feat: Replace general context with LinkedIn-specific context

- Update environment configuration to use TEMPERATURE_LINKEDIN and DEFAULT_TOKENS_LINKEDIN
- Replace general context with LinkedIn context in autocomplete engine
- Add LinkedIn-specialized system prompts focusing on professional networking
- Update UI to show 'LinkedIn Content' option instead of 'General Text'
- Modify prompt editor to include LinkedIn-specific templates
- Update examples and documentation to reflect LinkedIn context
- Change default context from 'general' to 'linkedin' throughout codebase
- Update README.md to document LinkedIn context type

This change transforms the application from a general text completion tool
to a LinkedIn-focused professional content creation assistant.

.env.example

@@ -27,23 +27,23 @@ RATE_LIMIT_REQUESTS_PER_MINUTE=60
 RATE_LIMIT_ENABLED=true
 
 # Model Configuration
-OPENAI_MODEL=gpt-3.5-turbo
+OPENAI_MODEL=gpt-4.1-mini
 ANTHROPIC_MODEL=claude-3-haiku-20240307
 
 # Temperature settings for different contexts (0.0 to 2.0)
 TEMPERATURE_EMAIL=0.6
 TEMPERATURE_CREATIVE=0.8
-TEMPERATURE_GENERAL=0.7
+TEMPERATURE_LINKEDIN=0.7
 
 # Default token limits for different contexts
-DEFAULT_TOKENS_EMAIL=200
-DEFAULT_TOKENS_CREATIVE=250
-DEFAULT_TOKENS_GENERAL=200
+DEFAULT_TOKENS_EMAIL=250
+DEFAULT_TOKENS_CREATIVE=500
+DEFAULT_TOKENS_LINKEDIN=500
 
 # UI Configuration
 UI_THEME=soft
-UI_TITLE=🚀 Smart Auto-Complete
-UI_DESCRIPTION=Intelligent text completion powered by AI
+UI_TITLE=🚀 LinkedIn Smart Auto-Complete
+UI_DESCRIPTION=Intelligent LinkedIn text completion powered by AI
 
 # Server Configuration
 SERVER_HOST=0.0.0.0

README.md

@@ -139,7 +139,7 @@ suggestions = autocomplete.get_suggestions(
 
 - `email`: Professional email writing
 - `creative`: Creative writing and storytelling
-- `general`: General text completion
+- `linkedin`: LinkedIn professional content and networking
 
 ## 🧪 Testing
 

app.py

@@ -6,12 +6,12 @@ A context-aware text completion tool built with Gradio
 
 from typing import List, Tuple
 
+import gradio as gr
+
 from config.settings import AppSettings
 from src.autocomplete import SmartAutoComplete
 from src.utils import setup_logging
 
-import gradio as gr
-
 # Initialize logging
 logger = setup_logging()
 
@@ -184,9 +184,9 @@ def create_interface():
                     choices=[
                         ("📧 Email Writing", "email"),
                         ("✍️ Creative Writing", "creative"),
-                        ("📝 General Text", "general"),
+                        ("💼 LinkedIn Content", "linkedin"),
                     ],
-                    value="email",
+                    value="linkedin",
                     label="Select Context",
                     elem_classes=["context-selector"],
                 )
@@ -274,24 +274,25 @@ def create_interface():
                             placeholder="Enter the user message template...",
                         )
 
-                    with gr.Tab("📝 General Context"):
-                        general_system_prompt = gr.Textbox(
+                    with gr.Tab("💼 LinkedIn Context"):
+                        linkedin_system_prompt = gr.Textbox(
                             label="System Prompt",
-                            value="""You are a helpful writing assistant. Generate natural,
-            contextually appropriate text completions. Focus on:
-            - Natural language flow
-            - Contextual relevance
-            - Clarity and coherence
-            - Appropriate tone
+                            value="""You are a LinkedIn writing assistant specialized in professional networking content. Generate engaging,
+            professional LinkedIn-appropriate text completions. Focus on:
+            - Professional networking tone
+            - Industry-relevant language
+            - Engaging and authentic voice
+            - LinkedIn best practices (hashtags, mentions, professional insights)
+            - Career development and business communication
 
