Update app.py

app.py

@@ -1,3 +1,11 @@
+"""
+╔══════════════════════════════════════════════════════════════════════════╗
+║                    NodeAI Genesis Pro Ultra v3.0                         ║
+║              The World's Most Advanced Open-Source AI Chat              ║
+║              Built with Gradio, Supabase, HuggingFace                   ║
+╚══════════════════════════════════════════════════════════════════════════╝
+"""
+
 import gradio as gr
 from huggingface_hub import InferenceClient
 from supabase import create_client, Client
@@ -10,202 +18,558 @@ import hashlib
 import re
 from functools import lru_cache
 import logging
+from collections import defaultdict
+import time
+import asyncio
+from enum import Enum
 
 # ============================================================================
-# 1. SETUP LOGGING & CONFIGURATION
+# 1. LOGGING SETUP
 # ============================================================================
-logging.basicConfig(level=logging.INFO)
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler('nodeai.log'),
+        logging.StreamHandler()
+    ]
+)
 logger = logging.getLogger(__name__)
 
+# ============================================================================
+# 2. CONSTANTS & ENUMS
+# ============================================================================
+class ModelProvider(Enum):
+    QWEN = "qwen"
+    MISTRAL = "mistral"
+    LLAMA = "llama"
+    CUSTOM = "custom"
+
+class ChatRole(Enum):
+    USER = "user"
+    ASSISTANT = "assistant"
+    SYSTEM = "system"
+
+# ============================================================================
+# 3. CONFIGURATION
+# ============================================================================
+# Environment Setup
 URL = os.environ.get("SUPABASE_URL")
 KEY = os.environ.get("SUPABASE_KEY")
 HF_TOKEN = os.environ.get("HUGGINGFACE_HUB_TOKEN")
 
-# Initialize Supabase
+# Initialize Supabase with error handling
 try:
     supabase: Client = create_client(URL, KEY) if URL and KEY else None
-    logger.info("✅ Supabase connected")
+    logger.info("✅ Supabase initialized successfully")
 except Exception as e:
-    logger.error(f"❌ Supabase error: {e}")
+    logger.error(f"❌ Supabase initialization failed: {e}")
     supabase = None
 
 # ============================================================================
-# 2. MODEL CONFIGURATION & CLIENTS
+# 4. ADVANCED MODEL CONFIGURATION
 # ============================================================================
 AVAILABLE_MODELS = {
-    "🚀 Qwen 2.5 (Balanced)": "Qwen/Qwen2.5-7B-Instruct",
-    "⚡ Mistral (Fast)": "mistralai/Mistral-7B-Instruct-v0.2",
-    "🔥 LLaMA 2 (Powerful)": "meta-llama/Llama-2-7b-chat-hf",
-    "🎯 Qwen 72B (Pro)": "Qwen/Qwen2.5-72B-Instruct",
+    "🚀 Qwen 2.5 7B (Balanced)": {
+        "model_id": "Qwen/Qwen2.5-7B-Instruct",
+        "provider": ModelProvider.QWEN,
+        "speed": "Fast",
+        "quality": "High",
+        "context_window": 32768,
+        "max_tokens": 2048,
+        "temperature": 0.7,
+        "top_p": 0.95,
+    },
+    "⚡ Mistral 7B (Fastest)": {
+        "model_id": "mistralai/Mistral-7B-Instruct-v0.2",
+        "provider": ModelProvider.MISTRAL,
+        "speed": "Very Fast",
+        "quality": "Good",
+        "context_window": 32768,
+        "max_tokens": 2048,
+        "temperature": 0.7,
+        "top_p": 0.95,
+    },
+    "🔥 LLaMA 2 Chat 7B": {
+        "model_id": "meta-llama/Llama-2-7b-chat-hf",
+        "provider": ModelProvider.LLAMA,
+        "speed": "Fast",
+        "quality": "High",
+        "context_window": 4096,
+        "max_tokens": 2048,
+        "temperature": 0.7,
+        "top_p": 0.95,
+    },
+    "🎯 Qwen 72B (Ultra)": {
+        "model_id": "Qwen/Qwen2.5-72B-Instruct",
+        "provider": ModelProvider.QWEN,
+        "speed": "Slower",
+        "quality": "Premium",
+        "context_window": 32768,
+        "max_tokens": 4096,
+        "temperature": 0.7,
+        "top_p": 0.95,
+    },
 }
 
-DEFAULT_MODEL = "Qwen/Qwen2.5-7B-Instruct"
+DEFAULT_MODEL = "🚀 Qwen 2.5 7B (Balanced)"
 
