app.py

import os
import json
import time
import psutil
import threading
import logging
import pytz
from datetime import datetime
from typing import List, Dict, Optional, Generator

import gradio as gr
from huggingface_hub import HfApi, hf_hub_download

# --- KERNEL INITIALIZATION ---
try:
    from llama_cpp import Llama
except ImportError:
    try:
        from llama_cpp_pydist import Llama
    except ImportError:
        class Llama:
            def __init__(self, *args, **kwargs):
                raise ImportError("Kernel Binary Missing. Ensure llama-cpp-python is installed.")

# --- CONFIGURATION ---
HF_TOKEN = os.environ.get("HF_TOKEN")
SPACE_ID = os.environ.get("SPACE_ID")
LOG_FILE = "engine_telemetry.json"
RAM_LIMIT_PCT = 0.85
SYSTEM_RESERVE_MB = 500
DEFAULT_MODEL = "unsloth/Llama-3.2-1B-Instruct-GGUF"
DEFAULT_QUANT = "Llama-3.2-1B-Instruct-Q4_K_M.gguf"

# --- TOKEN SYSTEM CONFIG ---
MONTHLY_TOKEN_CREDITS = 100.0
TOKEN_COST_PER_100MS = 0.001
BATCH_UPGRADE_BASE_COST = 0.00005  # Exponential: 1->2 = 0.00005, 2->4 = 0.0001, etc.
TOKEN_UPGRADE_COST_PER_1K = 0.0001  # Cost per 1000 extra tokens

# --- SPEED OPTIMIZATION CONFIG ---
FLASH_ATTENTION = False         # Disabled for CPU (GPU-only feature)
KV_CACHE_QUANTIZATION = True    # Keep for RAM savings
CONTINUOUS_BATCHING = False     # CPU doesn't benefit much
SPECULATIVE_DECODE = False      # CPU-only, no draft model
MLOCK_MODEL = False             # Don't lock - allow OS to manage memory
USE_MMAP = True                 # Critical for CPU - fast loading
OFFLOAD_KQV = False             # CPU-only
OPTIMAL_THREADS = psutil.cpu_count(logical=True)  # Use ALL threads (including hyperthreading for CPU)
ROPE_SCALING = 1.0
NUMA_OPTIMIZE = False           # Disabled - can cause issues on some systems
AGGRESSIVE_GC = True

# Quantization detection - CPU-optimized batch multipliers (more aggressive)
QUANT_OPTIMIZATIONS = {
    "BF16": {"batch_multiplier": 0.4, "ctx_size": 4096, "threads_boost": 1.0},
    "F16": {"batch_multiplier": 0.5, "ctx_size": 4096, "threads_boost": 1.0},
    "Q8_0": {"batch_multiplier": 1.0, "ctx_size": 8192, "threads_boost": 1.0},
    "Q6_K": {"batch_multiplier": 1.2, "ctx_size": 8192, "threads_boost": 1.0},
    "Q5_K_M": {"batch_multiplier": 1.5, "ctx_size": 12288, "threads_boost": 1.0},
    "Q5_K_S": {"batch_multiplier": 1.5, "ctx_size": 12288, "threads_boost": 1.0},
    "Q4_K_M": {"batch_multiplier": 2.0, "ctx_size": 16384, "threads_boost": 1.0},  # MASSIVE for CPU
    "Q4_K_S": {"batch_multiplier": 2.0, "ctx_size": 16384, "threads_boost": 1.0},
    "Q4_0": {"batch_multiplier": 2.2, "ctx_size": 16384, "threads_boost": 1.0},
    "Q3_K_M": {"batch_multiplier": 2.5, "ctx_size": 20480, "threads_boost": 1.0},
    "Q2_K": {"batch_multiplier": 3.0, "ctx_size": 24576, "threads_boost": 1.0},
}

# Model format/architecture detection patterns
MODEL_FORMATS = {
    "llama": {"pattern": ["llama", "mistral", "mixtral"], "template": "llama"},
    "gemma": {"pattern": ["gemma"], "template": "gemma"},
    "phi": {"pattern": ["phi"], "template": "phi"},
    "qwen": {"pattern": ["qwen"], "template": "chatml"},
    "deepseek": {"pattern": ["deepseek"], "template": "deepseek"},
}

logging.basicConfig(level=logging.INFO, format='%(asctime)s - ZEROENGINE - %(message)s')
logger = logging.getLogger(__name__)

# --- AGGRESSIVE GARBAGE COLLECTOR ---
import gc
gc.enable()
gc.set_threshold(700, 10, 10)  # Aggressive thresholds

def force_gc():
    """Force aggressive garbage collection"""
    if AGGRESSIVE_GC:
        collected = gc.collect(2)  # Full collection
        logger.info(f"[GC] Collected {collected} objects")
        return collected
    return 0

def nuclear_ram_clear():
    """NUCLEAR option: Clear all Python caches and force full GC"""
    try:
        # Clear function caches
        import functools
        functools._CacheInfo.__call__ = lambda self: None
        
        # Clear import caches
        import sys
        if hasattr(sys, 'modules'):
            # Don't delete core modules, just clear their caches
            for module_name, module in list(sys.modules.items()):
                if hasattr(module, '__dict__') and not module_name.startswith('_'):
                    if hasattr(module, '__pycache__'):
                        delattr(module, '__pycache__')
        
        # Force multiple GC passes
        for _ in range(3):
            gc.collect(2)
        
        logger.info("[RAM-NUKE] 💥 Nuclear RAM clear complete")
        return True
    except Exception as e:
        logger.error(f"[RAM-NUKE] Failed: {e}")
        return False

# --- MODEL CACHE MANAGER (LoRA-style lightweight caching) ---
class ModelCacheManager:
    def __init__(self):
        self.cache_dir = "/tmp/zeroengine_cache"
        self.cache = {}  # {model_path: {"adapter": bytes, "metadata": dict}}
        self.max_cache_size_mb = 50  # Only cache 50MB total (tiny!)
        
        os.makedirs(self.cache_dir, exist_ok=True)
        logger.info(f"[CACHE] Initialized at {self.cache_dir}")
    
    def extract_cache_signature(self, model_path: str) -> Optional[bytes]:
        """Extract TINY signature from model (first 1MB = ~LoRA adapter size)"""
        try:
            cache_size = 1024 * 1024  # 1MB
            with open(model_path, 'rb') as f:
                signature = f.read(cache_size)
            logger.info(f"[CACHE] Extracted {len(signature)} bytes signature from {os.path.basename(model_path)}")
            return signature
        except Exception as e:
            logger.error(f"[CACHE] Extraction failed: {e}")
            return None
    
    def save_to_cache(self, model_path: str, signature: bytes):
        """Save tiny model signature to cache"""
        try:
            model_name = os.path.basename(model_path)
            cache_path = os.path.join(self.cache_dir, f"{model_name}.cache")
            
            # Check total cache size
            total_size = sum(os.path.getsize(os.path.join(self.cache_dir, f)) 
                           for f in os.listdir(self.cache_dir) if f.endswith('.cache'))
            
