CensorBot/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

CensorBot is a Python application that acts as a data sanitization tool for IT service companies. It uses a small LLM (like DeepSeek) to automatically detect and censor sensitive customer information in text inputs. Users input text containing customer data, and CensorBot returns a sanitized version with all sensitive information replaced by placeholders. This censored text can then be safely used with any external LLM service (Claude, GPT-4, etc.) without risking data breaches. The application uses NiceGUI for the frontend.

Key Architecture

Core Components

• Frontend: NiceGUI-based web interface (to be implemented in src/main.py)
• LLM Integration: src/lib/llm.py provides async HTTP client for LLM API communication
  • Supports both streaming and non-streaming responses
  • Uses httpx for async HTTP requests
  • Expects OpenAI-compatible chat completions API

Configuration

• Environment Variables (via .env file):
  • BACKEND_BASE_URL: Censoring LLM backend URL (e.g., DeepSeek API)
  • BACKEND_API_TOKEN: API authentication token for the censoring LLM
  • BACKEND_MODEL: Model to use for censoring (e.g., "deepseek-chat")
• System Prompt: Located in src/prompt.md - defines the censoring LLM's behavior for identifying and redacting sensitive data

Development Commands

Package Management (using uv)

# Install dependencies
uv sync

# Add a dependency
uv add <package>

# Run the application
uv run src/main.py

Running the Application

# Run the NiceGUI application (once implemented)
uv run python src/main.py

Important Implementation Notes

1. LLM Integration: The get_response function in src/lib/llm.py is fully functional and expects:

   • Backend configuration with BACKEND_BASE_URL, BACKEND_API_TOKEN and BACKEND_MODEL
   • Messages in OpenAI format with roles: "system", "assistant", "user"
   • Returns async generators for both streaming and non-streaming modes
   • Used exclusively for the censoring functionality

2. Security Focus: This application handles sensitive customer data. Always:

   • Ensure proper data sanitization before and after LLM processing
   • Never log or expose raw customer information
   • Keep API tokens secure and never commit them

3. Frontend Development: When implementing the NiceGUI interface in src/main.py:

   • Provide input field for text containing sensitive data
   • Display censored output that users can copy
   • Use async handlers to integrate with the LLM backend
   • Implement proper error handling for API failures
   • Consider showing before/after comparison
   • Add copy-to-clipboard functionality for the censored text

4. System Prompt: The src/prompt.md file should contain clear instructions for the censoring LLM on:

   • What constitutes customer information (names, addresses, phone numbers, emails, etc.)
   • How to censor/redact sensitive data (e.g., replace with placeholders like [CUSTOMER_NAME], [EMAIL], etc.)
   • Maintaining context while protecting privacy
   • Ensuring the output remains useful for the downstream processing LLM

5. Usage Flow:

   • User pastes text with customer data into CensorBot
   • CensorBot uses small LLM to identify and replace sensitive information
   • User receives censored text with placeholders
   • User can copy censored text and use it with any external LLM service
   • No direct integration with external LLMs - CensorBot is a standalone sanitization tool