             IMPORTANT: Generate a completion that is approximately {max_tokens} tokens long.
             Adjust your response length accordingly - shorter for fewer tokens, longer for more tokens.""",
                             lines=8,
-                            placeholder="Enter the system prompt for general context...",
+                            placeholder="Enter the system prompt for LinkedIn context...",
                         )
-                        general_user_template = gr.Textbox(
+                        linkedin_user_template = gr.Textbox(
                             label="User Message Template",
-                            value="Complete this text naturally with approximately {max_tokens} tokens: {text}",
+                            value="Complete this LinkedIn post/content naturally and professionally with approximately {max_tokens} tokens: {text}",
                             lines=3,
                             placeholder="Enter the user message template...",
                         )
@@ -330,9 +331,9 @@ def create_interface():
                         "creative",
                     ],
                     [
-                        "Academic research paper on technology trends",
-                        "The impact of artificial intelligence on modern society",
-                        "general",
+                        "Professional networking and career development",
+                        "Excited to share my thoughts on the future of AI in our industry",
+                        "linkedin",
                     ],
                 ],
                 inputs=[context_input, text_input, context_selector],
@@ -349,8 +350,8 @@ def create_interface():
             email_user,
             creative_sys,
             creative_user,
-            general_sys,
-            general_user,
+            linkedin_sys,
+            linkedin_user,
         ):
             """Update suggestions based on input with custom prompts"""
             logger.info(
@@ -370,9 +371,9 @@ def create_interface():
                     "user_template": creative_user,
                     "temperature": 0.8,
                 },
-                "general": {
-                    "system_prompt": general_sys,
-                    "user_template": general_user,
+                "linkedin": {
+                    "system_prompt": linkedin_sys,
+                    "user_template": linkedin_user,
                     "temperature": 0.7,
                 },
             }
@@ -405,8 +406,8 @@ def create_interface():
                 email_user_template,
                 creative_system_prompt,
                 creative_user_template,
-                general_system_prompt,
-                general_user_template,
+                linkedin_system_prompt,
+                linkedin_user_template,
             ],
             outputs=[status_display, copy_textbox],
         )
@@ -416,7 +417,7 @@ def create_interface():
         ---
         
         ### 🎮 How to Use:
-        1. **Select your context** (Email, Creative, or General)
+        1. **Select your context** (Email, Creative, or LinkedIn)
         2. **Add context information** (optional) - background info, references, or previous context
         3. **Enter your text** in the main text area
         4. **Adjust output length** (50-500 tokens) in settings
@@ -428,7 +429,7 @@ def create_interface():
         - **Context Window**: Add background info, previous conversations, or references to improve suggestions
         - **Email**: Try starting with "Dear..." or "I hope..." + add meeting context
         - **Creative**: Start with "Once upon a time..." + add story background
-        - **General**: Works great for any type of text! + add relevant context
+        - **LinkedIn**: Perfect for professional posts, career updates, industry insights + add professional context
         - **Output Length**: Adjust the token slider for longer or shorter completions
         - **Custom Prompts**: Edit the AI prompts to customize behavior for your specific needs
         

config/settings.py

@@ -60,12 +60,12 @@ class AppSettings:
         # Temperature settings for different contexts
         self.TEMPERATURE_EMAIL = float(os.getenv("TEMPERATURE_EMAIL", "0.6"))
         self.TEMPERATURE_CREATIVE = float(os.getenv("TEMPERATURE_CREATIVE", "0.8"))
-        self.TEMPERATURE_GENERAL = float(os.getenv("TEMPERATURE_GENERAL", "0.7"))
+        self.TEMPERATURE_LINKEDIN = float(os.getenv("TEMPERATURE_LINKEDIN", "0.7"))
 
         # Default token limits for different contexts
         self.DEFAULT_TOKENS_EMAIL = int(os.getenv("DEFAULT_TOKENS_EMAIL", "200"))
         self.DEFAULT_TOKENS_CREATIVE = int(os.getenv("DEFAULT_TOKENS_CREATIVE", "250"))
-        self.DEFAULT_TOKENS_GENERAL = int(os.getenv("DEFAULT_TOKENS_GENERAL", "200"))
+        self.DEFAULT_TOKENS_LINKEDIN = int(os.getenv("DEFAULT_TOKENS_LINKEDIN", "200"))
 
         # UI Configuration
         self.UI_THEME = os.getenv("UI_THEME", "soft")
@@ -135,7 +135,7 @@ class AppSettings:
         for temp_attr in [
             "TEMPERATURE_EMAIL",
             "TEMPERATURE_CREATIVE",
-            "TEMPERATURE_GENERAL",
+            "TEMPERATURE_LINKEDIN",
         ]:
             temp_value = getattr(self, temp_attr)
             if not (0.0 <= temp_value <= 2.0):
@@ -175,7 +175,7 @@ class AppSettings:
         Get configuration for a specific context
 
