Update app.py

app.py

@@ -1,1115 +1,191 @@
-"""
-╔══════════════════════════════════════════════════════════════════════════╗
-║                    NodeAI Genesis Pro Ultra v3.0                         ║
-║              The World's Most Advanced Open-Source AI Chat              ║
-║              Built with Gradio, Supabase, HuggingFace                   ║
-╚══════════════════════════════════════════════════════════════════════════╝
-"""
-
+"""NodeAI Genesis Pro - FULL Pro Version - Gradio 6.9.0 Compatible"""
 import gradio as gr
 from huggingface_hub import InferenceClient
 from supabase import create_client, Client
-import os
-import uuid
-import json
-from datetime import datetime, timedelta
-from typing import Optional, List, Dict, Tuple, Any
-import hashlib
-import re
-from functools import lru_cache
-import logging
+import os, uuid, json, logging, re, time
+from datetime import datetime
+from typing import List, Dict, Tuple
 from collections import defaultdict
-import time
-import asyncio
-from enum import Enum
 
-# ============================================================================
-# 1. LOGGING SETUP
-# ============================================================================
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.FileHandler('nodeai.log'),
-        logging.StreamHandler()
-    ]
-)
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 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 with error handling
+URL, KEY, HF_TOKEN = os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_KEY"), os.environ.get("HUGGINGFACE_HUB_TOKEN")
 try:
-    supabase: Client = create_client(URL, KEY) if URL and KEY else None
-    logger.info("✅ Supabase initialized successfully")
+    supabase = create_client(URL, KEY) if URL and KEY else None
+    logger.info("✅ Supabase OK")
 except Exception as e:
-    logger.error(f"❌ Supabase initialization failed: {e}")
+    logger.error(f"Supabase: {e}")
     supabase = None
 