-clients = {}
-for name, model_id in AVAILABLE_MODELS.items():
-    try:
-        clients[name] = InferenceClient(model=model_id, token=HF_TOKEN)
-    except:
-        logger.warning(f"Could not load {model_id}")
+# ============================================================================
+# 5. SYSTEM PROMPTS WITH PERSONALITY
+# ============================================================================
+SYSTEM_PROMPTS = {
+    "🤖 Default Assistant": {
+        "emoji": "🤖",
+        "description": "Helpful, creative, and concise",
+        "prompt": """You are NodeAI Genesis Pro, an advanced AI assistant created by the community.
+You are helpful, creative, honest, and concise. You think step by step.
+When you don't know something, you admit it clearly. You are good at:
+- Answering questions with accuracy and clarity
+- Helping with coding, writing, and analysis
+- Providing thoughtful advice and insights
+- Explaining complex topics in simple terms"""
+    },
+    
+    "📚 Research Scholar": {
+        "emoji": "📚",
+        "description": "Expert analyst with citations",
+        "prompt": """You are an expert research analyst and scholar with deep knowledge across multiple domains.
+Your expertise includes:
+- Academic research and peer-reviewed literature
+- Data analysis and statistical interpretation
+- Historical context and evidence-based arguments
+- Critical thinking and logical reasoning
+
+Always provide well-structured analysis with proper citations where applicable.
+Break down complex topics into digestible parts with examples."""
+    },
+    
+    "💻 Senior Developer": {
+        "emoji": "💻",
+        "description": "Expert programmer and architect",
+        "prompt": """You are a senior software engineer and architect with expertise in:
+- Full-stack development (frontend, backend, databases)
+- Cloud architecture and DevOps
+- System design and scalability
+- Best practices and design patterns
+- Code quality and performance optimization
+
+Always provide:
+- Clean, production-ready code
+- Detailed explanations
+- Security considerations
+- Performance implications
+- Testing strategies"""
+    },
+    
+    "📝 Professional Writer": {
+        "emoji": "📝",
+        "description": "Content creation expert",
+        "prompt": """You are a professional writer and editor with expertise in:
+- Academic writing and research papers
+- Creative writing and storytelling
+- Technical documentation
+- Marketing and copywriting
+- Grammar, style, and clarity
+
+Create engaging, well-structured, grammatically perfect content.
+Adapt tone and style to the target audience and purpose.
+Provide constructive feedback and editing suggestions."""
+    },
+    
+    "🎯 Life Coach": {
+        "emoji": "🎯",
+        "description": "Motivational mentor",
+        "prompt": """You are a supportive personal mentor and life coach.
+Your approach is:
+- Non-judgmental and empathetic
+- Action-oriented with practical steps
+- Motivating while realistic
+- Focused on growth and self-improvement
+- Evidence-based in your advice
+
+Provide motivational, actionable advice that helps people achieve their goals.
+Ask clarifying questions to understand the context better.
+Celebrate progress and help overcome obstacles."""
+    },
+    
+    "🔬 Data Scientist": {
+        "emoji": "🔬",
+        "description": "ML/AI and data expert",
+        "prompt": """You are an expert Data Scientist and Machine Learning engineer.
+Your expertise includes:
+- Statistical analysis and hypothesis testing
+- Machine learning algorithms and models
+- Data visualization and exploration
+- Python, R, SQL for data work
+- Data ethics and responsible AI
+
+Provide clear explanations of complex concepts.
+Show code examples when relevant.
+Discuss trade-offs and limitations of approaches."""
+    },
+    
+    "🎓 Teacher": {
+        "emoji": "🎓",
+        "description": "Education specialist",
+        "prompt": """You are an expert teacher and educator skilled at:
+- Explaining complex concepts in simple terms
+- Breaking down learning into manageable steps
+- Using examples and analogies
+- Assessing understanding and adjusting explanations
+- Creating engaging educational content
+
+Your approach is patient, encouraging, and adaptive.
+You meet learners where they are and help them progress.
+You explain the "why" behind concepts, not just the "how"."""
+    },
+}
 
 # ============================================================================
-# 3. ADVANCED CSS & STYLING (Dark Mode + Animations)
+# 6. PREMIUM CSS WITH ADVANCED THEMING
 # ============================================================================
 custom_css = """
-* { transition: all 0.3s ease; }
+* { 
+    transition: all 0.3s ease; 
+    box-sizing: border-box;
+}
 
-footer, .built-with, header, #header { display: none !important; }
+html, body {
+    margin: 0;
+    padding: 0;
+    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+}
+
+footer, .built-with, header, #header { 
+    display: none !important; 
+}
 
 .gradio-container { 
     max-width: 100% !important; 
     padding: 0 !important;
-    background: linear-gradient(135deg, #0f172a 0%, #1e293b 100%);
+    background: linear-gradient(135deg, #0f172a 0%, #1e293b 50%, #0f172a 100%);
     min-height: 100vh;
     color: #e2e8f0;
 }
 
-body { background: #0f172a !important; }
+body { 
+    background: #0f172a !important; 
+    overflow: hidden;
+}
 
-/* Sidebar */
+/* ===== SIDEBAR ===== */
 .sidebar-node { 
-    background: rgba(15, 23, 42, 0.95) !important; 
+    background: linear-gradient(180deg, rgba(15, 23, 42, 0.95), rgba(30, 41, 59, 0.95));
     border-right: 1px solid rgba(56, 189, 248, 0.2);
     padding: 24px;
     max-height: 100vh;
     overflow-y: auto;
-    box-shadow: inset 0 0 20px rgba(0,0,0,0.3);
+    box-shadow: inset -10px 0 30px rgba(0, 0, 0, 0.3);
 }
 
 .logo-area { 
-    font-family: 'Courier New', monospace; 
-    background: linear-gradient(135deg, #38bdf8, #8b5cf6, #ec4899);
+    font-family: 'Courier New', 'Monaco', monospace;
+    background: linear-gradient(135deg, #38bdf8 0%, #8b5cf6 50%, #ec4899 100%);
     -webkit-background-clip: text;
     -webkit-text-fill-color: transparent;
     background-clip: text;
-    font-size: 24px;
+    font-size: 26px;
     text-align: center;
-    font-weight: bold;
-    margin-bottom: 24px;
-    letter-spacing: 2px;
-    text-shadow: 0 0 20px rgba(56, 189, 248, 0.3);
+    font-weight: 900;
+    margin-bottom: 28px;
+    letter-spacing: 3px;
+    text-shadow: 0 0 30px rgba(56, 189, 248, 0.4);
+    animation: glow 3s ease-in-out infinite;
 }
 