         Args:
-            context: Context name (email, code, creative, general)
+            context: Context name (email, code, creative, linkedin)
 
         Returns:
             Dictionary with context-specific configuration
@@ -191,14 +191,14 @@ class AppSettings:
                 "default_tokens": self.DEFAULT_TOKENS_CREATIVE,
                 "model_preference": "anthropic",  # Often better for creative content
             },
-            "general": {
-                "temperature": self.TEMPERATURE_GENERAL,
-                "default_tokens": self.DEFAULT_TOKENS_GENERAL,
+            "linkedin": {
+                "temperature": self.TEMPERATURE_LINKEDIN,
+                "default_tokens": self.DEFAULT_TOKENS_LINKEDIN,
                 "model_preference": self.DEFAULT_PROVIDER,
             },
         }
 
-        return context_configs.get(context, context_configs["general"])
+        return context_configs.get(context, context_configs["linkedin"])
 
     def get_model_for_provider(self, provider: str) -> str:
         """

settings.py

@@ -60,12 +60,12 @@ class AppSettings:
         # Temperature settings for different contexts
         self.TEMPERATURE_EMAIL = float(os.getenv("TEMPERATURE_EMAIL", "0.6"))
         self.TEMPERATURE_CREATIVE = float(os.getenv("TEMPERATURE_CREATIVE", "0.8"))
-        self.TEMPERATURE_GENERAL = float(os.getenv("TEMPERATURE_GENERAL", "0.7"))
+        self.TEMPERATURE_LINKEDIN = float(os.getenv("TEMPERATURE_LINKEDIN", "0.7"))
 
         # Default token limits for different contexts
         self.DEFAULT_TOKENS_EMAIL = int(os.getenv("DEFAULT_TOKENS_EMAIL", "200"))
         self.DEFAULT_TOKENS_CREATIVE = int(os.getenv("DEFAULT_TOKENS_CREATIVE", "250"))
-        self.DEFAULT_TOKENS_GENERAL = int(os.getenv("DEFAULT_TOKENS_GENERAL", "200"))
+        self.DEFAULT_TOKENS_LINKEDIN = int(os.getenv("DEFAULT_TOKENS_LINKEDIN", "200"))
 
         # UI Configuration
         self.UI_THEME = os.getenv("UI_THEME", "soft")
@@ -135,7 +135,7 @@ class AppSettings:
         for temp_attr in [
             "TEMPERATURE_EMAIL",
             "TEMPERATURE_CREATIVE",
-            "TEMPERATURE_GENERAL",
+            "TEMPERATURE_LINKEDIN",
         ]:
             temp_value = getattr(self, temp_attr)
             if not (0.0 <= temp_value <= 2.0):
@@ -175,7 +175,7 @@ class AppSettings:
         Get configuration for a specific context
 
         Args:
-            context: Context name (email, code, creative, general)
+            context: Context name (email, code, creative, linkedin)
 
         Returns:
             Dictionary with context-specific configuration
@@ -191,14 +191,14 @@ class AppSettings:
                 "default_tokens": self.DEFAULT_TOKENS_CREATIVE,
                 "model_preference": "anthropic",  # Often better for creative content
             },
-            "general": {
-                "temperature": self.TEMPERATURE_GENERAL,
-                "default_tokens": self.DEFAULT_TOKENS_GENERAL,
+            "linkedin": {
+                "temperature": self.TEMPERATURE_LINKEDIN,
+                "default_tokens": self.DEFAULT_TOKENS_LINKEDIN,
                 "model_preference": self.DEFAULT_PROVIDER,
             },
         }
 
-        return context_configs.get(context, context_configs["general"])
+        return context_configs.get(context, context_configs["linkedin"])
 
     def get_model_for_provider(self, provider: str) -> str:
         """

src/autocomplete.py

@@ -57,17 +57,18 @@ class SmartAutoComplete:
             "user_template": "Continue this creative writing piece naturally with approximately {max_tokens} tokens: {text}",
             "temperature": 0.8,
         },
-        "general": {
-            "system_prompt": """You are a helpful writing assistant. Generate natural,
-            contextually appropriate text completions. Focus on:
-            - Natural language flow
-            - Contextual relevance
-            - Clarity and coherence
-            - Appropriate tone
+        "linkedin": {
+            "system_prompt": """You are a LinkedIn writing assistant specialized in professional networking content. Generate engaging,
+            professional LinkedIn-appropriate text completions. Focus on:
+            - Professional networking tone
+            - Industry-relevant language
+            - Engaging and authentic voice
+            - LinkedIn best practices (hashtags, mentions, professional insights)
+            - Career development and business communication
 
