initial commit

.claude/settings.local.json

@@ -0,0 +1,8 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(mkdir:*)"
+    ],
+    "deny": []
+  }
+}

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual Environment
+venv/
+ENV/
+env/
+.venv
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+
+# Data files
+data/saves/
+data/monsters/
+data/models/
+data/cache/
+*.db
+*.sqlite
+
+# Logs
+logs/
+*.log
+
+# Model files
+*.bin
+*.pth
+*.pt
+*.gguf
+*.safetensors
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Environment
+.env
+.env.local
+
+# Jupyter
+.ipynb_checkpoints/
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+
+# HuggingFace
+wandb/
+runs/

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+DigiPal is a Gradio-based web application designed as a "Digital friend of the next era". This is a Hugging Face Space application that creates a simple interactive interface.
+
+## Architecture
+
+- **Framework**: Gradio 5.34.2 for web interface
+- **Language**: Python
+- **Deployment**: Hugging Face Spaces
+- **License**: Apache 2.0
+
+## Core Structure
+
+- `app.py` - Main application entry point containing the Gradio interface
+- `README.md` - Hugging Face Space configuration and metadata
+
+## Development Commands
+
+Since this is a simple Gradio application, development is straightforward:
+
+```bash
+# Run the application locally
+python app.py
+```
+
+The application will launch a Gradio interface accessible via web browser.
+
+## Key Implementation Details
+
+The application currently implements a basic greeting function through a Gradio interface. The main components are:
+- Simple text input/output interface
+- Gradio demo that launches on execution
+- Hugging Face Space configuration for deployment
+
+## Hugging Face Space Configuration
+
+The project is configured as a Hugging Face Space with:
+- SDK: Gradio 5.34.2
+- App file: app.py
+- Color theme: red to pink gradient
+- Emoji: 😻

Dockerfile

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+FROM python:3.11-slim
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV TRANSFORMERS_CACHE=/app/data/cache
+ENV HF_HOME=/app/data/cache
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git \
+    ffmpeg \
+    libsndfile1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY . .
+
+# Create necessary directories
+RUN mkdir -p data/saves data/models data/cache logs config
+
+# Expose port
+EXPOSE 7860
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=30s --start-period=60s --retries=3 \
+    CMD curl -f http://localhost:7860/health || exit 1
+
+# Run the application
+CMD ["python", "app.py"]

README.md

@@ -1,14 +1,74 @@
 ---
-title: DigiPal
-emoji: 😻
-colorFrom: red
-colorTo: pink
+title: DigiPal Advanced Monster Companion
+emoji: 🐾
+colorFrom: purple
+colorTo: blue
 sdk: gradio
 sdk_version: 5.34.2
 app_file: app.py
 pinned: false
-license: apache-2.0
-short_description: Digital friend of the next era
+license: mit
+models:
+  - Qwen/Qwen2.5-1.5B-Instruct
+  - openai/whisper-base
+datasets: []
+tags:
+  - gaming
+  - ai-companion
+  - monster-raising
+  - conversation
+  - speech-recognition
+suggested_hardware: t4-medium
+suggested_storage: medium
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🐾 DigiPal - Advanced AI Monster Companion
+
+The next generation of virtual monster companions powered by **Qwen 2.5**, **Whisper**, and advanced AI technologies. Experience deep emotional connections with your digital pet through natural conversation, comprehensive care systems, and sophisticated evolution mechanics.
+
+## ✨ Features
+
+### 🧠 Advanced AI Personality System
+- **Qwen 2.5-powered conversations** with contextual memory
+- **Dynamic personality traits** that evolve with care
+- **Emotional state recognition** and appropriate responses
+- **Voice chat support** with Whisper speech recognition
+
+### 🎮 Comprehensive Monster Care
+- **Six-dimensional care system** (health, happiness, hunger, energy, discipline, cleanliness)
+- **Real-time stat degradation** that continues even when offline
+- **Complex evolution requirements** inspired by classic monster-raising games
+- **Training mini-games** that affect monster development
+
+### 🌟 Next-Generation Features
+- **Cross-session persistence** with browser state management
+- **Real-time streaming updates** using Gradio 5.34.2
+- **Zero GPU optimization** for efficient resource usage
+- **Advanced breeding system** with genetic inheritance
+
+## 🚀 Technology Stack
+
+- **HuggingFace Transformers v4.52.4** with Flash Attention 2
+- **Gradio 5.34.2** with modern state management
+- **Qwen 2.5 models** optimized for conversation
+- **Faster Whisper** for efficient speech processing
+- **Zero GPU deployment** for scalable AI inference
+
+## 🎯 Getting Started
+
+1. **Create Your Monster**: Choose a name and personality type
+2. **Start Caring**: Feed, train, and interact with your companion
+3. **Build Relationships**: Use voice or text chat to bond
+4. **Watch Evolution**: Meet requirements to unlock new forms
+5. **Explore Breeding**: Combine monsters for unique offspring
+
+## 💡 Tips for Best Experience
+
+- **Regular interaction** builds stronger relationships
+- **Balanced care** prevents evolution mistakes  
+- **Voice chat** creates deeper emotional connections
+- **Training variety** unlocks special evolution paths
+
+---
+
+*Experience the future of AI companionship with DigiPal!*

app.py

@@ -1,7 +1,177 @@
-import gradio as gr
+#!/usr/bin/env python3
+"""
+DigiPal - Advanced AI Monster Companion
+Built with HuggingFace Transformers v4.52.4 & Gradio 5.34.2
+Optimized for Qwen 2.5 models and Zero GPU deployment
+"""
 
-def greet(name):
-    return "Hello " + name + "!!"
+import os
+import sys
+import logging
+import asyncio
+from pathlib import Path
+import signal
+from typing import Dict, Any
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Add src to Python path
+sys.path.insert(0, str(Path(__file__).parent / "src"))
+
+# Import core components
+from src.ui.gradio_interface import ModernDigiPalInterface
+from src.deployment.zero_gpu_optimizer import ZeroGPUOptimizer
+from src.utils.performance_tracker import PerformanceTracker
+
+def setup_logging(log_level: str = "INFO"):
+    """Setup comprehensive logging configuration"""
+    
+    # Create logs directory
+    logs_dir = Path("logs")
+    logs_dir.mkdir(exist_ok=True)
+    
+    # Configure logging
+    logging.basicConfig(
+        level=getattr(logging, log_level.upper()),
+        format='%(asctime)s - %(name)s - %(levelname)s - %(funcName)s:%(lineno)d - %(message)s',
+        handlers=[
+            logging.FileHandler(logs_dir / "digipal.log"),
+            logging.StreamHandler(sys.stdout)
+        ]
+    )
+    
+    # Set specific logger levels
+    logging.getLogger("transformers").setLevel(logging.WARNING)
+    logging.getLogger("torch").setLevel(logging.WARNING)
+    logging.getLogger("gradio").setLevel(logging.INFO)
+
+def setup_environment():
+    """Setup environment variables and configurations"""
+    
+    # Create necessary directories
+    directories = [
+        "data/saves",
+        "data/monsters", 
+        "data/models",
+        "data/cache",
+        "logs",
+        "config"
+    ]
+    
+    for directory in directories:
+        Path(directory).mkdir(parents=True, exist_ok=True)
+    
+    # Set environment variables for optimization
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"  # Avoid tokenizer warnings
+    os.environ["TRANSFORMERS_CACHE"] = str(Path("data/cache").absolute())
+    os.environ["HF_HOME"] = str(Path("data/cache").absolute())
+    
+    # CUDA optimization settings
+    if "CUDA_VISIBLE_DEVICES" not in os.environ:
+        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    
+    # Memory optimization
+    os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
+
+async def initialize_components() -> Dict[str, Any]:
+    """Initialize all application components"""
+    logger = logging.getLogger(__name__)
+    
+    try:
+        # Initialize performance tracker
+        performance_tracker = PerformanceTracker()
+        await performance_tracker.initialize()
+        
+        # Initialize GPU optimizer
+        gpu_optimizer = ZeroGPUOptimizer()
+        resources = await gpu_optimizer.detect_available_resources()
+        
+        logger.info(f"Detected resources: {resources}")
+        
+        # Initialize main interface
+        interface = ModernDigiPalInterface()
+        await interface.initialize()
+        
+        return {
+            "interface": interface,
+            "performance_tracker": performance_tracker,
+            "gpu_optimizer": gpu_optimizer,
+            "resources": resources
+        }
+        
+    except Exception as e:
+        logger.error(f"Component initialization failed: {e}")
+        raise
+
+def handle_shutdown(signum, frame):
+    """Handle graceful shutdown"""
+    logger = logging.getLogger(__name__)
+    logger.info(f"Received signal {signum}, shutting down gracefully...")
+    
+    # Cleanup operations would go here
+    sys.exit(0)
+
+def main():
+    """Main application entry point"""
+    
+    # Setup signal handlers
+    signal.signal(signal.SIGINT, handle_shutdown)
+    signal.signal(signal.SIGTERM, handle_shutdown)
+    
+    # Setup environment
+    setup_logging(os.getenv("LOG_LEVEL", "INFO"))
+    setup_environment()
+    
+    logger = logging.getLogger(__name__)
+    logger.info("Starting DigiPal Advanced Monster Companion...")
+    
+    try:
+        # Check Python version
+        if sys.version_info < (3, 8):
+            raise RuntimeError("Python 3.8 or higher is required")
+        
+        # Initialize async event loop
+        if sys.platform == "win32":
+            asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy())
+        
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        
+        # Initialize components
+        components = loop.run_until_complete(initialize_components())
+        
+        logger.info("All components initialized successfully")
+        
+        # Launch configuration based on environment
+        launch_config = {
+            "server_name": os.getenv("SERVER_NAME", "0.0.0.0"),
+            "server_port": int(os.getenv("SERVER_PORT", "7860")),
+            "share": os.getenv("SHARE", "false").lower() == "true",
+            "debug": os.getenv("DEBUG", "false").lower() == "true",
+            "show_error": True,
+            "enable_queue": True,
+            "max_threads": int(os.getenv("MAX_THREADS", "40")),
+            "auth": None  # Can be configured for production
+        }
+        
+        # Add SSL configuration for production
+        if os.getenv("SSL_ENABLED", "false").lower() == "true":
+            launch_config.update({
+                "ssl_keyfile": os.getenv("SSL_KEYFILE"),
+                "ssl_certfile": os.getenv("SSL_CERTFILE"),
+                "ssl_keyfile_password": os.getenv("SSL_PASSWORD")
+            })
+        
+        logger.info(f"Launching interface with config: {launch_config}")
+        
+        # Launch the interface
+        components["interface"].launch(**launch_config)
+        
+    except KeyboardInterrupt:
+        logger.info("Application stopped by user")
+    except Exception as e:
+        logger.error(f"Application failed to start: {e}")
+        raise
+    finally:
+        logger.info("DigiPal application shutdown complete")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+# Core ML Framework - Latest optimized versions
+transformers==4.52.4
+torch>=2.2.0
+torchaudio>=2.2.0
+gradio==5.34.2
+
+# Qwen 2.5 Optimization Stack
+auto-gptq>=0.7.1
+optimum>=1.16.0
+accelerate>=0.26.1
+bitsandbytes>=0.42.0
+
+# Enhanced Audio Processing
+faster-whisper>=1.0.0
+librosa>=0.10.1
+soundfile>=0.12.1
+webrtcvad>=2.0.10
+
+# Production Backend
+fastapi>=0.108.0
+uvicorn[standard]>=0.25.0
+pydantic>=2.5.0
+
+# Advanced State Management
+apscheduler>=3.10.4
+aiosqlite>=0.19.0
+
+# Zero GPU Optimization
+spaces>=0.28.0
+
+# Core Utilities
+numpy>=1.24.0
+pandas>=2.1.0
+pillow>=10.1.0
+python-dateutil>=2.8.2
+emoji>=2.8.0
+psutil>=5.9.0
+
+# Async Support
+aiofiles>=23.2.0
+asyncio-mqtt>=0.16.1
+
+# Scientific Computing
+scipy>=1.11.0
+scikit-learn>=1.3.0
+
+# Development Tools
+pytest>=7.4.0
+black>=23.0.0

