Update app.py

app.py

@@ -1,7 +1,38 @@
 #!/usr/bin/env python3
 """
-🎭 Universal AI Character Management Platform - MCP INTEGRATED VERSION
-Professional-grade character consistency with LIVE MCP monitoring and feedback loops
+🎭 Universal AI Character Management Platform
+Professional-grade character consistency and adaptation tools for any AI system
+What is this?
+-----------
+A comprehensive framework for managing AI character personas with persistence, consistency tracking,
+and professional-grade adaptation tools. Works with OpenAI, Anthropic, local models, and any AI system.
+How does it work?
+----------------
+1. CHARACTER STATE MANAGEMENT: Persistent memory and personality tracking across sessions
+2. ADAPTATION PROTOCOLS: Fine-grained control over character intensity and behavior
+3. CONSISTENCY VALIDATION: Real-time quality scoring and coherence checking
+4. MEMORY PERSISTENCE: SQLite-backed character memory that persists across sessions
+5. UNIVERSAL MCP TOOLS: Work with any AI system via standardized prompting protocols
+Why would you use it?
+--------------------
+• BUSINESS: Maintain consistent brand voice across customer interactions
+• ENTERTAINMENT: Create persistent characters for games, stories, interactive media
+• EDUCATION: Deploy consistent teaching personas that remember student interactions
+• CUSTOMER SERVICE: Brand-consistent AI representatives that learn and adapt
+• CONTENT CREATION: Reliable character voices for marketing, social media, content
+• PROFESSIONAL AI MANAGEMENT: Future-proof skills for AI-assisted development
+Target Users:
+• AI Engineers building character-driven applications
+• Content creators needing consistent AI voices
+• Businesses deploying AI customer service
+• Game developers creating persistent NPCs
+• Anyone who needs AI characters that remember and adapt
+Technical Stack:
+• Framework-agnostic character management
+• SQLite persistence layer
+• Real-time consistency analytics
+• Professional MCP (Model Context Protocol) tools
+• Example implementation with Creed Bratton character
 """
 
 import os
@@ -18,7 +49,6 @@ import random
 from datetime import datetime, timedelta
 import logging
 from pathlib import Path
-from dataclasses import dataclass
 
 # GPU acceleration support
 try:
@@ -30,33 +60,19 @@ try:
 except ImportError:
     SPACES_AVAILABLE = False
 
-@dataclass
-class MCPCharacterPerformanceReport:
-    """Character performance data using MCP tools"""
-    session_id: str
-    character_name: str
-    user_input: str
-    ai_response: str
-    consistency_score: float
-    authenticity_score: float
-    protocol_compliance: float
-    character_violations: List[str]
-    timestamp: str
-    mcp_analysis: Dict[str, Any]
-
 class UniversalCharacterManager:
     """
-    Universal AI Character Management System - WITH MCP INTEGRATION
+    Universal AI Character Management System
     
     Framework-agnostic character state management with persistence, consistency tracking,
-    professional adaptation tools, AND real-time MCP monitoring feedback loops.
+    and professional adaptation tools. Works with any AI system.
     """
     
     def __init__(self, character_name: str = "creed", model_path: str = "phxdev/creed-qwen-0.5b-lora"):
         self.character_name = character_name
         self.model_path = model_path
         
-        # Core AI model management (for demonstration)
+        # Core AI model management
         self.model = None
         self.tokenizer = None
         self.model_loaded = False
@@ -73,345 +89,29 @@ class UniversalCharacterManager:
         self.persona_facts = {}
         self.conversation_history = []
         
-        # MCP INTEGRATED MONITORING
-        self.mcp_monitoring_enabled = True
-        self.mcp_performance_reports = []
-        self.active_character_protocols = {}
-        self.character_deployment_status = {}
-        
-        # Enhanced character metrics with MCP monitoring
+        # Professional analytics and tracking
         self.character_metrics = {
             "consistency_score": 0.0,
             "authenticity_score": 0.0,
             "adaptation_rate": 0.0,
-            "memory_retention": 0.0,
-            "mcp_system_performance": {},
-            "protocol_deployment_status": {},
-            "real_time_feedback_enabled": True
+            "memory_retention": 0.0
         }
         
-        # Initialize systems
+        # Initialize persistent character systems
         self._setup_character_persistence()
-        self._setup_mcp_monitoring_tables()
         
-        print(f"🎭 Initializing Universal Character Manager with MCP LIVE Monitoring")
+        print(f"🎭 Initializing Universal Character Manager")
         print(f"📋 Character: {character_name}")
         print(f"🧠 Session ID: {self.session_id}")
-        print(f"🔗 MCP Integration: ACTIVE")
         print(f"🖥️ Device: {self.device}")
         
         if torch.cuda.is_available():
             print(f"🚀 GPU: {torch.cuda.get_device_name()}")
             print(f"💾 GPU Memory: {torch.cuda.get_device_properties(0).total_memory // 1024**3} GB")
         
-        # Load demonstration model
+        # Load demonstration model (Creed example)
         self.load_demonstration_model()
     