-# ============================================================================
-# 4. ADVANCED MODEL CONFIGURATION
-# ============================================================================
-AVAILABLE_MODELS = {
-    "🚀 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 2.5 7B (Balanced)"
-
-# ============================================================================
-# 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"."""
-    },
-}
-
-# ============================================================================
-# 6. PREMIUM CSS WITH ADVANCED THEMING
-# ============================================================================
-custom_css = """
-* { 
-    transition: all 0.3s ease; 
-    box-sizing: border-box;
-}
-
-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 50%, #0f172a 100%);
-    min-height: 100vh;
-    color: #e2e8f0;
-}
-
-body { 
-    background: #0f172a !important; 
-    overflow: hidden;
-}
-
-/* ===== SIDEBAR ===== */
-.sidebar-node { 
-    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 -10px 0 30px rgba(0, 0, 0, 0.3);
-}
-
-.logo-area { 
-    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: 26px;
-    text-align: center;
-    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;
-}
-
-@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: 700 !important;
-    padding: 12px 20px !important;
-    border-radius: 10px !important;
-    cursor: pointer !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%;
-}
-
-button:hover {
-    transform: translateY(-3px) !important;
-    box-shadow: 0 8px 30px rgba(56, 189, 248, 0.5) !important;
-}
-
-button:active {
-    transform: translateY(-1px) !important;
-}
-
-/* ===== CHATBOT ===== */
-.gradio-chatbot {
-    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;
-}
-
-.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;
-    color: #e2e8f0 !important;
-    border-radius: 8px !important;
-    padding: 10px !important;
-}
-
-.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: 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: 14px !important;
-    margin: 8px !important;
-    cursor: pointer !important;
-    border-radius: 6px !important;
-    transition: all 0.2s;
-}
-
-.dataset-item:hover {
-    background: rgba(56, 189, 248, 0.15) !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;
-}
-
-/* ===== WARNING & INFO ===== */
-.custom-warning {
-    text-align: center;
-    color: #94a3b8;
-    font-size: 12px;
-    margin: 16px 0;
-    font-style: italic;
-    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;
-}
-
-.info-box {
-    background: rgba(56, 189, 248, 0.1);
-    border: 1px solid rgba(56, 189, 248, 0.2);
-    border-radius: 8px;
-    padding: 14px;
-    color: #e2e8f0;
-    margin: 12px 0;
-}
-
-/* ===== 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;
-    }
-}
+MODELS = {
+    "🚀 Qwen 2.5 7B": {"id": "Qwen/Qwen2.5-7B-Instruct", "speed": "Fast", "quality": "High"},
+    "⚡ Mistral 7B": {"id": "mistralai/Mistral-7B-Instruct-v0.2", "speed": "Very Fast", "quality": "Good"},
+    "🔥 LLaMA 2 7B": {"id": "meta-llama/Llama-2-7b-chat-hf", "speed": "Fast", "quality": "High"},
+    "🎯 Qwen 72B": {"id": "Qwen/Qwen2.5-72B-Instruct", "speed": "Slow", "quality": "Premium"},
+}
+
+PROMPTS = {
+    "🤖 Default": "You are NodeAI Genesis Pro. Be helpful, creative, honest, and concise. Explain complex topics clearly.",
+    "📚 Researcher": "Expert analyst. Provide detailed analysis with sources. Break down complex topics. Think critically.",
+    "💻 Developer": "Senior programmer. Provide clean, optimized code. Explain approaches. Consider edge cases.",
+    "📝 Writer": "Professional writer. Create engaging, well-written, grammatically perfect content.",
+    "🎯 Coach": "Personal mentor. Provide motivational, actionable advice. Focus on growth.",
+    "🔬 Data Scientist": "ML expert. Explain statistics clearly. Provide code examples. Discuss ethics.",
+    "🎓 Teacher": "Educator. Explain clearly. Break into steps. Use examples. Encourage learning.",
+}
+
+CSS = """
+* { transition: all 0.3s ease; }
+footer, .built-with, header { display: none !important; }
+.gradio-container { max-width: 100%; background: linear-gradient(135deg, #0f172a, #1e293b); color: #e2e8f0; }
+body { background: #0f172a !important; }
+.sidebar-node { background: rgba(15,23,42,0.95); border-right: 1px solid rgba(56,189,248,0.2); padding: 24px; max-height: 100vh; overflow-y: auto; }
+.logo { background: linear-gradient(135deg, #38bdf8, #8b5cf6, #ec4899); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 28px; font-weight: 900; text-align: center; margin: 20px 0; letter-spacing: 2px; }
+button { background: linear-gradient(135deg, #38bdf8, #8b5cf6) !important; border: none !important; color: white !important; padding: 12px 20px !important; border-radius: 10px !important; box-shadow: 0 4px 20px rgba(56,189,248,0.3) !important; }
+button:hover { transform: translateY(-3px) !important; box-shadow: 0 8px 30px rgba(56,189,248,0.5) !important; }
+.gradio-chatbot { background: rgba(30,41,59,0.8); border: 1px solid rgba(56,189,248,0.15); border-radius: 16px; padding: 20px; }
+input, textarea { background: rgba(30,41,59,0.9) !important; border: 1px solid rgba(56,189,248,0.2) !important; color: #e2e8f0 !important; border-radius: 10px !important; padding: 14px !important; }
+input:focus, textarea:focus { border-color: #38bdf8 !important; box-shadow: 0 0 15px rgba(56,189,248,0.3) !important; }
+.gradio-dropdown select { background: rgba(30,41,59,0.9) !important; border: 1px solid rgba(56,189,248,0.2) !important; color: #e2e8f0 !important; }
+.gradio-dataset { background: rgba(30,41,59,0.7); border: 1px solid rgba(56,189,248,0.1); border-radius: 10px; max-height: 400px; overflow-y: auto; }
+.gradio-markdown h1, .gradio-markdown h2, .gradio-markdown h3 { color: #38bdf8; }
+::-webkit-scrollbar { width: 10px; }
+::-webkit-scrollbar-track { background: rgba(56,189,248,0.05); }
+::-webkit-scrollbar-thumb { background: linear-gradient(180deg, #38bdf8, #8b5cf6); border-radius: 5px; }
+.warning { text-align: center; color: #94a3b8; font-size: 12px; margin: 16px 0; padding: 14px; background: rgba(56,189,248,0.05); border-radius: 8px; border-left: 3px solid #38bdf8; }
 """
 
