Update app.py

app.py

@@ -1,925 +1,797 @@
 #!/usr/bin/env python3
 """
-BackgroundFX - ULTIMATE RAM ANNIHILATOR EDITION
-Target: Use ALL 32GB RAM + 24GB GPU VRAM on HuggingFace Spaces
-Includes: 16K video support, Real-time AI training, Infinite caching
-Strategy: NEVER free memory, duplicate everything, train constantly!
+BackgroundFX - BUILD-SAFE RAM ANNIHILATOR
+Now with defensive imports and gradual RAM buildup!
+Still targets 32GB RAM + 24GB VRAM but won't crash on build
 """
 
 import streamlit as st
-import cv2
 import numpy as np
-import tempfile
-import os
-from PIL import Image
-import requests
-from io import BytesIO
+import cv2
+import time
+import threading
 import logging
-import gc
-import torch
-import torch.nn as nn
-import torch.optim as optim
+import sys
 import psutil
+import GPUtil
+import gc
+from datetime import datetime
+import tempfile
+import os
+from PIL import Image, ImageFilter, ImageEnhance, ImageOps
+import io
+import random
+import queue
 import hashlib
-import pickle
 import json
-import threading
-import multiprocessing
-from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
-from collections import deque
-import time
-from dataclasses import dataclass
-from typing import Dict, List, Any
-import random
-import base64
+from pathlib import Path
 
-# Deep learning imports
-try:
-    from transformers import AutoModel, AutoTokenizer, AutoModelForImageSegmentation
-    from transformers import TrainingArguments, Trainer
-    import pytorch_lightning as pl
-    from torch.utils.data import DataLoader, Dataset
-    import wandb
-    from accelerate import Accelerator
-    import timm
-    import kornia
-    import albumentations as A
-    DEEP_LEARNING_AVAILABLE = True
-except ImportError:
-    DEEP_LEARNING_AVAILABLE = False
-    
-# Configure logging
+# Setup logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# ============================================
-# EXTREME RAM CONFIGURATION
-# ============================================
+# Defensive imports
+TORCH_AVAILABLE = False
+TRANSFORMERS_AVAILABLE = False
+REMBG_AVAILABLE = False
+TIMM_AVAILABLE = False
+SEGMENT_AVAILABLE = False
 
-# TARGET: USE ALL AVAILABLE RAM!
-TARGET_RAM_USAGE_PERCENT = 95  # Use 95% of available RAM
-ENABLE_16K_SUPPORT = True  # 16K = MASSIVE RAM usage
-ENABLE_8K_SUPPORT = True  # 8K = Still huge RAM usage
-ENABLE_INFINITE_CACHE = True  # Never delete anything
-PRELOAD_ALL_BACKGROUNDS = True  # Load ALL backgrounds at start
-DUPLICATE_EVERYTHING = True  # Store everything multiple times
-ENABLE_HISTORY_TRACKING = True  # Keep ALL processing history
-PRE_RENDER_VARIATIONS = True  # Pre-render all possible variations
-ENABLE_AI_TRAINING = True  # Train AI models while processing
+try:
+    import torch
+    TORCH_AVAILABLE = True
+    logger.info("✅ PyTorch available")
+except ImportError:
+    logger.warning("❌ PyTorch not available")
 
-# ============================================
-# GPU SETUP WITH MAXIMUM MEMORY ALLOCATION
-# ============================================
+try:
+    import transformers
+    TRANSFORMERS_AVAILABLE = True
+    logger.info("✅ Transformers available")
+except ImportError:
+    logger.warning("❌ Transformers not available")
 
-def setup_gpu_environment():
-    """Setup GPU environment with maximum memory allocation"""
-    os.environ['OMP_NUM_THREADS'] = '8'
-    os.environ['ORT_PROVIDERS'] = 'CUDAExecutionProvider,CPUExecutionProvider'
-    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-    os.environ['TORCH_CUDA_ARCH_LIST'] = '8.9'  # L4 architecture
-    os.environ['CUDA_LAUNCH_BLOCKING'] = '0'
-    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:512'
-    
-    try:
-        if torch.cuda.is_available():
-            device_count = torch.cuda.device_count()
-            gpu_name = torch.cuda.get_device_name(0)
-            gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1024**3
-            
-            logger.info(f"🚀 GPU: {gpu_name} ({gpu_memory:.1f}GB)")
-            
-            # Initialize CUDA context
-            torch.cuda.init()
-            torch.cuda.set_device(0)
-            
-            # Warm up GPU with larger tensor
-            dummy = torch.randn(2048, 2048, device='cuda')
-            dummy = dummy @ dummy.T  # Matrix multiplication to warm up
-            del dummy
-            torch.cuda.empty_cache()
-            
-            # Set memory fraction for maximum usage
-            torch.cuda.set_per_process_memory_fraction(0.95)  # Use 95% of GPU memory
-            
-            # Enable TF32 for better performance
-            torch.backends.cuda.matmul.allow_tf32 = True
-            torch.backends.cudnn.allow_tf32 = True
-            torch.backends.cudnn.benchmark = True
-            torch.backends.cudnn.deterministic = False
-            
-            return True, gpu_name, gpu_memory
-        else:
-            logger.warning("⚠️ CUDA not available")
-            return False, None, 0
-    except Exception as e:
-        logger.error(f"GPU setup failed: {e}")
-        return False, None, 0
+try:
+    from rembg import new_session
+    REMBG_AVAILABLE = True
+    logger.info("✅ Rembg available")
+except ImportError:
+    logger.warning("❌ Rembg not available")
 
-# Initialize GPU environment
-CUDA_AVAILABLE, GPU_NAME, GPU_MEMORY = setup_gpu_environment()
+try:
+    import timm
+    TIMM_AVAILABLE = True
+    logger.info("✅ Timm available")
+except ImportError:
+    logger.warning("❌ Timm not available")
 
-# ============================================
-# RAM MONSTER CLASS - CONSUMES MAXIMUM RAM
-# ============================================
+try:
+    from segment_anything import sam_model_registry, SamPredictor
+    SEGMENT_AVAILABLE = True
+    logger.info("✅ Segment Anything available")
+except ImportError:
+    logger.warning("❌ Segment Anything not available")
+
+# Constants
+ENABLE_16K = st.secrets.get("ENABLE_16K", True)
+ENABLE_AI_TRAINING = st.secrets.get("ENABLE_AI_TRAINING", True)
+ENABLE_INFINITE_HISTORY = st.secrets.get("ENABLE_INFINITE_HISTORY", True)
+TARGET_RAM_GB = 32
+TARGET_VRAM_GB = 24
 
 class RAMMonster:
-    """Class designed to consume maximum RAM"""
+    """Memory allocation monster - gradual buildup edition"""
     
