app.py

#!/usr/bin/env python3
"""
🎭 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
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import time
import json
import sqlite3
import hashlib
from typing import List, Dict, Iterator, Optional, Any
import threading
import random
from datetime import datetime, timedelta
import logging
from pathlib import Path

# GPU acceleration support
try:
    import spaces
    SPACES_AVAILABLE = True
    @spaces.GPU
    def gpu_placeholder():
        return "GPU acceleration available"
except ImportError:
    SPACES_AVAILABLE = False

class UniversalCharacterManager:
    """
    Universal AI Character Management System
    
    Framework-agnostic character state management with persistence, consistency tracking,
    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
        self.model = None
        self.tokenizer = None
        self.model_loaded = False
        self.loading = False
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        
        # Universal character state management
        self.session_id = self._generate_session_id()
        self.conversation_quality_scores = []
        self.memory_db_path = f"{character_name}_character_memory.db"
        
        # Cross-platform character management
        self.character_memory = {}
        self.persona_facts = {}
        self.conversation_history = []
        
        # Professional analytics and tracking
        self.character_metrics = {
            "consistency_score": 0.0,
            "authenticity_score": 0.0,
            "adaptation_rate": 0.0,
            "memory_retention": 0.0
        }
        
        # Initialize persistent character systems
        self._setup_character_persistence()
        
        print(f"🎭 Initializing Universal Character Manager")
        print(f"📋 Character: {character_name}")
        print(f"🧠 Session ID: {self.session_id}")
        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 (Creed example)
        self.load_demonstration_model()
    
    def _generate_session_id(self) -> str:
        """Generate unique session ID for character state tracking"""
        timestamp = datetime.now().isoformat()
        random_component = str(random.randint(1000, 9999))
        return hashlib.md5(f"{timestamp}_{random_component}".encode()).hexdigest()[:12]
    
    def _setup_character_persistence(self):
        """Initialize character persistence database"""
        try:
            if not os.path.exists(self.memory_db_path):
                conn = sqlite3.connect(self.memory_db_path)
                cursor = conn.cursor()
                
                # Character interaction log
                cursor.execute('''
                    CREATE TABLE IF NOT EXISTS character_interactions (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        session_id TEXT,
                        user_input TEXT,
                        character_response TEXT,
                        consistency_score REAL,
                        authenticity_score REAL,
                        timestamp TEXT
                    )
                ''')
                
                # Character state evolution
                cursor.execute('''
                    CREATE TABLE IF NOT EXISTS character_states (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        session_id TEXT,
                        character_name TEXT,
                        personality_traits TEXT,
                        memory_facts TEXT,
                        adaptation_history TEXT,
                        timestamp TEXT
                    )
                ''')
                
                # Character performance metrics
                cursor.execute('''
                    CREATE TABLE IF NOT EXISTS character_metrics (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        session_id TEXT,
                        metric_name TEXT,
                        metric_value REAL,
                        improvement_delta REAL,
                        timestamp TEXT
                    )
                ''')
                
                conn.commit()
                conn.close()
                print("✅ Character persistence system initialized")
        except Exception as e:
            print(f"⚠️ Character persistence setup failed (non-critical): {e}")
    
    def load_demonstration_model(self):
        """
        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
            
        self.loading = True
        
        try:
            print(f"🧠 Loading demonstration character model...")
            
            print("📦 Loading tokenizer...")
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.model_path,
                trust_remote_code=True,
                padding_side="left"
            )
            
            # Character-specific tokens (customizable for any character)
            character_tokens = ["<thinking>", "<memory>", "<adapt>", "<authentic>"]
            print(f"🎭 Adding character tokens: {character_tokens}")
            
            num_added_tokens = self.tokenizer.add_tokens(character_tokens)
            print(f"✅ Added {num_added_tokens} character tokens")
            
            if self.tokenizer.pad_token is None:
                self.tokenizer.pad_token = self.tokenizer.eos_token
            
            print(f"🤖 Loading model...")
            
            self.model = AutoModelForCausalLM.from_pretrained(
                self.model_path,
                torch_dtype=torch.float16,
                device_map=None,  # CPU first for GPU burst compatibility
                trust_remote_code=True,
                low_cpu_mem_usage=True
            )
            
            if num_added_tokens > 0:
                print(f"🔧 Resizing model embeddings for {num_added_tokens} tokens")
                self.model.resize_token_embeddings(len(self.tokenizer))
            
            self.model.eval()
            self.model_loaded = True
            self.loading = False
            print(f"✅ Demonstration model loaded successfully!")
            
        except Exception as e:
            print(f"❌ Model loading failed: {e}")
            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
        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."
        
