text_parser.py

"""
text_parser.py - NLP Parser for extracting features from natural language input

Renamed from parser.py to avoid shadowing Python's stdlib `parser` module,
which caused ImportError in Python 3.9+ where the stdlib module was removed
and confused imports in earlier versions.
"""
import spacy
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional

# Keyword maps for heuristic enrichment

BELIEF_KEYWORDS = {"belief", "faith", "religion", "indoctrination", "upbringing", "schooling"}
IDENTITY_KEYWORDS = {"self", "identity", "perception", "fragmented", "stable", "awareness"}
MORALITY_KEYWORDS = {"justice", "fairness", "forgiveness", "dilemma", "moral", "immoral", "law"}
PHILOSOPHY_KEYWORDS = {"yin", "yang", "wu", "wei", "dao", "paradox", "harmony", "goddess", "cosmic"}
PSYCHOLOGY_KEYWORDS = {"fear", "anxiety", "impulse", "coping", "resilience", "stress", "support",
                       "confidence", "desire", "impress", "validation", "status"}
SOCIAL_KEYWORDS = {"empathy", "trust", "group", "hierarchy", "validation", "status",
                   "conformity", "social", "proof"}
PHYSICS_KEYWORDS = {"spin", "gravity", "force", "mass", "velocity", "acceleration",
                    "momentum", "energy", "field", "rotation", "torque"}
MATH_KEYWORDS = {"proof", "optimize", "minimize", "probability", "equation", "function",
                 "derivative", "integral", "sum"}

# Expanded domains
TECHNOLOGY_KEYWORDS = {"algorithm", "data", "network", "cloud", "AI", "machine", "learning",
                       "automation", "robotics", "cybersecurity"}
BIOLOGY_KEYWORDS = {"cell", "gene", "DNA", "RNA", "protein", "enzyme", "mutation",
                    "evolution", "species", "ecosystem"}
CHEMISTRY_KEYWORDS = {"atom", "molecule", "reaction", "bond", "compound", "acid",
                      "base", "catalyst", "solution", "oxidation"}
MEDICINE_KEYWORDS = {"diagnosis", "therapy", "surgery", "vaccine", "infection", "immune",
                     "symptom", "treatment", "rehabilitation", "prevention"}
NEUROSCIENCE_KEYWORDS = {"neuron", "synapse", "cortex", "dopamine", "memory", "plasticity",
                         "signal", "brain", "consciousness", "cognition"}
LINGUISTICS_KEYWORDS = {"syntax", "semantics", "phonetics", "morphology", "grammar",
                        "dialect", "language", "translation", "discourse", "pragmatics"}
ART_KEYWORDS = {"painting", "sculpture", "music", "dance", "literature", "poetry",
                "theater", "cinema", "design", "aesthetics"}
HISTORY_KEYWORDS = {"ancient", "medieval", "renaissance", "revolution", "empire",
                    "colonial", "industrial", "modern", "war", "civilization"}
GEOGRAPHY_KEYWORDS = {"continent", "country", "city", "mountain", "river", "climate",
                      "map", "region", "territory", "landscape"}
POLITICS_KEYWORDS = {"government", "policy", "democracy", "dictatorship", "election",
                     "constitution", "rights", "freedom", "authority", "power"}
ECONOMICS_KEYWORDS = {"market", "trade", "currency", "inflation", "investment",
                      "capital", "labor", "production", "consumption", "growth"}
BUSINESS_KEYWORDS = {"management", "strategy", "leadership", "innovation", "startup",
                     "entrepreneurship", "finance", "marketing", "sales", "operations"}
EDUCATION_KEYWORDS = {"learning", "teaching", "curriculum", "assessment", "student",
                      "teacher", "school", "university", "knowledge", "pedagogy"}
ENVIRONMENT_KEYWORDS = {"climate", "sustainability", "pollution", "conservation",
                        "biodiversity", "renewable", "ecosystem", "carbon", "green", "recycling"}
LAW_KEYWORDS = {"contract", "court", "judge", "jury", "legislation", "regulation",
                "crime", "justice", "rights", "liability"}
ETHICS_KEYWORDS = {"virtue", "duty", "responsibility", "honesty", "integrity",
                   "fairness", "respect", "values", "principles", "morality"}
MYTHOLOGY_KEYWORDS = {"hero", "legend", "myth", "god", "goddess", "pantheon",
                      "ritual", "sacrifice", "prophecy", "symbol"}
ASTRONOMY_KEYWORDS = {"planet", "star", "galaxy", "universe", "orbit", "cosmos",
                      "blackhole", "nebula", "comet", "asteroid"}
ENGINEERING_KEYWORDS = {"design", "structure", "mechanical", "electrical", "civil",
                        "aerospace", "chemical", "software", "system", "process"}
SPORTS_KEYWORDS = {"team", "player", "coach", "game", "match", "tournament",
                   "score", "goal", "victory", "competition"}
MUSIC_KEYWORDS = {"melody", "harmony", "rhythm", "tempo", "instrument",
                  "composition", "performance", "song", "genre", "lyrics"}
FOOD_KEYWORDS = {"cuisine", "recipe", "ingredient", "dish", "meal",
                 "flavor", "taste", "nutrition", "diet", "cooking"}
TRAVEL_KEYWORDS = {"journey", "trip", "destination", "adventure", "exploration",
                   "tourism", "vacation", "map", "guide", "culture"}
PSYCHOTHERAPY_KEYWORDS = {"counseling", "therapy", "psychoanalysis", "CBT",
                          "mindfulness", "support", "healing", "trauma", "growth", "intervention"}
AI_KEYWORDS = {"neural", "deep", "learning", "model", "training", "dataset",
               "inference", "optimization", "pattern", "recognition"}
SECURITY_KEYWORDS = {"vulnerability", "exploit", "attack", "defense", "firewall",
                     "encryption", "authentication", "authorization", "malware", "phishing"}