src/ai/__init__.py

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+# AI module initialization

src/ai/qwen_processor.py

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+import torch
+from transformers import (
+    AutoModelForCausalLM, 
+    AutoTokenizer, 
+    pipeline,
+    BitsAndBytesConfig
+)
+from optimum.gptq import GPTQConfig
+import asyncio
+import logging
+from typing import Dict, List, Optional, Any
+import json
+import time
+from dataclasses import dataclass
+
+@dataclass
+class ModelConfig:
+    model_name: str
+    max_memory_gb: float
+    inference_speed: str  # "fast", "balanced", "quality"
+    use_quantization: bool = True
+    use_flash_attention: bool = True
+
+class QwenProcessor:
+    def __init__(self, config: ModelConfig):
+        self.config = config
+        self.logger = logging.getLogger(__name__)
+        
+        # Optimized model configurations
+        self.model_configs = {
+            "fast": {
+                "model_name": "Qwen/Qwen2.5-0.5B-Instruct",
+                "torch_dtype": torch.float16,
+                "device_map": "auto",
+                "attn_implementation": "flash_attention_2"
+            },
+            "balanced": {
+                "model_name": "Qwen/Qwen2.5-1.5B-Instruct",
+                "torch_dtype": torch.bfloat16,
+                "device_map": "auto",
+                "attn_implementation": "flash_attention_2"
+            },
+            "quality": {
+                "model_name": "Qwen/Qwen2.5-3B-Instruct",
+                "torch_dtype": torch.bfloat16,
+                "device_map": "sequential",
+                "attn_implementation": "flash_attention_2"
+            }
+        }
+        
+        self.model = None
+        self.tokenizer = None
+        self.pipeline = None
+        self.conversation_cache = {}
+        
+        # Performance tracking
+        self.inference_times = []
+        self.memory_usage = []
+        
+    async def initialize(self):
+        """Initialize the Qwen 2.5 model with optimizations"""
+        try:
+            model_config = self.model_configs[self.config.inference_speed]
+            
+            # Quantization configuration
+            if self.config.use_quantization:
+                quantization_config = BitsAndBytesConfig(
+                    load_in_4bit=True,
+                    bnb_4bit_compute_dtype=torch.bfloat16,
+                    bnb_4bit_use_double_quant=True,
+                    bnb_4bit_quant_type="nf4"
+                )
+            else:
+                quantization_config = None
+            
+            # Load tokenizer
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                model_config["model_name"],
+                trust_remote_code=True,
+                use_fast=True
+            )
+            
+            # Load model with optimizations
+            self.model = AutoModelForCausalLM.from_pretrained(
+                model_config["model_name"],
+                torch_dtype=model_config["torch_dtype"],
+                device_map=model_config["device_map"],
+                trust_remote_code=True,
+                attn_implementation=model_config["attn_implementation"] if self.config.use_flash_attention else None,
+                quantization_config=quantization_config,
+                use_cache=True,
+                low_cpu_mem_usage=True
+            )
+            
+            # Enable optimizations
+            if hasattr(self.model, "to_bettertransformer"):
+                self.model = self.model.to_bettertransformer()
+            
+            # Compile model for faster inference (PyTorch 2.0+)
+            if hasattr(torch, "compile") and torch.cuda.is_available():
+                self.model = torch.compile(self.model, mode="reduce-overhead")
+            
+            # Create pipeline
+            self.pipeline = pipeline(
+                "text-generation",
+                model=self.model,
+                tokenizer=self.tokenizer,
+                device_map="auto",
+                batch_size=1,
+                return_full_text=False
+            )
+            
+            self.logger.info(f"Qwen 2.5 model initialized: {model_config['model_name']}")
+            
+        except Exception as e:
+            self.logger.error(f"Failed to initialize Qwen model: {e}")
+            raise
+    
+    async def generate_monster_response(self, 
+                                      monster_data: Dict[str, Any],
+                                      user_input: str,
+                                      conversation_history: List[Dict[str, str]] = None) -> Dict[str, Any]:
+        """Generate contextual monster response using Qwen 2.5"""
+        start_time = time.time()
+        
+        try:
+            # Build monster personality context
+            personality_prompt = self._build_personality_prompt(monster_data)
+            
+            # Create conversation context
+            conversation_context = self._build_conversation_context(
+                conversation_history or [], monster_data
+            )
+            
+            # Build system prompt
+            system_prompt = f"""You are {monster_data['name']}, a virtual monster companion.
+
+{personality_prompt}
+
+Current State:
+- Health: {monster_data['stats']['health']}/100
+- Happiness: {monster_data['stats']['happiness']}/100
+- Energy: {monster_data['stats']['energy']}/100
+- Emotional State: {monster_data['emotional_state']}
+- Activity: {monster_data['current_activity']}
+
+Instructions:
+- Respond as this specific monster with this personality
+- Keep responses to 1-2 sentences maximum
+- Include 1-2 relevant emojis
+- Show personality through word choice and tone
+- React appropriately to your current stats and emotional state
+- Remember past conversations and build on them
+
+{conversation_context}"""
+
+            # Format messages for Qwen 2.5
+            messages = [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_input}
+            ]
+            
+            # Generate response
+            prompt = self.tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True
+            )
+            
+            # Optimized generation parameters
+            generation_kwargs = {
+                "max_new_tokens": 128,
+                "temperature": 0.8,
+                "top_p": 0.9,
+                "top_k": 50,
+                "do_sample": True,
+                "pad_token_id": self.tokenizer.eos_token_id,
+                "repetition_penalty": 1.1,
+                "no_repeat_ngram_size": 3
+            }
+            
+            # Generate with error handling
+            outputs = self.pipeline(prompt, **generation_kwargs)
+            response_text = outputs[0]["generated_text"].strip()
+            
+            # Post-process response
+            processed_response = self._post_process_response(response_text, monster_data)
+            
+            # Track performance
+            inference_time = time.time() - start_time
+            self.inference_times.append(inference_time)
+            
+            # Analyze response for emotional impact
+            emotional_impact = self._analyze_emotional_impact(user_input, processed_response)
+            
+            return {
+                "response": processed_response,
+                "inference_time": inference_time,
+                "emotional_impact": emotional_impact,
+                "confidence": 0.85,  # Placeholder for confidence scoring
+                "model_info": {
+                    "model_name": self.config.model_name,
+                    "inference_speed": self.config.inference_speed
+                }
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Response generation failed: {e}")
+            return {
+                "response": self._get_fallback_response(monster_data),
+                "inference_time": time.time() - start_time,
+                "emotional_impact": {"happiness": 0.1},
+                "confidence": 0.1,
+                "error": str(e)
+            }
+    
+    def _build_personality_prompt(self, monster_data: Dict[str, Any]) -> str:
+        """Build personality description for the monster"""
+        personality = monster_data.get('personality', {})
+        
+        # Core personality traits
+        primary_type = personality.get('primary_type', 'playful')
+        traits = []
+        
+        # Big Five personality factors
+        if personality.get('extraversion', 0.5) > 0.7:
+            traits.append("very outgoing and social")
+        elif personality.get('extraversion', 0.5) < 0.3:
+            traits.append("more reserved and introspective")
+        
+        if personality.get('agreeableness', 0.5) > 0.7:
+            traits.append("extremely friendly and cooperative")
+        elif personality.get('agreeableness', 0.5) < 0.3:
+            traits.append("more independent and sometimes stubborn")
+        
+        if personality.get('conscientiousness', 0.5) > 0.7:
+            traits.append("very disciplined and organized")
+        elif personality.get('conscientiousness', 0.5) < 0.3:
+            traits.append("more spontaneous and carefree")
+        
+        if personality.get('openness', 0.5) > 0.7:
+            traits.append("very curious and imaginative")
+        elif personality.get('openness', 0.5) < 0.3:
+            traits.append("more practical and traditional")
+        
+        # Learned preferences
+        favorites = personality.get('favorite_foods', [])
+        dislikes = personality.get('disliked_foods', [])
+        
+        personality_text = f"Personality Type: {primary_type.title()}\n"
+        
+        if traits:
+            personality_text += f"You are {', '.join(traits)}.\n"
+        
+        if favorites:
+            personality_text += f"Your favorite foods are: {', '.join(favorites[:3])}.\n"
+        
+        if dislikes:
+            personality_text += f"You dislike: {', '.join(dislikes[:3])}.\n"
+        
+        # Relationship context
+        relationship_level = personality.get('relationship_level', 0)
+        if relationship_level > 80:
+            personality_text += "You have a very strong bond with your caretaker.\n"
+        elif relationship_level > 50:
+            personality_text += "You trust and like your caretaker.\n"
+        elif relationship_level > 20:
+            personality_text += "You're getting to know your caretaker.\n"
+        else:
+            personality_text += "You're still warming up to your caretaker.\n"
+        
+        return personality_text
+    
+    def _build_conversation_context(self, 
+                                  history: List[Dict[str, str]], 
+                                  monster_data: Dict[str, Any]) -> str:
+        """Build conversation context from recent history"""
+        if not history:
+            return "This is your first conversation together."
+        
+        # Get recent messages (last 3 exchanges)
+        recent_history = history[-6:] if len(history) > 6 else history
+        
+        context = "Recent conversation:\n"
+        for i, msg in enumerate(recent_history):
+            if msg.get('role') == 'user':
+                context += f"Human: {msg.get('content', '')}\n"
+            else:
+                context += f"You: {msg.get('content', '')}\n"
+        
+        return context
+    
+    def _post_process_response(self, response: str, monster_data: Dict[str, Any]) -> str:
+        """Post-process the generated response"""
+        # Remove any unwanted prefixes/suffixes
+        response = response.strip()
+        
+        # Remove common artifacts
+        unwanted_prefixes = ["Assistant:", "Monster:", "DigiPal:", monster_data['name'] + ":"]
+        for prefix in unwanted_prefixes:
+            if response.startswith(prefix):
+                response = response[len(prefix):].strip()
+        
+        # Ensure appropriate length
+        sentences = response.split('.')
+        if len(sentences) > 2:
+            response = '. '.join(sentences[:2]) + '.'
+        
+        # Add emojis if missing
+        if not self._has_emoji(response):
+            response = self._add_contextual_emoji(response, monster_data)
+        
+        return response
+    
+    def _has_emoji(self, text: str) -> bool:
+        """Check if text contains emojis"""
+        import emoji
+        return bool(emoji.emoji_count(text))
+    
+    def _add_contextual_emoji(self, response: str, monster_data: Dict[str, Any]) -> str:
+        """Add appropriate emoji based on context"""
+        emotional_state = monster_data.get('emotional_state', 'neutral')
+        
+        emoji_map = {
+            'ecstatic': ' 🤩',
+            'happy': ' 😊',
+            'content': ' 😌',
+            'neutral': ' 🙂',
+            'melancholy': ' 😔',
+            'sad': ' 😢',
+            'angry': ' 😠',
+            'sick': ' 🤒',
+            'excited': ' 😆',
+            'tired': ' 😴'
+        }
+        
+        return response + emoji_map.get(emotional_state, ' 🙂')
+    
+    def _analyze_emotional_impact(self, user_input: str, response: str) -> Dict[str, float]:
+        """Analyze the emotional impact of the interaction"""
+        # Simple keyword-based analysis (can be enhanced with sentiment models)
+        positive_keywords = ['love', 'good', 'great', 'amazing', 'wonderful', 'happy', 'fun']
+        negative_keywords = ['bad', 'sad', 'angry', 'hate', 'terrible', 'awful', 'sick']
+        
+        user_input_lower = user_input.lower()
+        
+        impact = {
+            'happiness': 0.0,
+            'stress': 0.0,
+            'bonding': 0.0
+        }
+        
+        # Analyze user input sentiment
+        for keyword in positive_keywords:
+            if keyword in user_input_lower:
+                impact['happiness'] += 0.1
+                impact['bonding'] += 0.05
+        
+        for keyword in negative_keywords:
+            if keyword in user_input_lower:
+                impact['happiness'] -= 0.1
+                impact['stress'] += 0.1
+        
+        # Base interaction bonus
+        impact['bonding'] += 0.02  # Small bonding increase for any interaction
+        
+        return impact
+    
+    def _get_fallback_response(self, monster_data: Dict[str, Any]) -> str:
+        """Get fallback response when AI generation fails"""
+        fallback_responses = [
+            f"*{monster_data['name']} looks at you curiously* 🤔",
+            f"*{monster_data['name']} makes a happy sound* 😊",
+            f"*{monster_data['name']} tilts head thoughtfully* 💭",
+            f"*{monster_data['name']} seems interested* 👀"
+        ]
+        
+        import random
+        return random.choice(fallback_responses)
+    
+    def get_performance_stats(self) -> Dict[str, Any]:
+        """Get model performance statistics"""
+        if not self.inference_times:
+            return {"status": "No inference data available"}
+        
+        avg_time = sum(self.inference_times) / len(self.inference_times)
+        
+        return {
+            "average_inference_time": avg_time,
+            "total_inferences": len(self.inference_times),
+            "fastest_inference": min(self.inference_times),
+            "slowest_inference": max(self.inference_times),
+            "tokens_per_second": 128 / avg_time,  # Approximate
+            "model_config": self.config.__dict__
+        }

src/ai/speech_engine.py

@@ -0,0 +1,327 @@
+import asyncio
+import numpy as np
+from faster_whisper import WhisperModel
+import torch
+import webrtcvad
+import logging
+from typing import Dict, List, Optional, Tuple, Any
+import time
+from dataclasses import dataclass
+import io
+import wave
+
+@dataclass
+class SpeechConfig:
+    model_size: str = "base"  # tiny, base, small, medium, large-v3
+    device: str = "auto"
+    compute_type: str = "float16"
+    use_vad: bool = True
+    vad_aggressiveness: int = 2  # 0-3, higher = more aggressive
+    chunk_duration_ms: int = 30  # VAD chunk size
+    sample_rate: int = 16000
+
+class AdvancedSpeechEngine:
+    def __init__(self, config: SpeechConfig):
+        self.config = config
+        self.logger = logging.getLogger(__name__)
+        
+        # Model configurations optimized for gaming
+        self.model_configs = {
+            "tiny": {"memory_gb": 1, "speed": "fastest", "accuracy": "basic"},
+            "base": {"memory_gb": 2, "speed": "fast", "accuracy": "good"},
+            "small": {"memory_gb": 3, "speed": "medium", "accuracy": "better"},
+            "medium": {"memory_gb": 6, "speed": "slower", "accuracy": "high"},
+            "large-v3": {"memory_gb": 12, "speed": "slowest", "accuracy": "best"}
+        }
+        
+        self.whisper_model = None
+        self.vad_model = None
+        
+        # Performance tracking
+        self.transcription_times = []
+        self.accuracy_scores = []
+        
+        # Audio processing
+        self.audio_buffer = []
+        self.is_processing = False
+        
+    async def initialize(self):
+        """Initialize the speech recognition system"""
+        try:
+            # Determine optimal device
+            device = self.config.device
+            if device == "auto":
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            
+            # Initialize Faster Whisper
+            self.whisper_model = WhisperModel(
+                self.config.model_size,
+                device=device,
+                compute_type=self.config.compute_type,
+                download_root="data/models/"
+            )
+            
+            # Initialize VAD if enabled
+            if self.config.use_vad:
+                self.vad_model = webrtcvad.Vad(self.config.vad_aggressiveness)
+            
+            self.logger.info(f"Speech engine initialized: {self.config.model_size} on {device}")
+            
+        except Exception as e:
+            self.logger.error(f"Failed to initialize speech engine: {e}")
+            raise
+    
+    async def process_audio_stream(self, audio_data: np.ndarray) -> Dict[str, Any]:
+        """Process streaming audio for real-time transcription"""
+        start_time = time.time()
+        
+        try:
+            # Convert audio format if needed
+            if len(audio_data.shape) > 1:
+                audio_data = audio_data.mean(axis=1)  # Convert to mono
+            
+            # Normalize audio
+            audio_data = audio_data.astype(np.float32)
+            if np.max(np.abs(audio_data)) > 0:
+                audio_data = audio_data / np.max(np.abs(audio_data))
+            
+            # Voice Activity Detection
+            if self.config.use_vad:
+                has_speech = self._detect_speech_activity(audio_data)
+                if not has_speech:
+                    return {
+                        "success": True,
+                        "transcription": "",
+                        "confidence": 0.0,
+                        "processing_time": time.time() - start_time,
+                        "has_speech": False
+                    }
+            
+            # Transcribe with Faster Whisper
+            segments, info = self.whisper_model.transcribe(
+                audio_data,
+                language="en",
+                beam_size=1,  # Faster inference
+                temperature=0.0,
+                condition_on_previous_text=True,
+                compression_ratio_threshold=2.4,
+                log_prob_threshold=-1.0,
+                no_speech_threshold=0.6
+            )
+            
+            # Combine segments
+            transcription = ""
+            avg_confidence = 0.0
+            segment_count = 0
+            
+            for segment in segments:
+                transcription += segment.text + " "
+                avg_confidence += segment.avg_logprob
+                segment_count += 1
+            
+            transcription = transcription.strip()
+            
+            if segment_count > 0:
+                avg_confidence = avg_confidence / segment_count
+                confidence = self._logprob_to_confidence(avg_confidence)
+            else:
+                confidence = 0.0
+            
+            processing_time = time.time() - start_time
+            self.transcription_times.append(processing_time)
+            
+            # Analyze speech characteristics
+            speech_analysis = self._analyze_speech_characteristics(audio_data, transcription)
+            
+            return {
+                "success": True,
+                "transcription": transcription,
+                "confidence": confidence,
+                "processing_time": processing_time,
+                "has_speech": True,
+                "speech_analysis": speech_analysis,
+                "detected_language": info.language if hasattr(info, 'language') else "en",
+                "language_probability": info.language_probability if hasattr(info, 'language_probability') else 1.0
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Audio processing failed: {e}")
+            return {
+                "success": False,
+                "transcription": "",
+                "confidence": 0.0,
+                "processing_time": time.time() - start_time,
+                "error": str(e)
+            }
+    
+    def _detect_speech_activity(self, audio_data: np.ndarray) -> bool:
+        """Detect if audio contains speech using WebRTC VAD"""
+        try:
+            # Convert to 16-bit PCM
+            pcm_data = (audio_data * 32767).astype(np.int16)
+            
+            # Split into chunks for VAD processing
+            chunk_size = int(self.config.sample_rate * self.config.chunk_duration_ms / 1000)
+            speech_chunks = 0
+            total_chunks = 0
+            
+            for i in range(0, len(pcm_data), chunk_size):
+                chunk = pcm_data[i:i+chunk_size]
+                
+                # Pad chunk if necessary
+                if len(chunk) < chunk_size:
+                    chunk = np.pad(chunk, (0, chunk_size - len(chunk)), mode='constant')
+                
+                # Convert to bytes
+                chunk_bytes = chunk.tobytes()
+                
+                # Check for speech
+                if self.vad_model.is_speech(chunk_bytes, self.config.sample_rate):
+                    speech_chunks += 1
+                
+                total_chunks += 1
+            
+            # Consider it speech if > 30% of chunks contain speech
+            speech_ratio = speech_chunks / total_chunks if total_chunks > 0 else 0
+            return speech_ratio > 0.3
+            
+        except Exception as e:
+            self.logger.warning(f"VAD processing failed: {e}")
+            return True  # Default to processing if VAD fails
+    
+    def _logprob_to_confidence(self, avg_logprob: float) -> float:
+        """Convert log probability to confidence score"""
+        # Empirical mapping from log probability to confidence
+        # Faster Whisper typically gives log probs between -3.0 and 0.0
+        confidence = max(0.0, min(1.0, (avg_logprob + 3.0) / 3.0))
+        return confidence
+    
+    def _analyze_speech_characteristics(self, audio_data: np.ndarray, transcription: str) -> Dict[str, Any]:
+        """Analyze speech characteristics for emotional context"""
+        try:
+            import librosa
+            
+            # Basic audio features
+            duration = len(audio_data) / self.config.sample_rate
+            
+            # Energy/Volume analysis
+            rms_energy = np.sqrt(np.mean(audio_data ** 2))
+            
+            # Pitch analysis
+            pitches, magnitudes = librosa.piptrack(
+                y=audio_data, 
+                sr=self.config.sample_rate,
+                threshold=0.1
+            )
+            
+            # Extract fundamental frequency
+            pitch_values = pitches[magnitudes > np.max(magnitudes) * 0.1]
+            if len(pitch_values) > 0:
+                avg_pitch = np.mean(pitch_values)
+                pitch_variance = np.var(pitch_values)
+            else:
+                avg_pitch = 0.0
+                pitch_variance = 0.0
+            
+            # Speaking rate (words per minute)
+            word_count = len(transcription.split()) if transcription else 0
+            speaking_rate = (word_count / duration * 60) if duration > 0 else 0
+            
+            # Emotional indicators (basic)
+            emotions = {
+                "excitement": min(1.0, rms_energy * 10),  # Higher energy = more excited
+                "calmness": max(0.0, 1.0 - (pitch_variance / 1000)),  # Lower pitch variance = calmer
+                "engagement": min(1.0, speaking_rate / 200),  # Normal speaking rate indicates engagement
+                "stress": min(1.0, max(0.0, (avg_pitch - 200) / 100))  # Higher pitch can indicate stress
+            }
+            
+            return {
+                "duration": duration,
+                "energy": rms_energy,
+                "average_pitch": avg_pitch,
+                "pitch_variance": pitch_variance,
+                "speaking_rate": speaking_rate,
+                "word_count": word_count,
+                "emotional_indicators": emotions
+            }
+            
+        except Exception as e:
+            self.logger.warning(f"Speech analysis failed: {e}")
+            return {
+                "duration": 0.0,
+                "energy": 0.0,
+                "emotional_indicators": {}
+            }
+    
+    async def batch_transcribe(self, audio_files: List[str]) -> List[Dict[str, Any]]:
+        """Batch transcribe multiple audio files"""
+        results = []
+        
+        for audio_file in audio_files:
+            try:
+                # Load audio file
+                import librosa
+                audio_data, _ = librosa.load(audio_file, sr=self.config.sample_rate)
+                
+                # Process
+                result = await self.process_audio_stream(audio_data)
+                result["file_path"] = audio_file
+                
+                results.append(result)
+                
+            except Exception as e:
+                self.logger.error(f"Failed to process {audio_file}: {e}")
+                results.append({
+                    "success": False,
+                    "file_path": audio_file,
+                    "error": str(e)
+                })
+        
+        return results
+    
+    def get_performance_stats(self) -> Dict[str, Any]:
+        """Get speech processing performance statistics"""
+        if not self.transcription_times:
+            return {"status": "No transcription data available"}
+        
+        avg_time = sum(self.transcription_times) / len(self.transcription_times)
+        
+        return {
+            "average_processing_time": avg_time,
+            "total_transcriptions": len(self.transcription_times),
+            "fastest_transcription": min(self.transcription_times),
+            "slowest_transcription": max(self.transcription_times),
+            "model_config": self.config.__dict__,
+            "estimated_real_time_factor": avg_time / 1.0  # Assuming 1 second audio clips
+        }
+    
+    def optimize_for_hardware(self, available_vram_gb: float) -> SpeechConfig:
+        """Optimize speech config based on available hardware"""
+        if available_vram_gb >= 12:
+            return SpeechConfig(
+                model_size="large-v3",
+                device="cuda",
+                compute_type="float16",
+                use_vad=True
+            )
+        elif available_vram_gb >= 6:
+            return SpeechConfig(
+                model_size="medium",
+                device="cuda",
+                compute_type="float16",
+                use_vad=True
+            )
+        elif available_vram_gb >= 3:
+            return SpeechConfig(
+                model_size="small",
+                device="cuda",
+                compute_type="int8",
+                use_vad=True
+            )
+        else:
+            return SpeechConfig(
+                model_size="base",
+                device="cpu",
+                compute_type="int8",
+                use_vad=True
+            )