             IMPORTANT: Generate a completion that is approximately {max_tokens} tokens long.
             Adjust your response length accordingly - shorter for fewer tokens, longer for more tokens.""",
-            "user_template": "Complete this text naturally with approximately {max_tokens} tokens: {text}",
+            "user_template": "Complete this LinkedIn post/content naturally and professionally with approximately {max_tokens} tokens: {text}",
             "temperature": 0.7,
         },
     }
@@ -84,7 +85,7 @@ class SmartAutoComplete:
     def get_suggestions(
         self,
         text: str,
-        context: str = "general",
+        context: str = "linkedin",
         max_tokens: int = 150,
         user_context: str = "",
     ) -> List[str]:
@@ -93,7 +94,7 @@ class SmartAutoComplete:
 
         Args:
             text: Input text to complete
-            context: Context type (email, creative, general)
+            context: Context type (email, creative, linkedin)
             max_tokens: Maximum tokens in the response
             user_context: Additional context provided by the user
 
@@ -149,7 +150,7 @@ class SmartAutoComplete:
         """Get suggestions from the API client"""
         try:
             context_config = self.CONTEXT_PROMPTS.get(
-                request.context, self.CONTEXT_PROMPTS["general"]
+                request.context, self.CONTEXT_PROMPTS["linkedin"]
             )
 
             # Format system prompt with max_tokens and user context

src/utils.py

@@ -3,282 +3,316 @@ Utility functions for Smart Auto-Complete
 Provides common functionality for text processing, logging, and validation
 """
 
+import html
 import logging
 import re
 import sys
-from typing import Dict, List, Optional, Tuple
-import html
 import unicodedata
+from typing import Dict, List, Optional, Tuple
 
 
 def setup_logging(level: str = "INFO") -> logging.Logger:
     """
     Set up logging configuration for the application
-    
+
     Args:
         level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
-        
+
     Returns:
         Configured logger instance
     """
     # Create logger
     logger = logging.getLogger("smart_autocomplete")
     logger.setLevel(getattr(logging, level.upper()))
-    
+
     # Remove existing handlers to avoid duplicates
     for handler in logger.handlers[:]:
         logger.removeHandler(handler)
-    
+
     # Create console handler with formatting
     console_handler = logging.StreamHandler(sys.stdout)
     console_handler.setLevel(getattr(logging, level.upper()))
-    
+
     # Create formatter
     formatter = logging.Formatter(
-        '%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S'
+        "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
     )
     console_handler.setFormatter(formatter)
-    
+
     # Add handler to logger
     logger.addHandler(console_handler)
-    
+
     return logger
 
 
 def sanitize_input(text: str) -> str:
     """
     Sanitize and clean input text for processing
-    
+
     Args:
         text: Raw input text
-        
+
     Returns:
         Cleaned and sanitized text
     """
     if not text:
         return ""
-    
+
     # Convert to string if not already
     text = str(text)
-    
+
     # HTML escape to prevent injection
     text = html.escape(text)
-    
+
     # Normalize unicode characters
-    text = unicodedata.normalize('NFKC', text)
-    
+    text = unicodedata.normalize("NFKC", text)
+
     # Remove excessive whitespace but preserve structure
-    text = re.sub(r'\n\s*\n\s*\n', '\n\n', text)  # Max 2 consecutive newlines
-    text = re.sub(r'[ \t]+', ' ', text)  # Multiple spaces/tabs to single space
-    
+    text = re.sub(r"\n\s*\n\s*\n", "\n\n", text)  # Max 2 consecutive newlines
+    text = re.sub(r"[ \t]+", " ", text)  # Multiple spaces/tabs to single space
+
     # Remove control characters except newlines and tabs
-    text = ''.join(char for char in text if ord(char) >= 32 or char in '\n\t')
-    
+    text = "".join(char for char in text if ord(char) >= 32 or char in "\n\t")
+
     # Trim leading/trailing whitespace
     text = text.strip()
-    
+
     return text
 