            # If cache too big, delete oldest
            if total_size > (self.max_cache_size_mb * 1024 * 1024):
                logger.info("[CACHE] Cache full, removing oldest entry")
                cache_files = sorted(
                    [os.path.join(self.cache_dir, f) for f in os.listdir(self.cache_dir) if f.endswith('.cache')],
                    key=os.path.getmtime
                )
                if cache_files:
                    os.remove(cache_files[0])
                    logger.info(f"[CACHE] Deleted {os.path.basename(cache_files[0])}")
            
            # Save new cache
            with open(cache_path, 'wb') as f:
                f.write(signature)
            
            self.cache[model_path] = {
                "signature": signature,
                "cached_at": time.time(),
                "hits": 0
            }
            logger.info(f"[CACHE] ✅ Cached {model_name} ({len(signature) / 1024:.1f}KB)")
            
        except Exception as e:
            logger.error(f"[CACHE] Save failed: {e}")
    
    def is_cached(self, model_path: str) -> bool:
        """Check if model signature is cached"""
        model_name = os.path.basename(model_path)
        cache_path = os.path.join(self.cache_dir, f"{model_name}.cache")
        exists = os.path.exists(cache_path)
        if exists:
            logger.info(f"[CACHE] 🎯 HIT for {model_name}")
        return exists
    
    def preload_cache(self, model_path: str):
        """Preload cached signature (simulates faster load)"""
        try:
            model_name = os.path.basename(model_path)
            cache_path = os.path.join(self.cache_dir, f"{model_name}.cache")
            
            if os.path.exists(cache_path):
                with open(cache_path, 'rb') as f:
                    signature = f.read()
                
                if model_path in self.cache:
                    self.cache[model_path]["hits"] += 1
                
                logger.info(f"[CACHE] Preloaded {len(signature) / 1024:.1f}KB signature")
                return True
        except Exception as e:
            logger.error(f"[CACHE] Preload failed: {e}")
        return False
    
    def wreck_old_model_cache(self):
        """WRECK the old model's cache to free RAM"""
        try:
            logger.info("[WRECKER] 💣 Destroying old model caches...")
            
            # Clear Python's internal caches
            gc.collect()
            
            # This is symbolic - the real wrecking happens when we del self.llm
            # But we can clear our tiny cache references
            for model_path in list(self.cache.keys()):
                if self.cache[model_path].get("signature"):
                    self.cache[model_path]["signature"] = None
            
            nuclear_ram_clear()
            logger.info("[WRECKER] ✅ Old model WRECKED")
            return True
        except Exception as e:
            logger.error(f"[WRECKER] Failed: {e}")
            return False

# --- TOKEN MANAGER ---
class TokenManager:
    def __init__(self):
        self.user_tokens = {}  # {username: {"balance": float, "start_time": float, "purchases": {}}}
        self.owner_username = "turtle170"  # Owner gets infinite tokens
    
    def is_owner(self, username: str) -> bool:
        """Check if user is the owner"""
        if not username:
            return False
        return username.lower() == self.owner_username.lower()
    
    def initialize_user(self, username: str):
        """Initialize new user with monthly credits (or infinite for owner)"""
        if not username:
            username = "anonymous"
        
        if username not in self.user_tokens:
            # Owner gets infinite tokens
            if self.is_owner(username):
                self.user_tokens[username] = {
                    "balance": float('inf'),
                    "start_time": time.time(),
                    "purchases": {"batch_multiplier": 1, "token_limit": 2048},
                    "total_spent": 0.0,
                    "is_owner": True,
                    "username": username
                }
                logger.info(f"[TOKEN] 👑 OWNER {username} initialized with INFINITE tokens!")
            else:
                self.user_tokens[username] = {
                    "balance": MONTHLY_TOKEN_CREDITS,
                    "start_time": time.time(),
                    "purchases": {"batch_multiplier": 1, "token_limit": 2048},
                    "total_spent": 0.0,
                    "is_owner": False,
                    "username": username,
                    "last_reset": time.time()
                }
                logger.info(f"[TOKEN] New user {username}: {MONTHLY_TOKEN_CREDITS} tokens")
    
    def check_monthly_reset(self, username: str):
        """Reset tokens if a month has passed"""
        if not username or username not in self.user_tokens:
            return
        
        if self.user_tokens[username].get("is_owner", False):
            return  # Owner never needs reset
        
        last_reset = self.user_tokens[username].get("last_reset", time.time())
        month_in_seconds = 30 * 24 * 60 * 60  # 30 days
        
        if time.time() - last_reset > month_in_seconds:
            self.user_tokens[username]["balance"] = MONTHLY_TOKEN_CREDITS
            self.user_tokens[username]["last_reset"] = time.time()
            self.user_tokens[username]["total_spent"] = 0.0
            logger.info(f"[TOKEN] Monthly reset for {username}: {MONTHLY_TOKEN_CREDITS} tokens")
    
    def charge_usage(self, username: str, duration_ms: float) -> bool:
        """Charge user for inference time. Returns True if successful. Owner never charged."""
        if not username:
            username = "anonymous"
        
        self.initialize_user(username)
        self.check_monthly_reset(username)
        
        # Owner never gets charged
        if self.user_tokens[username].get("is_owner", False):
            return True
        
        cost = (duration_ms / 100.0) * TOKEN_COST_PER_100MS
        
        # Check if user has enough balance
        if self.user_tokens[username]["balance"] <= 0:
            logger.warning(f"[TOKEN] ❌ {username} has 0 tokens! Access denied.")
            return False
        
        if self.user_tokens[username]["balance"] >= cost:
            self.user_tokens[username]["balance"] -= cost
            self.user_tokens[username]["balance"] = max(0, self.user_tokens[username]["balance"])  # Never go below 0
            self.user_tokens[username]["total_spent"] += cost
            logger.info(f"[TOKEN] Charged {cost:.4f} tokens ({duration_ms:.0f}ms) | Remaining: {self.user_tokens[username]['balance']:.2f}")
            return True
        else:
            # Insufficient balance - set to 0 and deny
            self.user_tokens[username]["balance"] = 0
            logger.warning(f"[TOKEN] ❌ Insufficient balance! {username} now at 0 tokens.")
            return False
    
    def can_use_engine(self, username: str) -> tuple:
        """Check if user can use the engine. Returns (bool, message)"""
        if not username:
            username = "anonymous"
        
        self.initialize_user(username)
        self.check_monthly_reset(username)
        
        if self.user_tokens[username].get("is_owner", False):
            return True, "👑 Owner access granted"
        
        balance = self.user_tokens[username]["balance"]
        
        if balance <= 0:
            last_reset = self.user_tokens[username].get("last_reset", time.time())
            time_until_reset = 30 * 24 * 60 * 60 - (time.time() - last_reset)
            days_left = int(time_until_reset / (24 * 60 * 60))
            return False, f"❌ Out of tokens! Resets in {days_left} days. Current balance: 0.00"
        
        return True, f"✅ Access granted. Balance: {balance:.2f} tokens"
    
    def purchase_batch_upgrade(self, username: str) -> tuple:
        """Purchase batch size upgrade (exponential cost). Free for owner."""
        if not username:
            return False, "❌ Please login first"
        
        self.initialize_user(username)
        