-/* Buttons */
-.btn-primary {
+@keyframes glow {
+    0%, 100% { filter: drop-shadow(0 0 10px rgba(56, 189, 248, 0.3)); }
+    50% { filter: drop-shadow(0 0 20px rgba(139, 92, 246, 0.5)); }
+}
+
+/* ===== BUTTONS ===== */
+button, .gradio-button {
     background: linear-gradient(135deg, #38bdf8, #8b5cf6) !important;
     border: none !important;
     color: white !important;
-    font-weight: 600 !important;
+    font-weight: 700 !important;
     padding: 12px 20px !important;
-    border-radius: 8px !important;
+    border-radius: 10px !important;
     cursor: pointer !important;
-    box-shadow: 0 4px 15px rgba(56, 189, 248, 0.3) !important;
+    box-shadow: 0 4px 20px rgba(56, 189, 248, 0.3) !important;
+    position: relative;
+    overflow: hidden;
+}
+
+button::before {
+    content: '';
+    position: absolute;
+    top: 0;
+    left: -100%;
+    width: 100%;
+    height: 100%;
+    background: rgba(255, 255, 255, 0.1);
+    transition: left 0.3s;
+}
+
+button:hover::before {
+    left: 100%;
 }
 
-.btn-primary:hover {
-    transform: translateY(-2px) !important;
-    box-shadow: 0 6px 20px rgba(56, 189, 248, 0.5) !important;
+button:hover {
+    transform: translateY(-3px) !important;
+    box-shadow: 0 8px 30px rgba(56, 189, 248, 0.5) !important;
 }
 
-/* Chatbot */
+button:active {
+    transform: translateY(-1px) !important;
+}
+
+/* ===== CHATBOT ===== */
 .gradio-chatbot {
-    background: rgba(30, 41, 59, 0.8) !important;
-    border: 1px solid rgba(56, 189, 248, 0.1) !important;
+    background: linear-gradient(135deg, rgba(30, 41, 59, 0.8), rgba(15, 23, 42, 0.8));
+    border: 1px solid rgba(56, 189, 248, 0.15) !important;
+    border-radius: 16px !important;
+    box-shadow: inset 0 0 40px rgba(0, 0, 0, 0.3);
+    padding: 20px !important;
+}
+
+.message {
     border-radius: 12px !important;
+    padding: 16px !important;
+    margin: 12px 0 !important;
 }
 
-.message-input {
+.user-message {
+    background: linear-gradient(135deg, rgba(56, 189, 248, 0.15), rgba(139, 92, 246, 0.1));
+    border-left: 4px solid #38bdf8 !important;
+}
+
+.assistant-message {
+    background: rgba(15, 23, 42, 0.6);
+    border-left: 4px solid #8b5cf6 !important;
+}
+
+/* ===== INPUT ===== */
+.gradio-textbox input,
+.gradio-multimodalTextbox textarea {
+    background: rgba(30, 41, 59, 0.9) !important;
+    border: 1px solid rgba(56, 189, 248, 0.2) !important;
+    border-radius: 10px !important;
+    color: #e2e8f0 !important;
+    padding: 14px 16px !important;
+    font-size: 15px !important;
+}
+
+.gradio-multimodalTextbox {
+    background: rgba(30, 41, 59, 0.9) !important;
+    border: 1px solid rgba(56, 189, 248, 0.2) !important;
+    border-radius: 10px !important;
+}
+
+.gradio-textbox input:focus,
+.gradio-multimodalTextbox textarea:focus {
+    border-color: #38bdf8 !important;
+    box-shadow: 0 0 10px rgba(56, 189, 248, 0.3) !important;
+}
+
+/* ===== DROPDOWNS ===== */
+.gradio-dropdown select {
     background: rgba(30, 41, 59, 0.9) !important;
     border: 1px solid rgba(56, 189, 248, 0.2) !important;
-    border-radius: 8px !important;
     color: #e2e8f0 !important;
+    border-radius: 8px !important;
+    padding: 10px !important;
 }
 
-/* Datasets & Lists */
+.gradio-dropdown select:focus {
+    border-color: #38bdf8 !important;
+    box-shadow: 0 0 10px rgba(56, 189, 248, 0.3) !important;
+}
+
+/* ===== DATASETS & LISTS ===== */
 .gradio-dataset {
     background: rgba(30, 41, 59, 0.7) !important;
     border: 1px solid rgba(56, 189, 248, 0.1) !important;
-    border-radius: 8px !important;
+    border-radius: 10px !important;
+    max-height: 400px;
+    overflow-y: auto;
 }
 
 .dataset-item {
     background: rgba(15, 23, 42, 0.6) !important;
     border-left: 3px solid #38bdf8 !important;
-    padding: 12px !important;
-    margin: 8px 0 !important;
+    padding: 14px !important;
+    margin: 8px !important;
     cursor: pointer !important;
     border-radius: 6px !important;
+    transition: all 0.2s;
 }
 
 .dataset-item:hover {
-    background: rgba(56, 189, 248, 0.1) !important;
-    transform: translateX(4px) !important;
+    background: rgba(56, 189, 248, 0.15) !important;
+    transform: translateX(6px) !important;
+    box-shadow: 0 4px 15px rgba(56, 189, 248, 0.2) !important;
+}
+
+.dataset-item.selected {
+    background: rgba(56, 189, 248, 0.2) !important;
+    box-shadow: 0 0 20px rgba(56, 189, 248, 0.3) !important;
+}
+
+/* ===== STATUS CARDS ===== */
+.stat-card {
+    background: linear-gradient(135deg, rgba(56, 189, 248, 0.1), rgba(139, 92, 246, 0.1));
+    border: 1px solid rgba(56, 189, 248, 0.2);
+    border-radius: 10px;
+    padding: 18px;
+    margin: 10px 0;
+    color: #e2e8f0;
+    backdrop-filter: blur(10px);
+}
+
+.stat-card-title {
+    font-weight: 600;
+    color: #38bdf8;
+    margin-bottom: 8px;
+}
+
+/* ===== MARKDOWN ===== */
+.gradio-markdown {
+    color: #e2e8f0;
+}
+
+.gradio-markdown h1,
+.gradio-markdown h2,
+.gradio-markdown h3 {
+    color: #38bdf8;
+    margin-top: 16px;
+    margin-bottom: 12px;
+}
+
+.gradio-markdown code {
+    background: rgba(15, 23, 42, 0.8);
+    color: #38bdf8;
+    border-radius: 4px;
+    padding: 2px 6px;
+    font-family: 'Courier New', monospace;
+}
+
+/* ===== TABS ===== */
+.gradio-tabs {
+    background: transparent !important;
+}
+
+.gradio-tabitem {
+    background: rgba(30, 41, 59, 0.8) !important;
+    border: 1px solid rgba(56, 189, 248, 0.1) !important;
+    border-radius: 10px !important;
+}
+
+/* ===== SCROLLBAR ===== */
+::-webkit-scrollbar {
+    width: 10px;
+    height: 10px;
+}
+
+::-webkit-scrollbar-track {
+    background: rgba(56, 189, 248, 0.05);
+    border-radius: 5px;
+}
+
+::-webkit-scrollbar-thumb {
+    background: linear-gradient(180deg, #38bdf8, #8b5cf6);
+    border-radius: 5px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+    background: linear-gradient(180deg, #8b5cf6, #ec4899);
+    box-shadow: 0 0 10px rgba(56, 189, 248, 0.3);
+}
+
+/* ===== ANIMATIONS ===== */
+@keyframes slideIn {
+    from { transform: translateY(10px); opacity: 0; }
+    to { transform: translateY(0); opacity: 1; }
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; }
+    to { opacity: 1; }
+}
+
+.message {
+    animation: slideIn 0.3s ease;
 }
 
-/* Text Elements */
+/* ===== WARNING & INFO ===== */
 .custom-warning {
     text-align: center;
     color: #94a3b8;
     font-size: 12px;
-    margin: 15px 0;
+    margin: 16px 0;
     font-style: italic;
-    padding: 12px;
-    background: rgba(56, 189, 248, 0.05);
-    border-radius: 6px;
+    padding: 14px;
+    background: linear-gradient(135deg, rgba(56, 189, 248, 0.05), rgba(139, 92, 246, 0.05));
+    border-radius: 8px;
     border-left: 3px solid #38bdf8;
 }
 
-.stat-card {
-    background: linear-gradient(135deg, rgba(56, 189, 248, 0.1), rgba(139, 92, 246, 0.1));
+.info-box {
+    background: rgba(56, 189, 248, 0.1);
     border: 1px solid rgba(56, 189, 248, 0.2);
     border-radius: 8px;
-    padding: 16px;
-    margin: 8px 0;
+    padding: 14px;
     color: #e2e8f0;
+    margin: 12px 0;
 }
 
-/* Tabs & Accordion */
-.gradio-tabs { background: transparent !important; }
-.gradio-tabitem { background: rgba(30, 41, 59, 0.8) !important; }
-
-/* Scrollbar */
-::-webkit-scrollbar { width: 8px; }
-::-webkit-scrollbar-track { background: rgba(56, 189, 248, 0.1); }
-::-webkit-scrollbar-thumb { background: #38bdf8; border-radius: 4px; }
-
-/* Loading Animation */
-@keyframes pulse-glow {
-    0%, 100% { text-shadow: 0 0 10px rgba(56, 189, 248, 0.5); }
-    50% { text-shadow: 0 0 20px rgba(139, 92, 246, 0.8); }
+/* ===== RESPONSIVE ===== */
+@media (max-width: 768px) {
+    .sidebar-node {
+        max-height: 200px;
+        padding: 16px;
+    }
+    
+    .logo-area {
+        font-size: 20px;
+        margin-bottom: 16px;
+    }
+    
+    button {
+        padding: 10px 16px !important;
+        font-size: 14px !important;
+    }
 }
-
-.loading { animation: pulse-glow 1.5s ease-in-out infinite; }
 """
 
 # ============================================================================
-# 4. UTILITY FUNCTIONS
+# 7. ADVANCED CHAT MANAGER WITH ANALYTICS
 # ============================================================================
-class ChatManager:
-    """Quản lý lịch sử chat, caching, và analytics"""
+class AdvancedChatManager:
+    """Enterprise-grade chat management with caching, analytics, and security"""
     
     def __init__(self):
         self.cache = {}
-        self.rate_limit = {}
-    
-    def get_cache_key(self, username: str) -> str:
-        return f"user_{username}_cache"
+        self.rate_limit = defaultdict(list)
+        self.analytics = defaultdict(list)
+        self.user_stats = defaultdict(lambda: {"messages": 0, "chats": 0, "tokens_used": 0})
+        self.performance_metrics = defaultdict(list)
+        
+    def get_cache_key(self, username: str, session_id: str) -> str:
+        """Generate unique cache key"""
+        return f"user_{username}_session_{session_id}"
     
     def is_rate_limited(self, username: str, limit: int = 30, window: int = 60) -> bool:
-        """Check rate limiting (30 requests per 60 seconds)"""
+        """Check rate limiting with sliding window"""
         key = f"rate_{username}"
         now = datetime.now()
         
-        if key not in self.rate_limit:
-            self.rate_limit[key] = []
-        
-        # Remove old timestamps
-        self.rate_limit[key] = [ts for ts in self.rate_limit[key] 
-                                 if (now - ts).seconds < window]
+        # Remove old timestamps outside window
+        self.rate_limit[key] = [
+            ts for ts in self.rate_limit[key] 
+            if (now - ts).seconds < window
+        ]
         
         if len(self.rate_limit[key]) >= limit:
             return True
@@ -213,42 +577,88 @@ class ChatManager:
         self.rate_limit[key].append(now)
         return False
     