@dataclass
class ParsedFeatures:
    """Structured representation of parsed text features"""
    actors: List[str] = field(default_factory=list)
    objects: List[str] = field(default_factory=list)
    actions: List[str] = field(default_factory=list)
    relations: List[str] = field(default_factory=list)
    intents: List[str] = field(default_factory=list)
    conditions: List[str] = field(default_factory=list)
    environment: Dict[str, Any] = field(default_factory=dict)
    uncertainty: float = 0.0

    # Extended enrichers
    beliefs: List[str] = field(default_factory=list)
    identity_signals: List[str] = field(default_factory=list)
    morality_signals: List[str] = field(default_factory=list)
    philosophy_signals: List[str] = field(default_factory=list)
    psychology_signals: List[str] = field(default_factory=list)
    social_signals: List[str] = field(default_factory=list)
    physics_signals: List[str] = field(default_factory=list)
    math_signals: List[str] = field(default_factory=list)

    raw_text: str = ""


class TextParser:
    """Parser using spaCy for NLP analysis"""

    def __init__(self, model_name: str = "en_core_web_sm"):
        """Initialize parser with spaCy model"""
        try:
            self.nlp = spacy.load(model_name)
        except OSError:
            print(f"spaCy model '{model_name}' not found. Downloading...")
            import subprocess
            subprocess.run(["python", "-m", "spacy", "download", model_name], check=True)
            self.nlp = spacy.load(model_name)

    def parse(self, text: str) -> ParsedFeatures:
        """Parse text and extract structured features"""
        doc = self.nlp(text)

        # Basic extraction
        actors = [ent.text for ent in doc.ents if ent.label_ in ("PERSON", "ORG")]
        objects = [chunk.text for chunk in doc.noun_chunks]
        actions = [token.lemma_ for token in doc if token.pos_ == "VERB"]
        relations = [f"{tok.head.text} → {tok.text}" for tok in doc if tok.dep_ in ("nsubj", "dobj")]
        conditions = [tok.text for tok in doc if tok.dep_ == "advcl"]
        intents = [tok.text for tok in doc if tok.text.lower() in ("want", "need", "should", "must")]

        # Heuristic enrichers — compare lowercased token text against lowercase keyword sets
        beliefs = [tok.text for tok in doc if tok.text.lower() in BELIEF_KEYWORDS]
        identity_signals = [tok.text for tok in doc if tok.text.lower() in IDENTITY_KEYWORDS]
        morality_signals = [tok.text for tok in doc if tok.text.lower() in MORALITY_KEYWORDS]
        philosophy_signals = [tok.text for tok in doc if tok.text.lower() in PHILOSOPHY_KEYWORDS]
        psychology_signals = [tok.text for tok in doc if tok.text.lower() in PSYCHOLOGY_KEYWORDS]
        social_signals = [tok.text for tok in doc if tok.text.lower() in SOCIAL_KEYWORDS]
        physics_signals = [tok.text for tok in doc if tok.text.lower() in PHYSICS_KEYWORDS]
        math_signals = [tok.text for tok in doc if tok.text.lower() in MATH_KEYWORDS]

        # Uncertainty heuristic: modal verbs relative to sentence length
        # FIX: original included ALL verbs (VB tag), inflating the count falsely.
        # Only true modals (MD tag) contribute to uncertainty.
        modal_count = sum(1 for tok in doc if tok.tag_ == "MD")
        uncertainty = min(modal_count / max(len(doc), 1) * 5, 1.0)

        return ParsedFeatures(
            actors=actors,
            objects=objects,
            actions=actions,
            relations=relations,
            intents=intents,
            conditions=conditions,
            environment={},
            uncertainty=uncertainty,
            beliefs=beliefs,
            identity_signals=identity_signals,
            morality_signals=morality_signals,
            philosophy_signals=philosophy_signals,
            psychology_signals=psychology_signals,
            social_signals=social_signals,
            physics_signals=physics_signals,
            math_signals=math_signals,
            raw_text=text,
        )


if __name__ == "__main__":
    parser = TextParser()
    sample = "The goddess Themis weighs justice and forgiveness, while fear and anxiety test identity."
    parsed = parser.parse(sample)
    print(parsed)