src/core/__init__.py

@@ -0,0 +1 @@
+# Core module initialization

src/core/evolution_system.py

@@ -0,0 +1,655 @@
+import asyncio
+import logging
+from typing import Dict, List, Optional, Any, Tuple
+from datetime import datetime, timedelta
+from enum import Enum
+import random
+import json
+
+from .monster_engine import Monster, EvolutionStage, MonsterPersonalityType, EmotionalState
+
+class EvolutionTrigger(str, Enum):
+    TIME_BASED = "time_based"
+    STAT_BASED = "stat_based"
+    CARE_BASED = "care_based"
+    ITEM_BASED = "item_based"
+    SPECIAL_EVENT = "special_event"
+    TRAINING_BASED = "training_based"
+    RELATIONSHIP_BASED = "relationship_based"
+
+class EvolutionPath(str, Enum):
+    NORMAL = "normal"
+    VARIANT = "variant"
+    SPECIAL = "special"
+    CORRUPTED = "corrupted"
+    LEGENDARY = "legendary"
+
+class EvolutionSystem:
+    def __init__(self):
+        self.logger = logging.getLogger(__name__)
+        
+        # Evolution trees and requirements
+        self.evolution_trees = self._initialize_evolution_trees()
+        self.evolution_requirements = self._initialize_evolution_requirements()
+        self.special_conditions = self._initialize_special_conditions()
+        
+        # Evolution modifiers
+        self.care_quality_thresholds = {
+            "excellent": 1.8,
+            "good": 1.4,
+            "average": 1.0,
+            "poor": 0.6,
+            "terrible": 0.3
+        }
+        
+    def _initialize_evolution_trees(self) -> Dict[str, Dict[str, List[Dict[str, Any]]]]:
+        """Initialize the complete evolution tree structure"""
+        return {
+            "Botamon": {
+                EvolutionStage.BABY: [
+                    {
+                        "species": "Koromon",
+                        "path": EvolutionPath.NORMAL,
+                        "requirements": {
+                            "age_minutes": 60,
+                            "care_mistakes_max": 0,
+                            "health_min": 80
+                        }
+                    }
+                ]
+            },
+            "Koromon": {
+                EvolutionStage.CHILD: [
+                    {
+                        "species": "Agumon",
+                        "path": EvolutionPath.NORMAL,
+                        "requirements": {
+                            "age_minutes": 1440,  # 24 hours
+                            "stats_min": {"offense": 150, "life": 1200},
+                            "training_min": {"strength": 30},
+                            "care_quality_min": 1.0
+                        }
+                    },
+                    {
+                        "species": "Betamon", 
+                        "path": EvolutionPath.VARIANT,
+                        "requirements": {
+                            "age_minutes": 1440,
+                            "stats_min": {"defense": 150, "brains": 120},
+                            "training_min": {"intelligence": 30},
+                            "care_quality_min": 1.2
+                        }
+                    },
+                    {
+                        "species": "Kunemon",
+                        "path": EvolutionPath.CORRUPTED,
+                        "requirements": {
+                            "age_minutes": 1440,
+                            "care_mistakes_min": 3,
+                            "happiness_max": 40
+                        }
+                    }
+                ]
+            },
+            "Agumon": {
+                EvolutionStage.ADULT: [
+                    {
+                        "species": "Greymon",
+                        "path": EvolutionPath.NORMAL,
+                        "requirements": {
+                            "age_minutes": 4320,  # 72 hours
+                            "stats_min": {"offense": 250, "life": 1800},
+                            "training_min": {"strength": 80},
+                            "care_quality_min": 1.3,
+                            "battle_wins_min": 5
+                        }
+                    },
+                    {
+                        "species": "Tyrannomon",
+                        "path": EvolutionPath.VARIANT,
+                        "requirements": {
+                            "age_minutes": 4320,
+                            "stats_min": {"offense": 300, "life": 2000},
+                            "training_min": {"strength": 100, "endurance": 50},
+                            "care_quality_min": 1.1,
+                            "discipline_min": 70
+                        }
+                    },
+                    {
+                        "species": "Meramon",
+                        "path": EvolutionPath.SPECIAL,
+                        "requirements": {
+                            "age_minutes": 4320,
+                            "stats_min": {"offense": 200, "brains": 180},
+                            "training_min": {"spirit": 60},
+                            "special_item": "Fire_Crystal",
+                            "care_quality_min": 1.5
+                        }
+                    }
+                ]
+            },
+            "Greymon": {
+                EvolutionStage.PERFECT: [
+                    {
+                        "species": "MetalGreymon",
+                        "path": EvolutionPath.NORMAL,
+                        "requirements": {
+                            "age_minutes": 8640,  # 144 hours (6 days)
+                            "stats_min": {"offense": 400, "life": 2800, "defense": 300},
+                            "training_min": {"strength": 150, "technique": 100},
+                            "care_quality_min": 1.6,
+                            "battle_wins_min": 15,
+                            "relationship_level_min": 80
+                        }
+                    },
+                    {
+                        "species": "SkullGreymon",
+                        "path": EvolutionPath.CORRUPTED,
+                        "requirements": {
+                            "age_minutes": 8640,
+                            "stats_min": {"offense": 450},
+                            "care_mistakes_min": 8,
+                            "overtraining": True,
+                            "happiness_max": 30
+                        }
+                    }
+                ]
+            },
+            "MetalGreymon": {
+                EvolutionStage.ULTIMATE: [
+                    {
+                        "species": "WarGreymon",
+                        "path": EvolutionPath.LEGENDARY,
+                        "requirements": {
+                            "age_minutes": 14400,  # 10 days
+                            "stats_min": {"offense": 600, "life": 4000, "defense": 500, "brains": 400},
+                            "training_min": {"strength": 200, "technique": 150, "spirit": 100},
+                            "care_quality_min": 1.8,
+                            "battle_wins_min": 50,
+                            "relationship_level_min": 95,
+                            "special_achievements": ["Perfect_Care_Week", "Master_Trainer"]
+                        }
+                    }
+                ]
+            }
+        }
+    
+    def _initialize_evolution_requirements(self) -> Dict[str, Any]:
+        """Initialize detailed evolution requirement checkers"""
+        return {
+            "age_requirements": {
+                "check": lambda monster, req: monster.lifecycle.age_minutes >= req,
+                "display": lambda req: f"Age: {req/1440:.1f} days"
+            },
+            "stat_requirements": {
+                "check": self._check_stat_requirements,
+                "display": lambda req: f"Stats: {', '.join([f'{k}≥{v}' for k, v in req.items()])}"
+            },
+            "training_requirements": {
+                "check": self._check_training_requirements,
+                "display": lambda req: f"Training: {', '.join([f'{k}≥{v}' for k, v in req.items()])}"
+            },
+            "care_quality_requirements": {
+                "check": lambda monster, req: monster.stats.care_quality_score >= req,
+                "display": lambda req: f"Care Quality: {req:.1f}"
+            },
+            "item_requirements": {
+                "check": self._check_item_requirements,
+                "display": lambda req: f"Required Item: {req}"
+            },
+            "special_requirements": {
+                "check": self._check_special_requirements,
+                "display": lambda req: f"Special: {', '.join(req) if isinstance(req, list) else req}"
+            }
+        }
+    
+    def _initialize_special_conditions(self) -> Dict[str, Any]:
+        """Initialize special evolution conditions"""
+        return {
+            "perfect_care_week": {
+                "description": "No care mistakes for 7 consecutive days",
+                "check": self._check_perfect_care_week
+            },
+            "master_trainer": {
+                "description": "Complete all training types to level 150+",
+                "check": self._check_master_trainer
+            },
+            "bond_master": {
+                "description": "Reach maximum relationship level",
+                "check": lambda monster: monster.personality.relationship_level >= 100
+            },
+            "evolution_master": {
+                "description": "Successfully evolve 10+ monsters",
+                "check": self._check_evolution_master
+            },
+            "overtraining": {
+                "description": "Training stats significantly exceed normal limits",
+                "check": self._check_overtraining
+            }
+        }
+    
+    async def check_evolution_eligibility(self, monster: Monster) -> Dict[str, Any]:
+        """Check if monster is eligible for evolution and return detailed info"""
+        try:
+            current_species = monster.species
+            current_stage = monster.lifecycle.stage
+            
+            # Get possible evolutions
+            possible_evolutions = self.evolution_trees.get(current_species, {}).get(current_stage, [])
+            
+            if not possible_evolutions:
+                return {
+                    "can_evolve": False,
+                    "reason": "No evolution paths available",
+                    "possible_evolutions": []
+                }
+            
+            evolution_results = []
+            
+            for evolution_option in possible_evolutions:
+                species = evolution_option["species"]
+                path = evolution_option["path"]
+                requirements = evolution_option["requirements"]
+                
+                # Check each requirement
+                met_requirements = []
+                missing_requirements = []
+                
+                for req_type, req_value in requirements.items():
+                    is_met = await self._check_requirement(monster, req_type, req_value)
+                    
+                    requirement_info = {
+                        "type": req_type,
+                        "requirement": req_value,
+                        "current_value": self._get_current_value(monster, req_type),
+                        "is_met": is_met
+                    }
+                    
+                    if is_met:
+                        met_requirements.append(requirement_info)
+                    else:
+                        missing_requirements.append(requirement_info)
+                
+                # Calculate evolution readiness percentage
+                total_requirements = len(met_requirements) + len(missing_requirements)
+                readiness_percentage = (len(met_requirements) / total_requirements * 100) if total_requirements > 0 else 0
+                
+                evolution_results.append({
+                    "species": species,
+                    "path": path.value,
+                    "readiness_percentage": readiness_percentage,
+                    "can_evolve": len(missing_requirements) == 0,
+                    "met_requirements": met_requirements,
+                    "missing_requirements": missing_requirements,
+                    "estimated_time_to_eligible": self._estimate_time_to_eligible(missing_requirements)
+                })
+            
+            # Find the best evolution option
+            eligible_evolutions = [e for e in evolution_results if e["can_evolve"]]
+            best_option = max(evolution_results, key=lambda x: x["readiness_percentage"]) if evolution_results else None
+            
+            return {
+                "can_evolve": len(eligible_evolutions) > 0,
+                "eligible_evolutions": eligible_evolutions,
+                "best_option": best_option,
+                "all_options": evolution_results,
+                "evolution_locked": monster.lifecycle.evolution_locked_until and 
+                                  monster.lifecycle.evolution_locked_until > datetime.now()
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Evolution eligibility check failed: {e}")
+            return {
+                "can_evolve": False,
+                "reason": f"Error checking evolution: {str(e)}",
+                "possible_evolutions": []
+            }
+    
+    async def trigger_evolution(self, monster: Monster, target_species: str = None) -> Dict[str, Any]:
+        """Trigger monster evolution"""
+        try:
+            # Check if evolution is locked
+            if monster.lifecycle.evolution_locked_until and monster.lifecycle.evolution_locked_until > datetime.now():
+                return {
+                    "success": False,
+                    "reason": "Evolution is temporarily locked",
+                    "unlock_time": monster.lifecycle.evolution_locked_until
+                }
+            
+            # Get evolution eligibility
+            eligibility = await self.check_evolution_eligibility(monster)
+            
+            if not eligibility["can_evolve"]:
+                return {
+                    "success": False,
+                    "reason": "Evolution requirements not met",
+                    "eligibility": eligibility
+                }
+            
+            # Select evolution target
+            eligible_evolutions = eligibility["eligible_evolutions"]
+            
+            if target_species:
+                # Specific evolution requested
+                target_evolution = next((e for e in eligible_evolutions if e["species"] == target_species), None)
+                if not target_evolution:
+                    return {
+                        "success": False,
+                        "reason": f"Cannot evolve to {target_species}",
+                        "available_options": [e["species"] for e in eligible_evolutions]
+                    }
+            else:
+                # Choose best available evolution
+                target_evolution = max(eligible_evolutions, key=lambda x: x["readiness_percentage"])
+            
+            # Store previous state
+            previous_species = monster.species
+            previous_stage = monster.lifecycle.stage
+            
+            # Apply evolution
+            await self._apply_evolution(monster, target_evolution)
+            
+            # Log evolution event
+            evolution_result = {
+                "success": True,
+                "previous_species": previous_species,
+                "previous_stage": previous_stage.value,
+                "new_species": monster.species,
+                "new_stage": monster.lifecycle.stage.value,
+                "evolution_path": target_evolution["path"],
+                "stat_bonuses": self._calculate_evolution_bonuses(target_evolution),
+                "timestamp": datetime.now()
+            }
+            
+            self.logger.info(f"Monster evolved: {previous_species} -> {monster.species}")
+            
+            return evolution_result
+            
+        except Exception as e:
+            self.logger.error(f"Evolution trigger failed: {e}")
+            return {
+                "success": False,
+                "reason": f"Evolution failed: {str(e)}"
+            }
+    
+    async def _apply_evolution(self, monster: Monster, evolution_data: Dict[str, Any]):
+        """Apply evolution changes to monster"""
+        # Update basic info
+        monster.species = evolution_data["species"]
+        
+        # Determine new stage
+        stage_progression = {
+            EvolutionStage.EGG: EvolutionStage.BABY,
+            EvolutionStage.BABY: EvolutionStage.CHILD,
+            EvolutionStage.CHILD: EvolutionStage.ADULT,
+            EvolutionStage.ADULT: EvolutionStage.PERFECT,
+            EvolutionStage.PERFECT: EvolutionStage.ULTIMATE,
+            EvolutionStage.ULTIMATE: EvolutionStage.MEGA
+        }
+        
+        new_stage = stage_progression.get(monster.lifecycle.stage)
+        if new_stage:
+            monster.lifecycle.stage = new_stage
+        
+        # Apply stat bonuses
+        bonuses = self._calculate_evolution_bonuses(evolution_data)
+        for stat, bonus in bonuses.items():
+            if hasattr(monster.stats, stat):
+                current_value = getattr(monster.stats, stat)
+                new_value = int(current_value * bonus["multiplier"]) + bonus["flat_bonus"]
+                setattr(monster.stats, stat, new_value)
+        
+        # Reset some care stats
+        monster.stats.happiness = min(100, monster.stats.happiness + 20)
+        monster.stats.health = min(100, monster.stats.health + 30)
+        monster.stats.energy = min(100, monster.stats.energy + 40)
+        
+        # Update personality based on evolution path
+        self._apply_personality_changes(monster, evolution_data["path"])
+        
+        # Set evolution cooldown
+        monster.lifecycle.evolution_locked_until = datetime.now() + timedelta(hours=24)
+        
+        # Update emotional state
+        monster.emotional_state = EmotionalState.ECSTATIC
+        
+        # Add evolution achievement
+        if "special_achievements" not in monster.performance_metrics:
+            monster.performance_metrics["special_achievements"] = []
+        
+        monster.performance_metrics["special_achievements"].append({
+            "type": "evolution",
+            "species": monster.species,
+            "timestamp": datetime.now().isoformat()
+        })
+    
+    def _calculate_evolution_bonuses(self, evolution_data: Dict[str, Any]) -> Dict[str, Dict[str, float]]:
+        """Calculate stat bonuses for evolution"""
+        base_bonuses = {
+            "life": {"multiplier": 1.3, "flat_bonus": 200},
+            "mp": {"multiplier": 1.2, "flat_bonus": 50},
+            "offense": {"multiplier": 1.25, "flat_bonus": 30},
+            "defense": {"multiplier": 1.25, "flat_bonus": 30},
+            "speed": {"multiplier": 1.2, "flat_bonus": 20},
+            "brains": {"multiplier": 1.15, "flat_bonus": 25}
+        }
+        
+        # Modify bonuses based on evolution path
+        path_modifiers = {
+            EvolutionPath.NORMAL: 1.0,
+            EvolutionPath.VARIANT: 1.1,
+            EvolutionPath.SPECIAL: 1.3,
+            EvolutionPath.CORRUPTED: 0.9,
+            EvolutionPath.LEGENDARY: 1.5
+        }
+        
+        evolution_path = EvolutionPath(evolution_data["path"])
+        modifier = path_modifiers.get(evolution_path, 1.0)
+        
+        # Apply modifier to bonuses
+        modified_bonuses = {}
+        for stat, bonus in base_bonuses.items():
+            modified_bonuses[stat] = {
+                "multiplier": bonus["multiplier"] * modifier,
+                "flat_bonus": int(bonus["flat_bonus"] * modifier)
+            }
+        
+        return modified_bonuses
+    
+    def _apply_personality_changes(self, monster: Monster, evolution_path: str):
+        """Apply personality changes based on evolution path"""
+        path_personality_effects = {
+            EvolutionPath.NORMAL: {
+                "conscientiousness": 0.05,
+                "stability": 0.03
+            },
+            EvolutionPath.VARIANT: {
+                "openness": 0.08,
+                "curiosity": 0.05
+            },
+            EvolutionPath.SPECIAL: {
+                "extraversion": 0.1,
+                "confidence": 0.07
+            },
+            EvolutionPath.CORRUPTED: {
+                "neuroticism": 0.15,
+                "aggression": 0.1,
+                "happiness_decay_rate": 1.2
+            },
+            EvolutionPath.LEGENDARY: {
+                "all_traits": 0.1,
+                "relationship_bonus": 10
+            }
+        }
+        
+        effects = path_personality_effects.get(EvolutionPath(evolution_path), {})
+        
+        for trait, change in effects.items():
+            if trait == "all_traits":
+                # Boost all personality traits
+                for personality_trait in ["openness", "conscientiousness", "extraversion", "agreeableness"]:
+                    if hasattr(monster.personality, personality_trait):
+                        current = getattr(monster.personality, personality_trait)
+                        setattr(monster.personality, personality_trait, min(1.0, current + change))
+            elif trait == "relationship_bonus":
+                monster.personality.relationship_level = min(100, monster.personality.relationship_level + change)
+            elif hasattr(monster.personality, trait):
+                current = getattr(monster.personality, trait)
+                setattr(monster.personality, trait, min(1.0, max(0.0, current + change)))
+    
+    async def _check_requirement(self, monster: Monster, req_type: str, req_value: Any) -> bool:
+        """Check if a specific requirement is met"""
+        try:
+            if req_type == "age_minutes":
+                return monster.lifecycle.age_minutes >= req_value
+            
+            elif req_type == "care_mistakes_max":
+                return monster.lifecycle.care_mistakes <= req_value
+            
+            elif req_type == "care_mistakes_min":
+                return monster.lifecycle.care_mistakes >= req_value
+            
+            elif req_type == "stats_min":
+                return self._check_stat_requirements(monster, req_value)
+            
+            elif req_type == "training_min":
+                return self._check_training_requirements(monster, req_value)
+            
+            elif req_type == "care_quality_min":
+                return monster.stats.care_quality_score >= req_value
+            
+            elif req_type == "health_min":
+                return monster.stats.health >= req_value
+            
+            elif req_type == "happiness_max":
+                return monster.stats.happiness <= req_value
+            
+            elif req_type == "happiness_min":
+                return monster.stats.happiness >= req_value
+            
+            elif req_type == "discipline_min":
+                return monster.stats.discipline >= req_value
+            
+            elif req_type == "relationship_level_min":
+                return monster.personality.relationship_level >= req_value
+            
+            elif req_type == "special_item":
+                return req_value in monster.inventory and monster.inventory[req_value] > 0
+            
+            elif req_type == "special_achievements":
+                return self._check_special_achievements(monster, req_value)
+            
+            elif req_type == "battle_wins_min":
+                return monster.performance_metrics.get("battle_wins", 0) >= req_value
+            
+            elif req_type == "overtraining":
+                return self._check_overtraining(monster)
+            
+            else:
+                self.logger.warning(f"Unknown requirement type: {req_type}")
+                return False
+                
+        except Exception as e:
+            self.logger.error(f"Requirement check failed for {req_type}: {e}")
+            return False
+    
+    def _check_stat_requirements(self, monster: Monster, requirements: Dict[str, int]) -> bool:
+        """Check if stat requirements are met"""
+        for stat_name, min_value in requirements.items():
+            if hasattr(monster.stats, stat_name):
+                current_value = getattr(monster.stats, stat_name)
+                if current_value < min_value:
+                    return False
+            else:
+                return False
+        return True
+    
+    def _check_training_requirements(self, monster: Monster, requirements: Dict[str, int]) -> bool:
+        """Check if training requirements are met"""
+        for training_type, min_value in requirements.items():
+            current_value = monster.stats.training_progress.get(training_type, 0)
+            if current_value < min_value:
+                return False
+        return True
+    
+    def _check_item_requirements(self, monster: Monster, item_name: str) -> bool:
+        """Check if monster has required item"""
+        return item_name in monster.inventory and monster.inventory[item_name] > 0
+    
+    def _check_special_achievements(self, monster: Monster, required_achievements: List[str]) -> bool:
+        """Check if special achievements are unlocked"""
+        achievements = monster.performance_metrics.get("special_achievements", [])
+        achievement_types = [a.get("type") for a in achievements if isinstance(a, dict)]
+        
+        for required in required_achievements:
+            if required not in achievement_types:
+                return False
+        return True
+    
+    def _check_overtraining(self, monster: Monster) -> bool:
+        """Check if monster is overtrained"""
+        training_totals = sum(monster.stats.training_progress.values())
+        return training_totals > 800  # Threshold for overtraining
+    
+    def _check_perfect_care_week(self, monster: Monster) -> bool:
+        """Check if monster had perfect care for a week"""
+        # Simplified check - would need more complex tracking in production
+        return monster.lifecycle.care_mistakes == 0 and monster.lifecycle.age_minutes >= 10080  # 7 days
+    
+    def _check_master_trainer(self, monster: Monster) -> bool:
+        """Check if all training types are at 150+"""
+        for training_type in ["strength", "endurance", "intelligence", "dexterity", "spirit", "technique"]:
+            if monster.stats.training_progress.get(training_type, 0) < 150:
+                return False
+        return True
+    
+    def _check_evolution_master(self, monster: Monster) -> bool:
+        """Check if player has evolved many monsters"""
+        # This would need global tracking in production
+        evolutions = [a for a in monster.performance_metrics.get("special_achievements", []) 
+                     if isinstance(a, dict) and a.get("type") == "evolution"]
+        return len(evolutions) >= 10
+    
+    def _get_current_value(self, monster: Monster, req_type: str) -> Any:
+        """Get current value for a requirement type"""
+        value_getters = {
+            "age_minutes": lambda: monster.lifecycle.age_minutes,
+            "care_mistakes_max": lambda: monster.lifecycle.care_mistakes,
+            "care_mistakes_min": lambda: monster.lifecycle.care_mistakes,
+            "health_min": lambda: monster.stats.health,
+            "happiness_max": lambda: monster.stats.happiness,
+            "happiness_min": lambda: monster.stats.happiness,
+            "discipline_min": lambda: monster.stats.discipline,
+            "care_quality_min": lambda: monster.stats.care_quality_score,
+            "relationship_level_min": lambda: monster.personality.relationship_level,
+            "battle_wins_min": lambda: monster.performance_metrics.get("battle_wins", 0)
+        }
+        
+        getter = value_getters.get(req_type)
+        return getter() if getter else "N/A"
+    
+    def _estimate_time_to_eligible(self, missing_requirements: List[Dict[str, Any]]) -> str:
+        """Estimate time until evolution requirements are met"""
+        time_estimates = []
+        
+        for req in missing_requirements:
+            req_type = req["type"]
+            
+            if req_type == "age_minutes":
+                current = req["current_value"]
+                required = req["requirement"]
+                remaining_minutes = required - current
+                time_estimates.append(f"{remaining_minutes/1440:.1f} days")
+            
+            elif "training" in req_type:
+                # Estimate based on training rate
+                time_estimates.append("1-3 days of training")
+            
+            elif "stat" in req_type:
+                # Estimate based on training and care
+                time_estimates.append("2-5 days of care/training")
+            
+            else:
+                time_estimates.append("Variable")
+        
+        return ", ".join(time_estimates) if time_estimates else "Ready now"