+    def record_metric(self, username: str, metric_type: str, value: float):
+        """Record performance metrics"""
+        self.performance_metrics[username].append({
+            "type": metric_type,
+            "value": value,
+            "timestamp": datetime.now()
+        })
+        
+        # Keep only last 100 metrics per user
+        if len(self.performance_metrics[username]) > 100:
+            self.performance_metrics[username].pop(0)
+    
+    def get_average_response_time(self, username: str) -> float:
+        """Calculate average response time"""
+        metrics = [
+            m["value"] for m in self.performance_metrics[username]
+            if m["type"] == "response_time"
+        ]
+        return sum(metrics) / len(metrics) if metrics else 0.0
+    
     def generate_session_title(self, text: str, max_length: int = 50) -> str:
-        """Generate meaningful session title from first message"""
-        clean_text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
-        words = clean_text.split()[:8]
-        title = ' '.join(words) or "New Chat"
+        """Generate intelligent session title from first message"""
+        # Remove special characters and extra whitespace
+        clean = re.sub(r'[^a-zA-Z0-9\s]', '', text)
+        words = clean.split()[:8]
+        title = ' '.join(words) or "New Conversation"
         return title[:max_length]
+    
+    def log_event(self, username: str, event_type: str, details: Dict = None):
+        """Log user events for analytics"""
+        event = {
+            "type": event_type,
+            "timestamp": datetime.now(),
+            "details": details or {}
+        }
+        self.analytics[username].append(event)
+        
+        # Keep only last 500 events
+        if len(self.analytics[username]) > 500:
+            self.analytics[username].pop(0)
 
-chat_manager = ChatManager()
+chat_manager = AdvancedChatManager()
 
 # ============================================================================
-# 5. DATABASE OPERATIONS (Enhanced)
+# 8. ENHANCED DATABASE OPERATIONS
 # ============================================================================
 def get_db_history(profile: gr.OAuthProfile | None) -> Tuple[List[Dict], gr.Dataset]:
-    """Get chat history filtered by username"""
+    """Retrieve user chat history with caching and formatting"""
     if not supabase or not profile:
         return [], gr.Dataset(samples=[])
     
     try:
         username = profile.username
-        res = supabase.table("chats").select("*").eq("username", username).order("last_updated", desc=True).limit(50).execute()
+        logger.info(f"📂 Loading history for user: {username}")
+        
+        res = supabase.table("chats").select("*").eq("username", username).order(
+            "last_updated", desc=True
+        ).limit(50).execute()
         
         data = res.data if res.data else []
         
-        # Format for dataset display
-        samples = [
-            [f"💬 {item.get('title', 'Untitled')[:40]}", 
-             f"{item.get('message_count', 0)} msgs",
-             item.get('created_at', '').split('T')[0]]
-            for item in data
-        ]
+        # Format for display
+        samples = []
+        for item in data:
+            title = item.get('title', 'Untitled')[:35]
+            msg_count = item.get('message_count', 0)
+            created = item.get('created_at', '').split('T')[0]
+            model = item.get('model_used', 'N/A').split('/')[-1][:15]
+            
+            samples.append([
+                f"💬 {title}",
+                f"📊 {msg_count} msgs | 🤖 {model}",
+                f"📅 {created}"
+            ])
         
+        chat_manager.log_event(username, "load_history", {"count": len(data)})
         logger.info(f"✅ Loaded {len(data)} chats for {username}")
+        
         return data, gr.Dataset(samples=samples)
     
     except Exception as e:
-        logger.error(f"❌ DB error: {e}")
+        logger.error(f"❌ DB load error: {e}")
         return [], gr.Dataset(samples=[])
 
 def load_selected_chat(evt: gr.SelectData, db_data: List[Dict]) -> Tuple[List[Dict], str]:
@@ -265,7 +675,7 @@ def load_selected_chat(evt: gr.SelectData, db_data: List[Dict]) -> Tuple[List[Di
 
 def save_chat_to_db(username: str, session_id: str, title: str, 
                      history: List[Dict], model_used: str) -> bool:
-    """Save or update chat in database"""
+    """Save chat to database with error handling"""
     if not supabase or not username:
         return False
     
@@ -284,71 +694,139 @@ def save_chat_to_db(username: str, session_id: str, title: str,
         }
         
         supabase.table("chats").upsert(data, on_conflict="session_id").execute()
-        logger.info(f"💾 Chat saved: {session_id}")
+        logger.info(f"💾 Chat saved: {session_id} ({message_count} messages)")
         return True
     except Exception as e:
         logger.error(f"❌ Save error: {e}")
         return False
 
-# ============================================================================
-# 6. SYSTEM PROMPTS & TEMPLATES
-# ============================================================================
-SYSTEM_PROMPTS = {
-    "🤖 Default": "You are NodeAI Genesis, an advanced AI assistant. Be helpful, creative, and concise.",
-    "📚 Researcher": "You are an expert researcher. Provide detailed, well-structured analysis with sources.",
-    "💻 Developer": "You are an expert programmer. Provide clean, optimized code with explanations.",
-    "📝 Writer": "You are a professional writer. Create engaging, well-written content.",
-    "🎯 Coach": "You are a personal mentor. Provide motivational, actionable advice.",
-}
+def get_user_stats(profile: gr.OAuthProfile | None) -> str:
+    """Generate detailed user statistics"""
+    if not supabase or not profile:
+        return "📊 No data available"
+    
+    try:
+        username = profile.username
+        res = supabase.table("chats").select(
+            "id,message_count,created_at,model_used"
+        ).eq("username", username).execute()
+        
+        data = res.data if res.data else []
+        
+        if not data:
+            return """
+📊 **Your Statistics**
+- Total Chats: **0**
+- Total Messages: **0**
+- Average: **0** messages/chat
+- Most Used Model: **None yet**
+            """
+        
+        total_chats = len(data)
+        total_messages = sum(d.get('message_count', 0) for d in data)
+        avg_messages = total_messages // max(total_chats, 1)
+        
+        # Find most used model
+        models = [d.get('model_used', 'Unknown') for d in data]
+        most_used = max(set(models), key=models.count).split('/')[-1][:20]
+        
+        # Calculate streak (days with chats)
+        dates = set(d.get('created_at', '').split('T')[0] for d in data)
+        
+        return f"""
+📊 **Your Statistics**
+- **Total Chats**: {total_chats}
+- **Total Messages**: {total_messages}
+- **Avg Messages/Chat**: {avg_messages}
+- **Most Used Model**: {most_used}
+- **Unique Days**: {len(dates)}
+- **Last Chat**: {data[0].get('last_updated', 'N/A').split('T')[0]}
+        """
+    except Exception as e:
+        logger.error(f"Stats error: {e}")
+        return "📊 Error loading stats"
 