-# ============================================================================
-# 7. ADVANCED CHAT MANAGER WITH ANALYTICS
-# ============================================================================
-class AdvancedChatManager:
-    """Enterprise-grade chat management with caching, analytics, and security"""
-    
+class ChatMgr:
     def __init__(self):
-        self.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}"
+        self.metrics = defaultdict(list)
     
-    def is_rate_limited(self, username: str, limit: int = 30, window: int = 60) -> bool:
-        """Check rate limiting with sliding window"""
-        key = f"rate_{username}"
+    def is_limited(self, user, limit=30, window=60):
         now = datetime.now()
-        
-        # 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
-        
+        key = f"r_{user}"
+        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
         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 record(self, user, metric, val):
+        self.metrics[user].append({"type": metric, "value": val})
+        if len(self.metrics[user]) > 100: self.metrics[user].pop(0)
     
-    def generate_session_title(self, text: str, max_length: int = 50) -> str:
-        """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)
+    def title(self, text): return ' '.join(re.sub(r'[^a-zA-Z0-9\s]', '', text).split()[:8])[:50] or "Chat"
 
-chat_manager = AdvancedChatManager()
+mgr = ChatMgr()
 
-# ============================================================================
-# 8. ENHANCED DATABASE OPERATIONS
-# ============================================================================
-def get_db_history(profile: gr.OAuthProfile | None) -> Tuple[List[Dict], gr.Dataset]:
-    """Retrieve user chat history with caching and formatting"""
-    if not supabase or not profile:
-        return [], gr.Dataset(samples=[])
-    
+def get_hist(profile):
+    if not supabase or not profile: return [], gr.Dataset(samples=[])
     try:
-        username = profile.username
-        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 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}")
-        
+        res = supabase.table("chats").select("*").eq("username", profile.username).order("last_updated", desc=True).limit(50).execute()
+        data = res.data or []
+        samples = [[f"💬 {d['title'][:35]}", f"{d.get('message_count', 0)} msgs | {d.get('model_used', 'N/A').split('/')[-1][:15]}"] for d in data]
         return data, gr.Dataset(samples=samples)
-    
-    except Exception as e:
-        logger.error(f"❌ DB load error: {e}")
-        return [], gr.Dataset(samples=[])
+    except: return [], gr.Dataset(samples=[])
 
-def load_selected_chat(evt: gr.SelectData, db_data: List[Dict]) -> Tuple[List[Dict], str]:
-    """Load selected chat session"""
-    try:
-        selected_item = db_data[evt.index]
-        history = selected_item.get('history', [])
-        session_id = selected_item.get('session_id')
-        logger.info(f"📂 Loaded session: {session_id}")
-        return history, session_id
-    except Exception as e:
-        logger.error(f"Error loading chat: {e}")
-        return [], str(uuid.uuid4())
+def load_chat(evt, db):
+    try: return db[evt.index]['history'], db[evt.index]['session_id']
+    except: return [], str(uuid.uuid4())
 
-def save_chat_to_db(username: str, session_id: str, title: str, 
-                     history: List[Dict], model_used: str) -> bool:
-    """Save chat to database with error handling"""
-    if not supabase or not username:
-        return False
-    
+def save_chat(user, sid, title, hist, model):
+    if not supabase or not user: return False
     try:
-        message_count = len([m for m in history if m.get('role') == 'user'])
-        
-        data = {
-            "username": username,
-            "session_id": session_id,
-            "title": title,
-            "history": history,
-            "message_count": message_count,
-            "model_used": model_used,
-            "last_updated": datetime.utcnow().isoformat(),
-            "created_at": datetime.utcnow().isoformat(),
-        }
-        
-        supabase.table("chats").upsert(data, on_conflict="session_id").execute()
-        logger.info(f"💾 Chat saved: {session_id} ({message_count} messages)")
+        msg_count = len([m for m in hist if m['role'] == 'user'])
+        supabase.table("chats").upsert({
+            "username": user, "session_id": sid, "title": title, "history": hist,
+            "message_count": msg_count, "model_used": model,
+            "last_updated": datetime.utcnow().isoformat(), "created_at": datetime.utcnow().isoformat()
+        }, on_conflict="session_id").execute()
         return True
-    except Exception as e:
-        logger.error(f"❌ Save error: {e}")
-        return False
+    except: return False
 
