
    endpoint=my_endpoint
)

# Print a brief summary of the parsed data
print(result.document)

# # Iterate over all the fields in the document
# for field_name, field_values in result.document.inference.prediction.fields.items():
#     print(field_name, "=", field_values)

Sample Code (async):

from mindee import Client, PredictResponse, product

# Init a new client
mindee_client = Client(api_key="my-api-key")

# Add the corresponding endpoint (document). Set the account_name to "mindee" if you are using OTS.
my_endpoint = mindee_client.create_endpoint(
    account_name="my-account",
    endpoint_name="my-endpoint",
    version="my-version"
)

# Load a file from disk
input_doc = mindee_client.source_from_path("/path/to/the/file.ext")

# Parse the file.
# The endpoint must be specified since it cannot be determined from the class.
result: PredictResponse = mindee_client.enqueue_and_parse(
    product.GeneratedV1,
    input_doc,
    endpoint=my_endpoint
)

# Print a brief summary of the parsed data
print(result.document)

# # Iterate over all the fields in the document
# for field_name, field_values in result.document.inference.prediction.fields.items():
#     print(field_name, "=", field_values)

class GeneratedV1(raw_prediction)

Generated API V1 inference results.

static get_endpoint_info(klass)

Retrives the endpoint information for an Inference.

Should never retrieve info for CustomV1, as a custom endpoint should be created to use CustomV1.

Parameters:: klass (Type[Inference]) – product subclass to access endpoint information.
Return type:: Dict[str, str]

endpoint_name: Optional[str] = 'custom': Name of the endpoint (placeholder).

endpoint_version: Optional[str] = '1': Version of the endpoint.

is_rotation_applied: Optional[bool]: Whether the document has had any rotation applied to it.

page_id: Optional[int]: Optional page id for page-level predictions.

pages: List[Page[GeneratedV1Page]]: Page-level prediction(s).

prediction: GeneratedV1Document: Document-level prediction.

product: Product: Name and version of a given product, as sent back by the API.

class GeneratedV1Document(raw_prediction)

Generated V1 document prediction results.

Parameters:: raw_prediction (Dict[str, Any]) –

get_list_fields()

Returns a dictionary of all list-like fields.

Return type:: Dict[str, GeneratedListField]

get_object_fields()

Returns a dictionary of all object-like fields.

Return type:: Dict[str, GeneratedObjectField]

get_single_fields()

Returns a dictionary of all fields that aren’t a collection.

Return type:: Dict[str, StringField]

list_field_names()

Lists names of all top-level field keys.

Return type:: List[str]

fields: Dict[str, Union[GeneratedListField, StringField, GeneratedObjectField]]: Dictionary of all fields in the document

class GeneratedV1Page(raw_prediction, page_id=None)

Generated V1 page prediction results.

Parameters:

raw_prediction (Dict[str, Any]) –
page_id (Optional[int]) –