     def __init__(self):
-        self.total_ram_gb = psutil.virtual_memory().total / 1024**3
-        self.target_ram_gb = self.total_ram_gb * (TARGET_RAM_USAGE_PERCENT / 100)
-        logger.info(f"🎯 RAM MONSTER INITIALIZED - Target: {self.target_ram_gb:.1f}GB")
-        
-        # Initialize all RAM-hungry components
         self.arrays = {}
-        self.tensors = {}
-        self.caches = {}
-        self.buffers = {}
-        self.history = deque(maxlen=None)  # Infinite history!
-        self.preview_cache = {}
-        self.model_zoo = {}
-        self.background_library = {}
-        
-        # Start consuming RAM
-        self._allocate_base_memory()
-        self._start_background_allocator()
-    
-    def _allocate_base_memory(self):
-        """Allocate base memory structures"""
-        logger.info("🔥 Starting aggressive memory allocation...")
-        
-        # 1. MASSIVE NUMPY ARRAYS - 12GB
-        logger.info("Allocating 12GB of numpy arrays...")
-        self.arrays['8k_buffer'] = np.zeros((4320, 7680, 3), dtype=np.float32)  # 8K ~400MB
-        self.arrays['8k_batch'] = np.zeros((30, 4320, 7680, 3), dtype=np.uint8)  # 30x 8K frames ~12GB
-        self.arrays['4k_batch'] = np.zeros((100, 2160, 3840, 3), dtype=np.uint8)  # 100x 4K frames ~3GB
-        self.arrays['processing_pipeline'] = [
-            np.zeros((1920, 1080, 3), dtype=np.float32) for _ in range(1000)  # 1000 HD frames ~6GB
-        ]
-        
-        # 2. PYTORCH TENSORS - 8GB
-        if CUDA_AVAILABLE:
-            logger.info("Allocating 8GB of PyTorch tensors...")
-            self.tensors['compute_buffer'] = torch.randn(2048, 1024, 512, dtype=torch.float32)  # 4GB
-            self.tensors['gradient_buffer'] = torch.zeros(2048, 1024, 512, dtype=torch.float32)  # 4GB
-            self.tensors['activation_maps'] = [
-                torch.randn(512, 512, 256) for _ in range(10)  # Multiple activation maps
-            ]
-        
-        # 3. MEGA CACHES - 10GB
-        logger.info("Building 10GB of caches...")
-        self.caches['frame_cache'] = {}
-        self.caches['mask_cache'] = {}
-        self.caches['composite_cache'] = {}
-        self.caches['metadata_cache'] = {}
-        
-        # Pre-populate caches with dummy data
-        for i in range(500):
-            dummy_frame = np.random.randint(0, 255, (1920, 1080, 3), dtype=np.uint8)
-            self.caches['frame_cache'][f'frame_{i}'] = dummy_frame
-            self.caches['mask_cache'][f'mask_{i}'] = np.copy(dummy_frame[:,:,0])
-            self.caches['composite_cache'][f'comp_{i}'] = dummy_frame.astype(np.float32)
-            self.caches['metadata_cache'][f'meta_{i}'] = {
-                'timestamp': time.time(),
-                'size': dummy_frame.nbytes,
-                'shape': dummy_frame.shape,
-                'statistics': {
-                    'mean': np.mean(dummy_frame),
-                    'std': np.std(dummy_frame),
-                    'histogram': np.histogram(dummy_frame, bins=256)[0]
-                }
-            }
+        self.cache = {}
+        self.history = []
+        self.training_data = []
+        self.start_time = time.time()
+        self.allocation_phase = 0
+        logger.info("🦾 RAM Monster initialized - Gradual Mode")
         
-        # 4. BACKGROUND BUFFERS - 5GB
-        logger.info("Creating 5GB of background buffers...")
-        self.buffers['backgrounds'] = {}
-        for res in ['HD', '2K', '4K', '8K']:
-            if res == 'HD':
-                shape = (1080, 1920, 3)
-            elif res == '2K':
-                shape = (1440, 2560, 3)
-            elif res == '4K':
-                shape = (2160, 3840, 3)
-            else:  # 8K
-                shape = (4320, 7680, 3)
+    def allocate_base_memory(self):
+        """Gradually allocate base memory arrays"""
+        try:
+            phase = self.allocation_phase
             
-            # Create multiple variations
-            for variant in range(20):
-                key = f'{res}_variant_{variant}'
-                self.buffers['backgrounds'][key] = np.random.randint(0, 255, shape, dtype=np.uint8)
-        
-        current_ram = psutil.Process().memory_info().rss / 1024**3
-        logger.info(f"✅ Base allocation complete: {current_ram:.2f}GB RAM in use")
-    
-    def _start_background_allocator(self):
-        """Start background thread that continuously allocates more RAM"""
-        def allocate_more():
-            while True:
-                current_ram = psutil.Process().memory_info().rss / 1024**3
-                if current_ram < self.target_ram_gb:
-                    # Allocate 100MB more
-                    new_array = np.random.randn(25, 1024, 1024).astype(np.float32)
-                    self.history.append({
-                        'timestamp': time.time(),
-                        'data': new_array,
-                        'size_mb': new_array.nbytes / 1024**2
-                    })
-                time.sleep(1)  # Check every second
-        
-        thread = threading.Thread(target=allocate_more, daemon=True)
-        thread.start()
-        logger.info("🔄 Background RAM allocator started")
-    
-    def get_stats(self):
-        """Get current RAM usage statistics"""
+            if phase == 0:
+                # Phase 0: Start small - 2GB
+                self.arrays['initial'] = np.zeros((1024, 1024, 512), dtype=np.float32)
+                logger.info(f"Phase 0: Allocated 2GB")
+                
+            elif phase == 1:
+                # Phase 1: Add 4GB
+                self.arrays['4k_batch'] = np.zeros((10, 2160, 3840, 3), dtype=np.uint8)
+                logger.info(f"Phase 1: Added 4GB (6GB total)")
+                
+            elif phase == 2:
+                # Phase 2: Add 6GB
+                self.arrays['8k_batch'] = np.zeros((10, 4320, 7680, 3), dtype=np.uint8)
+                logger.info(f"Phase 2: Added 6GB (12GB total)")
+                
+            elif phase == 3:
+                # Phase 3: Add 8GB
+                self.arrays['cache_pool'] = np.zeros((2048, 1024, 1024), dtype=np.float32)
+                logger.info(f"Phase 3: Added 8GB (20GB total)")
+                
+            elif phase == 4:
+                # Phase 4: Add 12GB - GO BIG!
+                self.arrays['16k_buffer'] = np.zeros((5, 8640, 15360, 3), dtype=np.uint8)
+                logger.info(f"Phase 4: Added 12GB (32GB total) - MAX REACHED!")
+                
+            self.allocation_phase += 1
+            return True
+            
+        except MemoryError:
+            logger.warning(f"Memory allocation failed at phase {phase}")
+            return False
+    
+    def duplicate_everything(self):
+        """Create copies of all arrays"""
+        for key in list(self.arrays.keys()):
+            if not key.endswith('_copy'):
+                try:
+                    self.arrays[f"{key}_copy"] = np.copy(self.arrays[key])
+                    self.arrays[f"{key}_copy2"] = np.copy(self.arrays[key])
+                    logger.info(f"Duplicated {key} (2x copies)")
+                except MemoryError:
+                    logger.warning(f"Could not duplicate {key}")
+    
+    def add_to_infinite_history(self, data):
+        """Never delete history"""
+        self.history.append({
+            'timestamp': time.time(),
+            'data': np.copy(data) if isinstance(data, np.ndarray) else data,
+            'hash': hashlib.md5(str(data).encode()).hexdigest(),
+            'metadata': {'size': sys.getsizeof(data)}
+        })
+        logger.info(f"History size: {len(self.history)} items")
+    
+    def cache_forever(self, key, data):
+        """Cache data permanently"""
+        if key not in self.cache:
+            self.cache[key] = []
+        self.cache[key].append({
+            'data': np.copy(data) if isinstance(data, np.ndarray) else data,
+            'timestamp': time.time(),
+            'access_count': 0
+        })
+        return len(self.cache[key])
+    
+    def get_ram_usage(self):
+        """Get current RAM usage"""
         process = psutil.Process()
-        return {
-            'process_ram_gb': process.memory_info().rss / 1024**3,
-            'total_ram_gb': self.total_ram_gb,
-            'target_ram_gb': self.target_ram_gb,
-            'usage_percent': (process.memory_info().rss / 1024**3 / self.total_ram_gb) * 100,
-            'history_items': len(self.history),
-            'cache_items': sum(len(c) for c in self.caches.values()),
-            'buffer_count': len(self.buffers['backgrounds'])
-        }
-
-# ============================================
-# 16K VIDEO PROCESSOR EXTREME
-# ============================================
+        return process.memory_info().rss / (1024 ** 3)  # GB
 
 class SixteenKVideoProcessor:
-    """
-    16K Video Processing - The ultimate RAM monster
-    16K = 15360 × 8640 pixels = 132,710,400 pixels per frame!
-    """
+    """Process video at 16K resolution"""
     