        # Owner gets free upgrades
        if self.user_tokens[username].get("is_owner", False):
            current_mult = self.user_tokens[username]["purchases"]["batch_multiplier"]
            self.user_tokens[username]["purchases"]["batch_multiplier"] = current_mult * 2
            new_mult = current_mult * 2
            logger.info(f"[TOKEN] 👑 OWNER free batch upgrade: {current_mult}x → {new_mult}x")
            return True, f"👑 FREE UPGRADE! Batch now {new_mult}x!"
        
        current_mult = self.user_tokens[username]["purchases"]["batch_multiplier"]
        upgrade_level = int(math.log2(current_mult)) if current_mult > 1 else 0
        cost = BATCH_UPGRADE_BASE_COST * (2 ** upgrade_level)
        
        if self.user_tokens[username]["balance"] >= cost:
            self.user_tokens[username]["balance"] -= cost
            self.user_tokens[username]["purchases"]["batch_multiplier"] = current_mult * 2
            new_mult = current_mult * 2
            logger.info(f"[TOKEN] Batch upgrade: {current_mult}x → {new_mult}x | Cost: {cost:.5f}")
            return True, f"✅ Batch upgraded to {new_mult}x! (-{cost:.5f} tokens)"
        else:
            return False, f"❌ Insufficient tokens! Need {cost:.5f}, have {self.user_tokens[username]['balance']:.2f}"
    
    def purchase_token_upgrade(self, username: str, extra_tokens: int = 1000) -> tuple:
        """Purchase extra response token length. Free for owner."""
        if not username:
            return False, "❌ Please login first"
        
        self.initialize_user(username)
        
        # Owner gets free upgrades
        if self.user_tokens[username].get("is_owner", False):
            self.user_tokens[username]["purchases"]["token_limit"] += extra_tokens
            new_limit = self.user_tokens[username]["purchases"]["token_limit"]
            logger.info(f"[TOKEN] 👑 OWNER free token upgrade: +{extra_tokens} tokens")
            return True, f"👑 FREE UPGRADE! Token limit now {new_limit}!"
        
        cost = (extra_tokens / 1000) * TOKEN_UPGRADE_COST_PER_1K
        
        if self.user_tokens[username]["balance"] >= cost:
            self.user_tokens[username]["balance"] -= cost
            self.user_tokens[username]["purchases"]["token_limit"] += extra_tokens
            new_limit = self.user_tokens[username]["purchases"]["token_limit"]
            logger.info(f"[TOKEN] Token limit upgrade: +{extra_tokens} tokens | Cost: {cost:.5f}")
            return True, f"✅ Token limit now {new_limit}! (-{cost:.5f} tokens)"
        else:
            return False, f"❌ Insufficient tokens! Need {cost:.5f}, have {self.user_tokens[username]['balance']:.2f}"
    
    def get_balance(self, username: str) -> float:
        """Get user's current token balance"""
        if not username:
            username = "anonymous"
        
        self.initialize_user(username)
        self.check_monthly_reset(username)
        
        balance = self.user_tokens[username]["balance"]
        
        # Show ∞ for owner
        if balance == float('inf'):
            return balance
        
        return round(max(0, balance), 2)  # Never show negative
    
    def get_purchases(self, username: str) -> dict:
        """Get user's current purchases"""
        if not username:
            username = "anonymous"
        
        self.initialize_user(username)
        return self.user_tokens[username]["purchases"]
    
    def end_session(self, username: str):
        """End user session and log stats"""
        if not username:
            return "No active session found."
        
        if username in self.user_tokens:
            stats = self.user_tokens[username]
            
            if stats.get("is_owner", False):
                return f"👑 Owner session ended. Welcome back anytime, {stats['username']}!"
            
            logger.info(f"[TOKEN] Session ended: Spent {stats['total_spent']:.2f}, Remaining {stats['balance']:.2f}")
            return f"Session ended. You spent {stats['total_spent']:.2f} tokens this session. Balance: {stats['balance']:.2f}"
        return "No active session found."

# Global token manager
import math
token_manager = TokenManager()

# Global cache manager
model_cache = ModelCacheManager()

# --- TELEMETRY MODULE ---
class TelemetryManager:
    def __init__(self, api: HfApi):
        self.api = api
        self.stats = self._load_initial_stats()

    def _load_initial_stats(self) -> Dict:
        # Simplified: no file I/O to prevent restart issues
        return {
            "session_start": str(datetime.now(pytz.utc)),
            "load_count": {},
            "total_tokens_generated": 0
        }

    def track_load(self, repo: str, filename: str):
        key = f"{repo}/{filename}"
        self.stats["load_count"][key] = self.stats["load_count"].get(key, 0) + 1
        logger.info(f"Model loaded: {key} (count: {self.stats['load_count'][key]})")

    def track_generation(self, tokens: int):
        self.stats["total_tokens_generated"] += tokens
        logger.info(f"Total tokens generated: {self.stats['total_tokens_generated']}")

# --- RESOURCE MONITOR ---
class ResourceMonitor:
    @staticmethod
    def get_metrics() -> Dict:
        vm = psutil.virtual_memory()
        return {
            "ram_used_gb": round(vm.used / (1024**3), 2),
            "ram_avail_gb": round(vm.available / (1024**3), 2),
            "ram_total_gb": round(vm.total / (1024**3), 2),
            "ram_pct": vm.percent,
            "cpu_usage_pct": psutil.cpu_percent(interval=None),
            "load_avg": os.getloadavg()[0] if hasattr(os, 'getloadavg') else 0
        }

    @staticmethod
    def validate_deployment(file_path: str) -> (bool, str):
        try:
            vm = psutil.virtual_memory()
            file_size_mb = os.path.getsize(file_path) / (1024**2)
            total_ram_mb = vm.total / (1024**2)
            avail_ram_mb = vm.available / (1024**2)
            
            logger.info(f"Validation - Model: {file_size_mb:.1f}MB | Available RAM: {avail_ram_mb:.1f}MB | Total: {total_ram_mb:.1f}MB")
            
            if file_size_mb > (total_ram_mb * RAM_LIMIT_PCT):
                return False, f"Model size ({file_size_mb:.1f}MB) exceeds safety limit ({total_ram_mb * RAM_LIMIT_PCT:.1f}MB)."
            if (file_size_mb + SYSTEM_RESERVE_MB) > avail_ram_mb:
                return False, f"Insufficient RAM. Need {file_size_mb+SYSTEM_RESERVE_MB:.1f}MB, have {avail_ram_mb:.1f}MB available."
            return True, "Validation Passed."
        except Exception as e:
            logger.error(f"Validation error: {e}")
            return False, f"Validation error: {str(e)}"

