privateai-client 4.0.0

A thin client for communicating with the Private AI de-identication API.

Private AI Python Client

A Python client library for communicating with the Private AI API. This document provides information about how to best use the client. For more information, see Private AI's API Documentation.

Quick Links

1. Installation
2. Quick Start
3. Running the tests
4. Working with the Client
5. Request Objects
6. Sample Use

Installation

pip install privateai_client

Quick Start

from privateai_client import PAIClient
from privateai_client import request_objects

client = PAIClient(url="http://localhost:8080")
text_request = request_objects.process_text_obj(text=["My sample name is John Smith"])
response = client.process_text(text_request)

print(text_request.text)
print(response.processed_text)

Output:

['My sample name is John Smith']
['My sample name is [NAME_1]']

Running the tests

We use pytest to run our tests in the tests folder.

To run from command line, ensure you have pytest installed, and then run pytest from the main project folder.

pip install -r requirements.dev.txt
pytest

Alternatively, you can run automatically run all tests from the Testing window in Visual Studio Code.

Working With The Client

Initializing the Client

The PAI client requires a scheme, host, and optional port to initialize. Alternatively, a full url can be used. Once created, the connection can be tested with the client's ping function

scheme = 'http'
host = 'localhost'
port= '8080'
client = PAIClient(scheme, host, port)

client.ping()


url = "http://localhost:8080"
client = PAIClient(url=url)

client.ping()

Output:

True
True

Adding Authorization to the Client

from privateai_client import PAIClient
# On initialization
client = PAIClient(url="http://localhost:8080", api_key='testkey')

# After initialization
client = PAIClient(url="http://localhost:8080")
client.ping()
client.add_api_key("testkey")
client.ping()

Output:

The request returned with a 401 Unauthorized
True

Making Requests

Once initialized the client can be used to make any request listed in the Private-AI documentation

Available requests:

Client Function	Endpoint
get_version()	/
ping()	/healthz
get_metrics()	/metrics
get_diagnostics()	/diagnostics
process_text()	/process/text
process_files_uri()	/process/files/uri
process_files_base64()	/process/files/base64
bleep()	/bleep

Requests can be made using dictionaries:

sample_text = ["This is John Smith's sample dictionary request"]
text_dict_request = {"text": sample_text}

response = client.process_text(text_dict_request)
print(response.processed_text)

Output:

["This is [NAME_1]'s sample dictionary request"]

or using built-in request objects:

from privateai_client import request_objects

sample_text = "This is John Smith's sample process text object request"
text_request_object =  request_objects.process_text_obj(text=[sample_text])

response = client.process_text(text_request_object)
print(response.processed_text)

Output:

["This is [NAME_1]'s sample process text object request"]

Request Objects

Request objects are a simple way of creating request bodies without the tediousness of writing dictionaries. Every post request (as listed in the Private-AI documentation) has its own request own request object.

from privateai_client import request_objects

sample_obj = request_objects.file_uri_obj(uri='path/to/file.jpg')
sample_obj.uri

Output:

'path/to/file.jpg'

Additionally there are request objects for each nested dictionary of a request:

from privateai_client import request_objects

sample_text = "This is John Smith from Sample Company to show a sample process text object request where organizations won't be removed, but John will be recognized as the same entity"

# sub-dictionary of entity_detection
sample_entity_type_selector = request_objects.entity_type_selector_obj(type="DISABLE", value=["ORGANIZATION"])

# sub-dictionary of a process text request
sample_entity_detection = request_objects.entity_detection_obj(entity_types=[sample_entity_type_selector])

# sub-dictionary of a process text request
sample_processed_text = request_objects.processed_text_obj(type="MARKER", pattern="[UNIQUE_NUMBERED_ENTITY_TYPE]", coreference_resolution="model")

# request object created using the sub-dictionaries
sample_request = request_objects.process_text_obj(text=[sample_text], entity_detection=sample_entity_detection, processed_text=sample_processed_text)
response = client.process_text(sample_request)
print(response.processed_text)

Output:

["This is [NAME_1] from Sample Company to show a sample process text object request where organizations won't be removed, but [NAME_1] will be recognized as the same entity"]

Building Request Objects

Request objects can initialized by passing in all the required values needed for the request as arguments or from a dictionary, using the object's fromdict function. Any object can be created as per the Private AI documentation.

# Passing arguments
sample_data = "JVBERi0xLjQKJdPr6eEKMSAwIG9iago8PC9UaXRsZSAoc2FtcGxlKQovUHJvZHVj..."
sample_content_type = "application/pdf"

sample_file_obj = request_objects.file_obj(data=sample_data, content_type=sample_content_type)

# Passing a dictionary using .fromdict()
sample_dict = {"data": "JVBERi0xLjQKJdPr6eEKMSAwIG9iago8PC9UaXRsZSAoc2FtcGxlKQovUHJvZHVj...",
               "content_type": "application/pdf"}

sample_file_obj2 = request_objects.file_obj.fromdict(sample_dict)

Request objects also can be formatted as dictionaries, using the request object's to_dict() function:

from privateai_client import request_objects

sample_text = "Sample text."
sample_accuracy = "standard"

# Create the nested request objects
sample_entity_type_selector = request_objects.entity_type_selector_obj(type="DISABLE", value=['HIPAA_SAFE_HARBOR'])
sample_entity_detection = request_objects.entity_detection_obj(
    entity_types=[sample_entity_type_selector],
    accuracy=sample_accuracy
)

# Create the request object
sample_request = request_objects.process_text_obj(text=[sample_text], entity_detection=sample_entity_detection)

# All nested request objects are also formatted
print(sample_request.to_dict())

Output:

{
 'text': ['Sample text.'],
 'link_batch': False,
 'entity_detection': {'accuracy': 'standard', 'entity_types': [{'type': 'DISABLE', 'value': ['HIPAA_SAFE_HARBOR']}], 'filter': [], 'return_entity': True},
 'processed_text': {'type': 'MARKER', 'pattern': '[UNIQUE_NUMBERED_ENTITY_TYPE]'}
}

Sample Use

Processing a directory of files

from privateai_client import PAIClient
from privateai_client.objects import request_objects
import os
import logging

file_dir = "/path/to/file/directory"
client = PAIClient(url="http://localhost:8080")
for file_name in os.listdir(file_dir):
    filepath = os.path.join(file_dir, file_name)
    if not os.path.isfile(filepath):
        continue
    req_obj = request_objects.file_uri_obj(uri=filepath)
    # NOTE this method of file processing requires the container to have an the input and output directories mounted
    resp = client.process_files_uri(req_obj)

Processing a Base64 file

from privateai_client import PAIClient
from privateai_client.objects import request_objects
import base64
import os
import logging

file_dir = "/path/to/your/file"
file_name = 'sample_file.pdf'
filepath = os.path.join(file_dir,file_name)
file_type= "type/of_file" #eg. application/pdf
client = PAIClient(url="http://localhost:8080")

# Read from file
with open(filepath, "rb") as b64_file:
    file_data = base64.b64encode(b64_file.read())
    file_data = file_data.decode("ascii")

# Make the request
file_obj = request_objects.file_obj(data=file_data, content_type=file_type)
request_obj = request_objects.file_base64_obj(file=file_obj)
resp = client.process_files_base64(request_object=request_obj)

# Write to file
with open(os.path.join(file_dir,f"redacted-{file_name}"), 'wb') as redacted_file:
    processed_file = resp.processed_file.encode("ascii")
    processed_file = base64.b64decode(processed_file, validate=True)
    redacted_file.write(processed_file)

Bleep an audio file

from privateai_client import PAIClient
from privateai_client.objects import request_objects
import base64
import os
import logging

file_dir = "/path/to/your/file"
file_name = 'sample_file.pdf'
filepath = os.path.join(file_dir,file_name)
file_type= "type/of_file" #eg. audio/mp3 or audio/wav
client = PAIClient(url="http://localhost:8080")


file_dir = "/home/adam/workstation/file_processing/test_audio"
file_name = "test_audio.mp3"
filepath = os.path.join(file_dir,file_name)
file_type = "audio/mp3"
with open(filepath, "rb") as b64_file:
    file_data = base64.b64encode(b64_file.read())
    file_data = file_data.decode("ascii")

file_obj = request_objects.file_obj(data=file_data, content_type=file_type)
timestamp = request_objects.timestamp_obj(start=1.12, end=2.14)
request_obj = request_objects.bleep_obj(file=file_obj, timestamps=[timestamp])

resp = client.bleep(request_object=request_obj)
with open(os.path.join(file_dir,f"redacted-{file_name}"), 'wb') as redacted_file:
    processed_file = resp.bleeped_file.encode("ascii")
    processed_file = base64.b64decode(processed_file, validate=True)
    redacted_file.write(processed_file)

Working with structured data

Redacting a data frame column by column

NOTE: When de-identifying smaller strings of structured data, more accurate results can be achieved by passing in the whole column as a string (including the header) and a delimiter. For example, making a request row by row for a column named SSN will return data identified as PHONE_NUMBER, even when the header is included

# Working with data frames
import pandas as pd
from privateai_client import PAIClient
from privateai_client.objects import request_objects

client = PAIClient(url="http://localhost:8080")
data_frame = pd.DataFrame(
    {
        "Name": [
            "Braund, Mr. Owen Harris",
            "Allen, Mr. William Henry",
            "Bonnell, Miss. Elizabeth",
        ],
        "Age": [22, 35, 58],
        "Sex": ["male", "male", "female"],
    }
)
print(data_frame)
text_req = request_objects.process_text_obj(text=[])
for column in data_frame.columns:
    text_req.text.append(f"{column}:{' | '.join([str(row) for row in data_frame[column]])}")

resp = client.process_text(text_req)
redacted_data = dict()
for row in resp.processed_text:
    data = row.split(':',1)
    redacted_data[data[0]] = data[1].split(' | ')
redacted_data_frame = pd.DataFrame(redacted_data)
print(redacted_data_frame)

Redacting cell by cell for columns with large text content

# Working with data frames
import pandas as pd
from privateai_client import PAIClient
from privateai_client.objects import request_objects

client = PAIClient(url="http://localhost:8080")
data_frame = pd.DataFrame(
    {
        "Book": [
            "Treasure Island",
            "Moby Dick",
        ],
        "chapter": [1,1],
        "paragraph": [1,1],
        "text": ["The Old Sea-dog at the Admiral Benbow\nSquire Trelawney, Dr. Livesey, and the rest of...",
                 "Call me Ishmael. Some years ago—never mind how long precisely—having little or no money in my purse..."
                 ]
    }
)
obj = request_objects.process_text_obj
func = client.process_text
data_frame['text'] = [(lambda x: func(obj(text=[x])).processed_text[0])(row) for row in data_frame['text']]

Reidentifying Text

from privateai_client import PAIClient
from privateai_client import request_objects

client = PAIClient(url="http://localhost:8080")

# Deidentify the text
initial_text = 'My name is John. I work for Private AI'
request_obj = request_objects.process_text_obj(text=[initial_text])
response_obj = client.process_text(request_obj)

# Build reidentify request object from the deidentified response
new_request_obj = response_obj.get_reidentify_request()
# Call the reidentify Route
new_response_obj = client.reidentify_text(new_request_obj)
print(new_response_obj.body)