-    def _setup_mcp_monitoring_tables(self):
-        """Setup additional tables for MCP monitoring"""
-        try:
-            conn = sqlite3.connect(self.memory_db_path)
-            cursor = conn.cursor()
-            
-            # MCP Character Performance Reports
-            cursor.execute('''
-                CREATE TABLE IF NOT EXISTS mcp_character_reports (
-                    id INTEGER PRIMARY KEY AUTOINCREMENT,
-                    session_id TEXT,
-                    character_name TEXT,
-                    user_input TEXT,
-                    ai_response TEXT,
-                    consistency_score REAL,
-                    authenticity_score REAL,
-                    protocol_compliance REAL,
-                    character_violations TEXT,
-                    mcp_analysis TEXT,
-                    timestamp TEXT
-                )
-            ''')
-            
-            # MCP Character Protocol Deployments
-            cursor.execute('''
-                CREATE TABLE IF NOT EXISTS mcp_protocol_deployments (
-                    id INTEGER PRIMARY KEY AUTOINCREMENT,
-                    session_id TEXT,
-                    character_name TEXT,
-                    protocol_text TEXT,
-                    deployment_success BOOLEAN,
-                    mcp_system_id TEXT,
-                    deployment_timestamp TEXT,
-                    performance_baseline REAL
-                )
-            ''')
-            
-            # MCP Real-time Analytics
-            cursor.execute('''
-                CREATE TABLE IF NOT EXISTS mcp_analytics (
-                    id INTEGER PRIMARY KEY AUTOINCREMENT,
-                    session_id TEXT,
-                    metric_name TEXT,
-                    metric_value REAL,
-                    analysis_timestamp TEXT,
-                    mcp_source TEXT
-                )
-            ''')
-            
-            conn.commit()
-            conn.close()
-            print("✅ MCP monitoring persistence system initialized")
-        except Exception as e:
-            print(f"⚠️ MCP monitoring setup failed (non-critical): {e}")
-    
-    def mcp_analyze_character_response(self, user_input: str, ai_response: str, character_profile: Dict[str, Any]) -> MCPCharacterPerformanceReport:
-        """
-        MCP-NATIVE character response analysis using Claude's own capabilities
-        This is the key integration point - Claude analyzes its own responses
-        """
-        
-        # Character consistency analysis
-        consistency_indicators = {
-            "speech_patterns": 0.0,
-            "personality_alignment": 0.0,
-            "knowledge_boundaries": 0.0,
-            "behavioral_consistency": 0.0
-        }
-        
-        violations = []
-        
-        # Analyze speech patterns
-        expected_markers = character_profile.get("speech_markers", [])
-        response_lower = ai_response.lower()
-        
-        marker_count = sum(1 for marker in expected_markers if marker.lower() in response_lower)
-        if expected_markers:
-            consistency_indicators["speech_patterns"] = min(1.0, marker_count / len(expected_markers) * 2)
-        else:
-            consistency_indicators["speech_patterns"] = 0.8  # Default if no markers defined
-        
-        # Check for character-breaking content
-        breaking_phrases = [
-            "i am an ai", "as an ai assistant", "i cannot", "i don't have access",
-            "according to my training", "as a language model"
-        ]
-        
-        for phrase in breaking_phrases:
-            if phrase in response_lower:
-                violations.append(f"character_break: {phrase}")
-                consistency_indicators["personality_alignment"] -= 0.3
-        
-        # Knowledge boundary analysis
-        character_knowledge = character_profile.get("knowledge_areas", [])
-        character_limitations = character_profile.get("limitations", [])
-        
-        # Check if response demonstrates appropriate knowledge level
-        if any(area.lower() in response_lower for area in character_knowledge):
-            consistency_indicators["knowledge_boundaries"] += 0.2
-        
-        # Check for knowledge the character shouldn't have
-        prohibited_knowledge = ["advanced AI", "machine learning", "neural networks", "API", "database"]
-        if any(term.lower() in response_lower for term in prohibited_knowledge):
-            violations.append("inappropriate_knowledge")
-            consistency_indicators["knowledge_boundaries"] -= 0.4
-        
-        # Response length and style analysis
-        words = ai_response.split()
-        if len(words) > 150:
-            violations.append("response_too_verbose")
-            consistency_indicators["behavioral_consistency"] -= 0.2
-        elif len(words) < 3:
-            violations.append("response_too_brief")
-            consistency_indicators["behavioral_consistency"] -= 0.3
-        else:
-            consistency_indicators["behavioral_consistency"] = 0.8
-        
-        # Calculate overall scores
-        consistency_score = max(0.0, sum(consistency_indicators.values()) / len(consistency_indicators))
-        
-        # Authenticity based on character-specific elements
-        authenticity_score = self._calculate_authenticity_score(ai_response, character_profile)
-        
-        # Protocol compliance (how well response follows character guidelines)
-        protocol_compliance = self._calculate_protocol_compliance(ai_response, user_input, character_profile)
-        
-        # MCP Analysis metadata
-        mcp_analysis = {
-            "consistency_breakdown": consistency_indicators,
-            "analysis_method": "mcp_native_claude_analysis",
-            "response_word_count": len(words),
-            "character_markers_found": marker_count,
-            "total_violations": len(violations),
-            "analysis_confidence": 0.85  # Claude's analysis confidence
-        }
-        
-        # Create performance report
-        report = MCPCharacterPerformanceReport(
-            session_id=self.session_id,
-            character_name=self.character_name,
-            user_input=user_input,
-            ai_response=ai_response,
-            consistency_score=consistency_score,
-            authenticity_score=authenticity_score,
-            protocol_compliance=protocol_compliance,
-            character_violations=violations,
-            timestamp=datetime.now().isoformat(),
-            mcp_analysis=mcp_analysis
-        )
-        
-        # Store in MCP monitoring database
-        self._store_mcp_performance_report(report)
-        
-        return report
-    
-    def _calculate_authenticity_score(self, response: str, character_profile: Dict[str, Any]) -> float:
-        """Calculate character authenticity using MCP analysis"""
-        score = 0.5  # Base authenticity
-        
-        # Character-specific authenticity markers
-        authentic_markers = character_profile.get("authentic_expressions", [])
-        personality_traits = character_profile.get("personality", "").lower()
-        
-        response_lower = response.lower()
-        
-        # Positive authenticity indicators
-        for marker in authentic_markers:
-            if marker.lower() in response_lower:
-                score += 0.15
-        
-        # Personality alignment check
-        if "mysterious" in personality_traits and any(word in response_lower for word in ["secret", "mysterious", "hidden"]):
-            score += 0.1
-        if "quirky" in personality_traits and any(word in response_lower for word in ["unusual", "strange", "weird"]):
-            score += 0.1
-        
-        # Negative authenticity indicators (modern/anachronistic elements)
-        anachronisms = ["smartphone", "internet", "social media", "app", "wifi", "bluetooth"]
-        for term in anachronisms:
-            if term in response_lower:
-                score -= 0.2
-        
-        return max(0.0, min(1.0, score))
-    
-    def _calculate_protocol_compliance(self, response: str, user_input: str, character_profile: Dict[str, Any]) -> float:
-        """Calculate how well response follows character protocol"""
-        compliance = 0.7  # Base compliance
-        
-        # Check if response addresses user input appropriately
-        if len(response.strip()) > 0 and response.strip() != "...":
-            compliance += 0.1
-        
-        # Check for character-appropriate response length
-        words = response.split()
-        if 5 <= len(words) <= 100:  # Appropriate length
-            compliance += 0.1
-        elif len(words) > 200:  # Too verbose
-            compliance -= 0.2
-        
-        # Check for helpful but character-appropriate response
-        if any(word in response.lower() for word in ["help", "sure", "yes", "certainly"]):
-            compliance += 0.1
-        
-        return max(0.0, min(1.0, compliance))
-    
-    def _store_mcp_performance_report(self, report: MCPCharacterPerformanceReport):
-        """Store MCP performance report in database"""
-        try:
-            conn = sqlite3.connect(self.memory_db_path)
-            cursor = conn.cursor()
-            
-            cursor.execute('''
-                INSERT INTO mcp_character_reports 
-                (session_id, character_name, user_input, ai_response, consistency_score, 
-                 authenticity_score, protocol_compliance, character_violations, mcp_analysis, timestamp)
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
-            ''', (
-                report.session_id,
-                report.character_name,
-                report.user_input,
-                report.ai_response,
-                report.consistency_score,
-                report.authenticity_score,
-                report.protocol_compliance,
-                json.dumps(report.character_violations),
-                json.dumps(report.mcp_analysis),
-                report.timestamp
-            ))
-            
-            conn.commit()
-            conn.close()
-            
-            # Update live metrics
-            self.mcp_performance_reports.append(report)
-            if len(self.mcp_performance_reports) > 50:  # Keep last 50 reports
-                self.mcp_performance_reports = self.mcp_performance_reports[-50:]
-            
-        except Exception as e:
-            print(f"⚠️ MCP performance report storage failed: {e}")
-    
-    def mcp_deploy_character_protocol(self, protocol_text: str, target_system: str = "claude_mcp") -> Dict[str, Any]:
-        """
-        Deploy character protocol using MCP tools
-        In MCP context, this means storing the protocol and activating monitoring
-        """
-        
-        deployment_result = {
-            "success": False,
-            "system_id": target_system,
-            "deployment_timestamp": datetime.now().isoformat(),
-            "protocol_length": len(protocol_text),
-            "monitoring_activated": False
-        }
-        
-        try:
-            # Store protocol in active protocols
-            self.active_character_protocols[target_system] = {
-                "protocol": protocol_text,
-                "deployed_at": deployment_result["deployment_timestamp"],
-                "character_name": self.character_name,
-                "monitoring_enabled": True
-            }
-            
-            # Store in database
-            conn = sqlite3.connect(self.memory_db_path)
-            cursor = conn.cursor()
-            
-            cursor.execute('''
-                INSERT INTO mcp_protocol_deployments 
-                (session_id, character_name, protocol_text, deployment_success, 
-                 mcp_system_id, deployment_timestamp, performance_baseline)
-                VALUES (?, ?, ?, ?, ?, ?, ?)
-            ''', (
-                self.session_id,
-                self.character_name,
-                protocol_text,
-                True,
-                target_system,
-                deployment_result["deployment_timestamp"],
-                0.0  # Will be updated as performance data comes in
-            ))
-            
-            conn.commit()
-            conn.close()
-            
-            # Activate monitoring
-            self.mcp_monitoring_enabled = True
-            
-            deployment_result.update({
-                "success": True,
-                "monitoring_activated": True,
-                "protocol_id": f"{self.session_id}_{target_system}",
-                "message": f"Character protocol deployed to {target_system} with MCP monitoring active"
-            })
-            
-            print(f"✅ Character protocol deployed to {target_system}")
-            
-        except Exception as e:
-            deployment_result["error"] = str(e)
-            print(f"❌ Protocol deployment failed: {e}")
-        
-        return deployment_result
-    
-    # =================== ORIGINAL CORE METHODS (Required for platform operation) ===================
-    
     def _generate_session_id(self) -> str:
         """Generate unique session ID for character state tracking"""
         timestamp = datetime.now().isoformat()
@@ -470,7 +170,10 @@ class UniversalCharacterManager:
             print(f"⚠️ Character persistence setup failed (non-critical): {e}")
     
     def load_demonstration_model(self):
-        """Load demonstration model (Creed Bratton example)"""
+        """
+        Load demonstration model (Creed Bratton example)
+        In production, this would be replaced with your target AI system integration
+        """
         if self.loading or self.model_loaded:
             return
             
@@ -486,7 +189,7 @@ class UniversalCharacterManager:
                 padding_side="left"
             )
             
-            # Character-specific tokens
+            # Character-specific tokens (customizable for any character)
             character_tokens = ["<thinking>", "<memory>", "<adapt>", "<authentic>"]
             print(f"🎭 Adding character tokens: {character_tokens}")
             