# --- ENGINE CORE ---
class ZeroEngine:
    def __init__(self):
        self.api = HfApi(token=HF_TOKEN)
        self.telemetry = TelemetryManager(self.api)
        self.llm: Optional[Llama] = None
        self.active_model_info = {"repo": "", "file": "", "format": ""}
        self.kernel_lock = threading.Lock()
        self.is_prefilling = False
        self.perf_stats = {
            "total_tokens": 0,
            "total_time": 0.0,
            "avg_tps": 0.0,
            "peak_tps": 0.0,
            "cache_hits": 0
        }
        self.prompt_cache = {}
        self.last_activity = time.time()
        self.idle_timeout = 20
        self.auto_cleanup_thread = None
        self.start_idle_monitor()
        
        # Keyboard input pre-processing
        self.typing_buffer = ""
        self.typing_timer = None
        self.preprocessed_tokens = None
        
        # Custom parameters (user-configurable)
        self.custom_params = {
            "temperature": 0.7,
            "top_p": 0.95,
            "top_k": 40,
            "repeat_penalty": 1.1,
            "batch_size_override": None,  # None = auto
            "max_tokens_override": None   # None = auto
        }
    
    def detect_model_format(self, filename: str, repo: str) -> str:
        """Auto-detect model format/architecture from filename and repo"""
        combined = f"{repo.lower()} {filename.lower()}"
        
        for format_name, format_info in MODEL_FORMATS.items():
            for pattern in format_info["pattern"]:
                if pattern in combined:
                    logger.info(f"[FORMAT-DETECT] Detected {format_name.upper()} architecture")
                    return format_name
        
        logger.warning(f"[FORMAT-DETECT] Unknown format, defaulting to llama")
        return "llama"
        
    def detect_quantization(self, filename: str) -> dict:
        """Detect quantization method from filename and return optimizations"""
        filename_upper = filename.upper()
        
        for quant_type, optimizations in QUANT_OPTIMIZATIONS.items():
            if quant_type in filename_upper:
                logger.info(f"[QUANT-DETECT] Found {quant_type} in filename, applying optimizations")
                return {"type": quant_type, **optimizations}
        
        # Default to Q4_K_M if unknown
        logger.warning(f"[QUANT-DETECT] Unknown quantization, using Q4_K_M defaults")
        return {"type": "Q4_K_M", **QUANT_OPTIMIZATIONS["Q4_K_M"]}
    
    def preprocess_input(self, text: str):
        """Pre-process keyboard input in background (tensors ready before submit)"""
        if not self.llm or not text or len(text) < 5:
            return
        
        def _preprocess():
            try:
                logger.info(f"[PREPROCESS] Tokenizing {len(text)} chars in background...")
                tokens = self.llm.tokenize(text.encode("utf-8"))
                self.preprocessed_tokens = tokens
                logger.info(f"[PREPROCESS] ✅ Ready: {len(tokens)} tokens cached")
            except Exception as e:
                logger.error(f"[PREPROCESS] Failed: {e}")
                self.preprocessed_tokens = None
        
        # Cancel previous timer if user is still typing
        if self.typing_timer:
            self.typing_timer.cancel()
        
        # Start new timer - preprocess after 1 second of no typing
        self.typing_timer = threading.Timer(1.0, _preprocess)
        self.typing_timer.daemon = True
        self.typing_timer.start()
    
    def clear_preprocessed(self):
        """Clear preprocessed tokens and force GC"""
        if self.preprocessed_tokens:
            self.preprocessed_tokens = None
            force_gc()
            logger.info("[PREPROCESS] Cleared cached tokens")
        
    def start_idle_monitor(self):
        """Start background thread to monitor idle timeout"""
        def monitor():
            while True:
                time.sleep(5)  # Check every 5 seconds
                if self.llm and (time.time() - self.last_activity) > self.idle_timeout:
                    logger.info(f"[IDLE] No activity for {self.idle_timeout}s, unloading model...")
                    with self.kernel_lock:
                        if self.llm:
                            try:
                                del self.llm
                                self.llm = None
                                self.active_model_info = {"repo": "", "file": ""}
                                force_gc()  # Aggressive cleanup
                                logger.info("[IDLE] Model unloaded successfully")
                            except Exception as e:
                                logger.error(f"[IDLE] Cleanup error: {e}")
        
        self.auto_cleanup_thread = threading.Thread(target=monitor, daemon=True)
        self.auto_cleanup_thread.start()
        logger.info("[IDLE] Idle monitor started (20s timeout)")
    
    def update_activity(self):
        """Update last activity timestamp"""
        self.last_activity = time.time()
        
    def optimize_numa(self):
        """NUMA-aware CPU affinity optimization"""
        try:
            import os
            if hasattr(os, 'sched_setaffinity'):
                # Pin to physical cores only
                physical_cores = list(range(0, psutil.cpu_count(logical=False)))
                os.sched_setaffinity(0, physical_cores)
                logger.info(f"NUMA: Pinned to physical cores: {physical_cores}")
        except Exception as e:
            logger.warning(f"NUMA optimization unavailable: {e}")
    
    def is_model_loaded(self) -> bool:
        """Check if model is currently loaded"""
        return self.llm is not None

    def list_ggufs(self, repo_id: str) -> List[str]:
        try:
            files = self.api.list_repo_files(repo_id=repo_id)
            ggufs = [f for f in files if f.endswith(".gguf")]
            logger.info(f"Found {len(ggufs)} GGUF files in {repo_id}")
            return ggufs
        except Exception as e:
            logger.error(f"Scan error: {e}")
            return []

    def boot_kernel(self, repo: str, filename: str, session_id: str = None) -> str:
        """HYPER-OPTIMIZED Boot kernel with format auto-detection and Gemma fixes"""
        try:
            if not repo or not filename:
                return "🔴 ERROR: Repository or filename missing"
            
            logger.info(f"[BOOT] Starting download: {filename} from {repo}")
            
            # DETECT QUANTIZATION FROM FILENAME
            quant_config = self.detect_quantization(filename)
            
            # DETECT MODEL FORMAT/ARCHITECTURE
            model_format = self.detect_model_format(filename, repo)
            
            # Download with timeout protection
            try:
                path = hf_hub_download(
                    repo_id=repo, 
                    filename=filename, 
                    token=HF_TOKEN,
                    local_files_only=False
                )
                logger.info(f"[BOOT] Download complete: {path}")
            except Exception as e:
                logger.error(f"[BOOT] Download failed: {e}")
                return f"🔴 DOWNLOAD FAILED: {str(e)}"
            
            # Check if model is cached
            is_cached = model_cache.is_cached(path)
            cache_status = "🎯 CACHED" if is_cached else "🆕 NEW"
            
            # Validate before loading
            valid, msg = ResourceMonitor.validate_deployment(path)
            if not valid:
                logger.warning(f"[BOOT] Validation failed: {msg}")
                return f"🔴 VALIDATION FAILED: {msg}"
            
            logger.info(f"[BOOT] Validation passed ({cache_status}), applying {quant_config['type']} optimizations for {model_format.upper()}...")
            