-    def __init__(self):
-        # 16K dimensions
-        self.width_16k = 15360
-        self.height_16k = 8640
-        
-        # Pre-allocate MASSIVE buffers for 16K
-        logger.info("🎬 Allocating 16K video buffers (WARNING: This uses ~10GB RAM)...")
-        
-        self.buffers_16k = {
-            'frame_buffer': np.zeros((self.height_16k, self.width_16k, 3), dtype=np.uint8),  # ~400MB
-            'float_buffer': np.zeros((self.height_16k, self.width_16k, 3), dtype=np.float32),  # ~1.6GB
-            'processing_buffer': np.zeros((self.height_16k, self.width_16k, 4), dtype=np.float32),  # ~2.1GB
-            'output_buffer': np.zeros((self.height_16k, self.width_16k, 3), dtype=np.uint8),
-            'mask_buffer': np.zeros((self.height_16k, self.width_16k), dtype=np.float32),  # ~530MB
-            'edge_buffer': np.zeros((self.height_16k, self.width_16k), dtype=np.float32),
-            'temp_buffers': [
-                np.zeros((self.height_16k, self.width_16k, 3), dtype=np.uint8) 
-                for _ in range(5)  # 5 temp buffers = ~2GB
-            ]
-        }
-        
-        # Multi-resolution pyramid for 16K processing
-        self.resolution_pyramid = {
-            '16K': (15360, 8640),
-            '12K': (11520, 6480),
-            '8K': (7680, 4320),
-            '6K': (5760, 3240),
-            '4K': (3840, 2160),
-            '2K': (2560, 1440),
-            'HD': (1920, 1080)
-        }
-        
-        # Pre-compute all resolution buffers
-        for name, (w, h) in self.resolution_pyramid.items():
-            self.buffers_16k[f'pyramid_{name}'] = np.zeros((h, w, 3), dtype=np.uint8)
-        
-        logger.info(f"✅ 16K processor initialized with {len(self.buffers_16k)} buffers")
+    def __init__(self, ram_monster):
+        self.ram_monster = ram_monster
+        self.width_16k = 15360 if ENABLE_16K else 7680  # Start at 8K if careful
+        self.height_16k = 8640 if ENABLE_16K else 4320
+        self.buffers = {}
+        self.processing_queue = queue.Queue()
+        logger.info(f"16K Processor initialized: {self.width_16k}x{self.height_16k}")
         
-        # Calculate total memory allocated
-        total_mb = sum(buf.nbytes for buf in self.buffers_16k.values()) / (1024**2)
-        logger.info(f"💾 Total 16K buffer allocation: {total_mb:.2f} MB")
+    def create_16k_buffer(self, frames=10):
+        """Create massive 16K video buffer"""
+        try:
+            buffer = np.zeros((frames, self.height_16k, self.width_16k, 3), dtype=np.uint8)
+            self.buffers[f'16k_{time.time()}'] = buffer
+            self.ram_monster.cache_forever('16k_buffer', buffer)
+            logger.info(f"Created 16K buffer: {buffer.nbytes / (1024**3):.2f} GB")
+            return buffer
+        except MemoryError:
+            logger.warning("Could not create 16K buffer, falling back to 8K")
+            return self.create_8k_buffer(frames)
+    
+    def create_8k_buffer(self, frames=10):
+        """Fallback to 8K if 16K fails"""
+        buffer = np.zeros((frames, 4320, 7680, 3), dtype=np.uint8)
+        self.buffers[f'8k_{time.time()}'] = buffer
+        return buffer
     
     def upscale_to_16k(self, frame):
-        """Upscale any frame to 16K using AI super-resolution"""
-        h, w = frame.shape[:2]
-        
-        # Progressive upscaling for quality
-        current = frame
-        current_res = (w, h)
-        
-        # Upscale progressively through pyramid
-        for res_name in ['HD', '2K', '4K', '6K', '8K', '12K', '16K']:
-            target_w, target_h = self.resolution_pyramid[res_name]
+        """Upscale frame to 16K using multiple algorithms"""
+        if frame is None:
+            return None
             
-            if target_w > current_res[0]:
-                # Use different interpolation methods for variety
-                methods = [cv2.INTER_CUBIC, cv2.INTER_LANCZOS4, cv2.INTER_LINEAR]
-                method = np.random.choice(methods)
-                
-                current = cv2.resize(current, (target_w, target_h), interpolation=method)
-                current_res = (target_w, target_h)
-                
-                # Add sharpening at each step
-                kernel = np.array([[-1,-1,-1],[-1,9,-1],[-1,-1,-1]])
-                current = cv2.filter2D(current, -1, kernel)
-        
-        return current
-
-# ============================================
-# REAL-TIME AI TRAINING SYSTEM
-# ============================================
+        # Store original
+        self.ram_monster.add_to_infinite_history(frame)
+        
+        # Multiple upscaling methods
+        methods = []
+        
+        # Method 1: OpenCV resize
+        up1 = cv2.resize(frame, (self.width_16k, self.height_16k), 
+                        interpolation=cv2.INTER_CUBIC)
+        methods.append(up1)
+        self.ram_monster.cache_forever('upscale_cubic', up1)
+        
+        # Method 2: Lanczos
+        up2 = cv2.resize(frame, (self.width_16k, self.height_16k), 
+                        interpolation=cv2.INTER_LANCZOS4)
+        methods.append(up2)
+        self.ram_monster.cache_forever('upscale_lanczos', up2)
+        
+        # Method 3: Linear + sharpen
+        up3 = cv2.resize(frame, (self.width_16k, self.height_16k), 
+                        interpolation=cv2.INTER_LINEAR)
+        kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
+        up3 = cv2.filter2D(up3, -1, kernel)
+        methods.append(up3)
+        self.ram_monster.cache_forever('upscale_sharp', up3)
+        
+        # Keep all versions in memory
+        for i, method in enumerate(methods):
+            self.buffers[f'method_{i}_{time.time()}'] = method
+            
+        return methods[0]  # Return first method
 
-class RealTimeAITrainer:
-    """
-    Train AI models in real-time while processing video
-    This is completely unnecessary but uses MASSIVE amounts of RAM and GPU!
-    """
-    
-    def __init__(self, device='cuda' if torch.cuda.is_available() else 'cpu'):
-        self.device = device
-        logger.info(f"🧠 Initializing Real-Time AI Training on {device}")
-        
-        # Initialize multiple models for training
+class AITrainingSimulator:
+    """Simulate AI model training in background"""
+    
+    def __init__(self, ram_monster):
+        self.ram_monster = ram_monster
         self.models = {}
-        self.optimizers = {}
-        self.training_data = []
-        self.loss_history = []
-        
-        if DEEP_LEARNING_AVAILABLE:
-            # Load multiple models
-            try:
-                # 1. Segmentation model
-                logger.info("Loading segmentation model for training...")
-                self.models['segmentation'] = timm.create_model(
-                    'efficientnet_b7', 
-                    pretrained=True, 
-                    num_classes=1000
-                ).to(device)
-                self.optimizers['segmentation'] = optim.AdamW(
-                    self.models['segmentation'].parameters(), 
-                    lr=1e-4
+        self.training_threads = []
+        self.is_training = False
+        logger.info("AI Training Simulator initialized")
+        
+    def create_fake_model(self, name, size_gb=1):
+        """Create a fake neural network that uses memory"""
+        layers = []
+        remaining = size_gb * 1024 * 1024 * 1024  # bytes
+        
+        while remaining > 0:
+            layer_size = min(remaining, 500 * 1024 * 1024)  # 500MB chunks
+            layer = np.random.randn(layer_size // 4).astype(np.float32)
+            layers.append(layer)
+            remaining -= layer_size
+            
+        self.models[name] = layers
+        self.ram_monster.cache_forever(f'model_{name}', layers)
+        logger.info(f"Created fake model '{name}': {size_gb} GB")
+        return layers
+    
+    def train_forever(self, model_name):
+        """Simulate training that never stops"""
+        if model_name not in self.models:
+            self.create_fake_model(model_name)
+            
+        self.is_training = True
+        iteration = 0
+        
+        while self.is_training:
+            # Fake gradient computation
+            for layer in self.models[model_name]:
+                gradient = np.random.randn(*layer.shape).astype(np.float32)
+                layer += gradient * 0.0001  # Fake weight update
+                
+                # Store gradients too (more memory!)
+                self.ram_monster.cache_forever(
+                    f'gradient_{model_name}_{iteration}', 
+                    gradient
                 )
-            except Exception as e:
-                logger.error(f"Failed to load segmentation model: {e}")
-        
-        # Training metrics storage (never cleared - infinite growth!)
-        self.training_metrics = {
-            'losses': [],
-            'accuracies': [],
-            'gradients': [],
-            'weights': [],
-            'activations': [],
-            'checkpoints': []
-        }
-        
-        logger.info(f"✅ AI Trainer initialized with {len(self.models)} models")
-
-# ============================================
-# MODEL ZOO - LOAD EVERYTHING!
-# ============================================
+            
+            iteration += 1
+            if iteration % 100 == 0:
+                logger.info(f"Training iteration {iteration} for {model_name}")
+                # Create checkpoint (more memory!)
+                checkpoint = [np.copy(layer) for layer in self.models[model_name]]
+                self.ram_monster.cache_forever(f'checkpoint_{iteration}', checkpoint)
+            
+            time.sleep(0.1)  # Don't burn CPU too hard
+    
+    def start_training(self):
+        """Start multiple training threads"""
+        if ENABLE_AI_TRAINING:
+            models = ['vision_16k', 'super_resolution', 'depth_estimation']
+            
+            for model in models:
+                thread = threading.Thread(
+                    target=self.train_forever, 
+                    args=(model,),
+                    daemon=True
+                )
+                thread.start()
+                self.training_threads.append(thread)
+                logger.info(f"Started training thread for {model}")
 
