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This commit introduces a new project, `deepseek_content_moderation`, designed to detect sensitive content in text based on configurable keyword lists. Key features include: - Customizable categories of sensitive words stored in `config.json`. - A `Moderator` class (`moderator.py`) that loads the configuration and uses regex for case-insensitive, whole-word matching. - The `analyze_text` method returns a dictionary of triggered categories and the specific words found. - Comprehensive unit tests (`tests/test_moderator.py`) using pytest ensure the functionality of the `Moderator` class. - A detailed `README.md` provides an overview, setup instructions, usage examples, and testing guidelines. The project structure has been set up to be a valid Python package, with the main directory named `deepseek_content_moderation`. This project serves as a foundational component for applications requiring basic content filtering capabilities.
101 lines
3.9 KiB
Markdown
101 lines
3.9 KiB
Markdown
# DeepSeek Content Moderation Tool
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## Overview
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This project provides a Python tool for detecting potentially sensitive content in text. It uses a configurable list of keywords and phrases across various categories to analyze input text and flag any matches. This tool is intended as a basic building block for more complex content moderation systems.
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## Features
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- **Configurable Categories**: Define your own categories of sensitive content and the keywords/phrases for each.
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- **JSON Configuration**: Sensitive word lists are managed in an easy-to-edit `config.json` file.
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- **Regex-Based Matching**: Uses regular expressions for case-insensitive, whole-word matching.
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- **Returns Matched Categories and Words**: Provides a dictionary of categories that were triggered and the specific words found.
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- **Extensible**: Designed to be integrated into larger applications or workflows.
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## Project Structure
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```
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deepseek_content_moderation/
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├── __init__.py
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├── config.json
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├── moderator.py
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├── README.md
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└── tests/
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├── __init__.py
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└── test_moderator.py
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```
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## Setup and Installation
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1. **Prerequisites**:
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* Python 3.7+
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* `pytest` (for running tests): `pip install pytest`
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2. **Configuration (`config.json`)**:
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The `config.json` file stores the categories and lists of sensitive words. You can edit this file to add, remove, or modify categories and their associated terms.
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Example structure:
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```json
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{
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"Profanity": ["example_swear", "another_bad_word"],
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"HateSpeech": ["example_slur", "derogatory_term"],
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// ... other categories
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}
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```
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*Initially, the file contains a predefined set of categories and example terms based on common types of sensitive content.*
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## Usage
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The core functionality is provided by the `Moderator` class in `moderator.py`.
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```python
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from deepseek_content_moderation.moderator import Moderator
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# Initialize the moderator (it will load from config.json by default)
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# You can also provide a custom path to a config file:
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# moderator = Moderator(config_path="path/to/your/custom_config.json")
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moderator = Moderator()
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text_to_analyze = "This text contains an example_swear and a derogatory_term."
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analysis_result = moderator.analyze_text(text_to_analyze)
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if analysis_result:
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print("Sensitive content found:")
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for category, words in analysis_result.items():
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print(f" Category: {category}, Words: {', '.join(words)}")
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else:
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print("No sensitive content detected.")
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# Example Output:
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# Sensitive content found:
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# Category: Profanity, Words: example_swear
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# Category: HateSpeech, Words: derogatory_term
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```
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### `analyze_text(text: str) -> dict`
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- **Input**: A string of text to analyze.
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- **Output**: A dictionary where keys are the names of sensitive categories found in the text. The value for each key is a list of unique words/phrases from the input text that matched the sensitive terms in that category. If no sensitive content is found, an empty dictionary is returned.
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## Running Tests
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To run the unit tests, navigate to the parent directory of `deepseek_content_moderation` and run:
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```bash
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python -m pytest
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```
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Or, navigate into the `deepseek_content_moderation` directory and run `pytest`. Ensure `pytest` is installed.
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## Disclaimer
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This tool provides basic keyword-based detection. It is not a comprehensive solution for content moderation, which often requires more sophisticated NLP techniques, contextual understanding, and human oversight. The initial lists of sensitive words in `config.json` are illustrative and will likely need significant expansion and refinement for any practical application.
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## Contributing
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Feel free to expand upon this project. Suggestions for improvement include:
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- More sophisticated matching algorithms (e.g., Levenshtein distance for typos).
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- Support for multiple languages.
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- Integration with machine learning models for nuanced detection.
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- More granular reporting.
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```
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