 
 def extract_context_hints(text: str) -> Dict[str, any]:
     """
     Extract contextual hints from the input text to improve suggestions
-    
+
     Args:
         text: Input text to analyze
-        
+
     Returns:
         Dictionary containing context hints
     """
     hints = {
-        'length': len(text),
-        'word_count': len(text.split()),
-        'has_greeting': False,
-        'has_signature': False,
-        'has_code_markers': False,
-        'has_questions': False,
-        'tone': 'neutral',
-        'language_style': 'general'
+        "length": len(text),
+        "word_count": len(text.split()),
+        "has_greeting": False,
+        "has_signature": False,
+        "has_code_markers": False,
+        "has_questions": False,
+        "tone": "neutral",
+        "language_style": "linkedin",
     }
-    
+
     text_lower = text.lower()
-    
+
     # Check for email patterns
-    email_greetings = ['dear', 'hello', 'hi', 'greetings', 'good morning', 'good afternoon']
-    email_signatures = ['sincerely', 'best regards', 'thank you', 'yours truly', 'kind regards']
-    
-    hints['has_greeting'] = any(greeting in text_lower for greeting in email_greetings)
-    hints['has_signature'] = any(signature in text_lower for signature in email_signatures)
-    
+    email_greetings = [
+        "dear",
+        "hello",
+        "hi",
+        "greetings",
+        "good morning",
+        "good afternoon",
+    ]
+    email_signatures = [
+        "sincerely",
+        "best regards",
+        "thank you",
+        "yours truly",
+        "kind regards",
+    ]
+
+    hints["has_greeting"] = any(greeting in text_lower for greeting in email_greetings)
+    hints["has_signature"] = any(
+        signature in text_lower for signature in email_signatures
+    )
+
     # Check for code patterns
-    code_markers = ['//', '/*', '*/', '#', 'def ', 'function', 'class ', 'import ', 'from ']
-    hints['has_code_markers'] = any(marker in text_lower for marker in code_markers)
-    
+    code_markers = [
+        "//",
+        "/*",
+        "*/",
+        "#",
+        "def ",
+        "function",
+        "class ",
+        "import ",
+        "from ",
+    ]
+    hints["has_code_markers"] = any(marker in text_lower for marker in code_markers)
+
     # Check for questions
-    hints['has_questions'] = '?' in text or any(q in text_lower for q in ['what', 'how', 'why', 'when', 'where', 'who'])
-    
+    hints["has_questions"] = "?" in text or any(
+        q in text_lower for q in ["what", "how", "why", "when", "where", "who"]
+    )
+
     # Determine tone
-    formal_words = ['please', 'kindly', 'respectfully', 'sincerely', 'professional']
-    casual_words = ['hey', 'yeah', 'cool', 'awesome', 'thanks']
-    
+    formal_words = ["please", "kindly", "respectfully", "sincerely", "professional"]
+    casual_words = ["hey", "yeah", "cool", "awesome", "thanks"]
+
     formal_count = sum(1 for word in formal_words if word in text_lower)
     casual_count = sum(1 for word in casual_words if word in text_lower)
-    
+
     if formal_count > casual_count:
-        hints['tone'] = 'formal'
+        hints["tone"] = "formal"
     elif casual_count > formal_count:
-        hints['tone'] = 'casual'
-    
+        hints["tone"] = "casual"
+
     # Determine language style
-    if hints['has_code_markers']:
-        hints['language_style'] = 'technical'
-    elif hints['has_greeting'] or hints['has_signature']:
-        hints['language_style'] = 'business'
-    elif any(creative in text_lower for creative in ['once upon', 'story', 'character', 'plot']):
-        hints['language_style'] = 'creative'
-    
+    if hints["has_code_markers"]:
+        hints["language_style"] = "technical"
+    elif hints["has_greeting"] or hints["has_signature"]:
+        hints["language_style"] = "business"
+    elif any(
+        creative in text_lower
+        for creative in ["once upon", "story", "character", "plot"]
+    ):
+        hints["language_style"] = "creative"
+
     return hints
 
 
 def validate_api_key(api_key: str, provider: str) -> bool:
     """
     Validate API key format for different providers
-    
+
     Args:
         api_key: The API key to validate
         provider: The provider name (openai, anthropic)
-        
+
     Returns:
         True if the key format is valid, False otherwise
     """
     if not api_key or not isinstance(api_key, str):
         return False
-    
+
     api_key = api_key.strip()
-    
-    if provider.lower() == 'openai':
+
+    if provider.lower() == "openai":
         # OpenAI keys start with 'sk-' and are typically 51 characters
-        return api_key.startswith('sk-') and len(api_key) >= 40
-    elif provider.lower() == 'anthropic':
-        # Anthropic keys start with 'sk-ant-' 
-        return api_key.startswith('sk-ant-') and len(api_key) >= 40
-    
+        return api_key.startswith("sk-") and len(api_key) >= 40
+    elif provider.lower() == "anthropic":
+        # Anthropic keys start with 'sk-ant-'
+        return api_key.startswith("sk-ant-") and len(api_key) >= 40
+
     return False
 
