RPA.DocumentAI.Nanonets

Get all available models related to the API key.

return:	object containing available models

Robot Framework example:

${models}=  Get All Models
FOR  ${model}  IN  @{models}
    Log To Console  Model ID: ${model}[model_id]
    Log To Console  Model Type: ${model}[model_type]
END

Python example:

models = nanolib.get_all_models()
for model in models:
    print(f"model id: {model['model_id']}")
    print(f"model type: {model['model_type']}")

Helper keyword to get found fields from a prediction result.

Arguments

Argument	Type	Default value	Description
prediction	Dict[Hashable, str | int | float | bool | list | dict | None], List[str | int | float | bool | list | dict | None], str, int, float, bool, list, dict, None	null	prediction result dictionary

For example. see Predict File keyword

param prediction:
	prediction result dictionary
return:	list of found fields

Helper keyword to get found tables from a prediction result.

Arguments

Argument	Type	Default value	Description
prediction	Dict[Hashable, str | int | float | bool | list | dict | None], List[str | int | float | bool | list | dict | None], str, int, float, bool, list, dict, None	null	prediction result dictionary

For another example. see Predict File keyword

param prediction:
	prediction result dictionary
return:	list of found tables

Robot Framework example:

# It is possible to create `RPA.Tables` compatible tables from the result
${tables}=    Get Tables From Prediction Result    ${result}
FOR    ${table}    IN    @{tables}
    ${rpatable}=    Create Table    ${table}[rows]
    FOR    ${row}    IN    @{rpatable}
        Log To Console    ${row}
    END
END

Python example:

# It is possible to create `RPA.Tables` compatible tables from the result
tables = nanolib.get_tables_from_prediction_result(result)
for table in tables:
    rpatable = Tables().create_table(table["rows"])
    for row in rpatable:
        print(row)

OCR fulltext a given file. Returns words and full text.

Arguments

Argument	Type	Default value	Description
filename	str	null	name of the file
filepath	str	null	path of the file

Filename and filepath needs to be given separately.

param filename:	name of the file
param filepath:	path of the file
return:	the result in a list format

Robot Framework example:

${results}=  OCR Fulltext
...   invoice.pdf
...   ${CURDIR}${/}invoice.pdf
FOR  ${result}  IN  @{results}
    Log To Console  Filename: ${result}[filename]
    FOR  ${pagenum}  ${page}  IN ENUMERATE  @{result.pagedata}   start=1
        Log To Console  Page ${pagenum} raw Text: ${page}[raw_text]
    END
END

Python example:

results = nanolib.ocr_fulltext("IMG_8277.jpeg", "./IMG_8277.jpeg")
for result in results:
    print(f"FILENAME: {result['filename']}")
    for page in result["page_data"]:
        print(f"Page {page['page']+1}: {page['raw_text']}")

Get prediction result for a file by a given model id.

Arguments

Argument	Type	Default value	Description
filepath	str	null	filepath to the file
model_id	str	null	id of the Nanonets model to categorize a file

param filepath:	filepath to the file
param model_id:	id of the Nanonets model to categorize a file
return:	the result in a list format

Robot Framework example:

${result}=  Predict File  ./document.pdf   ${MODEL_ID}
${fields}=    Get Fields From Prediction Result    ${result}
FOR    ${field}    IN    @{fields}
    Log To Console    Label:${field}[label] Text:${field}[ocr_text]
END
${tables}=    Get Tables From Prediction Result    ${result}
FOR    ${table}    IN    @{tables}
    FOR    ${rows}    IN    ${table}[rows]
        FOR    ${row}    IN    @{rows}
            ${cells}=    Evaluate    [cell['text'] for cell in $row]
            Log To Console    ROW:${{" | ".join($cells)}}
        END
    END
END

Python example:

result = nanolib.predict_file("./docu.pdf", secrets["receipts-model-id"])
fields = nanolib.get_fields_from_prediction_result(result)
for field in fields:
    print(f"Label: {field['label']} Text: {field['ocr_text']}")
tables = nanolib.get_tables_from_prediction_result(result)
for table in tables:
    for row in table["rows"]:
        cells = [cell["text"] for cell in row]
        print(f"ROW: {' | '.join(cells)}")

Set Nanonets request headers with key related to API.

Arguments

Argument	Type	Default value	Description
apikey	str	null	key related to the API

param apikey:	key related to the API

Robot Framework example:

${secrets}=   Get Secret  nanonets-auth
Set Authorization    ${secrets}[apikey]

Python example:

secrets = Vault().get_secret("nanonets-auth")
nanolib = Nanonets()
nanolib.set_authorization(secrets["apikey"])