[WIP] Design modular text-correction app with Pipes and Filters architecture

[Copilot]

Implementation Plan for SATCN Text-Correction App

• Set up project structure with proper Python package layout
• Create requirements.txt with dependencies (language-tool-python, jamspell, pytest)
• Implement Pipes and Filters architecture base classes
• Create filter modules:
  • Input parser filter (Markdown/EPUB support)
  • Text extraction filter
  • Spelling correction filter (JamSpell)
  • Grammar correction filter (language-tool-python)
  • TTS normalization filter (regex-based rules)
  • Output generator filter (preserves structure)
• Implement pipeline orchestrator
• Add logging system throughout the pipeline
• Create comprehensive unit tests for each filter
• Add regression test corpus
• Create CLI entry point
• Add documentation and examples

Original prompt

Design a modular, reliable text-correction app using a Pipes and Filters architecture. Implement filters for input parsing, text extraction, spelling, grammar, and TTS normalization, ending with an output generator that preserves Markdown/EPUB structure. Use language-tool-python, JamSpell, and rule-based regex for TTS. Prioritize unit tests, regression corpus, logging, and scalability. Goal: automated, extensible pipeline for text normalization and correction.

---

💬 We'd love your input! Share your thoughts on Copilot coding agent in our 2 minute survey.