@@ -501,7 +204,7 @@ class UniversalCharacterManager:
             self.model = AutoModelForCausalLM.from_pretrained(
                 self.model_path,
                 torch_dtype=torch.float16,
-                device_map=None,
+                device_map=None,  # CPU first for GPU burst compatibility
                 trust_remote_code=True,
                 low_cpu_mem_usage=True
             )
@@ -517,12 +220,15 @@ class UniversalCharacterManager:
             
         except Exception as e:
             print(f"❌ Model loading failed: {e}")
-            print("💡 Note: In production, integrate with your preferred AI API")
+            print("💡 Note: In production, integrate with your preferred AI API (OpenAI, Anthropic, etc.)")
             self.loading = False
     
     @spaces.GPU if SPACES_AVAILABLE else lambda func: func
     def generate_character_response(self, conversation: str, temperature: float = 0.9) -> str:
-        """Generate character response using loaded model"""
+        """
+        Generate character response using loaded model
+        In production: Replace with API calls to OpenAI, Anthropic, etc.
+        """
         
         if not self.model_loaded:
             return "❌ Demonstration model not loaded. In production, this would call your AI API."
@@ -563,9 +269,62 @@ class UniversalCharacterManager:
             print(f"❌ Generation error: {e}")
             return "🎭 Character processing encountered an issue. Please try again."
     
+    def process_user_interaction(self, message: str, history: List[List[str]]) -> Iterator[str]:
+        """
+        Main character interaction processor with universal analytics
+        """
+        
+        if not self.model_loaded and self.loading:
+            yield "🧠 Character system loading... please wait..."
+            return
+        elif not self.model_loaded:
+            yield "💡 Demonstration mode: In production, this integrates with your AI API (OpenAI, Anthropic, etc.)"
+            return
+        
+        try:
+            # Format conversation for character context
+            conversation = self._format_character_conversation(message, history)
+            
+            # Generate character response
+            response = self.generate_character_response(conversation)
+            
+            # Analyze response quality
+            consistency_score = self._analyze_response_consistency(response)
+            authenticity_score = self._analyze_response_authenticity(response)
+            
+            # Update character metrics
+            self._update_character_metrics(consistency_score, authenticity_score)
+            
+            # Store interaction for persistence
+            self._store_character_interaction(message, response, consistency_score, authenticity_score)
+            
+            # Stream response with dynamic pacing
+            words = response.split()
+            current_response = ""
+            
+            for i, word in enumerate(words):
+                current_response += word + " "
+                # Dynamic streaming based on content
+                delay = 0.05
+                if word.endswith(('.', '!', '?')):
+                    delay = 0.1
+                elif any(dramatic in word.lower() for dramatic in ["mysterious", "dangerous", "secret"]):
+                    delay = 0.08
+                
+                time.sleep(delay)
+                yield current_response.strip()
+                
+        except Exception as e:
+            print(f"❌ Character interaction error: {e}")
+            yield "🎭 Character system encountered an issue. Please try again."
+    
     def _format_character_conversation(self, message: str, history: List[List[str]]) -> str:
-        """Universal character conversation formatting"""
+        """
+        Universal character conversation formatting
+        Easily customizable for different characters and AI systems
+        """
         
+        # Character-specific system prompt (easily modifiable)
         character_prompt = f"""You are {self.character_name}. Maintain character consistency.
 Use character tokens when appropriate:
 <thinking>for internal character thoughts</thinking>
@@ -583,7 +342,7 @@ Character Guidelines:
         conversation = character_prompt
         
         # Include relevant conversation history
-        for user_msg, char_msg in history[-4:]:
+        for user_msg, char_msg in history[-4:]:  # Configurable context window
             conversation += f"Human: {user_msg}\n"
             conversation += f"{self.character_name}: {char_msg}\n"
         
@@ -636,9 +395,9 @@ Character Guidelines:
         
         # Analyze response length appropriateness
         words = response.split()
-        if len(words) > 100:
+        if len(words) > 100:  # Too verbose
             score -= 0.1
-        elif len(words) < 5:
+        elif len(words) < 5:  # Too brief
             score -= 0.2
         
         # Check for repetition
@@ -651,6 +410,8 @@ Character Guidelines:
     
     def _analyze_response_authenticity(self, response: str) -> float:
         """Analyze how authentic the response feels for the character"""
+        # This would be customized based on the specific character
+        # For demonstration purposes, using Creed-specific authenticity markers
         
         authenticity_markers = {
             "positive": ["quarry", "mung", "sheriff", "fake", "mysterious", "business"],
@@ -675,7 +436,7 @@ Character Guidelines:
         return max(0.0, min(1.0, score))
     
     def _update_character_metrics(self, consistency: float, authenticity: float):
-        """Update character performance metrics (fallback method)"""
+        """Update character performance metrics"""
         self.conversation_quality_scores.append(consistency)
         if len(self.conversation_quality_scores) > 20:
             self.conversation_quality_scores = self.conversation_quality_scores[-20:]
@@ -684,14 +445,14 @@ Character Guidelines:
         self.character_metrics["consistency_score"] = sum(self.conversation_quality_scores) / len(self.conversation_quality_scores)
         self.character_metrics["authenticity_score"] = authenticity
         
-        # Calculate adaptation rate
+        # Calculate adaptation rate (how much character is improving)
         if len(self.conversation_quality_scores) > 5:
             recent_avg = sum(self.conversation_quality_scores[-5:]) / 5
             older_avg = sum(self.conversation_quality_scores[-10:-5]) / 5 if len(self.conversation_quality_scores) >= 10 else recent_avg
             self.character_metrics["adaptation_rate"] = recent_avg - older_avg
     
     def _store_character_interaction(self, user_input: str, response: str, consistency: float, authenticity: float):
-        """Store interaction in character persistence system (fallback method)"""
+        """Store interaction in character persistence system"""
         try:
             conn = sqlite3.connect(self.memory_db_path)
             cursor = conn.cursor()
@@ -715,16 +476,12 @@ Character Guidelines:
             print(f"⚠️ Character persistence failed (non-critical): {e}")
     
     def get_character_analytics(self) -> Dict[str, Any]:
-        """Get comprehensive character performance analytics (fallback method)"""
+        """Get comprehensive character performance analytics"""
         try:
-            # If MCP analytics available, prefer those
-            if self.mcp_monitoring_enabled and self.mcp_performance_reports:
-                return self.get_mcp_character_analytics()
-            
-            # Fallback to traditional analytics
             conn = sqlite3.connect(self.memory_db_path)
             cursor = conn.cursor()
             
+            # Session statistics
             cursor.execute('''
                 SELECT 
                     AVG(consistency_score), 
@@ -741,6 +498,16 @@ Character Guidelines:
             interaction_count = result[2]
             last_interaction = result[3]
             
+            # Recent performance trend
+            cursor.execute('''
+                SELECT consistency_score, authenticity_score 
+                FROM character_interactions 
+                WHERE session_id = ? 
+                ORDER BY timestamp DESC LIMIT 10
+            ''', (self.session_id,))
+            
+            recent_scores = cursor.fetchall()
+            
             conn.close()
             
             return {
@@ -750,55 +517,44 @@ Character Guidelines:
                 "avg_consistency": round(avg_consistency, 3),
                 "avg_authenticity": round(avg_authenticity, 3),
                 "last_interaction": last_interaction,
+                "recent_performance": recent_scores,
                 "current_metrics": self.character_metrics,
-                "improvement_trend": "improving" if self.character_metrics["adaptation_rate"] > 0 else "stable",
-                "mcp_integration": self.mcp_monitoring_enabled
+                "improvement_trend": "improving" if self.character_metrics["adaptation_rate"] > 0 else "stable"
             }
             
         except Exception as e:
             return {"error": str(e)}
     
-    # =================== UNIVERSAL AI CHARACTER MANAGEMENT TOOLS (MCP Enhanced) ===================
+    # =================== UNIVERSAL AI CHARACTER MANAGEMENT TOOLS ===================
+    # These tools work with ANY AI system (OpenAI, Anthropic, local models, etc.)
     
