Current State:

1. We’ve created an archive/ folder containing the full versions of our notebooks (with “_full” suffix)
2. We’ve created a current/ folder with a clean copy of initial_data_exploration.ipynb
3. We’ve created text_processor_cp.py as a safe working copy for our refactoring

Our Goals:

1. Create a modular, well-structured pipeline for text analysis
2. Ensure clear separation between core functionality and experimental code
3. Implement proper MCP database integration
4. Meet the assessment requirements for CLD6000

The error you’re seeing (“PreprocessingLogger is not defined”) is telling us something important - we need to build our structure from the ground up, starting with dependencies. Let me explain how we should proceed:

First, we need to establish the correct import structure. In the pipeline folder, your modules should follow this dependency chain:

preprocessing_logger.py (Base)
    ↓
text_processor.py (Core)
    ↓
enhanced_preprocessor.py (Advanced Features)

Let’s create a proper PreprocessingLogger first. We should modify text_processor.py to look like this:

Then, we can properly import this in text_processor.py:

This modular structure gives us several advantages:

1. Clear Separation of Concerns:
   • PreprocessingLogger handles all logging
   • TextProcessor handles core text analysis and database operations
   • EnhancedPreprocessor (which we’ll build next) will handle advanced features
2. MCP Integration:
   • Database operations are centralized
   • Proper error handling and logging
   • Configurable connection settings
3. Assessment Requirements:
   • Clear documentation of process
   • Modular design for maintainability
   • Logging for tracking progress