| import json |
| from typing import Dict, Any |
|
|
|
|
| class ContextlessMeaningEngine: |
| """ |
| Contextless Meaning Engine v0.1 |
| - Takes a single input string. |
| - Produces a deterministic meaning-state. |
| - Ignores any history or conversation context. |
| """ |
|
|
| def __init__(self, config: Dict[str, Any] = None): |
| self.config = config or {} |
|
|
| @classmethod |
| def from_pretrained(cls, repo_path: str = ".", **kwargs): |
| """Simple loader that reads config.json from the repo directory.""" |
| try: |
| with open(f"{repo_path}/config.json", "r", encoding="utf-8") as f: |
| config = json.load(f) |
| except FileNotFoundError: |
| config = {} |
| return cls(config=config) |
|
|
| def __call__(self, text: str) -> Dict[str, Any]: |
| """Core engine: takes ONLY the current input text.""" |
| cleaned = text.strip() |
| tone = self._infer_tone(cleaned) |
| intent = self._infer_intent(cleaned) |
| complexity = self._infer_complexity(cleaned) |
| keywords = self._extract_keywords(cleaned) |
|
|
| return { |
| "input": cleaned, |
| "meaning_state": { |
| "tone": tone, |
| "intent": intent, |
| "complexity": complexity, |
| "keywords": keywords, |
| }, |
| } |
|
|
| def _infer_tone(self, text: str) -> str: |
| text_lower = text.lower() |
| if any(w in text_lower for w in ["thank", "appreciate", "grateful"]): |
| return "appreciative" |
| if any(w in text_lower for w in ["angry", "upset", "frustrated"]): |
| return "frustrated" |
| if "?" in text: |
| return "inquisitive" |
| return "neutral" |
|
|
| def _infer_intent(self, text: str) -> str: |
| text_lower = text.lower() |
| if any(w in text_lower for w in ["how", "what", "why", "where"]): |
| return "seeking_information" |
| if any(w in text_lower for w in ["please", "could you", "can you"]): |
| return "request" |
| return "statement" |
|
|
| def _infer_complexity(self, text: str) -> str: |
| length = len(text.split()) |
| if length <= 5: |
| return "simple" |
| if length <= 20: |
| return "moderate" |
| return "complex" |
|
|
| def _extract_keywords(self, text: str): |
| words = [w.strip(".,!?").lower() for w in text.split()] |
| stopwords = {"the", "and", "or", "a", "an", "to", "of", "in", "on", "for"} |
| return [w for w in words if w and w not in stopwords] |
|
|