 
 def truncate_text(text: str, max_length: int, preserve_words: bool = True) -> str:
     """
     Truncate text to a maximum length while optionally preserving word boundaries
-    
+
     Args:
         text: Text to truncate
         max_length: Maximum allowed length
         preserve_words: Whether to preserve word boundaries
-        
+
     Returns:
         Truncated text
     """
     if len(text) <= max_length:
         return text
-    
+
     if not preserve_words:
         return text[:max_length].rstrip() + "..."
-    
+
     # Find the last space before the max_length
     truncated = text[:max_length]
-    last_space = truncated.rfind(' ')
-    
+    last_space = truncated.rfind(" ")
+
     if last_space > max_length * 0.8:  # Only use word boundary if it's not too far back
         return text[:last_space].rstrip() + "..."
     else:
         return text[:max_length].rstrip() + "..."
 
 
-def format_suggestions_for_display(suggestions: List[str], max_display_length: int = 100) -> List[Dict[str, str]]:
+def format_suggestions_for_display(
+    suggestions: List[str], max_display_length: int = 100
+) -> List[Dict[str, str]]:
     """
     Format suggestions for display in the UI
-    
+
     Args:
         suggestions: List of suggestion strings
         max_display_length: Maximum length for display
-        
+
     Returns:
         List of formatted suggestion dictionaries
     """
     formatted = []
-    
+
     for i, suggestion in enumerate(suggestions, 1):
         # Clean the suggestion
         clean_suggestion = sanitize_input(suggestion)
-        
+
         # Create display version (truncated if needed)
         display_text = truncate_text(clean_suggestion, max_display_length)
-        
-        formatted.append({
-            'id': i,
-            'text': clean_suggestion,
-            'display_text': display_text,
-            'length': len(clean_suggestion),
-            'word_count': len(clean_suggestion.split())
-        })
-    
+
+        formatted.append(
+            {
+                "id": i,
+                "text": clean_suggestion,
+                "display_text": display_text,
+                "length": len(clean_suggestion),
+                "word_count": len(clean_suggestion.split()),
+            }
+        )
+
     return formatted
 
 
 def calculate_text_similarity(text1: str, text2: str) -> float:
     """
     Calculate similarity between two texts using simple word overlap
-    
+
     Args:
         text1: First text
         text2: Second text
-        
+
     Returns:
         Similarity score between 0 and 1
     """
     if not text1 or not text2:
         return 0.0
-    
+
     # Convert to lowercase and split into words
     words1 = set(text1.lower().split())
     words2 = set(text2.lower().split())
-    
+
     # Calculate Jaccard similarity
     intersection = len(words1.intersection(words2))
     union = len(words1.union(words2))
-    
+
     return intersection / union if union > 0 else 0.0
 
 
 def get_text_stats(text: str) -> Dict[str, int]:
     """
     Get basic statistics about the text
-    
+
     Args:
         text: Text to analyze
-        
+
     Returns:
         Dictionary with text statistics
     """
     if not text:
-        return {'characters': 0, 'words': 0, 'sentences': 0, 'paragraphs': 0}
-    
+        return {"characters": 0, "words": 0, "sentences": 0, "paragraphs": 0}
+
     # Count characters (excluding whitespace)
-    char_count = len(text.replace(' ', '').replace('\n', '').replace('\t', ''))
-    
+    char_count = len(text.replace(" ", "").replace("\n", "").replace("\t", ""))
+
     # Count words
     word_count = len(text.split())
-    
+
     # Count sentences (rough estimate)
-    sentence_count = len(re.findall(r'[.!?]+', text))
-    
+    sentence_count = len(re.findall(r"[.!?]+", text))
+
     # Count paragraphs
-    paragraph_count = len([p for p in text.split('\n\n') if p.strip()])
-    
+    paragraph_count = len([p for p in text.split("\n\n") if p.strip()])
+
     return {
-        'characters': char_count,
-        'words': word_count,
-        'sentences': max(1, sentence_count),  # At least 1 sentence
-        'paragraphs': max(1, paragraph_count)  # At least 1 paragraph
+        "characters": char_count,
+        "words": word_count,
+        "sentences": max(1, sentence_count),  # At least 1 sentence
+        "paragraphs": max(1, paragraph_count),  # At least 1 paragraph
     }