[Python] How to build a lightweight rule-based NLP tagger that mimics GPT-style subtext...

I'm working on a small NLP tool that tags user input with basic subtext labels using plain Python rules.

Instead of using large LLMs, I'm trying to compress interpretive logic into conditionals.

For example:

if "not mad" in text and "forget" in text:
tags.append("Tone Conflict")
interpretation.append("Message implies contradiction between tone and intent.")


I hash the input text to track unique echoes:

import hashlib
echo_id = hashlib.sha1(text.encode()).hexdigest()[:8]


Right now I'm just using FastAPI and basic logic blocks. Looking for advice on:

• 
  How to scale this rule set modularly?
  
  
• 
  Keeping it lightweight but still expressive?
  
  
• 
  Tagging deeper subtext without hardcoding every phrase?
  

This is more of a logic compression exercise than a production tool.

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