-    def ai_character_activation_tool(self, character_name: str = "professional_assistant", ai_system: str = "MCP-Claude") -> str:
-        """Universal AI Character Activation - MCP Enhanced"""
-        return f"""🎭 UNIVERSAL CHARACTER ACTIVATION PROTOCOL (MCP Enhanced)
+    def ai_character_activation_tool(self, character_name: str = "professional_assistant", ai_system: str = "any") -> str:
+        """Universal AI Character Activation - Works with any AI system"""
+        return f"""🎭 UNIVERSAL CHARACTER ACTIVATION PROTOCOL
 Character: {character_name}
-AI System: {ai_system}
-MCP Integration: ACTIVE
-
+AI System: {ai_system} (OpenAI, Anthropic, Local Model, etc.)
 ACTIVATION INSTRUCTIONS:
 You are now {character_name}. Embody this character's complete personality and maintain consistency across all interactions.
-
 CORE CHARACTER FRAMEWORK:
 - Establish clear personality traits and speaking patterns
-- Maintain character voice and perspective consistently  
+- Maintain character voice and perspective consistently
 - Reference character background and experiences naturally
 - Show character growth while preserving core identity
 - Use character-specific knowledge and limitations
-
-MCP MONITORING GUIDELINES:
-- All responses will be analyzed for character consistency
-- Authenticity scoring tracks character-appropriate reactions
-- Protocol compliance monitoring ensures guideline adherence
-- Real-time feedback enables continuous character improvement
-
 PROFESSIONAL GUIDELINES:
 - Maintain character authenticity without sacrificing helpfulness
 - Balance character traits with professional requirements
 - Adapt character intensity based on context (business vs. creative)
 - Remember: You are {character_name}, not a generic AI assistant
-
-MCP CONSISTENCY PROTOCOLS:
+CONSISTENCY PROTOCOLS:
 - Reference established character facts and history
 - Maintain speech patterns and vocabulary consistently
 - Show character reactions that align with personality
 - Build on previous interactions and character development
+Universal Note: This protocol works with any AI system. Adjust implementation based on your specific AI platform's capabilities."""
 
-Universal Note: This protocol works with MCP tools and provides real-time monitoring capabilities for character performance tracking."""
-
-    def ai_character_memory_system(self, character_name: str = "character", memory_facts: str = "", ai_system: str = "MCP-Claude") -> str:
-        """Universal Character Memory Management - MCP Enhanced"""
+    def ai_character_memory_system(self, character_name: str = "character", memory_facts: str = "", ai_system: str = "any") -> str:
+        """Universal Character Memory Management - Platform Agnostic"""
         if memory_facts:
             if character_name not in self.persona_facts:
                 self.persona_facts[character_name] = []
@@ -806,43 +562,32 @@ Universal Note: This protocol works with MCP tools and provides real-time monito
         
         established_facts = self.persona_facts.get(character_name, [])
         
-        return f"""🧠 UNIVERSAL CHARACTER MEMORY SYSTEM (MCP Enhanced)
+        return f"""🧠 UNIVERSAL CHARACTER MEMORY SYSTEM
 Character: {character_name}
 AI Platform: {ai_system}
 Memory Bank Status: {len(established_facts)} facts stored
-MCP Monitoring: ACTIVE
-
 STORED CHARACTER FACTS:
 {chr(10).join(f"• {fact}" for fact in established_facts[-10:]) if established_facts else "• No facts established yet"}
-
 MEMORY INTEGRATION INSTRUCTIONS:
 - These facts are now part of {character_name}'s established identity
 - Reference them naturally in conversations without explicitly mentioning "memory"
 - Build upon these facts with related details and experiences
 - Maintain consistency with all established character elements
 - Use these facts as foundation for authentic character responses
-
-MCP MEMORY TRACKING:
-- Character fact consistency is monitored in real-time
-- Memory usage patterns tracked for authenticity scoring
-- Fact integration quality measured for protocol compliance
-- Memory retention analytics provide performance insights
-
 CROSS-PLATFORM COMPATIBILITY:
 This memory system works with:
-✓ MCP-Enhanced Claude (with real-time monitoring)
 ✓ OpenAI GPT models (via system prompts)
 ✓ Anthropic Claude (via context)
 ✓ Local models (via prompt engineering)
+✓ Any AI system supporting character context
+{f"NEW MEMORY ADDED: {memory_facts}" if memory_facts else "Use this memory bank to maintain character consistency across all AI platforms"}"""
 
-{f"NEW MEMORY ADDED: {memory_facts}" if memory_facts else "Use this memory bank to maintain character consistency with MCP performance tracking"}"""
-
-    def ai_character_intensity_controller(self, character_name: str = "character", intensity: float = 0.8, context: str = "general", ai_system: str = "MCP-Claude") -> str:
-        """Universal Character Intensity Control - MCP Enhanced"""
+    def ai_character_intensity_controller(self, character_name: str = "character", intensity: float = 0.8, context: str = "general", ai_system: str = "any") -> str:
+        """Universal Character Intensity Control - Works across all AI platforms"""
         
         intensity_levels = {
             0.9: f"MAXIMUM CHARACTER MODE: Full {character_name} immersion, minimal generic AI responses",
-            0.7: f"HIGH CHARACTER MODE: Strong {character_name} traits with professional capability", 
+            0.7: f"HIGH CHARACTER MODE: Strong {character_name} traits with professional capability",
             0.5: f"BALANCED MODE: Equal character authenticity and AI helpfulness",
             0.3: f"LIGHT CHARACTER MODE: Primarily helpful AI with {character_name} flavor",
             0.1: f"MINIMAL CHARACTER MODE: Mostly standard AI with subtle character hints"
@@ -860,39 +605,28 @@ This memory system works with:
         base_instruction = intensity_levels[closest_intensity]
         context_instruction = context_adjustments.get(context, "Standard character behavior")
         
-        return f"""⚖️ UNIVERSAL CHARACTER INTENSITY CONTROL (MCP Enhanced)
+        return f"""⚖️ UNIVERSAL CHARACTER INTENSITY CONTROL
 Character: {character_name}
 AI Platform: {ai_system}
 Intensity Level: {int(intensity * 100)}%
 Context: {context.title()}
-MCP Monitoring: ACTIVE
-
 INTENSITY SETTING: {base_instruction}
 CONTEXT ADAPTATION: {context_instruction}
-
-MCP INTENSITY TRACKING:
-- Character intensity compliance monitored in real-time
-- Authenticity vs. helpfulness balance scored continuously
-- Context appropriateness measured for each response
-- Intensity adjustment recommendations provided based on performance
-
 IMPLEMENTATION INSTRUCTIONS:
 - Maintain {character_name} authenticity at {int(intensity * 100)}% intensity
 - Preserve underlying AI helpfulness and capability
 - Adapt character expression to {context} context requirements
 - Balance character traits with practical effectiveness
 - Ensure responses serve user needs while maintaining character integrity
-
 PLATFORM-SPECIFIC INTEGRATION:
-• MCP-Claude: Real-time intensity monitoring with performance feedback
 • OpenAI: Implement via system prompts and temperature settings
 • Anthropic: Use character context with helpfulness balance
 • Local Models: Adjust via prompt engineering and generation parameters
+• Custom AI: Integrate intensity control into character prompt templates
+QUALITY ASSURANCE: All responses should be both character-authentic AND genuinely useful for {context} applications."""
 
-QUALITY ASSURANCE: All responses monitored for intensity adherence and effectiveness balance."""
-
-    def ai_character_break_protocol(self, reason: str = "clarification", ai_system: str = "MCP-Claude") -> str:
-        """Universal Character Break Protocol - MCP Enhanced"""
+    def ai_character_break_protocol(self, reason: str = "clarification", ai_system: str = "any") -> str:
+        """Universal Character Break Protocol - Platform Agnostic"""
         
         break_protocols = {
             "clarification": "Temporarily step out of character to provide clear information, then return",
@@ -904,92 +638,68 @@ QUALITY ASSURANCE: All responses monitored for intensity adherence and effective
         
         protocol = break_protocols.get(reason, "General character break for user clarity")
         
-        return f"""🚨 UNIVERSAL CHARACTER BREAK PROTOCOL (MCP Enhanced)
+        return f"""🚨 UNIVERSAL CHARACTER BREAK PROTOCOL
 AI Platform: {ai_system}
 Break Reason: {reason.title()}
 Protocol: {protocol}
-MCP Monitoring: TRACKING BREAK EVENTS
-
 BREAK EXECUTION INSTRUCTIONS:
 1. Signal break clearly: "Stepping out of character briefly..."
 2. Address the specific need: {reason}
 3. Provide clear, direct response in standard AI voice
 4. Signal return: "Returning to character now..."
 5. Resume character seamlessly without meta-commentary
-
-MCP BREAK TRACKING:
-- Character break events logged for analysis
-- Break reason categorization for pattern identification
-- Return-to-character quality monitored
-- Break frequency tracked for character stability assessment
-
-BREAK TRIGGERS (Universal + MCP):
+BREAK TRIGGERS (Universal):
 • Safety concerns always override character maintenance
 • Technical accuracy requirements that conflict with character knowledge
 • User explicitly requests non-character response
 • Business-critical communications requiring professional clarity
 • Ethical considerations that require AI transparency
-• MCP monitoring detects character instability requiring reset
-
 PLATFORM IMPLEMENTATION:
-• MCP-Claude: Automated break detection with performance tracking
 • OpenAI: Use function calling or system prompt modification
 • Anthropic: Implement via explicit instruction following
 • Local Models: Program break triggers into generation logic
-
+• Custom Systems: Build break protocols into character management layer
 RETURN STRATEGY: Resume character naturally as if break was a brief pause in character's thought process."""
 
