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clean-text-to-keywords/.ipynb_checkpoints/keyword_extractor-checkpoint.py

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+"""Rule-based keyword extraction and normalization for Pokemon card generation."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, Set, Tuple
+
+# Canonical concept -> synonym list
+from typing import Dict, List
+
+DEFAULT_NORMALIZATION_MAP: Dict[str, List[str]] = {
+    "normal": ["basic", "common", "regular", "plain"],
+    "fire": ["flame", "flames", "burn", "burning", "blaze", "fiery", "heat", "inferno"],
+    "water": ["wave", "ocean", "sea", "river", "aqua", "splash", "tidal"],
+    "grass": ["plant", "leaf", "forest", "nature", "vine", "seed", "flora"],
+    "flying": ["air", "wind", "sky", "wing", "wings", "flight", "soar"],
+    "fighting": ["punch", "kick", "strike", "martial", "combat", "brawl"],
+    "poison": ["toxic", "venom", "acid", "poisonous", "toxin"],
+    "electric": ["lightning", "thunder", "shock", "volt", "spark", "electricity"],
+    "ground": ["earth", "soil", "sand", "mud", "quake", "dust"],
+    "rock": ["stone", "boulder", "crystal", "rocky", "pebble"],
+    "psychic": ["mind", "mental", "telepathy", "psyonic", "brain", "illusion"],
+    "ice": ["freeze", "frozen", "snow", "frost", "blizzard", "icy"],
+    "bug": ["insect", "ant", "beetle", "spider", "crawler"],
+    "ghost": ["spirit", "phantom", "haunt", "shadow", "specter"],
+    "steel": ["metal", "iron", "armor", "blade", "alloy"],
+    "dragon": ["drake", "wyrm", "serpent", "legendary"],
+    "dark": ["shadow", "evil", "night", "doom", "darkness"],
+    "fairy": ["magic", "magical", "sparkle", "light", "charm"],
+}
+
+DEFAULT_ALLOWED_POS: Tuple[str, ...] = ("NOUN", "ADJ", "VERB")
+
+
+def _invert_normalization_map(normalization_map: Mapping[str, Iterable[str]]) -> Dict[str, str]:
+    """Build synonym -> canonical mapping for O(1) normalization lookup."""
+    inverse: Dict[str, str] = {}
+    for canonical, synonyms in normalization_map.items():
+        canonical_normalized = canonical.strip().lower()
+        inverse[canonical_normalized] = canonical_normalized
+        for synonym in synonyms:
+            synonym_normalized = synonym.strip().lower()
+            if synonym_normalized:
+                inverse[synonym_normalized] = canonical_normalized
+    return inverse
+
+
+def _deduplicate_preserve_order(items: Iterable[str]) -> List[str]:
+    seen: Set[str] = set()
+    output: List[str] = []
+    for item in items:
+        if item not in seen:
+            seen.add(item)
+            output.append(item)
+    return output
+
+
+@dataclass
+class KeywordExtractor:
+    """Deterministic spaCy + rule-based keyword extraction pipeline."""
+
+    nlp: Any
+    normalization_map: Mapping[str, Iterable[str]] = field(default_factory=lambda: DEFAULT_NORMALIZATION_MAP)
+    allowed_pos: Sequence[str] = field(default_factory=lambda: DEFAULT_ALLOWED_POS)
+
+    def __post_init__(self) -> None:
+        self._normalization_lookup = _invert_normalization_map(self.normalization_map)
+        self._allowed_pos_set = set(self.allowed_pos)
+
+    @classmethod
+    def from_default_model(
+        cls,
+        model_name: str = "en_core_web_sm",
+        normalization_map: Optional[Mapping[str, Iterable[str]]] = None,
+        allowed_pos: Sequence[str] = DEFAULT_ALLOWED_POS,
+    ) -> "KeywordExtractor":
+        """Initialize extractor with a spaCy English pipeline."""
+        try:
+            import spacy
+
+            nlp = spacy.load(model_name)
+        except OSError as exc:
+            raise OSError(
+                f"spaCy model '{model_name}' is not installed. "
+                "Run: python -m spacy download en_core_web_sm"
+            ) from exc
+        except Exception as exc:
+            raise RuntimeError(
+                "spaCy could not be loaded in this Python environment. "
+                "Try Python 3.13 or lower, then install spaCy and en_core_web_sm."
+            ) from exc
+
+        return cls(
+            nlp=nlp,
+            normalization_map=normalization_map or DEFAULT_NORMALIZATION_MAP,
+            allowed_pos=allowed_pos,
+        )
+
+    def extract(self, text: str) -> List[str]:
+        """Extract and normalize keywords from already-cleaned text."""
+        if not text or not text.strip():
+            return []
+
+        doc = self.nlp(text)
+
+        # Step 1: POS filtering + base normalization to lowercase lemmas/tokens.
+        raw_keywords: List[str] = []
+        for token in doc:
+            if token.is_stop or token.is_punct or token.pos_ not in self._allowed_pos_set:
+                continue
+
+            # Use lemma where possible to collapse inflections.
+            base = token.lemma_.lower().strip() if token.lemma_ and token.lemma_ != "-PRON-" else token.text.lower().strip()
+            if base:
+                raw_keywords.append(base)
+
+        # Step 2: Deduplicate before domain normalization (as requested in README).
+        deduplicated = _deduplicate_preserve_order(raw_keywords)
+
+        # Step 3: Map variants/synonyms to canonical concepts.
+        normalized = [self._normalize_keyword(keyword) for keyword in deduplicated]
+
+        # Step 4: Deduplicate again, since multiple words can map to one concept.
+        return _deduplicate_preserve_order(normalized)
+
+    def _normalize_keyword(self, keyword: str) -> str:
+        keyword_lower = keyword.lower()
+        return self._normalization_lookup.get(keyword_lower, keyword_lower)
+
+
+def extract_keywords(
+    text: str,
+    extractor: Optional[KeywordExtractor] = None,
+) -> List[str]:
+    """Convenience API to extract keywords with default extractor config."""
+    active_extractor = extractor or KeywordExtractor.from_default_model()
+    return active_extractor.extract(text)