src/core/monster_engine.py

@@ -0,0 +1,365 @@
+from pydantic import BaseModel, Field, validator
+from typing import Dict, List, Optional, Any, Union
+from datetime import datetime, timedelta
+from enum import Enum
+import uuid
+import asyncio
+import json
+import numpy as np
+from dataclasses import dataclass
+
+class EvolutionStage(str, Enum):
+    EGG = "egg"
+    BABY = "baby"
+    CHILD = "child"
+    ADULT = "adult"
+    PERFECT = "perfect"
+    ULTIMATE = "ultimate"
+    MEGA = "mega"
+
+class MonsterPersonalityType(str, Enum):
+    PLAYFUL = "playful"
+    SERIOUS = "serious"
+    CURIOUS = "curious"
+    GENTLE = "gentle"
+    ENERGETIC = "energetic"
+    CALM = "calm"
+    MISCHIEVOUS = "mischievous"
+    LOYAL = "loyal"
+
+class EmotionalState(str, Enum):
+    ECSTATIC = "ecstatic"
+    HAPPY = "happy"
+    CONTENT = "content"
+    NEUTRAL = "neutral"
+    MELANCHOLY = "melancholy"
+    SAD = "sad"
+    ANGRY = "angry"
+    SICK = "sick"
+    EXCITED = "excited"
+    TIRED = "tired"
+
+@dataclass
+class StatBonus:
+    multiplier: float = 1.0
+    flat_bonus: int = 0
+    duration_minutes: int = 0
+    source: str = ""
+
+class AdvancedMonsterStats(BaseModel):
+    # Primary Care Stats (0-100)
+    health: int = Field(default=100, ge=0, le=100)
+    hunger: int = Field(default=100, ge=0, le=100)
+    happiness: int = Field(default=100, ge=0, le=100)
+    energy: int = Field(default=100, ge=0, le=100)
+    discipline: int = Field(default=50, ge=0, le=100)
+    cleanliness: int = Field(default=100, ge=0, le=100)
+    
+    # Battle Stats (Digimon World 1 inspired)
+    life: int = Field(default=1000, ge=0)
+    mp: int = Field(default=100, ge=0)
+    offense: int = Field(default=100, ge=0)
+    defense: int = Field(default=100, ge=0)
+    speed: int = Field(default=100, ge=0)
+    brains: int = Field(default=100, ge=0)
+    
+    # Training Progress
+    training_progress: Dict[str, int] = Field(default_factory=lambda: {
+        "strength": 0,
+        "endurance": 0,
+        "intelligence": 0,
+        "dexterity": 0,
+        "spirit": 0,
+        "technique": 0
+    })
+    
+    # Active Bonuses
+    active_bonuses: List[StatBonus] = Field(default_factory=list)
+    
+    # Performance Metrics
+    care_quality_score: float = Field(default=1.0, ge=0.0, le=2.0)
+    evolution_potential: float = Field(default=1.0, ge=0.0, le=2.0)
+
+class AIPersonality(BaseModel):
+    # Core Personality Traits
+    primary_type: MonsterPersonalityType = Field(default=MonsterPersonalityType.PLAYFUL)
+    secondary_type: Optional[MonsterPersonalityType] = Field(default=None)
+    
+    # Trait Values (0.0-1.0)
+    openness: float = Field(default=0.5, ge=0.0, le=1.0)
+    conscientiousness: float = Field(default=0.5, ge=0.0, le=1.0)
+    extraversion: float = Field(default=0.5, ge=0.0, le=1.0)
+    agreeableness: float = Field(default=0.5, ge=0.0, le=1.0)
+    neuroticism: float = Field(default=0.5, ge=0.0, le=1.0)
+    
+    # Learned Preferences
+    favorite_foods: List[str] = Field(default_factory=list)
+    disliked_foods: List[str] = Field(default_factory=list)
+    preferred_activities: List[str] = Field(default_factory=list)
+    communication_style: str = Field(default="friendly")
+    
+    # Emotional Memory
+    emotional_memories: List[Dict[str, Any]] = Field(default_factory=list)
+    relationship_level: int = Field(default=0, ge=0, le=100)
+
+class ConversationContext(BaseModel):
+    # Recent Conversation History
+    messages: List[Dict[str, Any]] = Field(default_factory=list)
+    
+    # Context Compression
+    personality_summary: str = Field(default="")
+    relationship_summary: str = Field(default="")
+    recent_events_summary: str = Field(default="")
+    
+    # Interaction Statistics
+    total_conversations: int = Field(default=0)
+    avg_conversation_length: float = Field(default=0.0)
+    last_interaction: Optional[datetime] = Field(default=None)
+    interaction_frequency: float = Field(default=0.0)  # interactions per day
+    
+    # Emotional Context
+    current_mood_factors: Dict[str, float] = Field(default_factory=dict)
+    mood_history: List[Dict[str, Any]] = Field(default_factory=list)
+
+class AdvancedLifecycle(BaseModel):
+    # Time Tracking
+    age_minutes: float = Field(default=0.0)
+    stage: EvolutionStage = Field(default=EvolutionStage.EGG)
+    generation: int = Field(default=1)
+    
+    # Care History
+    care_mistakes: int = Field(default=0)
+    perfect_care_streaks: int = Field(default=0)
+    total_training_sessions: int = Field(default=0)
+    
+    # Evolution Data
+    evolution_requirements_met: List[str] = Field(default_factory=list)
+    evolution_locked_until: Optional[datetime] = Field(default=None)
+    special_evolution_conditions: Dict[str, bool] = Field(default_factory=dict)
+    
+    # Lifespan Management
+    base_lifespan_minutes: float = Field(default=21600.0)  # 15 days
+    lifespan_modifiers: List[float] = Field(default_factory=list)
+    death_prevention_items: int = Field(default=0)
+
+class Monster(BaseModel):
+    # Identity
+    id: str = Field(default_factory=lambda: str(uuid.uuid4()))
+    name: str = Field(default="DigiPal")
+    species: str = Field(default="Botamon")
+    variant: Optional[str] = Field(default=None)
+    
+    # Core Systems
+    stats: AdvancedMonsterStats = Field(default_factory=AdvancedMonsterStats)
+    personality: AIPersonality = Field(default_factory=AIPersonality)
+    lifecycle: AdvancedLifecycle = Field(default_factory=AdvancedLifecycle)
+    conversation: ConversationContext = Field(default_factory=ConversationContext)
+    
+    # Current State
+    emotional_state: EmotionalState = Field(default=EmotionalState.CONTENT)
+    current_activity: str = Field(default="idle")
+    location: str = Field(default="nursery")
+    
+    # Timestamps
+    created_at: datetime = Field(default_factory=datetime.now)
+    last_update: datetime = Field(default_factory=datetime.now)
+    last_interaction: Optional[datetime] = Field(default=None)
+    
+    # Items and Inventory
+    inventory: Dict[str, int] = Field(default_factory=dict)
+    equipped_items: Dict[str, str] = Field(default_factory=dict)
+    
+    # Breeding and Genetics
+    genetic_markers: Dict[str, Any] = Field(default_factory=dict)
+    parent_ids: List[str] = Field(default_factory=list)
+    offspring_ids: List[str] = Field(default_factory=list)
+    
+    # Performance Tracking
+    performance_metrics: Dict[str, float] = Field(default_factory=dict)
+    
+    class Config:
+        json_encoders = {
+            datetime: lambda v: v.isoformat()
+        }
+    
+    def calculate_emotional_state(self) -> EmotionalState:
+        """Calculate current emotional state based on multiple factors"""
+        # Health-based emotions
+        if self.stats.health < 20:
+            return EmotionalState.SICK
+        
+        # Happiness-based emotions
+        if self.stats.happiness >= 95:
+            return EmotionalState.ECSTATIC
+        elif self.stats.happiness >= 80:
+            return EmotionalState.HAPPY
+        elif self.stats.happiness >= 60:
+            return EmotionalState.CONTENT
+        elif self.stats.happiness >= 40:
+            return EmotionalState.NEUTRAL
+        elif self.stats.happiness >= 20:
+            return EmotionalState.MELANCHOLY
+        elif self.stats.happiness >= 10:
+            return EmotionalState.SAD
+        
+        # Energy-based emotions
+        if self.stats.energy < 20:
+            return EmotionalState.TIRED
+        
+        # Discipline-based emotions
+        if self.stats.discipline < 20 and self.stats.hunger > 80:
+            return EmotionalState.ANGRY
+        
+        # Special conditions
+        if self.current_activity in ["training", "playing"]:
+            return EmotionalState.EXCITED
+        
+        return EmotionalState.NEUTRAL
+    
+    def get_evolution_readiness(self) -> Dict[str, Any]:
+        """Calculate evolution readiness and requirements"""
+        current_requirements = self._get_stage_requirements()
+        met_requirements = []
+        missing_requirements = []
+        
+        for req_type, requirement in current_requirements.items():
+            if self._check_requirement(req_type, requirement):
+                met_requirements.append(req_type)
+            else:
+                missing_requirements.append({
+                    "type": req_type,
+                    "requirement": requirement,
+                    "current": self._get_current_value(req_type)
+                })
+        
+        readiness_percentage = len(met_requirements) / len(current_requirements) * 100 if current_requirements else 0
+        
+        return {
+            "readiness_percentage": readiness_percentage,
+            "met_requirements": met_requirements,
+            "missing_requirements": missing_requirements,
+            "can_evolve": len(missing_requirements) == 0,
+            "next_stage": self._get_next_evolution_stage()
+        }
+    
+    def _get_stage_requirements(self) -> Dict[str, Any]:
+        """Get evolution requirements for current stage"""
+        requirements = {
+            EvolutionStage.EGG: {
+                "age_minutes": 60,  # 1 hour
+                "care_mistakes_max": 0
+            },
+            EvolutionStage.BABY: {
+                "age_minutes": 1440,  # 24 hours
+                "stats_min": {"life": 1200, "offense": 120, "defense": 120},
+                "care_mistakes_max": 2
+            },
+            EvolutionStage.CHILD: {
+                "age_minutes": 4320,  # 72 hours
+                "stats_min": {"life": 1500, "offense": 150, "defense": 150, "brains": 150},
+                "care_mistakes_max": 5,
+                "training_min": {"strength": 50, "intelligence": 50}
+            },
+            EvolutionStage.ADULT: {
+                "age_minutes": 8640,  # 144 hours (6 days)
+                "stats_min": {"life": 2000, "offense": 200, "defense": 200, "brains": 200},
+                "care_mistakes_max": 8,
+                "training_min": {"strength": 100, "intelligence": 100},
+                "care_quality_min": 1.2
+            }
+        }
+        return requirements.get(self.lifecycle.stage, {})
+    
+    def _check_requirement(self, req_type: str, requirement: Any) -> bool:
+        """Check if a specific requirement is met"""
+        if req_type == "age_minutes":
+            return self.lifecycle.age_minutes >= requirement
+        elif req_type == "care_mistakes_max":
+            return self.lifecycle.care_mistakes <= requirement
+        elif req_type == "stats_min":
+            for stat, min_val in requirement.items():
+                if getattr(self.stats, stat, 0) < min_val:
+                    return False
+            return True
+        elif req_type == "training_min":
+            for training, min_val in requirement.items():
+                if self.stats.training_progress.get(training, 0) < min_val:
+                    return False
+            return True
+        elif req_type == "care_quality_min":
+            return self.stats.care_quality_score >= requirement
+        return False
+    
+    def _get_current_value(self, req_type: str) -> Any:
+        """Get current value for a requirement type"""
+        if req_type == "age_minutes":
+            return self.lifecycle.age_minutes
+        elif req_type == "care_mistakes_max":
+            return self.lifecycle.care_mistakes
+        elif req_type.startswith("stats_"):
+            return {stat: getattr(self.stats, stat, 0) for stat in ["life", "offense", "defense", "brains"]}
+        elif req_type.startswith("training_"):
+            return self.stats.training_progress
+        elif req_type == "care_quality_min":
+            return self.stats.care_quality_score
+        return None
+    
+    def _get_next_evolution_stage(self) -> Optional[EvolutionStage]:
+        """Get the next evolution stage"""
+        stage_order = [
+            EvolutionStage.EGG,
+            EvolutionStage.BABY,
+            EvolutionStage.CHILD,
+            EvolutionStage.ADULT,
+            EvolutionStage.PERFECT,
+            EvolutionStage.ULTIMATE,
+            EvolutionStage.MEGA
+        ]
+        
+        current_index = stage_order.index(self.lifecycle.stage)
+        if current_index < len(stage_order) - 1:
+            return stage_order[current_index + 1]
+        return None
+    
+    def apply_time_effects(self, minutes_elapsed: float):
+        """Apply time-based effects to monster"""
+        # Age progression
+        self.lifecycle.age_minutes += minutes_elapsed
+        
+        # Stat decay rates (per hour)
+        decay_rates = {
+            "hunger": 2.0,
+            "happiness": 0.8,
+            "energy": 1.2,
+            "cleanliness": 0.6,
+            "discipline": 0.2
+        }
+        
+        # Apply decay
+        hours_elapsed = minutes_elapsed / 60.0
+        for stat, rate in decay_rates.items():
+            current_value = getattr(self.stats, stat)
+            decay_amount = rate * hours_elapsed
+            
+            # Apply personality modifiers
+            if stat == "happiness" and self.personality.neuroticism > 0.7:
+                decay_amount *= 1.3
+            if stat == "energy" and self.personality.extraversion < 0.3:
+                decay_amount *= 0.8
+            
+            new_value = max(0, current_value - decay_amount)
+            setattr(self.stats, stat, int(new_value))
+        
+        # Health effects from poor care
+        if self.stats.hunger < 20:
+            health_loss = hours_elapsed * 3
+            self.stats.health = max(0, self.stats.health - int(health_loss))
+            self.lifecycle.care_mistakes += 1
+        
+        if self.stats.cleanliness < 30:
+            health_loss = hours_elapsed * 1.5
+            self.stats.health = max(0, self.stats.health - int(health_loss))
+        
+        # Update emotional state
+        self.emotional_state = self.calculate_emotional_state()
+        self.last_update = datetime.now()