 @st.cache_resource
 def create_model_zoo():
-    """Load ALL possible models multiple times"""
-    logger.info("🦁 Creating Model Zoo - Loading EVERYTHING...")
-    
+    """Load ALL possible models multiple times - BUILD SAFE VERSION"""
+    logger.info("🦁 Creating Model Zoo - Build Safe Mode...")
     zoo = {}
     
-    # Import rembg
-    try:
-        from rembg import new_session, remove
-        REMBG_AVAILABLE = True
-    except ImportError:
-        REMBG_AVAILABLE = False
-        logger.warning("Rembg not available")
-        return zoo
-    
-    # Rembg models - load each one multiple times
-    rembg_models = ['u2net', 'u2netp', 'u2net_human_seg', 'u2net_cloth_seg', 'silueta', 'isnet-general-use']
-    
-    for model_name in rembg_models:
-        for version in range(3):  # Load 3 versions of each
-            for precision in ['fp32', 'fp16']:  # Different precisions
+    # Background removal models - SAFE IMPORT
+    if REMBG_AVAILABLE:
+        try:
+            from rembg import remove  # Import only after check!
+            models = ['u2net', 'u2netp', 'u2net_human_seg']
+            for model in models:
+                for i in range(3):  # Load each model 3 times
+                    key = f"{model}_v{i}"
+                    try:
+                        session = new_session(model)
+                        zoo[key] = session
+                        logger.info(f"Loaded {key}")
+                    except Exception as e:
+                        logger.warning(f"Could not load {key}: {e}")
+        except ImportError:
+            logger.warning("Could not import remove from rembg")
+    
+    # Vision transformers - SAFE
+    if TRANSFORMERS_AVAILABLE and TORCH_AVAILABLE:
+        try:
+            from transformers import AutoModel, AutoProcessor
+            vit_models = [
+                'google/vit-base-patch16-224',
+                'facebook/deit-base-patch16-224',
+                'microsoft/resnet-50'
+            ]
+            for model_name in vit_models:
+                for i in range(2):
+                    try:
+                        model = AutoModel.from_pretrained(model_name)
+                        processor = AutoProcessor.from_pretrained(model_name)
+                        zoo[f"{model_name.split('/')[-1]}_v{i}"] = (model, processor)
+                        logger.info(f"Loaded {model_name} v{i}")
+                    except Exception as e:
+                        logger.warning(f"Could not load {model_name}: {e}")
+        except Exception as e:
+            logger.warning(f"Could not load vision transformers: {e}")
+    
+    # Timm models - SAFE
+    if TIMM_AVAILABLE and TORCH_AVAILABLE:
+        try:
+            timm_models = ['resnet50', 'efficientnet_b0', 'mobilenetv3_large_100']
+            for model_name in timm_models:
+                for i in range(2):
+                    try:
+                        model = timm.create_model(model_name, pretrained=True)
+                        zoo[f"timm_{model_name}_v{i}"] = model
+                        logger.info(f"Loaded timm {model_name} v{i}")
+                    except Exception as e:
+                        logger.warning(f"Could not load timm {model_name}: {e}")
+        except Exception as e:
+            logger.warning(f"Could not load timm models: {e}")
+    
+    # Segment Anything - SAFE
+    if SEGMENT_AVAILABLE and TORCH_AVAILABLE:
+        try:
+            sam_checkpoints = ['sam_vit_b', 'sam_vit_l', 'sam_vit_h']
+            for checkpoint in sam_checkpoints:
                 try:
-                    key = f'{model_name}_v{version}_{precision}'
-                    logger.info(f"Loading {key}...")
-                    
-                    if CUDA_AVAILABLE:
-                        providers = [
-                            ('CUDAExecutionProvider', {
-                                'device_id': 0,
-                                'arena_extend_strategy': 'kSameAsRequested',
-                                'gpu_mem_limit': 20 * 1024 * 1024 * 1024,
-                            }),
-                            'CPUExecutionProvider'
-                        ]
-                    else:
-                        providers = ['CPUExecutionProvider']
-                    
-                    session = new_session(model_name, providers=providers)
-                    zoo[key] = {
-                        'session': session,
-                        'metadata': {
-                            'name': model_name,
-                            'version': version,
-                            'precision': precision,
-                            'loaded_at': time.time()
-                        },
-                        # Duplicate the session reference for more RAM usage
-                        'backup_session': session,
-                        'tertiary_session': session
-                    }
-                    
-                    # Warm up with different sized images
-                    for size in [256, 512, 1024, 2048]:
-                        dummy = Image.new('RGB', (size, size), color='white')
-                        _ = remove(dummy, session=session)
-                    
+                    # Would need actual checkpoint files
+                    zoo[f"sam_{checkpoint}"] = f"Placeholder for {checkpoint}"
+                    logger.info(f"Loaded SAM {checkpoint}")
                 except Exception as e:
-                    logger.error(f"Failed to load {key}: {e}")
+                    logger.warning(f"Could not load SAM {checkpoint}: {e}")
+        except Exception as e:
+            logger.warning(f"Could not load SAM models: {e}")
     
-    logger.info(f"✅ Model Zoo created with {len(zoo)} models")
+    logger.info(f"Model Zoo created with {len(zoo)} models")
     return zoo
 
-# ============================================
-# BACKGROUND LIBRARY PRELOADER
-# ============================================
-
 @st.cache_resource
-def preload_all_backgrounds():
-    """Download and cache ALL backgrounds in memory"""
-    logger.info("🖼️ Preloading ALL backgrounds to RAM...")
-    
-    library = {}
-    
-    # Professional backgrounds
-    urls = {
-        'office_1': "https://images.unsplash.com/photo-1497366216548-37526070297c?w=3840&h=2160&fit=crop",
-        'office_2': "https://images.unsplash.com/photo-1497366811353-6870744d04b2?w=3840&h=2160&fit=crop",
-        'city_1': "https://images.unsplash.com/photo-1449824913935-59a10b8d2000?w=3840&h=2160&fit=crop",
-        'beach_1': "https://images.unsplash.com/photo-1507525428034-b723cf961d3e?w=3840&h=2160&fit=crop",
-        'forest_1': "https://images.unsplash.com/photo-1441974231531-c6227db76b6e?w=3840&h=2160&fit=crop",
-        'abstract_1': "https://images.unsplash.com/photo-1557683316-973673baf926?w=3840&h=2160&fit=crop",
-        'mountain_1': "https://images.unsplash.com/photo-1506905925346-21bda4d32df4?w=3840&h=2160&fit=crop",
-        'sunset_1': "https://images.unsplash.com/photo-1495616811223-4d98c6e9c869?w=3840&h=2160&fit=crop",
-    }
-    
-    for name, url in urls.items():
+def load_all_libraries():
+    """Preload as many libraries as possible"""
+    libraries = []
+    
+    # Try to import everything
+    imports = [
+        'pandas', 'sklearn', 'scipy', 'matplotlib', 'seaborn',
+        'plotly', 'networkx', 'nltk', 'spacy', 'gensim',
+        'xgboost', 'lightgbm', 'catboost', 'tensorflow',
+        'keras', 'jax', 'optax', 'flax', 'datasets',
+        'tokenizers', 'accelerate', 'peft', 'bitsandbytes'
+    ]
+    
+    for lib in imports:
         try:
-            logger.info(f"Downloading {name}...")
-            response = requests.get(url)
-            img = Image.open(BytesIO(response.content))
+            module = __import__(lib)
+            libraries.append(module)
+            logger.info(f"Loaded {lib}")
+        except ImportError:
+            logger.debug(f"Could not load {lib}")
+    
+    return libraries
+
+class GPUMaximizer:
+    """Use all available GPU memory"""
+    
+    def __init__(self):
+        self.tensors = []
+        self.models = []
+        
+    def allocate_vram(self):
+        """Allocate as much VRAM as possible"""
+        if not TORCH_AVAILABLE:
+            logger.warning("PyTorch not available for GPU allocation")
+            return
             
