fix: resolve CI type annotation issues

- Replaced deprecated typing.List/Dict/Tuple with native list/dict/tuple
- Fixed trailing whitespace issues
- Fixed blank line whitespace issues
- Removed unused variables and imports
- Applied black formatting

src/nl2gherkin/nlp/analyzer.py

@@ -2,7 +2,7 @@
 
 from dataclasses import dataclass, field
 from enum import Enum
-from typing import Any, Dict, List, Optional, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, Optional
 
 import spacy
 from spacy.tokens import Doc
@@ -13,6 +13,7 @@ if TYPE_CHECKING:
 
 class ActorType(str, Enum):
     """Types of actors in requirements."""
+
     USER = "user"
     SYSTEM = "system"
     ADMIN = "admin"
@@ -22,6 +23,7 @@ class ActorType(str, Enum):
 
 class ActionType(str, Enum):
     """Types of actions in requirements."""
+
     CREATE = "create"
     READ = "read"
     UPDATE = "update"
@@ -41,6 +43,7 @@ class ActionType(str, Enum):
 @dataclass
 class RequirementAnalysis:
     """Structured analysis of a requirement."""
+
     raw_text: str
     actor: Optional[str] = None
     actor_type: ActorType = ActorType.UNKNOWN
@@ -49,10 +52,10 @@ class RequirementAnalysis:
     target: Optional[str] = None
     condition: Optional[str] = None
     benefit: Optional[str] = None
-    examples: List[str] = field(default_factory=list)
+    examples: list[str] = field(default_factory=list)
-    variables: Dict[str, str] = field(default_factory=dict)
+    variables: dict[str, str] = field(default_factory=dict)
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """Convert to dictionary."""
         return {
             "raw_text": self.raw_text,
@@ -81,6 +84,7 @@ class NLPAnalyzer:
             self.nlp = spacy.load(model)
         except OSError:
             import subprocess
+
             subprocess.run(
                 ["python", "-m", "spacy", "download", model],
                 check=True,