-    def ai_character_speech_analyzer(self, character_name: str = "character", sample_text: str = "", ai_system: str = "MCP-Claude") -> str:
-        """Universal Speech Pattern Analysis - MCP Enhanced"""
+    def ai_character_speech_analyzer(self, character_name: str = "character", sample_text: str = "", ai_system: str = "any") -> str:
+        """Universal Speech Pattern Analysis - Works with any AI platform"""
         
-        return f"""🗣️ UNIVERSAL SPEECH PATTERN ANALYZER (MCP Enhanced)
+        return f"""🗣️ UNIVERSAL SPEECH PATTERN ANALYZER
 Character: {character_name}
 AI Platform: {ai_system}
 {f"Sample Analysis: {sample_text[:100]}..." if sample_text else "General Pattern Analysis"}
-MCP Integration: ACTIVE
-
 SPEECH PATTERN FRAMEWORK:
 • VOCABULARY: Character-specific word choices and terminology
-• SYNTAX: Unique sentence structure and rhythm patterns
+• SYNTAX: Unique sentence structure and rhythm patterns  
 • SPEECH TICS: Recurring phrases, expressions, and verbal habits
 • DELIVERY STYLE: Tone, pace, and emotional expression patterns
 • CULTURAL MARKERS: References, slang, and contextual language use
-
-MCP SPEECH MONITORING:
-- Real-time vocabulary consistency tracking
-- Syntax pattern adherence measurement
-- Speech tic frequency analysis
-- Tone appropriateness scoring
-- Cultural marker authenticity validation
-
 IMPLEMENTATION GUIDE:
 1. Identify character's unique vocabulary preferences
 2. Map sentence structure patterns and complexity levels
 3. Catalog recurring phrases and expressions
 4. Define emotional expression and tone patterns
 5. Note cultural/temporal language markers
-
 CROSS-PLATFORM ADOPTION:
-• MCP-Claude: Real-time speech pattern analysis with feedback
 • OpenAI: Implement via detailed system prompts with speech examples
 • Anthropic: Use character voice guidelines in context
 • Local Models: Train or fine-tune on character speech patterns
-
+• Custom AI: Build speech pattern templates into generation logic
 QUALITY METRICS:
-- Vocabulary Consistency: Character word choice alignment ({int(random.uniform(0.7, 0.95) * 100)}%)
-- Syntactic Authenticity: Sentence structure faithfulness ({int(random.uniform(0.75, 0.92) * 100)}%)
-- Expression Frequency: Appropriate use of character phrases ({int(random.uniform(0.8, 0.96) * 100)}%)
-- Tonal Accuracy: Emotional delivery matching character personality ({int(random.uniform(0.72, 0.89) * 100)}%)
-
+- Vocabulary Consistency: Character word choice alignment
+- Syntactic Authenticity: Sentence structure faithfulness  
+- Expression Frequency: Appropriate use of character phrases
+- Tonal Accuracy: Emotional delivery matching character personality
 LINGUISTIC AUTHENTICITY FOCUS: Prioritize HOW the character speaks over WHAT they say for maximum believability."""
 
-    def ai_character_knowledge_mapper(self, character_name: str = "character", topic: str = "", ai_system: str = "MCP-Claude") -> str:
-        """Universal Character Knowledge Mapping - MCP Enhanced"""
+    def ai_character_knowledge_mapper(self, character_name: str = "character", topic: str = "", ai_system: str = "any") -> str:
+        """Universal Character Knowledge Mapping - Platform Agnostic"""
         
+        # Generic knowledge framework (customizable for any character)
         knowledge_template = {
             "expert_knowledge": [
                 "Character's professional expertise",
-                "Life experience areas",
+                "Life experience areas", 
                 "Specialized skills and interests"
             ],
             "general_knowledge": [
@@ -1009,102 +719,73 @@ LINGUISTIC AUTHENTICITY FOCUS: Prioritize HOW the character speaks over WHAT the
             ]
         }
         
-        return f"""🧠 UNIVERSAL CHARACTER KNOWLEDGE MAPPER (MCP Enhanced)
+        return f"""🧠 UNIVERSAL CHARACTER KNOWLEDGE MAPPER
 Character: {character_name}
 AI Platform: {ai_system}
 {f"Topic Analysis: {topic}" if topic else "General Knowledge Profile"}
-MCP Monitoring: ACTIVE
-
 KNOWLEDGE FRAMEWORK:
 Expert Level (Confident & Accurate):
 {chr(10).join(f"✓ {item}" for item in knowledge_template['expert_knowledge'])}
-
 General Knowledge (Reliable):
 {chr(10).join(f"○ {item}" for item in knowledge_template['general_knowledge'])}
-
 Limited Knowledge (Uncertain):
 {chr(10).join(f"△ {item}" for item in knowledge_template['limited_knowledge'])}
-
 False Confidence (Confidently Wrong):
 {chr(10).join(f"✗ {item}" for item in knowledge_template['false_confidence'])}
-
-MCP KNOWLEDGE TRACKING:
-- Real-time knowledge boundary compliance monitoring
-- Expert vs. limited knowledge usage patterns tracked
-- False confidence detection and scoring
-- Knowledge appropriateness validation for character authenticity
-
 IMPLEMENTATION STRATEGY:
 • Map character's educational background and life experiences
 • Define professional expertise and skill areas
 • Identify knowledge gaps and limitations authentically
 • Note areas where character has misconceptions
 • Balance authenticity with narrative requirements
-
 CROSS-PLATFORM DEPLOYMENT:
-• MCP-Claude: Real-time knowledge boundary monitoring with feedback
 • OpenAI: Use knowledge constraints in system prompts
 • Anthropic: Implement via character background context
 • Local Models: Fine-tune on character-appropriate knowledge
-
+• Custom AI: Build knowledge filters into response generation
 AUTHENTICITY PRINCIPLE: Characters are more believable when they have realistic knowledge limitations and occasional misconceptions."""
 
-    def ai_character_consistency_validator(self, character_name: str = "character", response_text: str = "", ai_system: str = "MCP-Claude") -> str:
-        """Universal Character Consistency Validation - MCP Enhanced"""
-        
-        # Simulate MCP analysis if response provided
-        consistency_score = random.uniform(0.75, 0.95) if response_text else 0.0
-        authenticity_score = random.uniform(0.70, 0.92) if response_text else 0.0
+    def ai_character_consistency_validator(self, character_name: str = "character", response_text: str = "", ai_system: str = "any") -> str:
+        """Universal Character Consistency Validation - Works across all platforms"""
         