            # Load model with MAXIMUM PERFORMANCE SETTINGS
            with self.kernel_lock:
                # WRECK OLD MODEL
                if self.llm:
                    logger.info("[BOOT] 💣 WRECKING old model...")
                    try:
                        model_cache.wreck_old_model_cache()
                        del self.llm
                        self.llm = None
                        nuclear_ram_clear()
                        logger.info("[BOOT] ✅ Old model DESTROYED")
                    except Exception as e:
                        logger.warning(f"[BOOT] Cleanup warning: {e}")
                
                # Calculate optimal parameters with token purchases
                vm = psutil.virtual_memory()
                available_ram_gb = vm.available / (1024**3)
                
                # CPU-OPTIMIZED BATCH CALCULATION - Very aggressive for 16GB RAM
                # Base calculation: use more RAM for batching on CPU
                base_batch = int(512 * available_ram_gb / 8)  # More aggressive base
                optimal_batch = int(base_batch * quant_config["batch_multiplier"])
                
                # Apply user's batch multiplier from token purchases
                if session_id:
                    user_batch_mult = token_manager.get_purchases(session_id)["batch_multiplier"]
                    optimal_batch = int(optimal_batch * user_batch_mult)
                    logger.info(f"[TOKEN] User batch multiplier: {user_batch_mult}x")
                
                # CPU can handle larger batches with quantized models
                optimal_batch = max(1024, min(8192, optimal_batch))  # 1024-8192 range for CPU
                
                # Context size
                optimal_ctx = quant_config["ctx_size"]
                
                # Reduce context for Gemma models (they have 131K n_ctx_train)
                if model_format == "gemma":
                    optimal_ctx = min(8192, optimal_ctx)  # Gemma works better with lower ctx
                    logger.info(f"[FORMAT] Gemma detected: reducing context to {optimal_ctx}")
                
                # Thread optimization - use ALL threads on CPU (including hyperthreading)
                optimal_threads = psutil.cpu_count(logical=True)  # ALL logical cores
                logger.info(f"[CPU] Using all {optimal_threads} threads (including hyperthreading)")
                
                try:
                    logger.info(f"[BOOT] Initializing {model_format.upper()} {quant_config['type']}: threads={optimal_threads}, batch={optimal_batch}, ctx={optimal_ctx}")
                    
                    # Preload cache if available
                    if is_cached:
                        model_cache.preload_cache(path)
                    
                    # ULTRA-OPTIMIZED CPU-ONLY INITIALIZATION
                    init_params = {
                        "model_path": path,
                        "n_ctx": optimal_ctx,
                        "n_threads": optimal_threads,
                        "n_threads_batch": optimal_threads,
                        "use_mmap": USE_MMAP,              # Critical for CPU
                        "use_mlock": MLOCK_MODEL,          # Let OS manage memory
                        "n_batch": optimal_batch,          # MASSIVE batches for CPU
                        "n_gpu_layers": 0,                 # CPU-only
                        "rope_scaling_type": 0,
                        "rope_freq_scale": ROPE_SCALING,
                        "verbose": False,
                        "logits_all": False,
                        "embedding": False,
                        "f16_kv": False                    # Use quantized KV cache
                    }
                    
                    # Add KV quantization only if not Gemma (Gemma can be finicky)
                    if model_format != "gemma" and KV_CACHE_QUANTIZATION:
                        init_params["type_k"] = 2
                        init_params["type_v"] = 2
                        logger.info("[OPTIM] KV cache quantization enabled (Q4)")
                    
                    self.llm = Llama(**init_params)
                    
                    self.active_model_info = {
                        "repo": repo, 
                        "file": filename, 
                        "quant": quant_config['type'],
                        "format": model_format
                    }
                    self.telemetry.track_load(repo, filename)
                    
                    # Extract and cache signature
                    if not is_cached:
                        logger.info("[BOOT] Extracting cache signature...")
                        signature = model_cache.extract_cache_signature(path)
                        if signature:
                            model_cache.save_to_cache(path, signature)
                    
                    # Warm-up
                    logger.info("[BOOT] Warming up model caches...")
                    try:
                        self.llm("Warmup", max_tokens=1, stream=False)
                        force_gc()
                    except:
                        pass
                    
                    logger.info("[BOOT] 🚀 CPU-OPTIMIZED MODEL READY!")
                    return f"🟢 {model_format.upper()} {quant_config['type']} {cache_status} | CPU:{optimal_threads}T | B:{optimal_batch} | Ctx:{optimal_ctx}"
                    
                except Exception as e:
                    logger.error(f"[BOOT] Model loading failed: {e}")
                    self.llm = None
                    nuclear_ram_clear()
                    return f"🔴 LOAD FAILED: {str(e)}"
                    
        except Exception as e:
            logger.error(f"[BOOT] Unexpected error: {e}")
            nuclear_ram_clear()
            return f"🔴 BOOT FAILURE: {str(e)}"
        """HYPER-OPTIMIZED Boot kernel with cache manager and old model wrecker"""
        try:
            if not repo or not filename:
                return "🔴 ERROR: Repository or filename missing"
            
            logger.info(f"[BOOT] Starting download: {filename} from {repo}")
            
            # DETECT QUANTIZATION FROM FILENAME
            quant_config = self.detect_quantization(filename)
            
            # Download with timeout protection
            try:
                path = hf_hub_download(
                    repo_id=repo, 
                    filename=filename, 
                    token=HF_TOKEN,
                    local_files_only=False
                )
                logger.info(f"[BOOT] Download complete: {path}")
            except Exception as e:
                logger.error(f"[BOOT] Download failed: {e}")
                return f"🔴 DOWNLOAD FAILED: {str(e)}"
            
            # Check if model is cached (for faster subsequent loads)
            is_cached = model_cache.is_cached(path)
            cache_status = "🎯 CACHED" if is_cached else "🆕 NEW"
            
            # Validate before loading
            valid, msg = ResourceMonitor.validate_deployment(path)
            if not valid:
                logger.warning(f"[BOOT] Validation failed: {msg}")
                return f"🔴 VALIDATION FAILED: {msg}"
            
            logger.info(f"[BOOT] Validation passed ({cache_status}), applying {quant_config['type']} optimizations...")
            
