framewise-secureline 0.1.3

A client library for content security detection

---

Framewise SecureLine

Framewise SecureLine is a Python library for analyzing text and audio for security concerns. The library supports text-based detection and audio chunk predictions, offering flexibility for content security use cases.

Features

• Text Detection: Analyze text for security concerns, such as banned topics, PII, or profanity.
• Audio Chunk Analysis: Process audio data in chunks for classification and security predictions.
• Custom Filters: Easily configure filters for denied topics, PII information, and word lists.
• Extensibility: Supports structured outputs for easy integration with your workflows.
• Robust Error Handling: Handles API errors, validation errors, and request timeouts gracefully.

---

Installation

Framewise SecureLine is available for installation using Poetry or pip.

Install using Poetry

poetry add framewise-secureline

Install using pip

pip install framewise-secureline

---

Getting Started

Initialization

To use Framewise SecureLine, you need an API key. Initialize the SecureLine client as shown below:

from framewise_secureline import SecureLine

# Initialize the client
client = SecureLine(
    api_key="your-api-key",
    denied_topics="medical diagnoses, competitors",
    ppi_information="SSN, Medical Records",
    word_filters="profanity",
)

---

Text Detection

Analyze a piece of text for security concerns.

result = client.detect("Sample text for analysis.")

# Access raw response
print(result)

---

Audio Chunk Analysis

Analyze an audio chunk for predictions. The library accepts a single chunk of type AudioSegment.

from pydub import AudioSegment

# Load an audio file and extract a chunk
audio = AudioSegment.from_file("/path/to/audio.mpga").set_frame_rate(16000).set_channels(1)
chunk = audio[:100]  # Extract first 100ms

# Analyze the chunk
result = client.analyze_audio_chunk(chunk)

# Access predictions
for prediction in result.predictions:
    print(f"Label: {prediction.label}, Score: {prediction.score}")
print(f"Latency: {result.latency}, Device: {result.device}")

---

Advanced Usage

Custom Filters

You can update the filters dynamically using the update_filters method:

client.update_filters(
    denied_topics="new denied topics",
    ppi_information="new PII types",
    word_filters="new word list",
)

Handling Multiple Audio Chunks

While SecureLine processes one audio chunk at a time, you can handle multiple chunks by iterating over the audio file:

chunks = [audio[i:i+100] for i in range(0, len(audio), 100)]  # Split into 100ms chunks

for i, chunk in enumerate(chunks):
    result = client.analyze_audio_chunk(chunk)
    print(f"Chunk {i}:")
    for prediction in result.predictions:
        print(f"  Label: {prediction.label}, Score: {prediction.score}")

---

Error Handling

Framewise SecureLine provides robust error handling for common scenarios:

• ValidationError: Raised for invalid inputs (e.g., empty text, incorrect audio format).
• APIError: Raised for server-side errors during API calls.
• TimeoutError: Raised when an API call exceeds the timeout.

Example

from framewise_secureline.exceptions import ValidationError, APIError, TimeoutError

try:
    result = client.detect("text to analyze")
except ValidationError as e:
    print(f"Validation failed: {e}")
except APIError as e:
    print(f"API error: {e}")
except TimeoutError as e:
    print(f"Request timed out: {e}")

---

Testing

The library includes a comprehensive suite of tests.

Run Tests

poetry run pytest

Coverage Report

poetry run pytest --cov=framewise_secureline

---

Support

For any issues or feature requests, please contact:
Email: [e-mail]

---

License

This project is licensed under the MIT License. See the LICENSE file for details.

---