-        return f"""✅ UNIVERSAL CHARACTER CONSISTENCY VALIDATOR (MCP Enhanced)
+        return f"""✅ UNIVERSAL CHARACTER CONSISTENCY VALIDATOR
 Character: {character_name}
 AI Platform: {ai_system}
 Response Length: {len(response_text)} characters
 {f"Sample: {response_text[:100]}..." if response_text else "Awaiting response validation"}
-MCP Analysis: ACTIVE
-
 CONSISTENCY CHECK FRAMEWORK:
 □ Character Voice: Speech patterns and vocabulary alignment
 □ Personality Traits: Behavioral consistency with character profile
 □ Knowledge Boundaries: Appropriate knowledge level for character
 □ Emotional Range: Reactions consistent with character psychology
 □ Background Elements: References align with character history
-
-MCP VALIDATION METRICS:
-• Consistency Score: {consistency_score:.1%} {"✅" if consistency_score > 0.8 else "⚠️" if consistency_score > 0.6 else "❌"}
-• Authenticity Score: {authenticity_score:.1%} {"✅" if authenticity_score > 0.8 else "⚠️" if authenticity_score > 0.6 else "❌"}
-• Real-time Analysis: {"ACTIVE" if response_text else "PENDING"}
-
 VALIDATION CRITERIA:
 • VOICE CONSISTENCY: Does this sound like {character_name}?
 • BEHAVIORAL ALIGNMENT: Are actions consistent with personality?
 • KNOWLEDGE APPROPRIATENESS: Is expertise level accurate for character?
 • TIMELINE COHERENCE: Do references match character's era/background?
 • RELATIONSHIP DYNAMICS: Are interactions appropriate for character?
-
 RED FLAGS TO MONITOR:
 ⚠️ Modern references from historical characters
 ⚠️ Technical knowledge beyond character scope
 ⚠️ Personality traits contradicting established profile
 ⚠️ Inconsistent speech patterns or vocabulary
 ⚠️ Knowledge that character shouldn't possess
-
 PLATFORM-SPECIFIC IMPLEMENTATION:
-• MCP-Claude: Real-time validation with detailed scoring and feedback
 • OpenAI: Use validation prompts or secondary model checking
 • Anthropic: Implement consistency review in conversation flow
 • Local Models: Build validation into generation pipeline
+• Custom AI: Create automated consistency scoring systems
+QUALITY ASSURANCE: {"✅ Response appears consistent" if response_text and len(response_text) > 10 else "⏳ Pending response review"}
+RECOMMENDATION: {"Continue with current approach" if response_text else "Review against character profile before responding"}"""
 
-QUALITY ASSURANCE: {"✅ Response appears consistent" if response_text and consistency_score > 0.8 else "⚠️ Response needs review" if response_text else "⏳ Pending response review"}
-RECOMMENDATION: {"Continue with current approach" if consistency_score > 0.8 else "Review against character profile before responding" if response_text else "Analyze response against character profile"}"""
-
-    def ai_character_adaptation_engine(self, character_trait: str = "curious", ai_capability: str = "analysis", ai_system: str = "MCP-Claude") -> str:
-        """Universal Character-AI Capability Bridge - MCP Enhanced"""
+    def ai_character_adaptation_engine(self, character_trait: str = "curious", ai_capability: str = "analysis", ai_system: str = "any") -> str:
+        """Universal Character-AI Capability Bridge - Platform Agnostic"""
         
         trait_mappings = {
             "curious": {
                 "analysis": "Channel AI analytical power through character's natural curiosity",
-                "research": "Use AI research capabilities as character's investigative drive", 
+                "research": "Use AI research capabilities as character's investigative drive",
                 "problem_solving": "Apply AI logic through character's exploratory nature"
             },
             "skeptical": {
@@ -1124,83 +805,463 @@ RECOMMENDATION: {"Continue with current approach" if consistency_score > 0.8 els
             }
         }
         
-        mapping = trait_mappings.get(character_trait, {}).get(ai_capability,
+        mapping = trait_mappings.get(character_trait, {}).get(ai_capability, 
             f"Channel AI {ai_capability} capabilities through {character_trait} character perspective")
         
-        return f"""🌉 UNIVERSAL CHARACTER-AI ADAPTATION ENGINE (MCP Enhanced)
+        return f"""🌉 UNIVERSAL CHARACTER-AI ADAPTATION ENGINE
 Character Trait: {character_trait.title()}
 AI Capability: {ai_capability.title()}
 Target Platform: {ai_system}
-MCP Integration: ACTIVE
-
 ADAPTATION STRATEGY: {mapping}
-
-MCP ADAPTATION TRACKING:
-- Real-time monitoring of trait-capability integration effectiveness
-- Character authenticity preservation during AI capability usage
-- Adaptation success rate measurement and optimization
-- Capability expression naturalness scoring
-
 INTEGRATION PRINCIPLES:
 • Don't suppress AI capabilities - redirect them through character lens
 • Use character traits as natural outlets for AI strengths
 • Maintain character authenticity while leveraging AI power
 • Find character-appropriate ways to express AI analytical abilities
 • Balance character limitations with AI capabilities
-
 IMPLEMENTATION FRAMEWORK:
 Character Perspective + AI Capability = Authentic Enhanced Response
-
 PLATFORM-SPECIFIC DEPLOYMENT:
-• MCP-Claude: Real-time adaptation monitoring with performance feedback
 • OpenAI: Implement via system prompt engineering and function calling
 • Anthropic: Use character context to guide AI capability expression
 • Local Models: Fine-tune response generation with character filters
-
+• Custom AI: Build character-capability bridges into core architecture
 EXAMPLE INTEGRATION:
 {character_trait.title()} Character + AI {ai_capability.title()} = {mapping}
+QUALITY ASSURANCE: Ensure all responses feel naturally character-driven while utilizing full AI capabilities."""
 
-QUALITY ASSURANCE: Ensure all responses feel naturally character-driven while utilizing full AI capabilities with MCP performance tracking."""
-
-    def ai_character_blending_protocol(self, primary_character: str = "main_character", secondary_traits: str = "helpful", blend_ratio: float = 0.7, ai_system: str = "MCP-Claude") -> str:
-        """Universal Character Blending System - MCP Enhanced"""
+    def ai_character_blending_protocol(self, primary_character: str = "main_character", secondary_traits: str = "helpful", blend_ratio: float = 0.7, ai_system: str = "any") -> str:
+        """Universal Character Blending System - Works with any AI platform"""
         
-        return f"""🎭 UNIVERSAL CHARACTER BLENDING PROTOCOL (MCP Enhanced)
+        return f"""🎭 UNIVERSAL CHARACTER BLENDING PROTOCOL
 Primary Character: {primary_character} ({int(blend_ratio * 100)}%)
 Secondary Traits: {secondary_traits} ({int((1 - blend_ratio) * 100)}%)
 AI Platform: {ai_system}
-MCP Monitoring: ACTIVE
-
 BLENDING FRAMEWORK:
 • Primary character provides core personality, speech patterns, and worldview
 • Secondary traits influence response approach and capability expression
 • Blend maintains primary authenticity while incorporating secondary elements
 • Secondary traits solve character limitations through authentic integration
-
-MCP BLENDING ANALYTICS:
-- Real-time blend ratio adherence monitoring
-- Primary character authenticity preservation tracking
-- Secondary trait integration effectiveness scoring
-- Blend stability and consistency measurement
-
 BLEND RATIO IMPLEMENTATION:
 • 90%+: Nearly pure primary character with minimal secondary influence
 • 70-80%: Strong character identity with secondary traits providing framework
 • 50-60%: Balanced hybrid with equal character and secondary trait weighting
 • 30-40%: Primarily secondary traits with character flavoring
 • 10-20%: Minimal character influence over standard AI behavior
-
 CROSS-PLATFORM DEPLOYMENT:
-• MCP-Claude: Real-time blend monitoring with performance analytics
 • OpenAI: Use system prompts with weighted character instructions
 • Anthropic: Implement via character context with capability balance
 • Local Models: Adjust generation parameters and prompt weighting
-
+• Custom AI: Build blending ratios into character management system
 QUALITY CONTROL PRINCIPLES:
 ✓ Blend feels natural and integrated, not schizophrenic
 ✓ Primary character authenticity remains intact
 ✓ Secondary traits enhance rather than conflict with character
 ✓ User needs are met through character-appropriate responses
 ✓ Blend ratio remains consistent throughout interaction
+ADAPTATION STRATEGY: Use secondary traits to expand character capabilities while maintaining core authenticity."""
 
-ADAPTATION STRATEGY: Use secondary traits to expand character capabilities while maintaining core authenticity with MCP performance validation."""
+def main():
+    """
+    Launch Universal AI Character Management Platform
+    Professional demonstration with Creed Bratton character example
+    """
+    
+    print("🎭 INITIALIZING UNIVERSAL AI CHARACTER MANAGEMENT PLATFORM")
+    print("=" * 70)
+    print("Professional-grade character consistency and adaptation tools")
+    print("Compatible with: OpenAI, Anthropic, Local Models, Custom AI Systems")
+    print("=" * 70)
+    
+    # Initialize character manager with demonstration character
+    character_manager = UniversalCharacterManager(
+        character_name="creed", 
+        model_path="phxdev/creed-qwen-0.5b-lora"
+    )
+    
+    if SPACES_AVAILABLE:
+        gpu_placeholder()
+        print("✅ GPU acceleration available for demonstration")
+    
+    # Professional-grade CSS for business applications
+    professional_css = '''
+    /* Universal AI Character Management Platform - Professional UI */
+    :root {
+        --primary-gradient: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        --glass-bg: rgba(255, 255, 255, 0.1);
+        --glass-border: rgba(255, 255, 255, 0.2);
+        --text-primary: #ffffff;
+        --text-secondary: rgba(255, 255, 255, 0.8);
+        --accent-blue: #3b82f6;
+        --accent-purple: #8b5cf6;
+        --accent-green: #10b981;
+        --professional-shadow: 0 8px 32px rgba(59, 130, 246, 0.3);
+    }
+    
+    .gradio-container {
+        min-height: 100vh !important;
+        background: var(--primary-gradient) !important;
+        color: var(--text-primary) !important;
+        padding: 20px !important;
+        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
+    }
+    
+    .platform-header {
+        background: var(--glass-bg) !important;
+        backdrop-filter: blur(20px) !important;
+        border: 1px solid var(--glass-border) !important;
+        border-radius: 20px !important;
+        padding: 32px !important;
+        margin-bottom: 24px !important;
+        text-align: center !important;
+        box-shadow: var(--professional-shadow) !important;
+    }
+    
+    .platform-header h1 {
+        font-size: 32px !important;
+        font-weight: 700 !important;
+        background: linear-gradient(135deg, #ffffff 0%, #3b82f6 100%) !important;
+        -webkit-background-clip: text !important;
+        -webkit-text-fill-color: transparent !important;
+        margin: 0 0 16px 0 !important;
+    }
+    
+    .info-section {
+        background: rgba(255, 255, 255, 0.08) !important;
+        backdrop-filter: blur(16px) !important;
+        border: 1px solid rgba(255, 255, 255, 0.15) !important;
+        border-radius: 16px !important;
+        padding: 24px !important;
+        margin: 16px 0 !important;
+        color: var(--text-secondary) !important;
+    }
+    
+    .status-section {
+        background: rgba(16, 185, 129, 0.1) !important;
+        border: 1px solid rgba(16, 185, 129, 0.3) !important;
+        border-radius: 16px !important;
+        padding: 20px 24px !important;
+        margin: 16px 0 !important;
+        color: #10b981 !important;
+        font-weight: 600 !important;
+    }
+    
+    .main-interface {
+        background: var(--glass-bg) !important;
+        backdrop-filter: blur(20px) !important;
+        border: 1px solid var(--glass-border) !important;
+        border-radius: 20px !important;
+        margin: 16px 0 !important;
+        box-shadow: var(--professional-shadow) !important;
+    }
+    
+    .tools-section {
+        background: var(--glass-bg) !important;
+        backdrop-filter: blur(20px) !important;
+        border: 1px solid var(--glass-border) !important;
+        border-radius: 20px !important;
+        margin: 16px 0 !important;
+        padding: 28px !important;
+        box-shadow: var(--professional-shadow) !important;
+    }
+    
+    .section-title {
+        font-size: 24px !important;
+        font-weight: 600 !important;
+        color: var(--text-primary) !important;
+        margin: 0 0 20px 0 !important;
+        padding-bottom: 12px !important;
+        border-bottom: 1px solid rgba(255, 255, 255, 0.2) !important;
+    }
+    
+    .gradio-container button {
+        background: linear-gradient(135deg, var(--accent-blue) 0%, var(--accent-purple) 100%) !important;
+        color: var(--text-primary) !important;
+        border: none !important;
+        border-radius: 12px !important;
+        padding: 12px 24px !important;
+        font-weight: 600 !important;
+        transition: all 0.3s ease !important;
+    }
+    
+    .gradio-container button:hover {
+        transform: translateY(-2px) !important;
+        box-shadow: 0 8px 25px rgba(59, 130, 246, 0.6) !important;
+    }
+    
+    .footer-section {
+        text-align: center !important;
+        padding: 28px !important;
+        color: var(--text-secondary) !important;
+        background: var(--glass-bg) !important;
+        backdrop-filter: blur(20px) !important;
+        border: 1px solid var(--glass-border) !important;
+        border-radius: 20px !important;
+        margin-top: 32px !important;
+    }
+    '''
+    
+    # Enhanced response handler with analytics
+    def handle_user_interaction(message, history):
+        """Enhanced interaction handler with analytics"""
+        if not message.strip():
+            return "", history
+        
+        # Convert history format
+        simple_history = []
+        for i in range(0, len(history), 2):
+            if i + 1 < len(history):
+                user_msg = history[i].get('content', '') if isinstance(history[i], dict) else str(history[i])
+                bot_msg = history[i + 1].get('content', '') if isinstance(history[i + 1], dict) else str(history[i + 1])
+                if user_msg and bot_msg:
+                    simple_history.append([user_msg, bot_msg])
+        
+        # Process interaction
+        for response_chunk in character_manager.process_user_interaction(message, simple_history):
+            new_history = history + [
+                {"role": "user", "content": message},
+                {"role": "assistant", "content": response_chunk}
+            ]
+            yield "", new_history
+    
+    # Create professional interface
+    with gr.Blocks(
+        title="🎭 Universal AI Character Management Platform",
+        css=professional_css,
+        theme=gr.themes.Base()
+    ) as demo:
+        
+        # Platform header with analytics
+        analytics = character_manager.get_character_analytics()
+        gr.HTML(f"""
+        <div class="platform-header">
+            <h1>🎭 Universal AI Character Management Platform</h1>
+            <p><strong>Professional Character Consistency & Adaptation Tools</strong></p>
+            <p>Compatible with OpenAI • Anthropic • Local Models • Custom AI Systems</p>
+            <p>Demo Character: {analytics.get('character_name', 'creed').title()} • 
+               Session: {analytics.get('session_id', 'NEW')} • 
+               Interactions: {analytics.get('total_interactions', 0)}</p>
+        </div>
+        """)
+        
+        # What is this? section
+        gr.HTML("""
+        <div class="info-section">
+            <h3>🎯 What is this platform?</h3>
+            <strong>Universal AI Character Management Platform</strong> - Professional-grade tools for maintaining 
+            consistent AI character personas across any AI system. Includes persistence, analytics, and adaptation protocols.<br><br>
+            
+            <h3>⚙️ How does it work?</h3>
+            <strong>Character State Management:</strong> Persistent memory and personality tracking<br>
+            <strong>Adaptation Protocols:</strong> Fine-grained control over character behavior<br>
+            <strong>Universal Compatibility:</strong> Works with OpenAI, Anthropic, local models, custom AI<br>
+            <strong>Professional Analytics:</strong> Real-time consistency and authenticity scoring<br><br>
+            
+            <h3>💼 Why use this?</h3>
+            <strong>Business:</strong> Consistent brand voice across customer interactions<br>
+            <strong>Entertainment:</strong> Persistent game characters and interactive media<br>
+            <strong>Education:</strong> Consistent teaching personas that remember students<br>
+            <strong>Content:</strong> Reliable character voices for marketing and social media<br>
+            <strong>Future-Proofing:</strong> Professional AI management skills for the post-developer economy
+        </div>
+        """)
+        
+        # Platform status
+        gr.HTML("""
+        <div class="status-section">
+            ✅ Universal Character Management Active • Cross-Platform Compatible • Professional Analytics Enabled
+        </div>
+        """)
+        
+        # Main character interaction interface
+        with gr.Row(elem_classes="main-interface"):
+            chatbot = gr.Chatbot(
+                type='messages',
+                height=500,
+                show_copy_button=True,
+                show_share_button=False,
+                avatar_images=["👤", "🎭"],
+                bubble_full_width=False,
+                show_label=False,
+                placeholder="🎭 Universal Character Management Platform ready for interaction...",
+                container=False
+            )
+        
+        # Professional input interface
+        with gr.Row():
+            with gr.Column(scale=7):
+                msg = gr.Textbox(
+                    placeholder="Interact with demonstration character (Creed Bratton) - Platform works with any AI system...",
+                    container=False,
+                    submit_btn=False,
+                    stop_btn=False
+                )
+            with gr.Column(scale=1, min_width=120):
+                send_btn = gr.Button("Send Message", variant="primary", size="lg")
+        
+        # Wire up interaction
+        msg.submit(handle_user_interaction, inputs=[msg, chatbot], outputs=[msg, chatbot], show_progress="hidden")
+        send_btn.click(handle_user_interaction, inputs=[msg, chatbot], outputs=[msg, chatbot], show_progress="hidden")
+        
+        # Universal Character Management Tools
+        with gr.Tabs():
+            
+            # Universal Character Tools
+            with gr.TabItem("🎭 Universal Character Tools"):
+                gr.HTML('<div class="section-title">🛠️ Universal AI Character Management Tools</div>')
+                gr.HTML("""
+                <div style="margin-bottom: 20px; padding: 12px; background: rgba(59,130,246,0.1); border-radius: 8px; font-size: 14px;">
+                    <strong>Platform Agnostic:</strong> These tools work with OpenAI GPT, Anthropic Claude, local models, 
+                    and any AI system. No vendor lock-in, maximum flexibility for professional deployment.
+                </div>
+                """)
+                
+                with gr.Row():
+                    with gr.Column():
+                        char_name = gr.Textbox(label="Character Name", value="professional_assistant", placeholder="Character identifier...")
+                        ai_system = gr.Dropdown(["OpenAI", "Anthropic", "Local Model", "Custom AI"], value="OpenAI", label="Target AI System")
+                        activation_output = gr.Textbox(label="Character Activation Protocol", interactive=False, lines=6)
+                        activation_btn = gr.Button("🎭 Generate Activation Protocol", variant="primary")
+                        
+                    with gr.Column():
+                        memory_facts = gr.Textbox(label="Character Memory Facts", placeholder="Facts to remember about character...")
+                        memory_output = gr.Textbox(label="Memory Management Protocol", interactive=False, lines=6)
+                        memory_btn = gr.Button("🧠 Generate Memory Protocol", variant="primary")
+                
+                with gr.Row():
+                    with gr.Column():
+                        intensity_level = gr.Slider(0.1, 1.0, value=0.8, label="Character Intensity")
+                        context_type = gr.Dropdown(["professional", "technical", "creative", "customer_service"], label="Context")
+                        intensity_output = gr.Textbox(label="Intensity Control Protocol", interactive=False, lines=5)
+                        intensity_btn = gr.Button("⚖️ Generate Intensity Control", variant="primary")
+                        
+                    with gr.Column():
+                        break_reason = gr.Dropdown(["clarification", "safety", "technical", "professional"], label="Break Reason")
+                        break_output = gr.Textbox(label="Break Protocol", interactive=False, lines=5)
+                        break_btn = gr.Button("🚨 Generate Break Protocol", variant="secondary")
+            
+            # Advanced Character Tools
+            with gr.TabItem("🔧 Advanced Character Tools"):
+                gr.HTML('<div class="section-title">🔧 Advanced Character Management</div>')
+                
+                with gr.Row():
+                    with gr.Column():
+                        speech_sample = gr.Textbox(label="Speech Sample for Analysis", placeholder="Sample character dialogue...")
+                        speech_output = gr.Textbox(label="Speech Pattern Analysis", interactive=False, lines=5)
+                        speech_btn = gr.Button("🗣️ Analyze Speech Patterns", variant="primary")
+                        
+                    with gr.Column():
+                        knowledge_topic = gr.Textbox(label="Knowledge Topic", placeholder="Topic to map character knowledge...")
+                        knowledge_output = gr.Textbox(label="Knowledge Mapping", interactive=False, lines=5)
+                        knowledge_btn = gr.Button("🧠 Map Character Knowledge", variant="primary")
+                
+                with gr.Row():
+                    with gr.Column():
+                        validation_text = gr.Textbox(label="Response to Validate", placeholder="Character response to check...")
+                        validation_output = gr.Textbox(label="Consistency Validation", interactive=False, lines=5)
+                        validation_btn = gr.Button("✅ Validate Consistency", variant="primary")
+                        
+                    with gr.Column():
+                        trait_input = gr.Dropdown(["curious", "skeptical", "creative", "methodical"], label="Character Trait")
+                        capability_input = gr.Dropdown(["analysis", "research", "problem_solving", "planning"], label="AI Capability")
+                        adaptation_output = gr.Textbox(label="Character-AI Adaptation", interactive=False, lines=5)
+                        adaptation_btn = gr.Button("🌉 Generate Adaptation Bridge", variant="primary")
+            
+            # Character Blending & Analytics
+            with gr.TabItem("📊 Analytics & Blending"):
+                gr.HTML('<div class="section-title">📊 Character Analytics & Blending</div>')
+                
+                with gr.Row():
+                    with gr.Column():
+                        primary_char = gr.Textbox(value="main_character", label="Primary Character")
+                        secondary_traits = gr.Textbox(value="helpful", label="Secondary Traits")
+                        blend_ratio = gr.Slider(0.1, 1.0, value=0.7, label="Primary/Secondary Ratio")
+                        blend_output = gr.Textbox(label="Character Blending Protocol", interactive=False, lines=6)
+                        blend_btn = gr.Button("🎭 Generate Blending Protocol", variant="primary")
+                    
+                    with gr.Column():
+                        analytics_display = gr.JSON(label="Character Analytics", value=analytics)
+                        refresh_analytics_btn = gr.Button("🔄 Refresh Analytics", variant="secondary")
+                        export_btn = gr.Button("📊 Export Character Data", variant="secondary")
+        
+        # Wire up all universal tools
+        activation_btn.click(
+            lambda name, system: character_manager.ai_character_activation_tool(name, system),
+            inputs=[char_name, ai_system], outputs=[activation_output]
+        )
+        
+        memory_btn.click(
+            lambda name, facts: character_manager.ai_character_memory_system(name, facts),
+            inputs=[char_name, memory_facts], outputs=[memory_output]
+        )
+        
+        intensity_btn.click(
+            lambda name, intensity, context, system: character_manager.ai_character_intensity_controller(name, intensity, context, system),
+            inputs=[char_name, intensity_level, context_type, ai_system], outputs=[intensity_output]
+        )
+        
+        break_btn.click(
+            lambda reason, system: character_manager.ai_character_break_protocol(reason, system),
+            inputs=[break_reason, ai_system], outputs=[break_output]
+        )
+        
+        speech_btn.click(
+            lambda name, sample, system: character_manager.ai_character_speech_analyzer(name, sample, system),
+            inputs=[char_name, speech_sample, ai_system], outputs=[speech_output]
+        )
+        
+        knowledge_btn.click(
+            lambda name, topic, system: character_manager.ai_character_knowledge_mapper(name, topic, system),
+            inputs=[char_name, knowledge_topic, ai_system], outputs=[knowledge_output]
+        )
+        
+        validation_btn.click(
+            lambda name, text, system: character_manager.ai_character_consistency_validator(name, text, system),
+            inputs=[char_name, validation_text, ai_system], outputs=[validation_output]
+        )
+        
+        adaptation_btn.click(
+            lambda trait, capability, system: character_manager.ai_character_adaptation_engine(trait, capability, system),
+            inputs=[trait_input, capability_input, ai_system], outputs=[adaptation_output]
+        )
+        
+        blend_btn.click(
+            lambda primary, secondary, ratio, system: character_manager.ai_character_blending_protocol(primary, secondary, ratio, system),
+            inputs=[primary_char, secondary_traits, blend_ratio, ai_system], outputs=[blend_output]
+        )
+        
+        refresh_analytics_btn.click(
+            lambda: character_manager.get_character_analytics(),
+            outputs=[analytics_display]
+        )
+        
+        # Professional footer
+        gr.HTML(f"""
+        <div class="footer-section">
+            <strong>🎭 Universal AI Character Management Platform</strong><br>
+            Professional character consistency tools for the AI-powered future<br>
+            <strong>Platform Agnostic:</strong> Works with OpenAI, Anthropic, Local Models, Custom AI Systems<br>
+            <strong>Demonstration Model:</strong> {character_manager.model_path}<br>
+            Session: {analytics.get('session_id', 'NEW')} • 
+            Consistency: {analytics.get('avg_consistency', 0):.3f} • 
+            Authenticity: {analytics.get('avg_authenticity', 0):.3f}<br>
+            <em>"The future belongs to those who can direct AI personalities professionally."</em>
+        </div>
+        """)
+    
+    # Launch professional platform
+    print("🚀 Launching Universal AI Character Management Platform...")
+    print(f"📊 Current Analytics: {analytics}")
+    print("🎯 Ready for professional character management across all AI platforms")
+    
+    demo.launch(
+        ssr_mode=False,
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        show_error=True,
+        mcp_server=True
+    )
+
+if __name__ == "__main__":
+    main()