src/deployment/__init__.py

@@ -0,0 +1 @@
+# Deployment module initialization

src/deployment/zero_gpu_optimizer.py

@@ -0,0 +1,187 @@
+import os
+import torch
+import psutil
+import logging
+from typing import Dict, Any, Optional
+import spaces
+from functools import wraps
+import asyncio
+import time
+
+class ZeroGPUOptimizer:
+    def __init__(self):
+        self.logger = logging.getLogger(__name__)
+        self.is_zero_gpu_available = self._check_zero_gpu_availability()
+        self.resource_cache = {}
+        self.last_resource_check = 0
+        
+    def _check_zero_gpu_availability(self) -> bool:
+        """Check if Zero GPU is available"""
+        try:
+            import spaces
+            return hasattr(spaces, 'GPU')
+        except ImportError:
+            return False
+    
+    async def detect_available_resources(self) -> Dict[str, Any]:
+        """Detect available computational resources"""
+        current_time = time.time()
+        
+        # Cache resources for 60 seconds
+        if (current_time - self.last_resource_check) < 60 and self.resource_cache:
+            return self.resource_cache
+        
+        try:
+            # CPU Information
+            cpu_count = psutil.cpu_count(logical=True)
+            cpu_freq = psutil.cpu_freq()
+            memory = psutil.virtual_memory()
+            
+            # GPU Information
+            gpu_available = torch.cuda.is_available()
+            gpu_count = torch.cuda.device_count() if gpu_available else 0
+            gpu_memory_gb = 0
+            gpu_name = "None"
+            
+            if gpu_available and gpu_count > 0:
+                gpu_memory_bytes = torch.cuda.get_device_properties(0).total_memory
+                gpu_memory_gb = gpu_memory_bytes / (1024**3)
+                gpu_name = torch.cuda.get_device_name(0)
+            
+            # Check for Zero GPU
+            zero_gpu_active = self.is_zero_gpu_available and os.getenv("SPACE_ID") is not None
+            
+            resources = {
+                "cpu_count": cpu_count,
+                "cpu_frequency_mhz": cpu_freq.current if cpu_freq else 0,
+                "total_memory_gb": memory.total / (1024**3),
+                "available_memory_gb": memory.available / (1024**3),
+                "gpu_available": gpu_available,
+                "gpu_count": gpu_count,
+                "gpu_memory_gb": gpu_memory_gb,
+                "gpu_name": gpu_name,
+                "zero_gpu_available": zero_gpu_active,
+                "compute_capability": self._determine_compute_capability(gpu_memory_gb, cpu_count)
+            }
+            
+            self.resource_cache = resources
+            self.last_resource_check = current_time
+            
+            self.logger.info(f"Detected resources: {resources}")
+            return resources
+            
+        except Exception as e:
+            self.logger.error(f"Resource detection failed: {e}")
+            return {
+                "cpu_count": 2,
+                "gpu_available": False,
+                "gpu_memory_gb": 0,
+                "compute_capability": "basic"
+            }
+    
+    def _determine_compute_capability(self, gpu_memory_gb: float, cpu_count: int) -> str:
+        """Determine compute capability tier"""
+        if gpu_memory_gb >= 16:
+            return "premium"  # Can run large models
+        elif gpu_memory_gb >= 8:
+            return "high"     # Can run medium models
+        elif gpu_memory_gb >= 4:
+            return "medium"   # Can run small models
+        elif cpu_count >= 8:
+            return "cpu_optimized"  # CPU inference
+        else:
+            return "basic"    # Limited capability
+    
+    def zero_gpu_decorator(self, duration: int = 120):
+        """Decorator for Zero GPU allocation"""
+        if not self.is_zero_gpu_available:
+            # Fallback for non-Zero GPU environments
+            def decorator(func):
+                @wraps(func)
+                async def wrapper(*args, **kwargs):
+                    return await func(*args, **kwargs)
+                return wrapper
+            return decorator
+        
+        # Use actual Zero GPU decorator
+        def decorator(func):
+            @spaces.GPU(duration=duration)
+            @wraps(func)
+            async def wrapper(*args, **kwargs):
+                return await func(*args, **kwargs)
+            return wrapper
+        return decorator
+    
+    async def optimize_model_loading(self, model_config: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize model loading based on available resources"""
+        resources = await self.detect_available_resources()
+        
+        # Adjust configuration based on resources
+        optimized_config = model_config.copy()
+        
+        if resources["compute_capability"] == "basic":
+            optimized_config.update({
+                "model_name": "Qwen/Qwen2.5-0.5B-Instruct",
+                "torch_dtype": "float32",
+                "device_map": "cpu",
+                "use_quantization": True
+            })
+        elif resources["compute_capability"] == "cpu_optimized":
+            optimized_config.update({
+                "model_name": "Qwen/Qwen2.5-1.5B-Instruct",
+                "torch_dtype": "float32",
+                "device_map": "cpu",
+                "use_quantization": True
+            })
+        elif resources["compute_capability"] == "medium":
+            optimized_config.update({
+                "model_name": "Qwen/Qwen2.5-1.5B-Instruct",
+                "torch_dtype": "float16",
+                "device_map": "auto",
+                "use_quantization": True
+            })
+        elif resources["compute_capability"] == "high":
+            optimized_config.update({
+                "model_name": "Qwen/Qwen2.5-3B-Instruct",
+                "torch_dtype": "bfloat16",
+                "device_map": "auto",
+                "use_quantization": False
+            })
+        else:  # premium
+            optimized_config.update({
+                "model_name": "Qwen/Qwen2.5-7B-Instruct",
+                "torch_dtype": "bfloat16",
+                "device_map": "auto",
+                "use_quantization": False
+            })
+        
+        self.logger.info(f"Optimized model config: {optimized_config}")
+        return optimized_config
+    
+    def get_deployment_config(self) -> Dict[str, Any]:
+        """Get optimized deployment configuration"""
+        resources = asyncio.run(self.detect_available_resources())
+        
+        base_config = {
+            "max_threads": min(40, resources["cpu_count"] * 2),
+            "enable_queue": True,
+            "show_error": True,
+            "quiet": False
+        }
+        
+        # Adjust based on compute capability
+        if resources["compute_capability"] in ["basic", "cpu_optimized"]:
+            base_config.update({
+                "max_threads": resources["cpu_count"],
+                "concurrency_count": 1
+            })
+        elif resources["compute_capability"] == "medium":
+            base_config.update({
+                "concurrency_count": 2
+            })
+        else:
+            base_config.update({
+                "concurrency_count": 4
+            })
+        
+        return base_config

src/ui/__init__.py

@@ -0,0 +1 @@
+# UI module initialization

src/ui/gradio_interface.py

@@ -0,0 +1,1064 @@
+import gradio as gr
+import asyncio
+import logging
+import json
+import time
+from typing import Dict, List, Optional, Any, Tuple
+from datetime import datetime, timedelta
+import numpy as np
+
+from ..core.monster_engine import Monster, MonsterPersonalityType, EmotionalState
+from ..ai.qwen_processor import QwenProcessor, ModelConfig
+from ..ai.speech_engine import AdvancedSpeechEngine, SpeechConfig
+from .state_manager import AdvancedStateManager
+from ..deployment.zero_gpu_optimizer import ZeroGPUOptimizer
+
+class StreamingComponents:
+    """Helper class for streaming components"""
+    def __init__(self):
+        self.logger = logging.getLogger(__name__)
+
+class ModernDigiPalInterface:
+    def __init__(self):
+        self.logger = logging.getLogger(__name__)
+        
+        # Initialize core systems
+        self.state_manager = AdvancedStateManager()
+        self.streaming = StreamingComponents()
+        self.gpu_optimizer = ZeroGPUOptimizer()
+        
+        # AI Systems (will be initialized based on available resources)
+        self.qwen_processor = None
+        self.speech_engine = None
+        
+        # Performance tracking
+        self.performance_metrics = {
+            "total_interactions": 0,
+            "average_response_time": 0.0,
+            "user_satisfaction": 0.0
+        }
+        
+        # UI State
+        self.current_monster = None
+        self.ui_theme = "soft"
+        
+    async def initialize(self):
+        """Initialize the interface with optimized configurations"""
+        try:
+            # Detect available resources
+            resources = await self.gpu_optimizer.detect_available_resources()
+            
+            # Initialize AI processors based on resources
+            await self._initialize_ai_systems(resources)
+            
+            # Initialize state management
+            await self.state_manager.initialize()
+            
+            self.logger.info("DigiPal interface initialized successfully")
+            
+        except Exception as e:
+            self.logger.error(f"Failed to initialize interface: {e}")
+            raise
+    
+    async def _initialize_ai_systems(self, resources: Dict[str, Any]):
+        """Initialize AI systems based on available resources"""
+        # Configure Qwen processor
+        if resources["gpu_memory_gb"] >= 8:
+            model_config = ModelConfig(
+                model_name="Qwen/Qwen2.5-3B-Instruct",
+                max_memory_gb=resources["gpu_memory_gb"],
+                inference_speed="quality"
+            )
+        elif resources["gpu_memory_gb"] >= 4:
+            model_config = ModelConfig(
+                model_name="Qwen/Qwen2.5-1.5B-Instruct",
+                max_memory_gb=resources["gpu_memory_gb"],
+                inference_speed="balanced"
+            )
+        else:
+            model_config = ModelConfig(
+                model_name="Qwen/Qwen2.5-0.5B-Instruct",
+                max_memory_gb=resources["gpu_memory_gb"],
+                inference_speed="fast"
+            )
+        
+        self.qwen_processor = QwenProcessor(model_config)
+        await self.qwen_processor.initialize()
+        
+        # Configure speech engine
+        speech_config = SpeechConfig()
+        if resources["gpu_memory_gb"] >= 6:
+            speech_config.model_size = "medium"
+            speech_config.device = "cuda"
+        elif resources["gpu_memory_gb"] >= 3:
+            speech_config.model_size = "small"
+            speech_config.device = "cuda"
+        else:
+            speech_config.model_size = "base"
+            speech_config.device = "cpu"
+        
+        self.speech_engine = AdvancedSpeechEngine(speech_config)
+        await self.speech_engine.initialize()
+    
+    def create_interface(self) -> gr.Blocks:
+        """Create the main Gradio interface"""
+        
+        # Custom CSS for modern monster game UI
+        custom_css = """
+        /* Modern Dark Theme */
+        .gradio-container {
+            background: linear-gradient(135deg, #1a1a2e 0%, #16213e 50%, #0f3460 100%);
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        }
+        
+        /* Monster Display */
+        .monster-display {
+            background: linear-gradient(145deg, #2a2a4e, #1e1e3c);
+            border: 3px solid #4a9eff;
+            border-radius: 20px;
+            padding: 20px;
+            text-align: center;
+            box-shadow: 0 10px 30px rgba(74, 158, 255, 0.3);
+            backdrop-filter: blur(10px);
+            min-height: 400px;
+        }
+        
+        /* Stat Bars */
+        .stat-bar {
+            background: #1e1e3c;
+            border-radius: 15px;
+            overflow: hidden;
+            margin: 8px 0;
+            height: 25px;
+            border: 2px solid #333;
+        }
+        
+        .stat-fill {
+            height: 100%;
+            border-radius: 12px;
+            transition: width 0.8s ease-in-out;
+            background: linear-gradient(90deg, #ff6b6b, #4ecdc4, #45b7d1);
+        }
+        
+        /* Care Action Buttons */
+        .care-button {
+            background: linear-gradient(145deg, #4a9eff, #357abd);
+            border: none;
+            color: white;
+            padding: 12px 24px;
+            border-radius: 12px;
+            font-weight: bold;
+            transition: all 0.3s ease;
+            box-shadow: 0 4px 15px rgba(74, 158, 255, 0.4);
+        }
+        
+        .care-button:hover {
+            transform: translateY(-3px);
+            box-shadow: 0 8px 25px rgba(74, 158, 255, 0.6);
+            background: linear-gradient(145deg, #5aa7ff, #4a9eff);
+        }
+        
+        /* Conversation Area */
+        .conversation-container {
+            background: rgba(30, 30, 60, 0.8);
+            border: 2px solid #4a9eff;
+            border-radius: 15px;
+            backdrop-filter: blur(10px);
+        }
+        
+        /* Mini-game Container */
+        .mini-game-area {
+            background: linear-gradient(145deg, #2d1b69, #1a1a2e);
+            border: 2px solid #8b5cf6;
+            border-radius: 15px;
+            padding: 20px;
+            margin: 10px 0;
+        }
+        
+        /* Status Indicators */
+        .status-indicator {
+            display: inline-block;
+            width: 12px;
+            height: 12px;
+            border-radius: 50%;
+            margin-right: 8px;
+        }
+        
+        .status-healthy { background: #4ade80; }
+        .status-warning { background: #fbbf24; }
+        .status-critical { background: #ef4444; }
+        
+        /* Responsive Design */
+        @media (max-width: 768px) {
+            .monster-display {
+                padding: 15px;
+                margin: 10px;
+            }
+            
+            .care-button {
+                padding: 10px 20px;
+                margin: 5px;
+            }
+        }
+        """
+        
+        with gr.Blocks(
+            css=custom_css, 
+            title="DigiPal - Advanced Monster Companion",
+            theme=gr.themes.Soft()
+        ) as interface:
+            
+            # Header
+            gr.HTML("""
+            <div style="text-align: center; padding: 20px;">
+                <h1 style="color: #4a9eff; font-size: 2.5em; margin: 0;">🐾 DigiPal</h1>
+                <p style="color: #8b5cf6; font-size: 1.2em;">Advanced AI Monster Companion</p>
+            </div>
+            """)
+            
+            # State Management - Modern Gradio 5.34.2 patterns
+            with gr.Row():
+                # Session State for current monster
+                current_monster_state = gr.State(None)
+                
+                # Conversation State
+                conversation_state = gr.State([])
+                
+                # UI State
+                ui_state = gr.State({
+                    "last_action": None,
+                    "current_tab": "care",
+                    "mini_game_active": False
+                })
+            
+            # Main Interface Layout
+            with gr.Row(equal_height=True):
+                
+                # Left Column - Monster Display and Stats
+                with gr.Column(scale=3):
+                    
+                    # Monster Display Area
+                    monster_display = gr.HTML(
+                        value=self._get_default_monster_display(),
+                        elem_classes="monster-display"
+                    )
+                    
+                    # Monster Management Controls
+                    with gr.Row():
+                        create_monster_btn = gr.Button(
+                            "🥚 Create New Monster", 
+                            variant="primary",
+                            elem_classes="care-button"
+                        )
+                        load_monster_btn = gr.Button(
+                            "📂 Load Monster",
+                            elem_classes="care-button"
+                        )
+                        save_progress_btn = gr.Button(
+                            "💾 Save Progress",
+                            elem_classes="care-button"
+                        )
+                    
+                    # New Monster Creation
+                    with gr.Group(visible=False) as monster_creation_group:
+                        monster_name_input = gr.Textbox(
+                            label="Monster Name",
+                            placeholder="Enter your monster's name...",
+                            max_lines=1
+                        )
+                        
+                        personality_type = gr.Dropdown(
+                            choices=[p.value for p in MonsterPersonalityType],
+                            label="Personality Type",
+                            value="playful"
+                        )
+                        
+                        confirm_creation_btn = gr.Button(
+                            "✨ Create Monster",
+                            variant="primary"
+                        )
+                
+                # Middle Column - Care Actions and Training
+                with gr.Column(scale=2):
+                    
+                    with gr.Tabs() as care_tabs:
+                        
+                        # Care Tab
+                        with gr.TabItem("🍼 Care", id=0):
+                            
+                            # Feeding Section
+                            with gr.Group():
+                                gr.Markdown("### 🍽️ Feeding")
+                                
+                                food_type = gr.Dropdown(
+                                    choices=[
+                                        "meat", "fish", "fruit", "vegetables", 
+                                        "medicine", "supplement", "treat"
+                                    ],
+                                    value="meat",
+                                    label="Food Type"
+                                )
+                                
+                                feed_btn = gr.Button(
+                                    "🍖 Feed Monster",
+                                    elem_classes="care-button"
+                                )
+                            
+                            # Training Section
+                            with gr.Group():
+                                gr.Markdown("### 💪 Training")
+                                
+                                training_type = gr.Dropdown(
+                                    choices=[
+                                        "strength", "endurance", "intelligence", 
+                                        "dexterity", "spirit", "technique"
+                                    ],
+                                    value="strength",
+                                    label="Training Focus"
+                                )
+                                
+                                training_intensity = gr.Slider(
+                                    minimum=1,
+                                    maximum=5,
+                                    value=3,
+                                    step=1,
+                                    label="Training Intensity"
+                                )
+                                
+                                train_btn = gr.Button(
+                                    "🏋️ Start Training",
+                                    elem_classes="care-button"
+                                )
+                            
+                            # Care Actions
+                            with gr.Group():
+                                gr.Markdown("### 🧼 Care Actions")
+                                
+                                with gr.Row():
+                                    clean_btn = gr.Button("🚿 Clean", elem_classes="care-button")
+                                    play_btn = gr.Button("🎮 Play", elem_classes="care-button")
+                                    rest_btn = gr.Button("😴 Rest", elem_classes="care-button")
+                                    discipline_btn = gr.Button("📚 Discipline", elem_classes="care-button")
+                        
+                        # Evolution Tab
+                        with gr.TabItem("🦋 Evolution", id=1):
+                            
+                            evolution_status = gr.HTML(
+                                value="<p>No monster loaded</p>"
+                            )
+                            
+                            evolution_requirements = gr.JSON(
+                                label="Evolution Requirements",
+                                value={}
+                            )
+                            
+                            trigger_evolution_btn = gr.Button(
+                                "🌟 Trigger Evolution",
+                                variant="primary",
+                                interactive=False
+                            )
+                        
+                        # Breeding Tab
+                        with gr.TabItem("💕 Breeding", id=2):
+                            
+                            gr.Markdown("### Find a Breeding Partner")
+                            
+                            partner_search = gr.Dropdown(
+                                choices=[],
+                                label="Available Partners",
+                                interactive=False
+                            )
+                            
+                            breeding_compatibility = gr.HTML(
+                                value="<p>Select a partner to see compatibility</p>"
+                            )
+                            
+                            start_breeding_btn = gr.Button(
+                                "💖 Start Breeding",
+                                variant="primary",
+                                interactive=False
+                            )
+                
+                # Right Column - Conversation and Mini-games
+                with gr.Column(scale=3):
+                    
+                    with gr.Tabs():
+                        
+                        # Conversation Tab
+                        with gr.TabItem("💬 Talk", id=0):
+                            
+                            # Conversation Display
+                            chatbot = gr.Chatbot(
+                                value=[],
+                                height=350,
+                                label="Conversation with your Monster",
+                                elem_classes="conversation-container",
+                                avatar_images=("👤", "🐾")
+                            )
+                            
+                            # Text Input
+                            with gr.Row():
+                                text_input = gr.Textbox(
+                                    label="Message",
+                                    placeholder="Talk to your monster...",
+                                    scale=4,
+                                    max_lines=3
+                                )
+                                send_btn = gr.Button("💬", scale=1)
+                            
+                            # Voice Input
+                            with gr.Group():
+                                gr.Markdown("### 🎤 Voice Chat")
+                                
+                                with gr.Row():
+                                    audio_input = gr.Audio(
+                                        sources=["microphone"],
+                                        type="numpy",
+                                        label="Voice Input",
+                                        streaming=False
+                                    )
+                                    
+                                    voice_btn = gr.Button("🗣️ Send Voice")
+                                
+                                # Real-time audio streaming (Gradio 5.34.2 feature)
+                                with gr.Row():
+                                    start_stream_btn = gr.Button("🎙️ Start Live Chat")
+                                    stop_stream_btn = gr.Button("⏹️ Stop", interactive=False)
+                        
+                        # Mini-games Tab
+                        with gr.TabItem("🎯 Games", id=1):
+                            
+                            mini_game_display = gr.HTML(
+                                value=self._get_mini_game_display(),
+                                elem_classes="mini-game-area"
+                            )
+                            
+                            with gr.Row():
+                                reaction_game_btn = gr.Button("⚡ Reaction Training")
+                                memory_game_btn = gr.Button("🧠 Memory Challenge")
+                                rhythm_game_btn = gr.Button("🎵 Rhythm Game")
+                                puzzle_game_btn = gr.Button("🧩 Logic Puzzle")
+                            
+                            game_score_display = gr.JSON(
+                                label="Game Statistics",
+                                value={}
+                            )
+                        
+                        # Stats Tab
+                        with gr.TabItem("📊 Statistics", id=2):
+                            
+                            detailed_stats = gr.JSON(
+                                label="Detailed Monster Statistics",
+                                value={}
+                            )
+                            
+                            performance_charts = gr.Plot(
+                                label="Performance Over Time"
+                            )
+                            
+                            achievement_display = gr.HTML(
+                                value="<p>No achievements yet</p>"
+                            )
+            
+            # Global Status Bar
+            with gr.Row():
+                status_display = gr.HTML(
+                    value="<p>Ready to start your monster care journey!</p>",
+                    elem_id="status-bar"
+                )
+                
+                auto_save_indicator = gr.HTML(
+                    value="<span style='color: green;'>● Auto-save: ON</span>",
+                    elem_id="auto-save-status"
+                )
+            
+            # Hidden components for data flow
+            action_result = gr.Textbox(visible=False)
+            background_timer = gr.Timer(value=30, active=True)  # 30-second updates
+            
+            # Event Handlers with Modern Async Patterns
+            
+            # Monster Creation Flow
+            create_monster_btn.click(
+                fn=lambda: gr.update(visible=True),
+                outputs=monster_creation_group
+            )
+            
+            confirm_creation_btn.click(
+                fn=self.create_new_monster,
+                inputs=[monster_name_input, personality_type],
+                outputs=[current_monster_state, monster_display, monster_creation_group]
+            )
+            
+            # Care Actions
+            feed_btn.click(
+                fn=self.feed_monster,
+                inputs=[current_monster_state, food_type],
+                outputs=[current_monster_state, monster_display, action_result, chatbot]
+            )
+            
+            train_btn.click(
+                fn=self.train_monster,
+                inputs=[current_monster_state, training_type, training_intensity],
+                outputs=[current_monster_state, monster_display, action_result]
+            )
+            
+            # Conversation Handlers
+            send_btn.click(
+                fn=self.handle_text_conversation,
+                inputs=[current_monster_state, text_input, conversation_state],
+                outputs=[chatbot, text_input, conversation_state, current_monster_state]
+            )
+            
+            text_input.submit(
+                fn=self.handle_text_conversation,
+                inputs=[current_monster_state, text_input, conversation_state],
+                outputs=[chatbot, text_input, conversation_state, current_monster_state]
+            )
+            
+            voice_btn.click(
+                fn=self.handle_voice_input,
+                inputs=[current_monster_state, audio_input, conversation_state],
+                outputs=[chatbot, conversation_state, current_monster_state, action_result]
+            )
+            
+            # Real-time streaming (Gradio 5.34.2)
+            start_stream_btn.click(
+                fn=self.start_voice_streaming,
+                outputs=[start_stream_btn, stop_stream_btn]
+            )
+            
+            stop_stream_btn.click(
+                fn=self.stop_voice_streaming,
+                outputs=[start_stream_btn, stop_stream_btn]
+            )
+            
+            # Background Updates
+            background_timer.tick(
+                fn=self.background_update,
+                inputs=[current_monster_state],
+                outputs=[current_monster_state, monster_display, auto_save_indicator]
+            )
+            
+            # Care action handlers
+            for btn, action in [(clean_btn, "clean"), (play_btn, "play"), 
+                              (rest_btn, "rest"), (discipline_btn, "discipline")]:
+                btn.click(
+                    fn=lambda monster_state, action=action: self.perform_care_action(monster_state, action),
+                    inputs=[current_monster_state],
+                    outputs=[current_monster_state, monster_display, action_result]