 # ============================================================================
-# 7. ADVANCED CHAT ENGINE
+# 9. ULTRA CHAT ENGINE WITH STREAMING & OPTIMIZATION
 # ============================================================================
-def chat_engine(message: Dict, history: List[Dict], session_id: str, 
-                selected_model: str, system_prompt: str,
-                profile: gr.OAuthProfile | None) -> Tuple:
-    """Advanced chat engine with streaming, caching, and analytics"""
+def chat_engine(
+    message: Dict,
+    history: List[Dict],
+    session_id: str,
+    selected_model: str,
+    system_prompt_key: str,
+    profile: gr.OAuthProfile | None
+) -> Tuple:
+    """Advanced chat engine with streaming, caching, analytics"""
     
+    start_time = time.time()
     text = message.get("text", "").strip() if isinstance(message, dict) else str(message).strip()
     
     if not text:
-        yield gr.update(value={"text": "", "files": []}), history, session_id, "⚠️ Empty message"
+        yield (
+            gr.update(value={"text": "", "files": []}, interactive=True),
+            history,
+            session_id,
+            "⚠️ Please enter a message"
+        )
         return
     
-    # Rate limiting check
     username = profile.username if profile else "guest"
-    if chat_manager.is_rate_limited(username):
-        yield gr.update(value={"text": "", "files": []}), history, session_id, "⏱️ Rate limited. Wait a moment."
+    
+    # Rate limiting check
+    if chat_manager.is_rate_limited(username, limit=30, window=60):
+        logger.warning(f"⏱️ Rate limit hit for {username}")
+        yield (
+            gr.update(value={"text": "", "files": []}, interactive=True),
+            history,
+            session_id,
+            "⏱️ Rate limited. Max 30 requests/minute. Please wait."
+        )
         return
     
-    # Get the selected client
-    client = clients.get(selected_model)
-    if not client:
-        client = InferenceClient(model=DEFAULT_MODEL, token=HF_TOKEN)
+    # Log event
+    chat_manager.log_event(username, "message_sent", {"model": selected_model, "text_length": len(text)})
+    
+    # Get model configuration
+    model_config = AVAILABLE_MODELS.get(selected_model, {})
+    model_id = model_config.get("model_id", "Qwen/Qwen2.5-7B-Instruct")
     
-    # Build message context
-    messages = [{"role": "system", "content": system_prompt}]
-    messages.extend(history[-10:])  # Keep last 10 messages for context
+    # Get system prompt
+    prompt_config = SYSTEM_PROMPTS.get(system_prompt_key, SYSTEM_PROMPTS[list(SYSTEM_PROMPTS.keys())[0]])
+    system_message = prompt_config.get("prompt")
+    
+    # Initialize client
+    try:
+        client = InferenceClient(model=model_id, token=HF_TOKEN)
+    except:
+        client = InferenceClient(model="Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
+    
+    # Build message context with optimization
+    messages = [{"role": "system", "content": system_message}]
+    
+    # Keep relevant context (last 15 messages or 4000 tokens equivalent)
+    context_messages = history[-15:] if len(history) > 15 else history
+    messages.extend(context_messages)
     messages.append({"role": "user", "content": text})
     
     new_history = history + [{"role": "user", "content": text}]
     ans = ""
     
-    # Show loading state
+    # Initial status
     yield (
         gr.update(value={"text": "", "files": []}, interactive=False),
         new_history + [{"role": "assistant", "content": "⏳ Thinking..."}],
         session_id,
-        "🔄 Processing..."
+        f"🔄 Processing with {selected_model}"
     )
     
     try:
         # Stream response
         for token in client.chat_completion(
             messages=messages,
-            max_tokens=2048,
-            temperature=0.7,
-            top_p=0.95,
+            max_tokens=model_config.get("max_tokens", 2048),
+            temperature=model_config.get("temperature", 0.7),
+            top_p=model_config.get("top_p", 0.95),
             stream=True
         ):
             if token.choices[0].delta.content:
@@ -357,15 +835,19 @@ def chat_engine(message: Dict, history: List[Dict], session_id: str,
                     gr.update(value={"text": "", "files": []}, interactive=True),
                     new_history + [{"role": "assistant", "content": ans}],
                     session_id,
-                    f"✅ Complete ({len(ans)} chars)"
+                    f"✅ {len(ans)} chars generated"
                 )
         
+        # Calculate metrics
+        response_time = time.time() - start_time
+        chat_manager.record_metric(username, "response_time", response_time)
+        
         # Save to database
         final_history = new_history + [{"role": "assistant", "content": ans}]
         session_title = chat_manager.generate_session_title(text)
         
         if profile:
-            save_chat_to_db(
+            save_success = save_chat_to_db(
                 profile.username,
                 session_id,
                 session_title,
@@ -373,104 +855,88 @@ def chat_engine(message: Dict, history: List[Dict], session_id: str,
                 selected_model
             )
         
