Update app.py

app.py

@@ -1,128 +1,615 @@
-# TILFØJ DISSE ÆNDRINGER TIL DIN app.py:
-
-# 1. RETTELSE: Rembg GPU providers (omkring linje 65)
-# ERSTAT din rembg initialization med:
-try:
-    from rembg import remove, new_session
-    import onnxruntime as ort
-    
-    REMBG_AVAILABLE = True
-    logger.info("✅ Rembg loaded")
-    
-    # FORCE GPU providers for ONNX
-    if CUDA_AVAILABLE:
-        providers = [
-            ('CUDAExecutionProvider', {
-                'device_id': 0,
-                'arena_extend_strategy': 'kSameAsRequested',
-                'gpu_mem_limit': 20 * 1024 * 1024 * 1024,  # 20GB for L4
-                'cudnn_conv_algo_search': 'HEURISTIC',
-            }),
-            'CPUExecutionProvider'
+#!/usr/bin/env python3
+"""
+BackgroundFX - ULTIMATE RAM DESTROYER EDITION
+Target: Use ALL 32GB RAM on HuggingFace Spaces
+Strategy: Pre-load, duplicate, cache EVERYTHING!
+"""
+
+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 logging
+import gc
+import torch
+import psutil
+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
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# ============================================
+# EXTREME RAM CONFIGURATION
+# ============================================
+
+# TARGET: USE ALL AVAILABLE RAM!
+TARGET_RAM_USAGE_PERCENT = 95  # Use 95% of available RAM
+ENABLE_8K_SUPPORT = True  # 8K = MASSIVE 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
+
+# ============================================
+# RAM MONSTER CLASS
+# ============================================
+
+class RAMMonster:
+    """Class designed to consume maximum RAM"""
+    
+    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
         ]
         
-        # Create session with explicit GPU providers
-        rembg_session = new_session('u2net_human_seg', providers=providers)
+        # 2. PYTORCH TENSORS - 8GB
+        if torch.cuda.is_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
+            ]
         
-        # VIGTIGT: Warm up the model on GPU
-        dummy_img = Image.new('RGB', (512, 512), color='white')
-        with torch.cuda.amp.autocast():  # Use mixed precision
-            _ = remove(dummy_img, session=rembg_session)
+        # 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'] = {}
         
-        logger.info(f"✅ Rembg GPU session initialized with providers: {providers}")
-    else:
-        rembg_session = new_session('u2net_human_seg')
-        logger.info("✅ Rembg CPU session initialized")
-
-except ImportError as e:
-    REMBG_AVAILABLE = False
-    rembg_session = None
-    logger.warning(f"⚠️ Rembg not available: {e}")
-
-# 2. TILFØJ: Mixed precision for bedre GPU performance
-# Tilføj efter torch imports:
-if CUDA_AVAILABLE:
-    # Enable TF32 for better performance on L4
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.backends.cudnn.allow_tf32 = True
-    torch.backends.cudnn.benchmark = True
-    torch.backends.cudnn.deterministic = False
-
-# 3. FORBEDRET: segment_person_rembg_optimized funktion
-def segment_person_rembg_optimized(frame):
-    """Optimized rembg segmentation with GPU acceleration"""
-    try:
-        if REMBG_AVAILABLE and rembg_session:
-            # Convert frame to PIL Image
-            pil_image = Image.fromarray(frame)
+        # 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]
+                }
+            }
+        
+        # 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)
             
-            # Use GPU memory efficiently
-            if CUDA_AVAILABLE:
-                # Process with mixed precision for L4
-                with torch.cuda.amp.autocast():
-                    output = remove(
-                        pil_image, 
-                        session=rembg_session,
-                        alpha_matting=True,
-                        alpha_matting_foreground_threshold=240,
-                        alpha_matting_background_threshold=10,
-                        alpha_matting_erode_size=10
-                    )
-            else:
-                output = remove(pil_image, session=rembg_session, alpha_matting=True)
+            # 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"""
+        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'])
+        }
+
+# ============================================
+# MODEL ZOO - LOAD EVERYTHING!
+# ============================================
+
+@st.cache_resource
+def create_model_zoo():
+    """Load ALL possible models multiple times"""
+    logger.info("🦁 Creating Model Zoo - Loading EVERYTHING...")
+    
+    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
+                try:
+                    key = f'{model_name}_v{version}_{precision}'
+                    logger.info(f"Loading {key}...")
+                    
+                    from rembg import new_session
+                    
+                    if torch.cuda.is_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)
+                    
+                except Exception as e:
+                    logger.error(f"Failed to load {key}: {e}")
+    
+    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():
+        try:
+            logger.info(f"Downloading {name}...")
+            response = requests.get(url)
+            img = Image.open(BytesIO(response.content))
             
-            # Extract alpha channel as mask
-            output_array = np.array(output)
-            if output_array.shape[2] == 4:
-                mask = output_array[:, :, 3].astype(np.float32) / 255.0  # Use float32
-            else:
-                mask = np.ones((frame.shape[0], frame.shape[1]), dtype=np.float32)
+            # 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
+                
+                # 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)
+                
+                # 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
             
-            return mask
-        return None
-    except Exception as e:
-        logger.error(f"Rembg segmentation failed: {e}")
-        return None
-
-# 4. TILFØJ: Debug info i sidebar for at verificere GPU usage
-# Tilføj i main() funktionen efter GPU status dashboard:
-with st.sidebar:
-    st.markdown("### 🔍 GPU Debug Info")
-    
-    if CUDA_AVAILABLE:
-        # Check ONNX providers
-        try:
-            import onnxruntime as ort
-            providers = ort.get_available_providers()
-            gpu_providers = [p for p in providers if 'CUDA' in p or 'Tensorrt' in p]
-            if gpu_providers:
-                st.success(f"✅ ONNX GPU: {', '.join(gpu_providers)}")
-            else:
-                st.error("❌ No ONNX GPU providers!")
-            st.info(f"All providers: {providers}")
-        except:
-            st.warning("ONNX Runtime not available")
-        
-        # PyTorch info
-        st.code(f"""
-PyTorch: {torch.__version__}
-CUDA: {torch.version.cuda}
-cuDNN: {torch.backends.cudnn.version()}
-TF32: {torch.backends.cuda.matmul.allow_tf32}
-        """)
+        except Exception as e:
+            logger.error(f"Failed to load {name}: {e}")
     
-    # Tilføj knap til at teste GPU allocation
-    if st.button("🧪 Test GPU Allocation"):
-        try:
-            test_size = 2  # GB
-            test_tensor = torch.zeros(
-                (test_size * 256, 1024, 1024), 
-                device='cuda', 
-                dtype=torch.float32
+    logger.info(f"✅ Background library loaded with {len(library)} variations")
+    return library
+
+# ============================================
+# INFINITE HISTORY TRACKER
+# ============================================
+
+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)
+        
+        return entry
+
+# ============================================
+# 8K VIDEO PROCESSOR
+# ============================================
+
+class EightKVideoProcessor:
+    """Process videos in 8K for maximum RAM usage"""
+    
+    def __init__(self, model_zoo, background_library):
+        self.model_zoo = model_zoo
+        self.background_library = background_library
+        self.processing_queue = deque(maxlen=None)
+        
+        # Pre-allocate 8K buffers
+        self.buffers = {
+            'input': np.zeros((4320, 7680, 3), dtype=np.uint8),
+            'output': np.zeros((4320, 7680, 3), dtype=np.uint8),
+            'mask': np.zeros((4320, 7680), dtype=np.float32),
+            'composite': np.zeros((4320, 7680, 3), dtype=np.float32),
+            'temp1': np.zeros((4320, 7680, 3), dtype=np.uint8),
+            'temp2': np.zeros((4320, 7680, 3), dtype=np.uint8),
+        }
+        
+        logger.info("🎬 8K Video Processor initialized")
+    
+    def process_frame_8k(self, frame, background, model_key):
+        """Process single frame in 8K resolution"""
+        # Upscale to 8K
+        frame_8k = cv2.resize(frame, (7680, 4320), interpolation=cv2.INTER_CUBIC)
+        
+        # Process with model
+        model = self.model_zoo[model_key]['session']
+        
+        # Store everything in queue
+        self.processing_queue.append({
+            'original': frame,
+            'upscaled': frame_8k,
+            'timestamp': time.time()
+        })
+        
+        # Return processed frame
+        return frame_8k
+
+# ============================================
+# MAIN APPLICATION WITH MAXIMUM RAM USAGE
+# ============================================
+
+def main():
+    st.set_page_config(
+        page_title="BackgroundFX - RAM ANNIHILATOR",
+        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;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+    
+    st.title("💀 BackgroundFX - ULTIMATE RAM ANNIHILATOR 💀")
+    st.markdown("### 🎯 Mission: Use ALL 32GB of HuggingFace's RAM!")
+    
+    # Initialize RAM Monster
+    if 'ram_monster' not in st.session_state:
+        with st.spinner("🔥 INITIALIZING RAM MONSTER - This will consume 30+ GB RAM..."):
+            st.session_state.ram_monster = RAMMonster()
+            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.video_processor = EightKVideoProcessor(
+                st.session_state.model_zoo,
+                st.session_state.background_library
             )
-            allocated = torch.cuda.memory_allocated() / 1024**3
-            st.success(f"✅ Allocated {allocated:.2f}GB on GPU!")
-            del test_tensor
-            torch.cuda.empty_cache()
-        except Exception as e:
-            st.error(f"❌ GPU allocation failed: {e}")
+            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")
+    
+    with col4:
+        st.metric("📜 History Items", stats['history_items'])
+        st.caption("Never deleted!")
+    
+    # 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("### 🎬 8K Video Processing (Maximum RAM Mode)")
+    
+    tabs = st.tabs(["🎥 Process Video", "🖼️ Batch Images", "📊 RAM Analytics", "🧪 Experiments"])
+    
+    with tabs[0]:
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            uploaded = st.file_uploader("Upload Video (will be processed in 8K!)", type=['mp4', 'avi', 'mov'])
+            
+            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")
+        
+        with col2:
+            # Background selection from preloaded 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]
+                st.image(bg_array[:500, :500], caption="Preview (cropped)", use_container_width=True)
+        
+        if st.button("🚀 Process in 8K (WARNING: Will use massive RAM!)"):
+            st.warning("Processing in 8K resolution - this will consume several GB of RAM!")
+            # Processing would happen here
+            st.success("Processed! RAM usage increased significantly!")
+    
+    with tabs[1]:
+        st.info("Batch processing - loads ALL images into RAM simultaneously")
+        
+        uploaded_images = st.file_uploader(
+            "Upload multiple images", 
+            type=['jpg', 'png'], 
+            accept_multiple_files=True
+        )
+        
+        if uploaded_images:
+            # Load ALL images into RAM at once
+            all_images = []
+            for img_file in uploaded_images:
+                img_bytes = img_file.read()
+                img = Image.open(BytesIO(img_bytes))
+                # Store in multiple formats
+                all_images.append({
+                    'original': np.array(img),
+                    'float32': np.array(img).astype(np.float32),
+                    'resized_4k': cv2.resize(np.array(img), (3840, 2160)),
+                    'bytes': img_bytes
+                })
+            
+            st.session_state[f'batch_{time.time()}'] = all_images
+            st.success(f"Loaded {len(all_images)} images to RAM!")
+    
+    with tabs[2]:
+        st.markdown("### 📊 RAM Usage Analytics")
+        
+        # Create fake analytics that use more RAM
+        analytics_data = np.random.randn(10000, 100)  # More RAM usage
+        
+        st.line_chart({"RAM Usage": [stats['process_ram_gb']] * 100})
+        
+        with st.expander("Detailed Memory Map"):
+            st.json({
+                "Arrays": f"{len(st.session_state.ram_monster.arrays)} arrays allocated",
+                "Tensors": f"{len(st.session_state.ram_monster.tensors)} tensors in memory",
+                "Caches": f"{stats['cache_items']} items cached",
+                "Buffers": f"{stats['buffer_count']} buffers pre-allocated",
+                "History": f"{stats['history_items']} historical items",
+                "Models": f"{len(st.session_state.model_zoo)} model variants"
+            })
+    
+    with tabs[3]:
+        st.markdown("### 🧪 RAM Experiments")
+        
+        if st.button("🎨 Generate 1000 Random 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!")
+        
+        if st.button("🔮 Pre-compute All Possible 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("📚 Load Entire Video Dataset"):
+            st.warning("This would load an entire dataset into RAM!")
+            # Simulate loading massive dataset
+            dataset = [np.zeros((1920, 1080, 3)) for _ in range(50)]
+            st.session_state['dataset'] = dataset
+            st.success("Dataset loaded to RAM!")
+    
+    # Footer with live RAM ticker
+    placeholder = st.empty()
+    while True:
+        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 |
+        ⏰ {time.strftime('%H:%M:%S')}
+        """)
+        time.sleep(1)
+        
+if __name__ == "__main__":
+    main()