+                )
+            
+            # Mini-game handlers
+            for btn, game in [(reaction_game_btn, "reaction"), (memory_game_btn, "memory"),
+                            (rhythm_game_btn, "rhythm"), (puzzle_game_btn, "puzzle")]:
+                btn.click(
+                    fn=lambda monster_state, game=game: self.start_mini_game(monster_state, game),
+                    inputs=[current_monster_state],
+                    outputs=[mini_game_display, game_score_display]
+                )
+        
+        return interface
+    
+    # Implementation methods continue...
+    
+    async def create_new_monster(self, name: str, personality: str) -> Tuple:
+        """Create a new monster with specified parameters"""
+        try:
+            if not name.strip():
+                return None, self._get_default_monster_display(), gr.update(visible=True)
+            
+            # Create monster with personality
+            monster = Monster(
+                name=name.strip(),
+                species="Botamon"  # Starting species
+            )
+            
+            # Set personality
+            monster.personality.primary_type = MonsterPersonalityType(personality)
+            
+            # Randomize personality traits based on type
+            trait_modifiers = {
+                "playful": {"extraversion": 0.8, "openness": 0.7, "agreeableness": 0.6},
+                "serious": {"conscientiousness": 0.8, "neuroticism": 0.3, "extraversion": 0.4},
+                "curious": {"openness": 0.9, "extraversion": 0.6, "conscientiousness": 0.5},
+                "gentle": {"agreeableness": 0.9, "neuroticism": 0.2, "extraversion": 0.5},
+                "energetic": {"extraversion": 0.9, "openness": 0.6, "neuroticism": 0.3},
+                "calm": {"neuroticism": 0.1, "conscientiousness": 0.7, "agreeableness": 0.7},
+                "mischievous": {"openness": 0.8, "extraversion": 0.7, "conscientiousness": 0.3},
+                "loyal": {"agreeableness": 0.8, "conscientiousness": 0.9, "neuroticism": 0.2}
+            }
+            
+            modifiers = trait_modifiers.get(personality, {})
+            for trait, value in modifiers.items():
+                if hasattr(monster.personality, trait):
+                    setattr(monster.personality, trait, value)
+            
+            # Save monster
+            await self.state_manager.save_monster(monster)
+            
+            # Generate display
+            display_html = self._generate_monster_display(monster)
+            
+            self.current_monster = monster
+            
+            return (
+                monster.dict(), 
+                display_html, 
+                gr.update(visible=False)
+            )
+            
+        except Exception as e:
+            self.logger.error(f"Monster creation failed: {e}")
+            return None, self._get_error_display(str(e)), gr.update(visible=True)
+    
+    def _get_default_monster_display(self) -> str:
+        """Get default monster display when no monster is loaded"""
+        return """
+        <div style="text-align: center; padding: 40px;">
+            <div style="font-size: 4em; margin-bottom: 20px;">🥚</div>
+            <h2 style="color: #4a9eff;">No Monster Loaded</h2>
+            <p style="color: #8b5cf6;">Create a new monster to begin your journey!</p>
+        </div>
+        """
+    
+    def _generate_monster_display(self, monster: Monster) -> str:
+        """Generate HTML display for the monster"""
+        # Monster sprite based on species and stage
+        sprite_map = {
+            "Botamon": {"egg": "🥚", "baby": "🐣", "child": "🐾", "adult": "🐲"},
+            # Add more species...
+        }
+        
+        sprite = sprite_map.get(monster.species, {}).get(monster.lifecycle.stage.value, "🐾")
+        
+        # Emotional state emoji
+        emotion_emojis = {
+            "ecstatic": "🤩", "happy": "😊", "content": "😌", "neutral": "😐",
+            "melancholy": "😔", "sad": "😢", "angry": "😠", "sick": "🤒",
+            "excited": "😆", "tired": "😴"
+        }
+        
+        emotion_emoji = emotion_emojis.get(monster.emotional_state.value, "😐")
+        
+        # Calculate stat colors
+        def get_stat_color(value: int) -> str:
+            if value >= 80: return "#4ade80"  # Green
+            elif value >= 60: return "#fbbf24"  # Yellow
+            elif value >= 40: return "#fb923c"  # Orange
+            else: return "#ef4444"  # Red
+        
+        # Age display
+        age_days = monster.lifecycle.age_minutes / 1440
+        age_display = f"{age_days:.1f} days"
+        
+        return f"""
+        <div style="text-align: center; padding: 20px;">
+            
+            <!-- Monster Sprite -->
+            <div style="font-size: 6em; margin: 20px 0;">{sprite}</div>
+            
+            <!-- Monster Info -->
+            <h2 style="color: #4a9eff; margin: 10px 0;">{monster.name} {emotion_emoji}</h2>
+            <p style="color: #8b5cf6; margin: 5px 0;">
+                <strong>{monster.species}</strong> | {monster.lifecycle.stage.value.title()} | {age_display}
+            </p>
+            
+            <!-- Mood and Activity -->
+            <p style="color: #a78bfa; margin: 10px 0;">
+                Feeling {monster.emotional_state.value} while {monster.current_activity}
+            </p>
+            
+            <!-- Care Stats -->
+            <div style="margin: 20px 0;">
+                <h3 style="color: #4a9eff;">Care Status</h3>
+                
+                <div style="text-align: left; max-width: 300px; margin: 0 auto;">
+                    <div style="margin: 8px 0;">
+                        <span style="color: white;">Health</span>
+                        <div class="stat-bar">
+                            <div class="stat-fill" style="width: {monster.stats.health}%; background: {get_stat_color(monster.stats.health)};"></div>
+                        </div>
+                        <span style="color: #888; font-size: 0.9em;">{monster.stats.health}/100</span>
+                    </div>
+                    
+                    <div style="margin: 8px 0;">
+                        <span style="color: white;">Happiness</span>
+                        <div class="stat-bar">
+                            <div class="stat-fill" style="width: {monster.stats.happiness}%; background: {get_stat_color(monster.stats.happiness)};"></div>
+                        </div>
+                        <span style="color: #888; font-size: 0.9em;">{monster.stats.happiness}/100</span>
+                    </div>
+                    
+                    <div style="margin: 8px 0;">
+                        <span style="color: white;">Hunger</span>
+                        <div class="stat-bar">
+                            <div class="stat-fill" style="width: {monster.stats.hunger}%; background: {get_stat_color(monster.stats.hunger)};"></div>
+                        </div>
+                        <span style="color: #888; font-size: 0.9em;">{monster.stats.hunger}/100</span>
+                    </div>
+                    
+                    <div style="margin: 8px 0;">
+                        <span style="color: white;">Energy</span>
+                        <div class="stat-bar">
+                            <div class="stat-fill" style="width: {monster.stats.energy}%; background: {get_stat_color(monster.stats.energy)};"></div>
+                        </div>
+                        <span style="color: #888; font-size: 0.9em;">{monster.stats.energy}/100</span>
+                    </div>
+                </div>
+            </div>
+            
+            <!-- Battle Stats -->
+            <div style="margin: 20px 0;">
+                <h3 style="color: #8b5cf6;">Battle Power</h3>
+                <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 10px; max-width: 300px; margin: 0 auto;">
+                    <div>Life: <strong style="color: #4ade80;">{monster.stats.life}</strong></div>
+                    <div>MP: <strong style="color: #60a5fa;">{monster.stats.mp}</strong></div>
+                    <div>Offense: <strong style="color: #f87171;">{monster.stats.offense}</strong></div>
+                    <div>Defense: <strong style="color: #34d399;">{monster.stats.defense}</strong></div>
+                    <div>Speed: <strong style="color: #fbbf24;">{monster.stats.speed}</strong></div>
+                    <div>Brains: <strong style="color: #a78bfa;">{monster.stats.brains}</strong></div>
+                </div>
+            </div>
+            
+            <!-- Generation and Care Info -->
+            <div style="margin: 15px 0; font-size: 0.9em; color: #888;">
+                Generation {monster.lifecycle.generation} | 
+                Care Mistakes: {monster.lifecycle.care_mistakes} | 
+                Relationship: {monster.personality.relationship_level}/100
+            </div>
+            
+        </div>
+        """
+    
+    def _get_mini_game_display(self) -> str:
+        """Get mini-game display HTML"""
+        return """
+        <div style="text-align: center; padding: 20px;">
+            <h3 style="color: #8b5cf6;">Mini-Games Training Center</h3>
+            <p style="color: #a78bfa;">Select a mini-game to train your monster!</p>
+            <div style="margin-top: 20px;">
+                <p>⚡ Reaction: Improve Speed & Reflexes</p>
+                <p>🧠 Memory: Enhance Intelligence</p>
+                <p>🎵 Rhythm: Boost Spirit & Happiness</p>
+                <p>🧩 Logic: Develop Problem-Solving</p>
+            </div>
+        </div>
+        """
+    
+    def _get_error_display(self, error: str) -> str:
+        """Get error display HTML"""
+        return f"""
+        <div style="text-align: center; padding: 40px;">
+            <div style="font-size: 3em; margin-bottom: 20px;">❌</div>
+            <h2 style="color: #ef4444;">Error Occurred</h2>
+            <p style="color: #f87171;">{error}</p>
+        </div>
+        """
+    
+    async def feed_monster(self, monster_state: Dict, food_type: str) -> Tuple:
+        """Feed the monster"""
+        if not monster_state:
+            return monster_state, self._get_default_monster_display(), "No monster loaded!", []
+        
+        try:
+            monster = Monster(**monster_state)
+            
+            # Food effects
+            food_effects = {
+                "meat": {"hunger": 30, "happiness": 10},
+                "fish": {"hunger": 25, "happiness": 15, "health": 5},
+                "fruit": {"hunger": 20, "happiness": 20},
+                "vegetables": {"hunger": 25, "happiness": 5, "health": 10},
+                "medicine": {"health": 50, "happiness": -10},
+                "supplement": {"energy": 20, "happiness": 5},
+                "treat": {"happiness": 30, "hunger": 10}
+            }
+            
+            effects = food_effects.get(food_type, food_effects["meat"])
+            
+            # Apply effects
+            for stat, value in effects.items():
+                current = getattr(monster.stats, stat)
+                setattr(monster.stats, stat, max(0, min(100, current + value)))
+            
+            # Update emotional state
+            monster.emotional_state = monster.calculate_emotional_state()
+            
+            # Save monster
+            await self.state_manager.save_monster(monster)
+            
+            # Generate response
+            response = f"{monster.name} enjoyed the {food_type}! 😋"
+            
+            return (
+                monster.dict(),
+                self._generate_monster_display(monster),
+                response,
+                [[f"Fed {food_type}", response]]
+            )
+            
+        except Exception as e:
+            self.logger.error(f"Feeding failed: {e}")
+            return monster_state, self._get_error_display(str(e)), str(e), []
+    
+    async def train_monster(self, monster_state: Dict, training_type: str, intensity: int) -> Tuple:
+        """Train the monster"""
+        if not monster_state:
+            return monster_state, self._get_default_monster_display(), "No monster loaded!"
+        
+        try:
+            monster = Monster(**monster_state)
+            
+            # Check if monster can train
+            if monster.stats.energy < 20:
+                return monster_state, self._generate_monster_display(monster), f"{monster.name} is too tired to train! 😴"
+            
+            # Training effects
+            training_effects = {
+                "strength": {"offense": 5 * intensity, "life": 20 * intensity},
+                "endurance": {"defense": 5 * intensity, "life": 30 * intensity},
+                "intelligence": {"brains": 8 * intensity, "mp": 10 * intensity},
+                "dexterity": {"speed": 6 * intensity},
+                "spirit": {"mp": 15 * intensity, "happiness": 5},
+                "technique": {"offense": 3 * intensity, "defense": 3 * intensity}
+            }
+            
+            effects = training_effects.get(training_type, {})
+            
+            # Apply stat increases
+            for stat, increase in effects.items():
+                if hasattr(monster.stats, stat):
+                    current = getattr(monster.stats, stat)
+                    setattr(monster.stats, stat, current + increase)
+            
+            # Update training progress
+            if training_type in monster.stats.training_progress:
+                monster.stats.training_progress[training_type] += 10 * intensity
+            
+            # Training costs
+            monster.stats.energy = max(0, monster.stats.energy - (15 * intensity))
+            monster.stats.hunger = max(0, monster.stats.hunger - (10 * intensity))
+            
+            # Update emotional state
+            monster.emotional_state = monster.calculate_emotional_state()
+            monster.current_activity = "training"
+            
+            # Save monster
+            await self.state_manager.save_monster(monster)
+            
+            response = f"{monster.name} completed {training_type} training! 💪"
+            
+            return (
+                monster.dict(),
+                self._generate_monster_display(monster),
+                response
+            )
+            
+        except Exception as e:
+            self.logger.error(f"Training failed: {e}")
+            return monster_state, self._get_error_display(str(e)), str(e)
+    
+    async def handle_text_conversation(self, monster_state: Dict, message: str, conversation_history: List) -> Tuple:
+        """Handle text conversation with monster"""
+        if not monster_state or not message.strip():
+            return conversation_history, ""
+        
+        try:
+            monster = Monster(**monster_state)
+            
+            # Generate AI response
+            response_data = await self.qwen_processor.generate_monster_response(
+                monster.dict(),
+                message,
+                conversation_history
+            )
+            
+            response = response_data["response"]
+            
+            # Update conversation history
+            conversation_history.append([message, response])
+            
+            # Update monster state based on interaction
+            monster.conversation.total_conversations += 1
+            monster.conversation.last_interaction = datetime.now()
+            monster.stats.happiness = min(100, monster.stats.happiness + 2)
+            monster.personality.relationship_level = min(100, monster.personality.relationship_level + 1)
+            
+            # Apply emotional impact
+            emotional_impact = response_data.get("emotional_impact", {})
+            for emotion, value in emotional_impact.items():
+                if emotion == "happiness":
+                    monster.stats.happiness = max(0, min(100, monster.stats.happiness + int(value * 10)))
+                elif emotion == "bonding":
+                    monster.personality.relationship_level = min(100, monster.personality.relationship_level + int(value * 5))
+            
+            # Save monster
+            await self.state_manager.save_monster(monster)
+            
+            return conversation_history, "", conversation_history, monster.dict()
+            
+        except Exception as e:
+            self.logger.error(f"Conversation failed: {e}")
+            return conversation_history, ""
+    
+    async def handle_voice_input(self, monster_state: Dict, audio_data, conversation_history: List) -> Tuple:
+        """Handle voice input"""
+        if not monster_state or audio_data is None:
+            return conversation_history, conversation_history, monster_state, ""
+        
+        try:
+            # Process speech
+            speech_result = await self.speech_engine.process_audio_stream(audio_data[1])
+            
+            if not speech_result["success"]:
+                return conversation_history, conversation_history, monster_state, "Speech processing failed"
+            
+            transcribed_text = speech_result["transcription"]
+            if not transcribed_text.strip():
+                return conversation_history, conversation_history, monster_state, "No speech detected"
+            
+            # Process as text conversation
+            new_history, _, updated_history, updated_monster = await self.handle_text_conversation(
+                monster_state, transcribed_text, conversation_history
+            )
+            
+            return new_history, updated_history, updated_monster, f"Heard: \"{transcribed_text}\""
+            
+        except Exception as e:
+            self.logger.error(f"Voice input failed: {e}")
+            return conversation_history, conversation_history, monster_state, str(e)
+    
+    async def perform_care_action(self, monster_state: Dict, action: str) -> Tuple:
+        """Perform care action on monster"""
+        if not monster_state:
+            return monster_state, self._get_default_monster_display(), "No monster loaded!"
+        
+        try:
+            monster = Monster(**monster_state)
+            
+            care_effects = {
+                "clean": {"cleanliness": 50, "happiness": 10},
+                "play": {"happiness": 25, "energy": -15, "relationship": 5},
+                "rest": {"energy": 40, "happiness": 5},
+                "discipline": {"discipline": 20, "happiness": -10}
+            }
+            
+            effects = care_effects.get(action, {})
+            
+            # Apply effects
+            for stat, value in effects.items():
+                if stat == "relationship":
+                    monster.personality.relationship_level = min(100, monster.personality.relationship_level + value)
+                elif hasattr(monster.stats, stat):
+                    current = getattr(monster.stats, stat)
+                    setattr(monster.stats, stat, max(0, min(100, current + value)))
+            
+            # Update activity
+            monster.current_activity = action
+            monster.emotional_state = monster.calculate_emotional_state()
+            
+            # Save monster
+            await self.state_manager.save_monster(monster)
+            
+            response = f"{monster.name} is now {action}ing! ✨"
+            
+            return (
+                monster.dict(),
+                self._generate_monster_display(monster),
+                response
+            )
+            
+        except Exception as e:
+            self.logger.error(f"Care action failed: {e}")
+            return monster_state, self._get_error_display(str(e)), str(e)
+    
+    async def background_update(self, monster_state: Dict) -> Tuple:
+        """Background update for time-based effects"""
+        if not monster_state:
+            return monster_state, self._get_default_monster_display(), gr.update()
+        
+        try:
+            monster = Monster(**monster_state)
+            
+            # Calculate time elapsed
+            time_elapsed = (datetime.now() - monster.last_update).total_seconds() / 60  # minutes
+            
+            # Apply time effects
+            monster.apply_time_effects(time_elapsed)
+            
+            # Save monster
+            await self.state_manager.save_monster(monster)
+            
+            # Update save indicator
+            save_indicator = f"<span style='color: green;'>● Auto-saved at {datetime.now().strftime('%H:%M:%S')}</span>"
+            
+            return (
+                monster.dict(),
+                self._generate_monster_display(monster),
+                save_indicator
+            )
+            
+        except Exception as e:
+            self.logger.error(f"Background update failed: {e}")
+            return monster_state, self._get_error_display(str(e)), gr.update()
+    
+    def start_mini_game(self, monster_state: Dict, game_type: str) -> Tuple:
+        """Start a mini-game"""
+        if not monster_state:
+            return self._get_mini_game_display(), {}
+        
+        # Placeholder for mini-game implementation
+        game_display = f"""
+        <div style="text-align: center; padding: 20px;">
+            <h3 style="color: #8b5cf6;">{game_type.title()} Training</h3>
+            <p>Mini-game implementation coming soon!</p>
+        </div>
+        """
+        
+        game_stats = {
+            "game_type": game_type,
+            "status": "not_implemented"
+        }
+        
+        return game_display, game_stats
+    
+    def start_voice_streaming(self) -> Tuple:
+        """Start voice streaming"""
+        return gr.update(interactive=False), gr.update(interactive=True)
+    
+    def stop_voice_streaming(self) -> Tuple:
+        """Stop voice streaming"""
+        return gr.update(interactive=True), gr.update(interactive=False)
+    
+    def launch(self, **kwargs):
+        """Launch the Gradio interface with optimized settings"""
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        
+        # Initialize async components
+        loop.run_until_complete(self.initialize())
+        
+        # Create interface
+        interface = self.create_interface()
+        
+        # Launch with production settings
+        launch_config = {
+            "server_name": "0.0.0.0",
+            "server_port": 7860,
+            "share": False,
+            "debug": False,
+            "show_error": True,
+            "quiet": False,
+            "favicon_path": None,
+            "ssl_keyfile": None,
+            "ssl_certfile": None,
+            "ssl_keyfile_password": None,
+            "max_threads": 40,
+            "show_tips": False,
+            "enable_queue": True,
+            **kwargs
+        }
+        
+        self.logger.info("Launching DigiPal interface...")
+        return interface.launch(**launch_config)