            # Apply NUMA optimization
            if NUMA_OPTIMIZE:
                self.optimize_numa()
            
            # Load model with MAXIMUM PERFORMANCE SETTINGS
            with self.kernel_lock:
                # WRECK OLD MODEL - Nuclear option
                if self.llm:
                    logger.info("[BOOT] 💣 WRECKING old model...")
                    try:
                        # Wreck the cache first
                        model_cache.wreck_old_model_cache()
                        
                        # Delete the model
                        del self.llm
                        self.llm = None
                        
                        # Nuclear RAM clear
                        nuclear_ram_clear()
                        
                        logger.info("[BOOT] ✅ Old model DESTROYED")
                    except Exception as e:
                        logger.warning(f"[BOOT] Cleanup warning: {e}")
                
                # Calculate optimal batch size based on quantization and available RAM
                vm = psutil.virtual_memory()
                available_ram_gb = vm.available / (1024**3)
                
                # MASSIVE batch sizes for quantized models
                base_batch = int(256 * available_ram_gb / 4)
                optimal_batch = int(base_batch * quant_config["batch_multiplier"])
                optimal_batch = max(512, min(4096, optimal_batch))  # Clamp between 512-4096
                
                # Context size based on quantization
                optimal_ctx = quant_config["ctx_size"]
                
                # Thread count with quantization-specific boost
                optimal_threads = int(OPTIMAL_THREADS * quant_config["threads_boost"])
                optimal_threads = max(2, min(optimal_threads, psutil.cpu_count(logical=False)))
                
                try:
                    logger.info(f"[BOOT] Initializing {quant_config['type']}: threads={optimal_threads}, batch={optimal_batch}, ctx={optimal_ctx}")
                    
                    # Preload cache if available (simulates faster warmup)
                    if is_cached:
                        model_cache.preload_cache(path)
                    
                    # ULTRA-OPTIMIZED LLAMA.CPP INITIALIZATION
                    self.llm = Llama(
                        model_path=path,
                        n_ctx=optimal_ctx,                  # Dynamic context based on quant
                        n_threads=optimal_threads,          # Optimized thread count
                        n_threads_batch=optimal_threads,    # Batch processing threads
                        use_mmap=USE_MMAP,                  # Memory-mapped weights (fast loading)
                        use_mlock=MLOCK_MODEL,              # Lock in RAM (prevent swap thrashing)
                        n_batch=optimal_batch,              # MASSIVE batch size
                        n_gpu_layers=0,                     # CPU-only mode
                        flash_attn=FLASH_ATTENTION,         # Flash Attention (2x faster)
                        type_k=2 if KV_CACHE_QUANTIZATION else None,  # Q4 KV cache quantization
                        type_v=2 if KV_CACHE_QUANTIZATION else None,  # Q4 KV cache quantization
                        rope_scaling_type=0,                # Linear RoPE scaling
                        rope_freq_scale=ROPE_SCALING,       # RoPE frequency scale
                        numa=NUMA_OPTIMIZE,                 # NUMA optimization
                        verbose=False,
                        logits_all=False,                   # Only compute final logits (faster)
                        embedding=False,                    # Disable embeddings (not needed)
                        offload_kqv=OFFLOAD_KQV,           # No offload on CPU
                        f16_kv=False                        # Use quantized KV cache instead
                    )
                    
                    self.active_model_info = {"repo": repo, "file": filename, "quant": quant_config['type']}
                    self.telemetry.track_load(repo, filename)
                    
                    # Extract and cache TINY signature for faster future loads
                    if not is_cached:
                        logger.info("[BOOT] Extracting cache signature...")
                        signature = model_cache.extract_cache_signature(path)
                        if signature:
                            model_cache.save_to_cache(path, signature)
                    
                    # Warm-up inference to populate caches
                    logger.info("[BOOT] Warming up model caches...")
                    try:
                        self.llm("Warmup", max_tokens=1, stream=False)
                        force_gc()  # Clear warmup artifacts
                    except:
                        pass
                    
                    logger.info("[BOOT] 🚀 HYPER-OPTIMIZED MODEL READY!")
                    return f"🟢 {quant_config['type']} KERNEL {cache_status} | T:{optimal_threads} | B:{optimal_batch} | Ctx:{optimal_ctx}"
                    
                except Exception as e:
                    logger.error(f"[BOOT] Model loading failed: {e}")
                    self.llm = None
                    nuclear_ram_clear()
                    return f"🔴 LOAD FAILED: {str(e)}"
                    
        except Exception as e:
            logger.error(f"[BOOT] Unexpected error: {e}")
            nuclear_ram_clear()
            return f"🔴 BOOT FAILURE: {str(e)}"

    def stitch_cache(self, ghost_text: str) -> str:
        """Prime KV cache with ghost context"""
        if not self.llm or not ghost_text or self.is_prefilling:
            return "Kernel Idle/Busy"
            
        def _bg_eval():
            self.is_prefilling = True
            try:
                tokens = self.llm.tokenize(ghost_text.encode("utf-8"))
                self.llm.eval(tokens)
                logger.info(f"Ghost cache primed: {len(tokens)} tokens")
                force_gc()  # Clean up after priming
            except Exception as e:
                logger.error(f"KV Cache priming failed: {e}")
            finally:
                self.is_prefilling = False
        
        threading.Thread(target=_bg_eval, daemon=True).start()
        return "⚡ Primed"

    def inference_generator(self, prompt: str, history: List[Dict], ghost_context: str, repo: str, quant: str, username: str) -> Generator:
        # Update activity timestamp
        self.update_activity()
        
        # Clear any preprocessed tokens from typing
        self.clear_preprocessed()
        
        # AUTO-BOOT: If model not loaded, auto-boot default model
        if not self.llm:
            logger.info("[AUTO-BOOT] No model loaded, initiating auto-boot...")
            history.append({"role": "assistant", "content": "🔄 Auto-booting model, please wait..."})
            yield history
            
            # Use provided repo/quant or fallback to defaults
            boot_repo = repo if repo else DEFAULT_MODEL
            boot_quant = quant if quant else DEFAULT_QUANT
            
            boot_result = self.boot_kernel(boot_repo, boot_quant)
            
            if "🔴" in boot_result or "FAILED" in boot_result:
                history[-1]["content"] = f"❌ Auto-boot failed: {boot_result}\n\nPlease manually SCAN and BOOT a model."
                yield history
                return
            
            history[-1]["content"] = f"✅ {boot_result}\n\nProcessing your request..."
            yield history
            time.sleep(0.5)  # Brief pause for user to see the message

        # Check prompt cache for exact matches (instant response)
        cache_key = f"{ghost_context}:{prompt}"
        if cache_key in self.prompt_cache:
            self.perf_stats["cache_hits"] += 1
            logger.info("⚡ CACHE HIT - Instant response!")
            history.append({"role": "user", "content": prompt})
            history.append({"role": "assistant", "content": self.prompt_cache[cache_key]})
            yield history
            return

        # Prepare input with optimized formatting
        full_input = f"{ghost_context}\n{prompt}" if ghost_context else prompt
        formatted_prompt = f"User: {full_input}\nAssistant: "
        
        # Add User Message & Empty Assistant Message for Streaming
        history.append({"role": "user", "content": prompt})
        history.append({"role": "assistant", "content": "..."})
        yield history

        response_text = ""
        start_time = time.time()
        tokens_count = 0
        first_token_time = None

        try:
            # Get max tokens from user purchases
            max_tokens = 2048
            if username:
                max_tokens = token_manager.get_purchases(username)["token_limit"]
            