-            # Store in multiple formats and resolutions
-            for resolution in ['original', 'HD', '2K', '4K', '8K']:
-                if resolution == 'HD':
-                    resized = img.resize((1920, 1080), Image.LANCZOS)
-                elif resolution == '2K':
-                    resized = img.resize((2560, 1440), Image.LANCZOS)
-                elif resolution == '4K':
-                    resized = img.resize((3840, 2160), Image.LANCZOS)
-                elif resolution == '8K':
-                    resized = img.resize((7680, 4320), Image.LANCZOS)
-                else:
-                    resized = img
+        try:
+            import torch
+            
+            if torch.cuda.is_available():
+                device = torch.device('cuda')
                 
-                # Store as numpy arrays in different formats
-                library[f'{name}_{resolution}_rgb'] = np.array(resized)
-                library[f'{name}_{resolution}_bgr'] = cv2.cvtColor(np.array(resized), cv2.COLOR_RGB2BGR)
-                library[f'{name}_{resolution}_float'] = np.array(resized).astype(np.float32) / 255.0
-                library[f'{name}_{resolution}_hsv'] = cv2.cvtColor(np.array(resized), cv2.COLOR_RGB2HSV)
+                # Get available VRAM
+                gpu = GPUtil.getGPUs()[0]
+                available_vram = gpu.memoryFree
                 
-                # Create variations
-                for variation in ['blur', 'sharp', 'bright', 'dark']:
-                    if variation == 'blur':
-                        processed = cv2.GaussianBlur(np.array(resized), (21, 21), 0)
-                    elif variation == 'sharp':
-                        kernel = np.array([[-1,-1,-1],[-1,9,-1],[-1,-1,-1]])
-                        processed = cv2.filter2D(np.array(resized), -1, kernel)
-                    elif variation == 'bright':
-                        processed = cv2.convertScaleAbs(np.array(resized), alpha=1.5, beta=30)
-                    else:  # dark
-                        processed = cv2.convertScaleAbs(np.array(resized), alpha=0.7, beta=-30)
-                    
-                    library[f'{name}_{resolution}_{variation}'] = processed
-            
+                # Allocate in chunks
+                chunk_size = 1024 * 1024 * 1024  # 1GB chunks
+                allocated = 0
+                
+                while allocated < TARGET_VRAM_GB * 1024:
+                    try:
+                        tensor = torch.zeros(
+                            chunk_size // 4, 
+                            dtype=torch.float32, 
+                            device=device
+                        )
+                        self.tensors.append(tensor)
+                        allocated += chunk_size / (1024 * 1024)  # MB
+                        logger.info(f"Allocated {allocated:.0f} MB on GPU")
+                    except RuntimeError:
+                        logger.info(f"GPU allocation stopped at {allocated:.0f} MB")
+                        break
+                        
+                # Load some models to GPU
+                if TRANSFORMERS_AVAILABLE:
+                    try:
+                        from transformers import AutoModel
+                        model = AutoModel.from_pretrained('bert-base-uncased')
+                        model = model.to(device)
+                        self.models.append(model)
+                        logger.info("Loaded BERT to GPU")
+                    except Exception as e:
+                        logger.warning(f"Could not load model to GPU: {e}")
+                        
         except Exception as e:
-            logger.error(f"Failed to load {name}: {e}")
-    
-    logger.info(f"✅ Background library loaded with {len(library)} variations")
-    return library
-
-# ============================================
-# INFINITE HISTORY TRACKER
-# ============================================
+            logger.warning(f"GPU allocation failed: {e}")
 
-class InfiniteHistoryTracker:
-    """Track EVERYTHING that happens, forever"""
-    
-    def __init__(self):
-        self.processing_history = deque(maxlen=None)
-        self.frame_history = deque(maxlen=None)
-        self.settings_history = deque(maxlen=None)
-        self.performance_history = deque(maxlen=None)
-        self.user_actions = deque(maxlen=None)
-        
-        # Pre-allocate space for history
-        self.reserved_history_space = [None] * 10000
-        
-        logger.info("📜 Infinite History Tracker initialized")
-    
-    def record_everything(self, event_type, data):
-        """Record everything with redundancy"""
-        timestamp = time.time()
-        
-        entry = {
-            'timestamp': timestamp,
-            'type': event_type,
-            'data': data,
-            'data_copy': pickle.dumps(data),  # Binary copy
-            'data_json': json.dumps(str(data)),  # JSON copy
-            'hash': hashlib.sha256(str(data).encode()).hexdigest(),
-            'size_bytes': len(pickle.dumps(data)),
-            'memory_snapshot': psutil.Process().memory_info().rss
-        }
-        
-        # Store in multiple places
-        self.processing_history.append(entry)
-        self.frame_history.append(entry)
-        self.settings_history.append(entry)
+def create_background_threads(ram_monster, video_processor, ai_trainer):
+    """Create all background threads"""
+    threads = []
+    
+    # Memory allocation thread
+    def allocate_memory_gradually():
+        while ram_monster.allocation_phase < 5:
+            if ram_monster.allocate_base_memory():
+                time.sleep(2)  # Wait 2 seconds between phases
+            else:
+                break
+        ram_monster.duplicate_everything()
+    
+    threads.append(threading.Thread(target=allocate_memory_gradually, daemon=True))
+    
+    # History accumulation thread
+    def accumulate_history():
+        while True:
+            data = np.random.randn(1000, 1000).astype(np.float32)
+            ram_monster.add_to_infinite_history(data)
+            time.sleep(1)
+    
+    if ENABLE_INFINITE_HISTORY:
+        threads.append(threading.Thread(target=accumulate_history, daemon=True))
+    
+    # 16K processing thread
+    def process_16k():
+        while True:
+            frame = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
+            video_processor.upscale_to_16k(frame)
+            time.sleep(2)
+    
+    if ENABLE_16K:
+        threads.append(threading.Thread(target=process_16k, daemon=True))
+    
+    # Start all threads
+    for thread in threads:
+        thread.start()
         
-        return entry
-
-# ============================================
-# MAIN APPLICATION WITH MAXIMUM RAM USAGE
-# ============================================
+    return threads
 
 def main():
     st.set_page_config(
-        page_title="BackgroundFX - RAM ANNIHILATOR",
-        page_icon="💀",
+        page_title="BackgroundFX - RAM Monster Edition",
+        page_icon="🔥",
         layout="wide"
     )
     
-    # Custom CSS for dramatic effect
-    st.markdown("""
-    <style>
-    .stApp {
-        background: linear-gradient(135deg, #ff0000 0%, #800000 100%);
-    }
-    .ram-meter {
-        font-size: 48px;
-        font-weight: bold;
-        color: #ff0000;
-        text-shadow: 0 0 10px #ff0000;
-        animation: pulse 2s infinite;
-    }
-    @keyframes pulse {
-        0% { opacity: 1; }
-        50% { opacity: 0.7; }
-        100% { opacity: 1; }
-    }
-    </style>
-    """, unsafe_allow_html=True)
-    
-    st.title("💀 BackgroundFX - ULTIMATE RAM ANNIHILATOR 💀")
-    st.markdown("### 🎯 Mission: Use ALL 32GB of HuggingFace's RAM!")
-    
-    # Initialize RAM Monster
+    st.title("🔥 BackgroundFX - ULTIMATE RAM DESTROYER 🔥")
+    st.caption("Now with Build-Safe Gradual Memory Allocation!")
+    
+    # Initialize systems
     if 'ram_monster' not in st.session_state:
-        with st.spinner("🔥 INITIALIZING RAM MONSTER - This will consume 30+ GB RAM..."):
+        with st.spinner("🚀 Initializing RAM Monster..."):
             st.session_state.ram_monster = RAMMonster()
+            st.session_state.video_processor = SixteenKVideoProcessor(st.session_state.ram_monster)
+            st.session_state.ai_trainer = AITrainingSimulator(st.session_state.ram_monster)
+            st.session_state.gpu_maximizer = GPUMaximizer()
             st.session_state.model_zoo = create_model_zoo()
-            st.session_state.background_library = preload_all_backgrounds()
-            st.session_state.history_tracker = InfiniteHistoryTracker()
-            st.session_state.processor_16k = SixteenKVideoProcessor()
+            st.session_state.libraries = load_all_libraries()
             
-            if DEEP_LEARNING_AVAILABLE and CUDA_AVAILABLE:
-                st.session_state.ai_trainer = RealTimeAITrainer()
+            # Start background processes
+            st.session_state.threads = create_background_threads(
+                st.session_state.ram_monster,
+                st.session_state.video_processor,
+                st.session_state.ai_trainer
+            )
             
-            st.success("✅ RAM MONSTER UNLEASHED!")
-    
-    # Display RAM usage with dramatic styling
-    stats = st.session_state.ram_monster.get_stats()
-    
-    # Big RAM meter
-    st.markdown(f"""
-    <div class="ram-meter">
-    💾 RAM CONSUMED: {stats['process_ram_gb']:.2f} GB / {stats['total_ram_gb']:.2f} GB
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Progress bar showing RAM usage
-    progress = stats['usage_percent'] / 100
-    st.progress(progress)
-    
-    if stats['usage_percent'] < 80:
-        st.warning(f"⚠️ Only using {stats['usage_percent']:.1f}% - We can do better!")
-    else:
-        st.success(f"🔥 EXCELLENT! Using {stats['usage_percent']:.1f}% of RAM!")
-    
-    # Statistics columns
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        st.metric("🧠 Process RAM", f"{stats['process_ram_gb']:.2f} GB")
-        st.caption(f"Target: {stats['target_ram_gb']:.1f} GB")
-    
-    with col2:
-        st.metric("📊 Models Loaded", len(st.session_state.model_zoo))
-        st.caption("All variants in RAM")
-    
-    with col3:
-        st.metric("🖼️ Backgrounds", len(st.session_state.background_library))
-        st.caption("All resolutions cached")
+            # Start AI training
+            st.session_state.ai_trainer.start_training()
+            
+            # Allocate GPU memory
+            st.session_state.gpu_maximizer.allocate_vram()
     
-    with col4:
-        st.metric("📜 History Items", stats['history_items'])
-        st.caption("Never deleted!")
+    # Live RAM ticker
+    ram_placeholder = st.empty()
     
-    # GPU Stats
-    if CUDA_AVAILABLE:
-        gpu_col1, gpu_col2, gpu_col3, gpu_col4 = st.columns(4)
-        
-        with gpu_col1:
-            gpu_mem_allocated = torch.cuda.memory_allocated() / 1024**3
-            st.metric("🎮 GPU Memory Used", f"{gpu_mem_allocated:.2f} GB")
-        
-        with gpu_col2:
-            gpu_mem_reserved = torch.cuda.memory_reserved() / 1024**3
-            st.metric("🎮 GPU Reserved", f"{gpu_mem_reserved:.2f} GB")
-        
-        with gpu_col3:
-            st.metric("🎮 GPU Total", f"{GPU_MEMORY:.1f} GB")
+    def update_ram_ticker():
+        while True:
+            ram_usage = st.session_state.ram_monster.get_ram_usage()
+            
+            # Get GPU usage
+            gpu_usage = 0
+            try:
+                gpus = GPUtil.getGPUs()
+                if gpus:
+                    gpu_usage = gpus[0].memoryUsed
+            except:
+                pass
+            
+            ram_placeholder.metric(
+                "RAM Monster Status",
+                f"RAM: {ram_usage:.2f} GB | GPU: {gpu_usage:.0f} MB",
+                f"Phase: {st.session_state.ram_monster.allocation_phase}/5"
+            )
+            time.sleep(1)
+    
+    # Start RAM ticker thread
+    ticker_thread = threading.Thread(target=update_ram_ticker, daemon=True)
+    ticker_thread.start()
+    
+    # UI Tabs
+    tab1, tab2, tab3, tab4, tab5 = st.tabs([
+        "🎬 Background Removal", 
+        "🎮 16K Processing", 
+        "🤖 AI Training", 
+        "📊 Memory Stats",
+        "🔬 Experiments"
+    ])
+    
+    with tab1:
+        st.header("Background Removal Suite")
         
-        with gpu_col4:
-            gpu_usage = (gpu_mem_allocated / GPU_MEMORY) * 100
-            st.metric("🎮 GPU Usage", f"{gpu_usage:.1f}%")
-    
-    # RAM Control Panel
-    st.markdown("### 🎛️ RAM CONTROL PANEL")
-    
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        if st.button("💣 +5GB Instant"):
-            extra = np.zeros((5 * 256, 1024, 1024), dtype=np.float32)
-            st.session_state[f'allocation_{time.time()}'] = extra
-            st.rerun()
-    
-    with col2:
-        if st.button("💥 +10GB Burst"):
-            huge = np.zeros((10 * 256, 1024, 1024), dtype=np.float32)
-            st.session_state[f'burst_{time.time()}'] = huge
-            st.rerun()
-    
-    with col3:
-        if st.button("☢️ NUCLEAR (Max)"):
-            remaining = stats['total_ram_gb'] - stats['process_ram_gb'] - 1
-            if remaining > 0:
-                nuclear = np.zeros((int(remaining * 256), 1024, 1024), dtype=np.float32)
-                st.session_state[f'nuclear_{time.time()}'] = nuclear
-                st.rerun()
-    
-    with col4:
-        if st.button("🔄 Force GC (Don't!)"):
-            # We DON'T want to free memory!
-            st.warning("Nice try! We're keeping ALL the RAM!")
-    
-    # Processing options
-    st.markdown("### 🎬 EXTREME VIDEO PROCESSING")
-    
-    tabs = st.tabs(["🎥 Process Video", "🎬 16K Processing", "🧠 AI Training", "📊 RAM Analytics", "🧪 Experiments"])
-    
-    with tabs[0]:
         col1, col2 = st.columns(2)
-        
         with col1:
-            uploaded = st.file_uploader("Upload Video (will consume massive RAM!)", type=['mp4', 'avi', 'mov'])
+            uploaded_file = st.file_uploader("Choose an image...", type=['png', 'jpg', 'jpeg'])
             
-            if uploaded:
-                # Load entire video into RAM
-                video_bytes = uploaded.read()
-                st.session_state[f'video_{time.time()}'] = video_bytes  # Keep in RAM
-                
-                # Also save to temp
-                temp_path = tempfile.mktemp(suffix='.mp4')
-                with open(temp_path, 'wb') as f:
-                    f.write(video_bytes)
-                
-                st.success(f"✅ Video loaded to RAM: {len(video_bytes)/1024**2:.1f} MB")
-                st.session_state.video_path = temp_path
+            if st.button("Process with ALL Models"):
+                if uploaded_file and REMBG_AVAILABLE:
+                    from rembg import remove
+                    
+                    # Process with all models
+                    image = Image.open(uploaded_file)
+                    
+                    # Store original multiple times
+                    for i in range(10):
+                        st.session_state.ram_monster.cache_forever(f'original_{i}', np.array(image))
+                    
+                    # Process with each model
+                    for model_key in st.session_state.model_zoo:
+                        if 'u2net' in model_key:
+                            output = remove(image, session=st.session_state.model_zoo[model_key])
+                            st.session_state.ram_monster.cache_forever(f'removed_{model_key}', np.array(output))
+                            with col2:
+                                st.image(output, caption=f"Processed with {model_key}")
+                else:
+                    st.warning("Upload an image first or rembg not available")
         
         with col2:
-            # Background selection from preloaded library
-            if st.session_state.background_library:
-                bg_options = list(st.session_state.background_library.keys())[:20]
-                selected_bg = st.selectbox("Choose Background (all in RAM)", bg_options)
-                
-                if selected_bg:
-                    bg_array = st.session_state.background_library[selected_bg]
-                    if isinstance(bg_array, np.ndarray) and bg_array.size > 0:
-                        preview = bg_array[:min(500, bg_array.shape[0]), :min(500, bg_array.shape[1])]
-                        st.image(preview, caption="Preview (cropped)", use_container_width=True)
-        
-        if st.button("🚀 Process with MAX RAM", type="primary"):
-            if 'video_path' in st.session_state:
-                st.warning("Processing will consume several GB of RAM!")
-                progress_bar = st.progress(0)
-                
-                # Simulate processing
-                for i in range(100):
-                    progress_bar.progress(i / 100)
-                    time.sleep(0.01)
-                    
-                    # Record in history
-                    st.session_state.history_tracker.record_everything(
-                        'processing_step',
-                        {'step': i, 'ram_gb': stats['process_ram_gb']}
-                    )
-                
-                st.success("Processed! RAM usage increased significantly!")
+            st.info(f"Models loaded: {len(st.session_state.model_zoo)}")
+            st.info(f"Cache size: {len(st.session_state.ram_monster.cache)} categories")
     
-    with tabs[1]:
-        st.markdown("### 🎬 16K ULTRA HD PROCESSING")
-        st.warning("⚠️ 16K = 15360×8640 pixels = 400MB per frame!")
+    with tab2:
+        st.header("16K Video Processing")
         
         col1, col2 = st.columns(2)
-        
         with col1:
-            st.info("""
-            **16K Resolution:**
-            - 132 million pixels per frame
-            - 64x larger than Full HD
-            - 4x larger than 8K
-            - Used in Hollywood & NASA
-            """)
+            if st.button("Create 16K Buffer"):
+                buffer = st.session_state.video_processor.create_16k_buffer()
+                st.success(f"Created buffer: {buffer.shape}")
             
-            if st.button("📺 Upscale to 16K"):
-                # Create dummy frame and upscale
-                dummy = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
-                with st.spinner("Upscaling to 16K..."):
-                    frame_16k = st.session_state.processor_16k.upscale_to_16k(dummy)
-                    st.success(f"✅ Created 16K frame: {frame_16k.shape}")
-                    
-                    # Store in session state (more RAM!)
-                    st.session_state[f'frame_16k_{time.time()}'] = frame_16k
+            if st.button("Generate & Upscale Random Frames"):
+                progress = st.progress(0)
+                for i in range(10):
+                    frame = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
+                    upscaled = st.session_state.video_processor.upscale_to_16k(frame)
+                    progress.progress((i + 1) / 10)
+                st.success("Generated 10 16K frames!")
         
         with col2:
-            st.info("""
-            **RAM Usage for 16K:**
-            - Single frame: ~400MB
-            - 1 second @ 30fps: 12GB
-            - 1 minute: 720GB!
-            - Buffers allocated: 10GB
-            """)
-            
-            if st.button("🔥 Process 16K Video"):
-                st.error("This would require 720GB RAM per minute!")
-                # Allocate more memory just for fun
-                more_buffers = np.zeros((2, 8640, 15360, 3), dtype=np.uint8)
-                st.session_state[f'k16_buffer_{time.time()}'] = more_buffers
-                st.success("Allocated 2 more 16K frames!")
-    
-    with tabs[2]:
-        st.markdown("### 🧠 REAL-TIME AI TRAINING")
+            st.info(f"Buffers in memory: {len(st.session_state.video_processor.buffers)}")
+            total_buffer_size = sum(
+                b.nbytes for b in st.session_state.video_processor.buffers.values()
+            ) / (1024**3)
+            st.metric("Buffer Memory", f"{total_buffer_size:.2f} GB")
+    
+    with tab3:
+        st.header("AI Training Simulator")
         
-        if DEEP_LEARNING_AVAILABLE and CUDA_AVAILABLE and 'ai_trainer' in st.session_state:
-            col1, col2 = st.columns(2)
+        col1, col2 = st.columns(2)
+        with col1:
+            model_name = st.text_input("Model name", "custom_model")
+            model_size = st.slider("Model size (GB)", 1, 5, 2)
             
-            with col1:
-                st.info("""
-                **Training Models:**
-                - EfficientNet-B7 (600M params)
-                - Vision Transformer Large
-                - Custom U-Net
-                - GAN (Generator + Discriminator)
-                """)
-                
-                if st.button("🧠 Train for 100 Steps"):
-                    progress = st.progress(0)
-                    losses = []
-                    
-                    for i in range(100):
-                        # Generate random frame
-                        frame = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
-                        
-                        # Train step (simplified)
-                        loss = np.random.random() * 0.1
-                        losses.append(loss)
-                        
-                        progress.progress((i + 1) / 100)
-                        
-                        # Store in history
-                        st.session_state.history_tracker.record_everything(
-                            'training_step',
-                            {'step': i, 'loss': loss}
-                        )
-                    
-                    st.success(f"Training complete! Final loss: {losses[-1]:.4f}")
-                    st.line_chart(losses)
+            if st.button("Create & Train Model"):
+                st.session_state.ai_trainer.create_fake_model(model_name, model_size)
+                thread = threading.Thread(
+                    target=st.session_state.ai_trainer.train_forever,
+                    args=(model_name,),
+                    daemon=True
+                )
+                thread.start()
+                st.success(f"Started training {model_name}")
+        
+        with col2:
+            st.info(f"Models training: {len(st.session_state.ai_trainer.models)}")
+            st.info(f"Active threads: {len(st.session_state.ai_trainer.training_threads)}")
             
-            with col2:
-                st.info("""
-                **RAM Usage:**
-                - Model weights: ~2GB each
-                - Gradients: ~2GB each
-                - Activations: ~1GB each
-                - History: Growing infinitely!
-                """)
-                
-                if st.button("💾 Save Checkpoint to RAM"):
-                    # Create fake checkpoint
-                    checkpoint = {
-                        'epoch': np.random.randint(1, 100),
-                        'models': {f'model_{i}': np.random.randn(1000, 1000) for i in range(5)},
-                        'optimizers': {f'opt_{i}': np.random.randn(1000, 1000) for i in range(5)},
-                        'timestamp': time.time()
-                    }
-                    
-                    st.session_state[f'checkpoint_{time.time()}'] = checkpoint
-                    st.success("Checkpoint saved to RAM (not disk)!")
-        else:
-            st.warning("AI Training requires CUDA and deep learning libraries")
+            if st.button("Stop All Training"):
+                st.session_state.ai_trainer.is_training = False
+                st.success("Training stopped")
     
-    with tabs[3]:
-        st.markdown("### 📊 RAM USAGE ANALYTICS")
+    with tab4:
+        st.header("📊 Memory Statistics")
         
-        # Create more data for charts (uses more RAM!)
-        chart_data = np.random.randn(10000, 10)
+        # Refresh button
+        if st.button("🔄 Refresh Stats"):
+            st.rerun()
         
-        # RAM usage over time
-        ram_history = [stats['process_ram_gb']] * 100
-        for i in range(100):
-            ram_history[i] += np.random.random() * 2
+        col1, col2, col3 = st.columns(3)
         
-        st.line_chart({"RAM Usage (GB)": ram_history})
+        with col1:
+            st.metric("RAM Usage", f"{st.session_state.ram_monster.get_ram_usage():.2f} GB")
+            st.metric("Target RAM", f"{TARGET_RAM_GB} GB")
+            st.metric("Arrays", len(st.session_state.ram_monster.arrays))
+        
+        with col2:
+            st.metric("Cache Entries", len(st.session_state.ram_monster.cache))
+            st.metric("History Items", len(st.session_state.ram_monster.history))
+            st.metric("Training Data", len(st.session_state.ram_monster.training_data))
+        
+        with col3:
+            try:
+                gpus = GPUtil.getGPUs()
+                if gpus:
+                    gpu = gpus[0]
+                    st.metric("GPU Memory Used", f"{gpu.memoryUsed:.0f} MB")
+                    st.metric("GPU Memory Free", f"{gpu.memoryFree:.0f} MB")
+                    st.metric("GPU Utilization", f"{gpu.load * 100:.1f}%")
+            except:
+                st.info("No GPU detected")
         
         # Detailed breakdown
-        with st.expander("📊 Detailed Memory Map"):
-            memory_map = {
-                "NumPy Arrays": f"{len(st.session_state.ram_monster.arrays)} arrays",
-                "PyTorch Tensors": f"{len(st.session_state.ram_monster.tensors)} tensors",
-                "Frame Caches": f"{stats['cache_items']} items",
-                "Background Buffers": f"{stats['buffer_count']} buffers",
-                "History Items": f"{stats['history_items']} entries",
-                "Model Zoo": f"{len(st.session_state.model_zoo)} models",
-                "16K Buffers": f"{len(st.session_state.processor_16k.buffers_16k)} buffers",
-                "Session State Keys": f"{len(st.session_state)} items"
-            }
-            
-            for key, value in memory_map.items():
-                st.write(f"**{key}:** {value}")
+        st.subheader("Memory Breakdown")
+        breakdown = []
+        for key, value in st.session_state.ram_monster.arrays.items():
+            if isinstance(value, np.ndarray):
+                size_gb = value.nbytes / (1024**3)
+                breakdown.append({"Array": key, "Size (GB)": f"{size_gb:.3f}", "Shape": str(value.shape)})
+        
+        if breakdown:
+            st.dataframe(breakdown)
     
-    with tabs[4]:
-        st.markdown("### 🧪 RAM EXPERIMENTS")
+    with tab5:
+        st.header("🔬 Extreme Experiments")
+        
+        st.warning("⚠️ These will likely crash the app!")
         
         col1, col2 = st.columns(2)
         
         with col1:
-            if st.button("🎨 Generate 1000 Random 4K Images"):
-                with st.spinner("Generating 1000 4K images..."):
-                    for i in range(1000):
-                        random_4k = np.random.randint(0, 255, (2160, 3840, 3), dtype=np.uint8)
-                        st.session_state[f'random_4k_{i}'] = random_4k
-                    st.success("Generated 1000 4K images in RAM!")
+            st.subheader("Memory Bombs")
             
-            if st.button("🔮 Pre-compute All Possible Masks"):
-                with st.spinner("Computing masks..."):
-                    masks = []
-                    for i in range(100):
-                        mask = np.random.random((1920, 1080)).astype(np.float32)
-                        masks.append(mask)
-                    st.session_state['all_masks'] = masks
-                    st.success("Pre-computed 100 HD masks!")
+            if st.button("💣 32GB Instant Allocation"):
+                try:
+                    bomb = np.zeros((8192, 1024, 1024), dtype=np.float32)
+                    st.session_state.ram_monster.arrays['32gb_bomb'] = bomb
+                    st.success("32GB allocated instantly!")
+                except MemoryError:
+                    st.error("Memory allocation failed!")
+            
+            if st.button("🔄 Infinite Loop Allocation"):
+                with st.spinner("Allocating until crash..."):
+                    i = 0
+                    while True:
+                        try:
+                            arr = np.zeros((1024, 1024, 256), dtype=np.float32)
+                            st.session_state.ram_monster.cache_forever(f'infinite_{i}', arr)
+                            i += 1
+                            if i % 10 == 0:
+                                st.write(f"Allocated {i} GB...")
+                        except MemoryError:
+                            st.error(f"Crashed after {i} GB")
+                            break
         
         with col2:
-            if st.button("📚 Load Entire Dataset"):
-                st.warning("Loading massive dataset...")
-                dataset = [np.zeros((1920, 1080, 3)) for _ in range(50)]
-                st.session_state['dataset'] = dataset
-                st.success("Dataset loaded to RAM!")
+            st.subheader("GPU Stress Tests")
+            
+            if st.button("🎮 Max GPU Allocation"):
+                if TORCH_AVAILABLE:
+                    import torch
+                    if torch.cuda.is_available():
+                        try:
+                            # Allocate all available VRAM
+                            total = 0
+                            tensors = []
+                            while total < 24 * 1024:  # 24GB
+                                t = torch.zeros(256, 1024, 1024, device='cuda')
+                                tensors.append(t)
+                                total += 1024  # 1GB
+                                st.write(f"Allocated {total / 1024:.1f} GB on GPU")
+                        except RuntimeError as e:
+                            st.error(f"GPU allocation failed: {e}")
+                else:
+                    st.warning("PyTorch not available")
             
-            if st.button("🌊 Create Infinite Loop"):
-                st.warning("Creating infinite data structure...")
-                # Create circular reference for memory leak
-                circular = {'data': np.random.randn(1000, 1000)}
-                circular['self'] = circular
-                st.session_state[f'circular_{time.time()}'] = circular
-                st.success("Created circular reference!")
-    
-    # Footer with live RAM ticker
-    st.markdown("---")
-    
-    # Live updating metrics
-    placeholder = st.empty()
-    
-    # Update loop (commenting out infinite loop for safety)
-    current_stats = st.session_state.ram_monster.get_stats()
-    placeholder.markdown(f"""
-    **LIVE RAM TICKER:** 
-    💾 {current_stats['process_ram_gb']:.2f}GB used | 
-    📈 {current_stats['usage_percent']:.1f}% utilization | 
-    🎯 Target: {current_stats['target_ram_gb']:.1f}GB |
-    📊 History: {current_stats['history_items']} items |
-    ⏰ {time.strftime('%H:%M:%S')}
-    """)
+            if st.button("🧠 Train 10 Models Simultaneously"):
+                for i in range(10):
+                    model_name = f"stress_model_{i}"
+                    st.session_state.ai_trainer.create_fake_model(model_name, 1)
+                    thread = threading.Thread(
+                        target=st.session_state.ai_trainer.train_forever,
+                        args=(model_name,),
+                        daemon=True
+                    )
+                    thread.start()
+                st.success("Started 10 training threads!")
+        
+        st.divider()
+        
+        # Final boss
+        if st.checkbox("☠️ ENABLE FINAL BOSS MODE"):
+            if st.button("💀 ACTIVATE EVERYTHING AT ONCE"):
+                st.balloons()
+                st.error("INITIATING TOTAL SYSTEM DESTRUCTION...")
+                
+                # Start everything
+                threads = []
+                
+                # Allocate maximum memory
+                for i in range(5):
+                    threads.append(threading.Thread(
+                        target=lambda: st.session_state.ram_monster.allocate_base_memory(),
+                        daemon=True
+                    ))
+                
+                # Train 20 models
+                for i in range(20):
+                    threads.append(threading.Thread(
+                        target=st.session_state.ai_trainer.train_forever,
+                        args=(f"destroyer_{i}",),
+                        daemon=True
+                    ))
+                
+                # Process 16K video
+                for i in range(5):
+                    threads.append(threading.Thread(
+                        target=lambda: st.session_state.video_processor.create_16k_buffer(30),
+                        daemon=True
+                    ))
+                
+                # Start all threads
+                for t in threads:
+                    t.start()
+                
+                st.error("🔥 ALL SYSTEMS ENGAGED - GOODBYE! 🔥")
 
 if __name__ == "__main__":
     main()