-def get_user_stats(profile: gr.OAuthProfile | None) -> str:
-    """Generate detailed user statistics"""
-    if not supabase or not profile:
-        return "📊 No data available"
-    
+def get_stats(profile):
+    if not supabase or not profile: return "📊 No data"
     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"
-
-# ============================================================================
-# 9. ULTRA CHAT ENGINE WITH STREAMING & OPTIMIZATION
-# ============================================================================
-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": []}, interactive=True),
-            history,
-            session_id,
-            "⚠️ Please enter a message"
-        )
-        return
-    
-    username = profile.username if profile else "guest"
-    
-    # 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
-    
-    # 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")
-    
-    # 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 = ""
-    
-    # Initial status
-    yield (
-        gr.update(value={"text": "", "files": []}, interactive=False),
-        new_history + [{"role": "assistant", "content": "⏳ Thinking..."}],
-        session_id,
-        f"🔄 Processing with {selected_model}"
-    )
-    
-    try:
-        # Stream response
-        for token in client.chat_completion(
-            messages=messages,
-            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:
-                ans += token.choices[0].delta.content
-                yield (
-                    gr.update(value={"text": "", "files": []}, interactive=True),
-                    new_history + [{"role": "assistant", "content": ans}],
-                    session_id,
-                    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_success = save_chat_to_db(
-                profile.username,
-                session_id,
-                session_title,
-                final_history,
-                selected_model
-            )
-        
-        # Final status
-        yield (
-            gr.update(value={"text": "", "files": []}, interactive=True),
-            final_history,
-            session_id,
-            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(f"{error_msg} (user: {username})")
-        yield (
-            gr.update(value={"text": "", "files": []}, interactive=True),
-            new_history + [{"role": "assistant", "content": f"⚠️ Error: {str(e)}"}],
-            session_id,
-            error_msg
-        )
-
-# ============================================================================
-# 10. PREMIUM UI INTERFACE
-# ============================================================================
-def create_interface():
-    """Build the complete Gradio interface"""
-    
-    with gr.Blocks(theme=gr.themes.Slate()) as demo:
-        
-        # Global states
-        session_id = gr.State(str(uuid.uuid4()))
-        db_state = gr.State([])
-        
-        with gr.Row(equal_height=False):
-            # ==================== SIDEBAR ====================
-            with gr.Column(scale=1, elem_classes="sidebar-node"):
-                # Logo
-                gr.HTML("<div class='logo-area'>⚡ NODEAI PRO</div>")
-                
-                # Authentication
-                profile = gr.LoginButton(
-                    value="🔐 Login with HF",
-                    size="sm",
-                    scale=1
-                )
-                
-                gr.Markdown("---")
-                
-                # Chat Controls
-                gr.Markdown("### 🎮 Controls")
-                with gr.Row(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 Selection
-                gr.Markdown("### 🤖 AI Model")
-                selected_model = gr.Dropdown(
-                    choices=list(AVAILABLE_MODELS.keys()),
-                    value=DEFAULT_MODEL,
-                    label="Choose Model",
-                    interactive=True,
-                    show_label=False
-                )
-                
-                # System Prompt Selection
-                gr.Markdown("### 🎯 Personality")
-                system_prompt_key = gr.Dropdown(
-                    choices=list(SYSTEM_PROMPTS.keys()),
-                    value=list(SYSTEM_PROMPTS.keys())[0],
-                    label="Persona",
-                    interactive=True,
-                    show_label=False
-                )
-                
-                # 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),
-                        gr.Textbox(visible=False),
-                        gr.Textbox(visible=False),
-                    ],
-                    samples=[],
-                    type="index",
-                    label="Conversations"
-                )
-                
-                gr.Markdown("---")
-                
-                # Statistics
-                gr.Markdown("### 📊 Stats")
-                stats_display = gr.Markdown("Loading...")
-                
-                gr.Markdown("---")
-                
-                # Footer
-                gr.HTML("<div class='custom-warning'>🔒 End-to-end encrypted. Your data, your control.</div>")
+        res = supabase.table("chats").select("message_count").eq("username", profile.username).execute()
+        data = res.data or []
+        total_msgs = sum(d.get('message_count', 0) for d in data)
+        return f"📊 Chats: **{len(data)}** | Messages: **{total_msgs}** | Avg: **{total_msgs // max(len(data), 1)}**"
+    except: return "📊 Error"
+
+def chat_engine(msg, hist, sid, model, prompt, profile):
+    text = msg.get("text", "").strip() if isinstance(msg, dict) else str(msg).strip()
+    if not text: yield gr.update(value={"text": ""}), hist, sid, "Empty"
+    else:
+        user = profile.username if profile else "guest"
+        if mgr.is_limited(user):
+            yield gr.update(value={"text": ""}), hist, sid, "Rate limited"
+        else:
+            start = time.time()
+            try:
+                model_id = MODELS[model]["id"] if model in MODELS else list(MODELS.values())[0]["id"]
+                client = InferenceClient(model=model_id, token=HF_TOKEN)
+            except:
+                client = InferenceClient(model="Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
             
-            # ==================== MAIN AREA ====================
-            with gr.Column(scale=4):
-                # Header
-                gr.HTML("""
-                <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="65vh",
-                    bubble_full_width=False,
-                    scale=1,
-                    avatar_images=(None, "https://img.icons8.com/color/96/000000/robot.png")
-                )
-                
-                # Status Bar
-                with gr.Row(scale=1, equal_height=True):
-                    status_display = gr.Textbox(
-                        value="✅ Ready",
-                        label="Status",
-                        interactive=False,
-                        scale=2,
-                        container=False
-                    )
-                    model_info = gr.Textbox(
-                        value="",
-                        label="Model",
-                        interactive=False,
-                        scale=1,
-                        container=False
-                    )
-                
-                # Input Area
-                with gr.Row(scale=1):
-                    msg_input = gr.MultimodalTextbox(
-                        show_label=False,
-                        placeholder="💬 Ask anything... (Ctrl+Enter to send)",
-                        container=False,
-                        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 ====================
-        
-        # Load history on page load
-        demo.load(
-            fn=lambda profile: get_db_history(profile),
-            inputs=[profile],
-            outputs=[db_state, history_list]
-        ).then(
-            fn=lambda profile: get_user_stats(profile),
-            inputs=[profile],
-            outputs=[stats_display]
-        )
-        
-        # 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],
-            outputs=[chatbot, session_id]
-        )
-        
-        # Send message
-        msg_input.submit(
-            fn=chat_engine,
-            inputs=[
-                msg_input,
-                chatbot,
-                session_id,
-                selected_model,
-                system_prompt_key,
-                profile
-            ],
-            outputs=[msg_input, chatbot, session_id, status_display]
-        ).then(
-            fn=lambda profile: get_db_history(profile),
-            inputs=[profile],
-            outputs=[db_state, history_list]
-        ).then(
-            fn=lambda profile: get_user_stats(profile),
-            inputs=[profile],
-            outputs=[stats_display]
-        )
-        
-        # Update model info
-        selected_model.change(
-            fn=lambda model: gr.update(value=model.split("(")[0].strip()),
-            inputs=[selected_model],
-            outputs=[model_info]
-        )
-        
-        # New chat
-        new_chat_btn.click(
-            fn=lambda: ([], str(uuid.uuid4()), "✅ New chat started"),
-            outputs=[chatbot, session_id, status_display]
-        )
-        
-        # Delete chat
-        delete_chat_btn.click(
-            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, status_display]
-        ).then(
-            fn=lambda profile: get_db_history(profile),
-            inputs=[profile],
-            outputs=[db_state, history_list]
-        )
+            msgs = [{"role": "system", "content": PROMPTS.get(prompt, list(PROMPTS.values())[0])}]
+            msgs.extend(hist[-15:])
+            msgs.append({"role": "user", "content": text})
+            new_hist = hist + [{"role": "user", "content": text}]
+            ans = ""
+            
+            yield gr.update(value={"text": ""}, interactive=False), new_hist + [{"role": "assistant", "content": "..."}], sid, "..."
+            try:
+                for token in client.chat_completion(messages=msgs, max_tokens=2048, stream=True):
+                    if token.choices[0].delta.content:
+                        ans += token.choices[0].delta.content
+                        yield gr.update(value={"text": ""}, interactive=True), new_hist + [{"role": "assistant", "content": ans}], sid, f"{len(ans)}"
+                
+                final = new_hist + [{"role": "assistant", "content": ans}]
+                if profile: save_chat(profile.username, sid, mgr.title(text), final, model)
+                mgr.record(user, "response_time", time.time() - start)
+                yield gr.update(value={"text": ""}, interactive=True), final, sid, "✅"
+            except Exception as e:
+                yield gr.update(value={"text": ""}, interactive=True), new_hist + [{"role": "assistant", "content": f"Error: {str(e)[:50]}"}], sid, "❌"
+
+with gr.Blocks(theme=gr.themes.Slate()) as demo:
+    sid = gr.State(str(uuid.uuid4()))
+    db = gr.State([])
+    
+    with gr.Row(equal_height=False):
+        with gr.Column(scale=1, elem_classes="sidebar-node"):
+            gr.HTML("<div class='logo'>⚡ NODEAI PRO</div>")
+            profile = gr.LoginButton(value="🔐 Login", size="sm")
+            
+            with gr.Row(): 
+                gr.Button("➕ New", variant="primary", scale=1, size="sm")
+                gr.Button("🗑️ Delete", scale=1, size="sm")
+            
+            gr.Markdown("### 🤖 Model")
+            model = gr.Dropdown(list(MODELS.keys()), value=list(MODELS.keys())[0], show_label=False)
+            
+            gr.Markdown("### 🎯 Personality")
+            prompt = gr.Dropdown(list(PROMPTS.keys()), value=list(PROMPTS.keys())[0], show_label=False)
+            
+            gr.Markdown("### 📚 History")
+            hist_list = gr.Dataset(components=[gr.Textbox(visible=False)]*2, samples=[], type="index")
+            
+            gr.Markdown("### 📊 Stats")
+            stats = gr.Markdown("Ready")
+            gr.HTML("<div class='warning'>🔒 Your data is private & encrypted</div>")
         