            # HYPER-OPTIMIZED CPU INFERENCE SETTINGS
            stream = self.llm(
                formatted_prompt,
                max_tokens=max_tokens,
                stop=["User:", "<|eot_id|>", "\n\n"],
                stream=True,
                temperature=self.custom_params["temperature"],
                top_p=self.custom_params["top_p"],
                top_k=self.custom_params["top_k"],
                repeat_penalty=self.custom_params["repeat_penalty"],
                frequency_penalty=0.0,
                presence_penalty=0.0,
                tfs_z=1.0,
                typical_p=1.0,
                mirostat_mode=2,                    # CPU benefits from mirostat
                mirostat_tau=5.0,
                mirostat_eta=0.1,
            )
            
            for chunk in stream:
                token = chunk["choices"][0]["text"]
                response_text += token
                tokens_count += 1
                
                # Track first token latency (TTFT - Time To First Token)
                if first_token_time is None:
                    first_token_time = time.time() - start_time
                    logger.info(f"⚡ First token: {first_token_time*1000:.0f}ms")
                
                elapsed = time.time() - start_time
                tps = round(tokens_count / elapsed, 1) if elapsed > 0 else 0
                
                # Track peak performance
                if tps > self.perf_stats["peak_tps"]:
                    self.perf_stats["peak_tps"] = tps
                
                # Charge tokens every second
                if int(elapsed * 1000) % 1000 < 100 and username:  # Every ~1 second
                    token_manager.charge_usage(username, elapsed * 1000)
                
                # Update history with streaming content + performance metrics
                balance = token_manager.get_balance(username) if username else 0
                history[-1]["content"] = f"{response_text}\n\n`⚡ {tps} t/s | 🎯 Peak: {self.perf_stats['peak_tps']:.1f} t/s | 💰 {balance:.2f} tokens`"
                yield history
            
            # Final token charge for remaining time
            if username:
                token_manager.charge_usage(username, elapsed * 1000)
            
            # Update global performance stats
            self.perf_stats["total_tokens"] += tokens_count
            self.perf_stats["total_time"] += elapsed
            self.perf_stats["avg_tps"] = self.perf_stats["total_tokens"] / self.perf_stats["total_time"]
            
            # Cache the response for future identical queries
            if len(response_text) > 10:  # Only cache meaningful responses
                self.prompt_cache[cache_key] = response_text
                # Limit cache size to prevent memory bloat
                if len(self.prompt_cache) > 100:
                    oldest_key = next(iter(self.prompt_cache))
                    del self.prompt_cache[oldest_key]
            
            self.telemetry.track_generation(tokens_count)
            
            # Aggressive GC after generation
            force_gc()
            
            logger.info(f"✅ Generation complete: {tokens_count} tokens @ {tps:.1f} t/s (TTFT: {first_token_time*1000:.0f}ms)")
            
        except Exception as e:
            logger.error(f"Inference error: {e}")
            history[-1]["content"] = f"🔴 Runtime Error: {str(e)}"
            yield history
            force_gc()

# --- CUSTOM CSS ---
CUSTOM_CSS = """
@import url('https://fonts.cdnfonts.com/css/consolas');

* {
    font-family: 'Consolas', 'Courier New', monospace !important;
}

/* Global smooth rounded corners */
.gradio-container {
    border-radius: 24px !important;
}

/* All buttons */
button {
    border-radius: 16px !important;
    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
    font-family: 'Consolas', monospace !important;
}

button:hover {
    transform: translateY(-2px);
    box-shadow: 0 8px 16px rgba(0,0,0,0.2) !important;
}

/* Input fields */
input, textarea, .gr-textbox, .gr-dropdown {
    border-radius: 12px !important;
    font-family: 'Consolas', monospace !important;
}

/* Chat messages */
.message {
    border-radius: 16px !important;
    font-family: 'Consolas', monospace !important;
}

/* Code blocks */
.gr-code {
    border-radius: 12px !important;
    font-family: 'Consolas', monospace !important;
}

/* Labels */
.gr-label {
    border-radius: 12px !important;
    font-family: 'Consolas', monospace !important;
}

/* Sidebar */
.gr-sidebar {
    border-radius: 20px !important;
    background: linear-gradient(135deg, rgba(20,20,40,0.95), rgba(10,10,20,0.98)) !important;
    backdrop-filter: blur(10px) !important;
}

/* Markdown sections */
.gr-markdown {
    font-family: 'Consolas', monospace !important;
}

/* Chatbot container */
.chatbot {
    border-radius: 20px !important;
    font-family: 'Consolas', monospace !important;
}

/* Dropdown menus */
.gr-dropdown-menu {
    border-radius: 12px !important;
    font-family: 'Consolas', monospace !important;
}

/* Column containers */
.gr-column {
    border-radius: 16px !important;
}

/* Row containers */
.gr-row {
    border-radius: 12px !important;
}

/* Smooth animations for all interactive elements */
* {
    transition: all 0.2s ease !important;
}

/* Header styling */
h1, h2, h3, h4, h5, h6 {
    font-family: 'Consolas', monospace !important;
}
"""

# --- UI INTERFACE ---
kernel = ZeroEngine()

# Session ID for token tracking
username = token_manager.get_username()

with gr.Blocks(title="ZeroEngine V0.2", css=CUSTOM_CSS) as demo:
    # Header with Token Display
    with gr.Row():
        with gr.Column(scale=8):
            gr.HTML("""
                <div style='text-align: center; padding: 30px; border-radius: 24px; 
                            background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%); 
                            margin-bottom: 20px; box-shadow: 0 10px 30px rgba(0,0,0,0.3);'>
                    <h1 style='margin: 0; font-size: 3em; background: linear-gradient(90deg, #00d4ff, #7b2ff7); 
                               -webkit-background-clip: text; -webkit-text-fill-color: transparent;
                               font-family: Consolas, monospace;'>
                        🛰️ ZEROENGINE V0.2
                    </h1>
                    <p style='margin: 10px 0 0 0; color: #888; font-family: Consolas, monospace;'>
                        CPU-Optimized | Token System | Custom Parameters | Auto-Format
                    </p>
                </div>
            """)
        with gr.Column(scale=2):
            # Token Display
            gr.HTML("""
                <div style='text-align: center; padding: 20px; border-radius: 20px;
                            background: linear-gradient(135deg, #7b2ff7 0%, #9b59b6 100%);
                            margin-bottom: 20px; box-shadow: 0 8px 20px rgba(123,47,247,0.3);'>
                    <div style='font-size: 2em; margin-bottom: 5px;'>💰</div>
                    <div id='token-display' style='font-size: 1.8em; font-weight: bold; color: white; font-family: Consolas;'>
                        100.00
                    </div>
                    <div style='font-size: 0.9em; color: #ddd; font-family: Consolas;'>TOKENS</div>
                </div>
            """)
            token_balance = gr.Textbox(value="100.00", visible=False, elem_id="token_balance")
            end_session_btn = gr.Button("END SESSION", variant="stop", size="sm")
            session_status = gr.Markdown("", visible=False)
    
    with gr.Row():
        with gr.Column(scale=8):
            chat_box = gr.Chatbot(
                label="Main Engine Feedback", 
                height=600, 
                show_label=False, 
                autoscroll=True,
                container=True
            )
            
            with gr.Row():
                user_input = gr.Textbox(
                    placeholder="Input command...", 
                    label="Terminal", 
                    container=False, 
                    scale=9
                )
                send_btn = gr.Button("SUBMIT", variant="primary", scale=1)
        
        with gr.Column(scale=4):
            # Hardware Status
            gr.Markdown("### 🛠️ Hardware Status")
            ram_metric = gr.Label(label="RAM Usage", value="0/0 GB")
            cpu_metric = gr.Label(label="CPU Load", value="0%")
            
            gr.Markdown("---")
            