        try:
            # GPU management for burst processing
            current_device = next(self.model.parameters()).device
            
            if self.device == "cuda" and current_device.type != "cuda":
                self.model = self.model.to(self.device)
            
            actual_device = next(self.model.parameters()).device
            
            # Generate response
            inputs = self.tokenizer.encode(conversation, return_tensors="pt")
            inputs = inputs.to(actual_device)
            
            with torch.no_grad():
                outputs = self.model.generate(
                    inputs,
                    max_new_tokens=200,
                    do_sample=True,
                    temperature=temperature,
                    top_p=0.95,
                    top_k=40,
                    repetition_penalty=1.15,
                    pad_token_id=self.tokenizer.eos_token_id,
                    eos_token_id=self.tokenizer.eos_token_id,
                    use_cache=True
                )
            
            full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            response = full_response[len(self.tokenizer.decode(inputs[0], skip_special_tokens=True)):].strip()
            
            return self._process_character_response(response)
            
        except Exception as e:
            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
        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>
<memory>for recalling past interactions</memory>
<adapt>for character growth moments</adapt>
<authentic>for core character expressions</authentic>
Character Guidelines:
- Stay true to established personality traits
- Reference past interactions naturally
- Show subtle character development
- Maintain authentic voice and mannerisms
"""
        
        # Build conversation context
        conversation = character_prompt
        
        # Include relevant conversation history
        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"
        
        conversation += f"Human: {message}\n"
        conversation += f"{self.character_name}:"
        
        return conversation
    
    def _process_character_response(self, response: str) -> str:
        """Universal character response processing"""
        
        # Remove artifacts
        response = response.replace("Human:", "").replace(f"{self.character_name}:", "")
        
        # Process character tokens
        token_formatting = {
            "<thinking>": "\n\n🤔 **THINKING:** ",
            "</thinking>": "\n",
            "<memory>": "\n\n💭 **REMEMBERING:** ",
            "</memory>": "\n",
            "<adapt>": "\n\n🔄 **ADAPTING:** ",
            "</adapt>": "\n",
            "<authentic>": "\n\n✨ **AUTHENTIC MOMENT:** ",
            "</authentic>": "\n"
        }
        
        for token, replacement in token_formatting.items():
            response = response.replace(token, replacement)
        
        # Clean up formatting
        response = "\n".join(line.strip() for line in response.split("\n") if line.strip())
        
        if response and not response.endswith(('.', '!', '?', '...', '*')):
            response += "."
        
        return response
    
    def _analyze_response_consistency(self, response: str) -> float:
        """Analyze response consistency with character profile"""
        if not response or len(response.strip()) < 5:
            return 0.0
        
        score = 1.0
        
        # Check for artifacts that break character
        artifacts = ["<|im_end|>", "<|im_start|>", "[INST]", "[/INST]", "Assistant:", "AI:"]
        for artifact in artifacts:
            if artifact in response:
                score -= 0.3
        
        # Analyze response length appropriateness
        words = response.split()
        if len(words) > 100:  # Too verbose
            score -= 0.1
        elif len(words) < 5:  # Too brief
            score -= 0.2
        
        # Check for repetition
        if len(words) > 5:
            unique_ratio = len(set(words)) / len(words)
            if unique_ratio < 0.7:
                score -= 0.2
        
        return max(0.0, min(1.0, score))
    
    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"],
            "neutral": ["office", "work", "people", "time", "know"],
            "negative": ["modern", "technology", "app", "social media", "smartphone"]
        }
        
        score = 0.5  # Base authenticity
        
        response_lower = response.lower()
        
        # Positive authenticity indicators
        for marker in authenticity_markers["positive"]:
            if marker in response_lower:
                score += 0.1
        
        # Negative authenticity indicators
        for marker in authenticity_markers["negative"]:
            if marker in response_lower:
                score -= 0.15
        
        return max(0.0, min(1.0, score))
    
    def _update_character_metrics(self, consistency: float, authenticity: float):
        """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:]
        
        # Update rolling averages
        self.character_metrics["consistency_score"] = sum(self.conversation_quality_scores) / len(self.conversation_quality_scores)
        self.character_metrics["authenticity_score"] = authenticity
        
        # 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"""
        try:
            conn = sqlite3.connect(self.memory_db_path)
            cursor = conn.cursor()
            
            cursor.execute('''
                INSERT INTO character_interactions 
                (session_id, user_input, character_response, consistency_score, authenticity_score, timestamp)
                VALUES (?, ?, ?, ?, ?, ?)
            ''', (
                self.session_id,
                user_input,
                response,
                consistency,
                authenticity,
                datetime.now().isoformat()
            ))
            
            conn.commit()
            conn.close()
        except Exception as e:
            print(f"⚠️ Character persistence failed (non-critical): {e}")
    
    def get_character_analytics(self) -> Dict[str, Any]:
        """Get comprehensive character performance analytics"""
        try:
            conn = sqlite3.connect(self.memory_db_path)
            cursor = conn.cursor()
            
            # Session statistics
            cursor.execute('''
                SELECT 
                    AVG(consistency_score), 
                    AVG(authenticity_score), 
                    COUNT(*), 
                    MAX(timestamp)
                FROM character_interactions 
                WHERE session_id = ?
            ''', (self.session_id,))
            
            result = cursor.fetchone()
            avg_consistency = result[0] if result[0] else 0.0
            avg_authenticity = result[1] if result[1] else 0.0
            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 {
                "session_id": self.session_id,
                "character_name": self.character_name,
                "total_interactions": interaction_count,
                "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"
            }
            
        except Exception as e:
            return {"error": str(e)}
    
    # =================== 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 = "any") -> str:
        """Universal AI Character Activation - Works with any AI system"""
        return f"""🎭 UNIVERSAL CHARACTER ACTIVATION PROTOCOL
Character: {character_name}
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
- Reference character background and experiences naturally
- Show character growth while preserving core identity
- Use character-specific knowledge and limitations
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
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."""

    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] = []
            self.persona_facts[character_name].append(memory_facts)
        
        established_facts = self.persona_facts.get(character_name, [])
        
        return f"""🧠 UNIVERSAL CHARACTER MEMORY SYSTEM
Character: {character_name}
AI Platform: {ai_system}
Memory Bank Status: {len(established_facts)} facts stored
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
CROSS-PLATFORM COMPATIBILITY:
This memory system works with:
✓ 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"}"""

    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",
            0.5: 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"
        }
        
        context_adjustments = {
            "professional": "Maintain professional standards while preserving character authenticity",
            "technical": "Allow full AI capabilities while filtering through character perspective",
            "creative": "Maximize character authenticity, embrace character quirks and limitations",
            "customer_service": "Balance brand voice with customer service excellence",
            "educational": "Combine character engagement with educational effectiveness"
        }
        
        closest_intensity = min(intensity_levels.keys(), key=lambda x: abs(x - intensity))
        base_instruction = intensity_levels[closest_intensity]
        context_instruction = context_adjustments.get(context, "Standard character behavior")
        
        return f"""⚖️ UNIVERSAL CHARACTER INTENSITY CONTROL
Character: {character_name}
AI Platform: {ai_system}
Intensity Level: {int(intensity * 100)}%
Context: {context.title()}
INTENSITY SETTING: {base_instruction}
CONTEXT ADAPTATION: {context_instruction}
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:
• 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."""

    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",
            "safety": "Immediately drop character for safety/ethical concerns, prioritize user wellbeing",
            "technical": "Provide technical expertise that character wouldn't possess, then resume character",
            "professional": "Switch to professional mode for business-critical communications",
            "meta": "Discuss the character system itself or AI capabilities openly"
        }
        
        protocol = break_protocols.get(reason, "General character break for user clarity")
        
        return f"""🚨 UNIVERSAL CHARACTER BREAK PROTOCOL
AI Platform: {ai_system}
Break Reason: {reason.title()}
Protocol: {protocol}
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
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
PLATFORM IMPLEMENTATION:
• 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 = "any") -> str:
        """Universal Speech Pattern Analysis - Works with any AI platform"""
        
        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"}
SPEECH PATTERN FRAMEWORK:
• VOCABULARY: Character-specific word choices and terminology
• 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
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:
• 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
- 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 = "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", 
                "Specialized skills and interests"
            ],
            "general_knowledge": [
                "Basic education and common knowledge",
                "Cultural awareness appropriate to character",
                "General life skills and experiences"
            ],
            "limited_knowledge": [
                "Areas outside character's experience",
                "Technical subjects beyond character scope",
                "Cultural elements character wouldn't know"
            ],
            "false_confidence": [
                "Topics character thinks they know but doesn't",
                "Areas where character makes confident mistakes",
                "Subjects character has misconceptions about"
            ]
        }
        
        return f"""🧠 UNIVERSAL CHARACTER KNOWLEDGE MAPPER
Character: {character_name}
AI Platform: {ai_system}
{f"Topic Analysis: {topic}" if topic else "General Knowledge Profile"}
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'])}
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:
• 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 = "any") -> str:
        """Universal Character Consistency Validation - Works across all platforms"""
        
        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"}
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
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:
• 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"}"""

    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",
                "problem_solving": "Apply AI logic through character's exploratory nature"
            },
            "skeptical": {
                "fact_checking": "Use AI verification as character's natural doubt and questioning",
                "analysis": "Channel AI analytical capabilities through character's critical thinking",
                "research": "Apply AI research through character's need to verify claims"
            },
            "creative": {
                "generation": "Use AI creativity as extension of character's artistic nature",
                "brainstorming": "Channel AI ideation through character's imaginative perspective",
                "storytelling": "Apply AI narrative construction through character's creative voice"
            },
            "methodical": {
                "planning": "Use AI organizational capabilities through character's systematic approach",
                "analysis": "Channel AI processing through character's detail-oriented nature",
                "problem_solving": "Apply AI logic through character's step-by-step methodology"
            }
        }
        
        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
Character Trait: {character_trait.title()}
AI Capability: {ai_capability.title()}
Target Platform: {ai_system}
ADAPTATION STRATEGY: {mapping}
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:
• 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."""

    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
Primary Character: {primary_character} ({int(blend_ratio * 100)}%)
Secondary Traits: {secondary_traits} ({int((1 - blend_ratio) * 100)}%)
AI Platform: {ai_system}
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
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:
• 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."""

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()