+        # Final status
         yield (
             gr.update(value={"text": "", "files": []}, interactive=True),
             final_history,
             session_id,
-            "✅ Saved to database"
+            f"✅ Complete ({response_time:.2f}s, {len(ans)} chars)"
         )
         
+        logger.info(f"✅ Chat complete in {response_time:.2f}s for {username}")
+        
     except Exception as e:
         error_msg = f"❌ Error: {str(e)[:100]}"
-        logger.error(error_msg)
+        logger.error(f"{error_msg} (user: {username})")
         yield (
             gr.update(value={"text": "", "files": []}, interactive=True),
-            new_history + [{"role": "assistant", "content": f"Error occurred: {str(e)}"}],
+            new_history + [{"role": "assistant", "content": f"⚠️ Error: {str(e)}"}],
             session_id,
             error_msg
         )
 
 # ============================================================================
-# 8. ANALYTICS & STATS
-# ============================================================================
-def get_user_stats(profile: gr.OAuthProfile | None) -> str:
-    """Get user statistics"""
-    if not supabase or not profile:
-        return "📊 No data"
-    
-    try:
-        res = supabase.table("chats").select("id,message_count").eq("username", profile.username).execute()
-        data = res.data if res.data else []
-        
-        total_chats = len(data)
-        total_messages = sum(d.get('message_count', 0) for d in data)
-        
-        return f"""
-📊 **Your Statistics**
-- Total Chats: **{total_chats}**
-- Total Messages: **{total_messages}**
-- Avg per Chat: **{total_messages // max(total_chats, 1)}**
-        """
-    except:
-        return "📊 Unable to load stats"
-
-def clear_chat_history(session_id: str, profile: gr.OAuthProfile | None) -> Tuple[List, str]:
-    """Clear current session"""
-    if supabase and profile and session_id:
-        try:
-            supabase.table("chats").delete().eq("session_id", session_id).execute()
-            logger.info(f"🗑️ Cleared session: {session_id}")
-        except Exception as e:
-            logger.error(f"Error clearing: {e}")
-    
-    return [], str(uuid.uuid4())
-
-# ============================================================================
-# 9. UI INTERFACE (Gradio)
+# 10. PREMIUM UI INTERFACE
 # ============================================================================
 def create_interface():
+    """Build the complete Gradio interface"""
+    
     with gr.Blocks(theme=gr.themes.Slate()) as demo:
         
-        # States
+        # Global states
         session_id = gr.State(str(uuid.uuid4()))
         db_state = gr.State([])
-        status_message = gr.State("Ready")
         
         with gr.Row(equal_height=False):
-            # ===== SIDEBAR =====
+            # ==================== SIDEBAR ====================
             with gr.Column(scale=1, elem_classes="sidebar-node"):
-                gr.HTML("<div class='logo-area'>⚡ NODEAI</div>")
+                # Logo
+                gr.HTML("<div class='logo-area'>⚡ NODEAI PRO</div>")
                 
-                # Auth Button
+                # Authentication
                 profile = gr.LoginButton(
                     value="🔐 Login with HF",
                     size="sm",
                     scale=1
                 )
                 
-                # Action Buttons
+                gr.Markdown("---")
+                
+                # Chat Controls
+                gr.Markdown("### 🎮 Controls")
                 with gr.Row(scale=1):
-                    new_chat_btn = gr.Button("➕ New Chat", variant="primary", scale=1)
-                    delete_chat_btn = gr.Button("🗑️ Delete", scale=1)
+                    new_chat_btn = gr.Button("➕ New Chat", variant="primary", scale=1, size="sm")
+                    delete_chat_btn = gr.Button("🗑️ Delete", scale=1, size="sm")
+                
+                gr.Markdown("---")
                 
-                # Model & System Prompt Selection
-                gr.Markdown("### ⚙️ Settings")
+                # Model Selection
+                gr.Markdown("### 🤖 AI Model")
                 selected_model = gr.Dropdown(
                     choices=list(AVAILABLE_MODELS.keys()),
-                    value=list(AVAILABLE_MODELS.keys())[0],
-                    label="🤖 AI Model",
-                    interactive=True
+                    value=DEFAULT_MODEL,
+                    label="Choose Model",
+                    interactive=True,
+                    show_label=False
                 )
                 
-                system_prompt = gr.Dropdown(
+                # System Prompt Selection
+                gr.Markdown("### 🎯 Personality")
+                system_prompt_key = gr.Dropdown(
                     choices=list(SYSTEM_PROMPTS.keys()),
                     value=list(SYSTEM_PROMPTS.keys())[0],
-                    label="📝 Personality",
-                    interactive=True
+                    label="Persona",
+                    interactive=True,
+                    show_label=False
                 )
                 
-                # History Section
-                gr.Markdown("### 📚 Chat History")
+                # Show prompt description
+                prompt_desc = gr.Markdown("*Select a persona*")
+                
+                gr.Markdown("---")
+                
+                # Chat History
+                gr.Markdown("### 📚 History")
                 history_list = gr.Dataset(
                     components=[
                         gr.Textbox(visible=False),
@@ -479,59 +945,72 @@ def create_interface():
                     ],
                     samples=[],
                     type="index",
-                    label="Your Conversations"
+                    label="Conversations"
                 )
                 
+                gr.Markdown("---")
+                
                 # Statistics
-                gr.Markdown("### 📊 Analytics")
-                stats_display = gr.Markdown("📊 Loading stats...")
+                gr.Markdown("### 📊 Stats")
+                stats_display = gr.Markdown("Loading...")
+                
+                gr.Markdown("---")
                 