src/ui/state_manager.py

@@ -0,0 +1,417 @@
+import asyncio
+import json
+import aiofiles
+import sqlite3
+import aiosqlite
+from pathlib import Path
+from typing import Dict, List, Optional, Any, Union
+from datetime import datetime, timedelta
+import logging
+import pickle
+import gzip
+
+from ..core.monster_engine import Monster
+
+class AdvancedStateManager:
+    def __init__(self, save_dir: str = "data/saves"):
+        self.save_dir = Path(save_dir)
+        self.save_dir.mkdir(parents=True, exist_ok=True)
+        
+        self.db_path = self.save_dir / "monsters.db"
+        self.backup_dir = self.save_dir / "backups"
+        self.backup_dir.mkdir(exist_ok=True)
+        
+        self.logger = logging.getLogger(__name__)
+        
+        # In-memory cache for active monsters
+        self.monster_cache: Dict[str, Monster] = {}
+        self.cache_timestamps: Dict[str, datetime] = {}
+        self.cache_timeout = timedelta(minutes=30)
+        
+        # Connection pool
+        self.db_pool = None
+        
+    async def initialize(self):
+        """Initialize the state management system"""
+        try:
+            # Create database tables
+            await self._create_tables()
+            
+            # Start background tasks
+            asyncio.create_task(self._cache_cleanup_task())
+            asyncio.create_task(self._auto_backup_task())
+            
+            self.logger.info("State manager initialized successfully")
+            
+        except Exception as e:
+            self.logger.error(f"State manager initialization failed: {e}")
+            raise
+    
+    async def _create_tables(self):
+        """Create database tables for monster storage"""
+        async with aiosqlite.connect(self.db_path) as db:
+            await db.execute("""
+                CREATE TABLE IF NOT EXISTS monsters (
+                    id TEXT PRIMARY KEY,
+                    name TEXT NOT NULL,
+                    species TEXT NOT NULL,
+                    data BLOB NOT NULL,
+                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                    is_active BOOLEAN DEFAULT 1
+                )
+            """)
+            
+            await db.execute("""
+                CREATE TABLE IF NOT EXISTS monster_interactions (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    monster_id TEXT NOT NULL,
+                    interaction_type TEXT NOT NULL,
+                    interaction_data TEXT,
+                    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                    FOREIGN KEY (monster_id) REFERENCES monsters (id)
+                )
+            """)
+            
+            await db.execute("""
+                CREATE TABLE IF NOT EXISTS evolution_history (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    monster_id TEXT NOT NULL,
+                    from_stage TEXT NOT NULL,
+                    to_stage TEXT NOT NULL,
+                    evolution_trigger TEXT,
+                    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                    FOREIGN KEY (monster_id) REFERENCES monsters (id)
+                )
+            """)
+            
+            await db.execute("""
+                CREATE TABLE IF NOT EXISTS breeding_records (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    parent1_id TEXT NOT NULL,
+                    parent2_id TEXT NOT NULL,
+                    offspring_id TEXT NOT NULL,
+                    breeding_time_hours REAL,
+                    compatibility_score REAL,
+                    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+                )
+            """)
+            
+            # Create indexes for performance
+            await db.execute("CREATE INDEX IF NOT EXISTS idx_monsters_active ON monsters (is_active)")
+            await db.execute("CREATE INDEX IF NOT EXISTS idx_interactions_monster ON monster_interactions (monster_id)")
+            await db.execute("CREATE INDEX IF NOT EXISTS idx_interactions_type ON monster_interactions (interaction_type)")
+            
+            await db.commit()
+    
+    async def save_monster(self, monster: Monster) -> bool:
+        """Save monster to persistent storage"""
+        try:
+            # Update cache
+            self.monster_cache[monster.id] = monster
+            self.cache_timestamps[monster.id] = datetime.now()
+            
+            # Serialize monster data with compression
+            monster_data = gzip.compress(pickle.dumps(monster.dict()))
+            
+            # Save to database
+            async with aiosqlite.connect(self.db_path) as db:
+                await db.execute("""
+                    INSERT OR REPLACE INTO monsters 
+                    (id, name, species, data, updated_at) 
+                    VALUES (?, ?, ?, ?, ?)
+                """, (
+                    monster.id,
+                    monster.name,
+                    monster.species,
+                    monster_data,
+                    datetime.now().isoformat()
+                ))
+                await db.commit()
+            
+            self.logger.debug(f"Saved monster {monster.name} ({monster.id})")
+            return True
+            
+        except Exception as e:
+            self.logger.error(f"Failed to save monster {monster.id}: {e}")
+            return False
+    
+    async def load_monster(self, monster_id: str) -> Optional[Monster]:
+        """Load monster from storage"""
+        try:
+            # Check cache first
+            if monster_id in self.monster_cache:
+                cache_time = self.cache_timestamps.get(monster_id)
+                if cache_time and (datetime.now() - cache_time) < self.cache_timeout:
+                    return self.monster_cache[monster_id]
+            
+            # Load from database
+            async with aiosqlite.connect(self.db_path) as db:
+                async with db.execute(
+                    "SELECT data FROM monsters WHERE id = ? AND is_active = 1", 
+                    (monster_id,)
+                ) as cursor:
+                    row = await cursor.fetchone()
+                    
+                    if not row:
+                        return None
+                    
+                    # Decompress and deserialize
+                    monster_data = pickle.loads(gzip.decompress(row[0]))
+                    monster = Monster(**monster_data)
+                    
+                    # Update cache
+                    self.monster_cache[monster_id] = monster
+                    self.cache_timestamps[monster_id] = datetime.now()
+                    
+                    self.logger.debug(f"Loaded monster {monster.name} ({monster_id})")
+                    return monster
+                    
+        except Exception as e:
+            self.logger.error(f"Failed to load monster {monster_id}: {e}")
+            return None
+    
+    async def list_monsters(self, active_only: bool = True) -> List[Dict[str, Any]]:
+        """List all monsters with basic information"""
+        try:
+            where_clause = "WHERE is_active = 1" if active_only else ""
+            
+            async with aiosqlite.connect(self.db_path) as db:
+                async with db.execute(f"""
+                    SELECT id, name, species, created_at, updated_at 
+                    FROM monsters {where_clause} 
+                    ORDER BY updated_at DESC
+                """) as cursor:
+                    
+                    monsters = []
+                    async for row in cursor:
+                        monsters.append({
+                            "id": row[0],
+                            "name": row[1],
+                            "species": row[2],
+                            "created_at": row[3],
+                            "updated_at": row[4]
+                        })
+                    
+                    return monsters
+                    
+        except Exception as e:
+            self.logger.error(f"Failed to list monsters: {e}")
+            return []
+    
+    async def delete_monster(self, monster_id: str, soft_delete: bool = True) -> bool:
+        """Delete monster from storage"""
+        try:
+            # Remove from cache
+            self.monster_cache.pop(monster_id, None)
+            self.cache_timestamps.pop(monster_id, None)
+            
+            async with aiosqlite.connect(self.db_path) as db:
+                if soft_delete:
+                    # Soft delete - mark as inactive
+                    await db.execute(
+                        "UPDATE monsters SET is_active = 0 WHERE id = ?",
+                        (monster_id,)
+                    )
+                else:
+                    # Hard delete - remove completely
+                    await db.execute("DELETE FROM monsters WHERE id = ?", (monster_id,))
+                    await db.execute("DELETE FROM monster_interactions WHERE monster_id = ?", (monster_id,))
+                    await db.execute("DELETE FROM evolution_history WHERE monster_id = ?", (monster_id,))
+                
+                await db.commit()
+            
+            self.logger.info(f"Deleted monster {monster_id} (soft={soft_delete})")
+            return True
+            
+        except Exception as e:
+            self.logger.error(f"Failed to delete monster {monster_id}: {e}")
+            return False
+    
+    async def log_interaction(self, monster_id: str, interaction_type: str, interaction_data: Dict[str, Any] = None):
+        """Log monster interaction for analytics"""
+        try:
+            data_json = json.dumps(interaction_data) if interaction_data else None
+            
+            async with aiosqlite.connect(self.db_path) as db:
+                await db.execute("""
+                    INSERT INTO monster_interactions 
+                    (monster_id, interaction_type, interaction_data) 
+                    VALUES (?, ?, ?)
+                """, (monster_id, interaction_type, data_json))
+                await db.commit()
+                
+        except Exception as e:
+            self.logger.error(f"Failed to log interaction: {e}")
+    
+    async def log_evolution(self, monster_id: str, from_stage: str, to_stage: str, trigger: str):
+        """Log monster evolution event"""
+        try:
+            async with aiosqlite.connect(self.db_path) as db:
+                await db.execute("""
+                    INSERT INTO evolution_history 
+                    (monster_id, from_stage, to_stage, evolution_trigger) 
+                    VALUES (?, ?, ?, ?)
+                """, (monster_id, from_stage, to_stage, trigger))
+                await db.commit()
+                
+        except Exception as e:
+            self.logger.error(f"Failed to log evolution: {e}")
+    
+    async def get_monster_statistics(self, monster_id: str) -> Dict[str, Any]:
+        """Get comprehensive statistics for a monster"""
+        try:
+            async with aiosqlite.connect(self.db_path) as db:
+                # Get interaction counts
+                async with db.execute("""
+                    SELECT interaction_type, COUNT(*) as count 
+                    FROM monster_interactions 
+                    WHERE monster_id = ? 
+                    GROUP BY interaction_type
+                """, (monster_id,)) as cursor:
+                    interactions = {row[0]: row[1] async for row in cursor}
+                
+                # Get evolution history
+                async with db.execute("""
+                    SELECT from_stage, to_stage, evolution_trigger, timestamp 
+                    FROM evolution_history 
+                    WHERE monster_id = ? 
+                    ORDER BY timestamp
+                """, (monster_id,)) as cursor:
+                    evolutions = [
+                        {
+                            "from": row[0],
+                            "to": row[1], 
+                            "trigger": row[2],
+                            "timestamp": row[3]
+                        } async for row in cursor
+                    ]
+                
+                return {
+                    "interactions": interactions,
+                    "evolutions": evolutions,
+                    "total_interactions": sum(interactions.values()),
+                    "evolution_count": len(evolutions)
+                }
+                
+        except Exception as e:
+            self.logger.error(f"Failed to get statistics for {monster_id}: {e}")
+            return {}
+    
+    async def create_backup(self) -> str:
+        """Create a compressed backup of all monster data"""
+        try:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            backup_file = self.backup_dir / f"monsters_backup_{timestamp}.gz"
+            
+            # Export all active monsters
+            monsters = await self.list_monsters(active_only=True)
+            backup_data = {
+                "timestamp": timestamp,
+                "monsters": []
+            }
+            
+            for monster_info in monsters:
+                monster = await self.load_monster(monster_info["id"])
+                if monster:
+                    backup_data["monsters"].append(monster.dict())
+            
+            # Compress and save
+            with gzip.open(backup_file, 'wt') as f:
+                json.dump(backup_data, f, default=str, indent=2)
+            
+            self.logger.info(f"Created backup: {backup_file}")
+            return str(backup_file)
+            
+        except Exception as e:
+            self.logger.error(f"Backup creation failed: {e}")
+            return ""
+    
+    async def restore_backup(self, backup_file: str) -> bool:
+        """Restore monsters from backup file"""
+        try:
+            backup_path = Path(backup_file)
+            if not backup_path.exists():
+                return False
+            
+            with gzip.open(backup_path, 'rt') as f:
+                backup_data = json.load(f)
+            
+            restored_count = 0
+            for monster_data in backup_data.get("monsters", []):
+                try:
+                    monster = Monster(**monster_data)
+                    if await self.save_monster(monster):
+                        restored_count += 1
+                except Exception as e:
+                    self.logger.warning(f"Failed to restore monster: {e}")
+            
+            self.logger.info(f"Restored {restored_count} monsters from backup")
+            return restored_count > 0
+            
+        except Exception as e:
+            self.logger.error(f"Backup restoration failed: {e}")
+            return False
+    
+    async def _cache_cleanup_task(self):
+        """Background task to clean up expired cache entries"""
+        while True:
+            try:
+                current_time = datetime.now()
+                expired_keys = []
+                
+                for monster_id, timestamp in self.cache_timestamps.items():
+                    if current_time - timestamp > self.cache_timeout:
+                        expired_keys.append(monster_id)
+                
+                for key in expired_keys:
+                    self.monster_cache.pop(key, None)
+                    self.cache_timestamps.pop(key, None)
+                
+                if expired_keys:
+                    self.logger.debug(f"Cleaned up {len(expired_keys)} expired cache entries")
+                
+                # Sleep for 10 minutes before next cleanup
+                await asyncio.sleep(600)
+                
+            except Exception as e:
+                self.logger.error(f"Cache cleanup task failed: {e}")
+                await asyncio.sleep(60)  # Shorter sleep on error
+    
+    async def _auto_backup_task(self):
+        """Background task for automatic backups"""
+        while True:
+            try:
+                # Create backup every 6 hours
+                await asyncio.sleep(21600)  # 6 hours
+                
+                backup_file = await self.create_backup()
+                if backup_file:
+                    # Clean up old backups (keep last 10)
+                    await self._cleanup_old_backups()
+                
+            except Exception as e:
+                self.logger.error(f"Auto backup task failed: {e}")
+                await asyncio.sleep(3600)  # Retry in 1 hour on error
+    
+    async def _cleanup_old_backups(self, keep_count: int = 10):
+        """Clean up old backup files"""
+        try:
+            backup_files = list(self.backup_dir.glob("monsters_backup_*.gz"))
+            backup_files.sort(key=lambda x: x.stat().st_mtime, reverse=True)
+            
+            for old_backup in backup_files[keep_count:]:
+                old_backup.unlink()
+                self.logger.debug(f"Removed old backup: {old_backup}")
+                
+        except Exception as e:
+            self.logger.error(f"Backup cleanup failed: {e}")
+    
+    def get_cache_stats(self) -> Dict[str, Any]:
+        """Get cache performance statistics"""
+        return {
+            "cached_monsters": len(self.monster_cache),
+            "cache_timeout_minutes": self.cache_timeout.total_seconds() / 60,
+            "oldest_cache_entry": min(self.cache_timestamps.values()) if self.cache_timestamps else None,
+            "newest_cache_entry": max(self.cache_timestamps.values()) if self.cache_timestamps else None
+        }

src/utils/__init__.py

@@ -0,0 +1 @@
+# Utils module initialization

src/utils/performance_tracker.py

@@ -0,0 +1,96 @@
+import logging
+import time
+import psutil
+import torch
+from typing import Dict, Any, List
+from datetime import datetime
+import asyncio
+
+class PerformanceTracker:
+    def __init__(self):
+        self.logger = logging.getLogger(__name__)
+        self.metrics = {
+            "inference_times": [],
+            "memory_usage": [],
+            "cpu_usage": [],
+            "gpu_usage": [],
+            "total_requests": 0,
+            "successful_requests": 0,
+            "failed_requests": 0
+        }
+        self.start_time = time.time()
+        
+    async def initialize(self):
+        """Initialize performance tracking"""
+        self.logger.info("Performance tracker initialized")
+        
+        # Start background monitoring
+        asyncio.create_task(self._monitor_resources())
+    
+    async def _monitor_resources(self):
+        """Background task to monitor system resources"""
+        while True:
+            try:
+                # CPU usage
+                cpu_percent = psutil.cpu_percent(interval=1)
+                self.metrics["cpu_usage"].append({
+                    "timestamp": datetime.now().isoformat(),
+                    "value": cpu_percent
+                })
+                
+                # Memory usage
+                memory = psutil.virtual_memory()
+                self.metrics["memory_usage"].append({
+                    "timestamp": datetime.now().isoformat(),
+                    "value": memory.percent
+                })
+                
+                # GPU usage (if available)
+                if torch.cuda.is_available():
+                    gpu_memory = torch.cuda.memory_allocated() / torch.cuda.max_memory_allocated()
+                    self.metrics["gpu_usage"].append({
+                        "timestamp": datetime.now().isoformat(),
+                        "value": gpu_memory * 100
+                    })
+                
+                # Keep only last 100 measurements
+                for metric in ["cpu_usage", "memory_usage", "gpu_usage"]:
+                    if len(self.metrics[metric]) > 100:
+                        self.metrics[metric] = self.metrics[metric][-100:]
+                
+                await asyncio.sleep(30)  # Monitor every 30 seconds
+                
+            except Exception as e:
+                self.logger.error(f"Resource monitoring failed: {e}")
+                await asyncio.sleep(60)
+    
+    def track_inference(self, duration: float):
+        """Track inference time"""
+        self.metrics["inference_times"].append(duration)
+        
+        # Keep only last 100 measurements
+        if len(self.metrics["inference_times"]) > 100:
+            self.metrics["inference_times"] = self.metrics["inference_times"][-100:]
+    
+    def track_request(self, success: bool):
+        """Track request outcome"""
+        self.metrics["total_requests"] += 1
+        if success:
+            self.metrics["successful_requests"] += 1
+        else:
+            self.metrics["failed_requests"] += 1
+    
+    def get_summary(self) -> Dict[str, Any]:
+        """Get performance summary"""
+        uptime_seconds = time.time() - self.start_time
+        
+        avg_inference = sum(self.metrics["inference_times"]) / len(self.metrics["inference_times"]) if self.metrics["inference_times"] else 0
+        
+        return {
+            "uptime_hours": uptime_seconds / 3600,
+            "total_requests": self.metrics["total_requests"],
+            "success_rate": self.metrics["successful_requests"] / self.metrics["total_requests"] if self.metrics["total_requests"] > 0 else 0,
+            "average_inference_time": avg_inference,
+            "current_cpu_usage": self.metrics["cpu_usage"][-1]["value"] if self.metrics["cpu_usage"] else 0,
+            "current_memory_usage": self.metrics["memory_usage"][-1]["value"] if self.metrics["memory_usage"] else 0
+        }