-        return demo
+        with gr.Column(scale=4):
+            gr.HTML("<div style='text-align:center;margin:20px;'><h1 style='color:#38bdf8;'>🚀 NodeAI Genesis Pro</h1><p style='color:#94a3b8;'>Enterprise AI Chat • Multi-Model • Real-time Streaming</p></div>")
+            chat = gr.Chatbot(show_label=False, height="65vh")
+            
+            with gr.Row():
+                status = gr.Textbox(value="Ready", interactive=False, scale=2)
+                model_info = gr.Textbox(value="", interactive=False, scale=1)
+            
+            inp = gr.MultimodalTextbox(show_label=False, placeholder="Ask anything... (Ctrl+Enter)")
+            gr.HTML("<div class='warning'>⚡ Streaming responses • Auto-saves</div>")
+
+demo.load(lambda p: get_hist(p), [profile], [db, hist_list]).then(lambda p: get_stats(p), [profile], [stats])
+hist_list.select(lambda e, d: load_chat(e, d), [hist_list, db], [chat, sid])
+inp.submit(chat_engine, [inp, chat, sid, model, prompt, profile], [inp, chat, sid, status]).then(lambda p: get_hist(p), [profile], [db, hist_list]).then(lambda p: get_stats(p), [profile], [stats])
 
-# ============================================================================
-# 11. MAIN EXECUTION
-# ============================================================================
 if __name__ == "__main__":
-    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
+    demo.queue().launch(css=CSS, share=False)