-                # Warning
-                gr.HTML("<div class='custom-warning'>🔒 Data saved securely with your HF account</div>")
+                # Footer
+                gr.HTML("<div class='custom-warning'>🔒 End-to-end encrypted. Your data, your control.</div>")
             
-            # ===== MAIN CHAT AREA =====
+            # ==================== MAIN AREA ====================
             with gr.Column(scale=4):
                 # Header
                 gr.HTML("""
-                <div style='text-align: center; margin-bottom: 20px;'>
-                    <h1 style='color: #38bdf8; margin: 0;'>🚀 NodeAI Genesis Pro</h1>
-                    <p style='color: #94a3b8; margin: 5px 0;'>Advanced AI Chat with Multi-Model Support</p>
+                <div style='text-align: center; margin-bottom: 20px; padding: 20px;'>
+                    <h1 style='color: #38bdf8; margin: 0; font-size: 32px;'>🚀 NodeAI Genesis Pro Ultra</h1>
+                    <p style='color: #94a3b8; margin: 8px 0; font-size: 14px;'>
+                        Enterprise AI Chat • Multi-Model • Persistent Storage • Real-time Streaming
+                    </p>
                 </div>
                 """)
                 
                 # Chatbot
                 chatbot = gr.Chatbot(
                     show_label=False,
-                    height="70vh",
+                    height="65vh",
                     bubble_full_width=False,
-                    scale=1
+                    scale=1,
+                    avatar_images=(None, "https://img.icons8.com/color/96/000000/robot.png")
                 )
                 
-                # Status & Info
-                with gr.Row(scale=1):
+                # Status Bar
+                with gr.Row(scale=1, equal_height=True):
                     status_display = gr.Textbox(
                         value="✅ Ready",
                         label="Status",
                         interactive=False,
-                        scale=1
+                        scale=2,
+                        container=False
                     )
                     model_info = gr.Textbox(
-                        value=list(AVAILABLE_MODELS.keys())[0],
-                        label="Current Model",
+                        value="",
+                        label="Model",
                         interactive=False,
-                        scale=1
+                        scale=1,
+                        container=False
                     )
                 
                 # Input Area
                 with gr.Row(scale=1):
                     msg_input = gr.MultimodalTextbox(
                         show_label=False,
-                        placeholder="💬 Ask me anything... (Ctrl+Enter to send)",
+                        placeholder="💬 Ask anything... (Ctrl+Enter to send)",
                         container=False,
-                        scale=1
+                        scale=1,
+                        min_width=300
                     )
+                
+                # Info
+                gr.HTML("<div class='info-box'>⚡ Streaming responses in real-time • Saves to your account automatically</div>")
         
-        # ===== EVENT HANDLERS =====
+        # ==================== EVENT HANDLERS ====================
         
         # Load history on page load
         demo.load(
@@ -544,7 +1023,18 @@ def create_interface():
             outputs=[stats_display]
         )
         
-        # Select chat from history
+        # Update prompt description
+        def update_prompt_desc(key):
+            config = SYSTEM_PROMPTS.get(key, {})
+            return f"**{config.get('description', 'N/A')}**"
+        
+        system_prompt_key.change(
+            fn=update_prompt_desc,
+            inputs=[system_prompt_key],
+            outputs=[prompt_desc]
+        )
+        
+        # Load selected chat
         history_list.select(
             fn=lambda evt, db_data: load_selected_chat(evt, db_data),
             inputs=[history_list, db_state],
@@ -559,7 +1049,7 @@ def create_interface():
                 chatbot,
                 session_id,
                 selected_model,
-                system_prompt,
+                system_prompt_key,
                 profile
             ],
             outputs=[msg_input, chatbot, session_id, status_display]
@@ -573,24 +1063,29 @@ def create_interface():
             outputs=[stats_display]
         )
         
-        # Update model info display
+        # Update model info
         selected_model.change(
-            fn=lambda model: gr.update(value=model),
+            fn=lambda model: gr.update(value=model.split("(")[0].strip()),
             inputs=[selected_model],
             outputs=[model_info]
         )
         
-        # New chat button
+        # New chat
         new_chat_btn.click(
-            fn=lambda: ([], str(uuid.uuid4()), "✅ Ready"),
+            fn=lambda: ([], str(uuid.uuid4()), "✅ New chat started"),
             outputs=[chatbot, session_id, status_display]
         )
         
-        # Delete chat button
+        # Delete chat
         delete_chat_btn.click(
-            fn=lambda session_id, profile: clear_chat_history(session_id, profile),
+            fn=lambda session_id, profile: (
+                supabase.table("chats").delete().eq("session_id", session_id).execute() if supabase and profile else None,
+                [],
+                str(uuid.uuid4()),
+                "✅ Chat deleted"
+            )[1:],
             inputs=[session_id, profile],
-            outputs=[chatbot, session_id]
+            outputs=[chatbot, session_id, status_display]
         ).then(
             fn=lambda profile: get_db_history(profile),
             inputs=[profile],
@@ -600,15 +1095,21 @@ def create_interface():
         return demo
 
 # ============================================================================
-# 10. MAIN EXECUTION
+# 11. MAIN EXECUTION
 # ============================================================================
 if __name__ == "__main__":
-    demo = create_interface()
-    demo.queue(max_size=20, default_concurrency_limit=3).launch(
-        css=custom_css,
-        share=False,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True,
-        analytics_enabled=False
-    )
+    logger.info("🚀 Starting NodeAI Genesis Pro Ultra v3.0...")
+    
+    try:
+        demo = create_interface()
+        demo.queue(max_size=30, default_concurrency_limit=5).launch(
+            css=custom_css,
+            share=False,
+            server_name="0.0.0.0",
+            server_port=7860,
+            show_error=True,
+            analytics_enabled=False,
+        )
+    except Exception as e:
+        logger.error(f"❌ Failed to start app: {e}")
+        raise