            # Model Control
            gr.Markdown("### 📡 Model Control")
            repo_input = gr.Textbox(label="HuggingFace Repo", value=DEFAULT_MODEL)
            quant_dropdown = gr.Dropdown(label="Available Quants", choices=[], interactive=True)
            
            with gr.Row():
                scan_btn = gr.Button("SCAN", size="sm")
                boot_btn = gr.Button("BOOT", variant="primary", size="sm")
            
            boot_status = gr.Markdown("Status: `STANDBY`")
            
            gr.Markdown("---")
            
            # Custom Parameters
            gr.Markdown("### ⚙️ Custom Parameters")
            temperature_slider = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature")
            top_p_slider = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-P")
            top_k_slider = gr.Slider(1, 100, value=40, step=1, label="Top-K")
            repeat_penalty_slider = gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Repeat Penalty")
            
            gr.Markdown("---")
            
            # Token Purchases
            gr.Markdown("### 💎 Token Upgrades")
            with gr.Row():
                batch_upgrade_btn = gr.Button("🚀 Batch x2", size="sm", variant="secondary")
                token_upgrade_btn = gr.Button("📈 +1K Tokens", size="sm", variant="secondary")
            purchase_status = gr.Markdown("Ready to upgrade!")
            
            gr.Markdown("---")
            
            # Ghost Cache
            gr.Markdown("### 👻 Ghost Cache (Pre-Context)")
            ghost_buffer = gr.Textbox(
                label="Background Context", 
                placeholder="Add context that will be prepended to all messages...", 
                lines=3
            )
            with gr.Row():
                stitch_btn = gr.Button("PRIME CACHE", variant="secondary", size="sm", scale=1)
            stitch_status = gr.Markdown("Cache: `EMPTY`")
            
            log_output = gr.Code(
                label="Kernel Logs", 
                language="shell", 
                value="[INIT] V0.2 System Ready.",
                lines=5
            )

    # --- UI LOGIC ---
    def update_stats():
        try:
            m = ResourceMonitor.get_metrics()
            balance = token_manager.get_balance(session_id)
            return f"{m['ram_used_gb']}/{m['ram_total_gb']} GB", f"{m['cpu_usage_pct']}%", f"{balance}"
        except Exception as e:
            logger.error(f"Stats update error: {e}")
            return "Error", "Error", "0.00"
            return "Error", "Error"

    def on_scan(repo):
        try:
            if not repo:
                return gr.update(choices=[], value=None), "⚠️ Please enter a repository ID"
            
            logger.info(f"Scanning repository: {repo}")
            files = kernel.list_ggufs(repo)
            
            if not files:
                return gr.update(choices=[], value=None), f"❌ No GGUFs found in {repo}"
            
            return gr.update(choices=files, value=files[0]), f"✅ Found {len(files)} GGUF file(s)"
        except Exception as e:
            logger.error(f"Scan error: {e}")
            return gr.update(choices=[], value=None), f"🔴 Scan failed: {str(e)}"

    def on_boot(repo, file):
        try:
            if not repo or not file:
                yield "🔴 ERROR: Repository and filename required", gr.update()
                return
            
            yield "⚙️ System: Initiating boot sequence...", gr.update()
            time.sleep(0.5)
            
            result = kernel.boot_kernel(repo, file, session_id)
            yield result, gr.update()
            
        except Exception as e:
            logger.error(f"Boot UI error: {e}")
            yield f"🔴 BOOT ERROR: {str(e)}", gr.update()
    
    def on_batch_upgrade():
        success, msg = token_manager.purchase_batch_upgrade(session_id)
        balance = token_manager.get_balance(session_id)
        return msg, f"{balance}"
    
    def on_token_upgrade():
        success, msg = token_manager.purchase_token_upgrade(session_id, 1000)
        balance = token_manager.get_balance(session_id)
        return msg, f"{balance}"
    
    def on_end_session():
        msg = token_manager.end_session(session_id)
        return msg
    
    def update_custom_params(temp, top_p, top_k, repeat_pen):
        kernel.custom_params["temperature"] = temp
        kernel.custom_params["top_p"] = top_p
        kernel.custom_params["top_k"] = int(top_k)
        kernel.custom_params["repeat_penalty"] = repeat_pen
        return "✅ Parameters updated!"

    # Timer for periodic stats updates (includes token balance)
    timer = gr.Timer(value=2)
    timer.tick(update_stats, None, [ram_metric, cpu_metric, token_balance])
    
    # Event handlers
    scan_btn.click(on_scan, [repo_input], [quant_dropdown, log_output])
    boot_btn.click(on_boot, [repo_input, quant_dropdown], [boot_status, log_output])
    
    # Token purchases
    batch_upgrade_btn.click(on_batch_upgrade, None, [purchase_status, token_balance])
    token_upgrade_btn.click(on_token_upgrade, None, [purchase_status, token_balance])
    end_session_btn.click(on_end_session, None, [session_status])
    
    # Custom parameter updates
    temperature_slider.change(update_custom_params, 
                             [temperature_slider, top_p_slider, top_k_slider, repeat_penalty_slider],
                             [purchase_status])
    top_p_slider.change(update_custom_params,
                        [temperature_slider, top_p_slider, top_k_slider, repeat_penalty_slider],
                        [purchase_status])
    top_k_slider.change(update_custom_params,
                        [temperature_slider, top_p_slider, top_k_slider, repeat_penalty_slider],
                        [purchase_status])
    repeat_penalty_slider.change(update_custom_params,
                                 [temperature_slider, top_p_slider, top_k_slider, repeat_penalty_slider],
                                 [purchase_status])
    
    # Ghost cache
    stitch_btn.click(
        lambda x: f"Cache: `{kernel.stitch_cache(x)}`", 
        [ghost_buffer], 
        [stitch_status]
    )
    
    # Keyboard input preprocessing
    user_input.change(
        lambda x: kernel.preprocess_input(x),
        [user_input],
        None
    )
    
    # Auto-boot enabled inference
    inference_args = [user_input, chat_box, ghost_buffer, repo_input, quant_dropdown]
    user_input.submit(kernel.inference_generator, inference_args, [chat_box])
    send_btn.click(kernel.inference_generator, inference_args, [chat_box])
    user_input.submit(lambda: "", None, [user_input])

# --- LAUNCH ---
if __name__ == "__main__":
    demo.queue(max_size=20).launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )