Upload app.py

app.py

@@ -0,0 +1,1461 @@
+# app.py
+print(">>> ENTERING app.py (top-level) <<<")
+"""
+Mimir Educational AI Assistant - Main Application
+Architecture:
+- Multi-page Gradio interface (Chatbot + Analytics with link to Mimir case study)
+- Agent-based orchestration (Tool, Routing, Thinking, Response)
+- Global state management with SQLite + HF dataset backup
+- Prompt state tracking per turn
+- LightEval for metrics tracking
+- Logger for timing functions
+- OPTIMIZED: Single Llama-3.2-3B model with lazy loading (loads on first use, ~1GB)
+"""
+import os
+import re
+import sys
+import time
+import json
+import base64
+import logging
+import sqlite3
+import subprocess
+import threading
+import warnings
+import uuid
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Any
+
+# ============================================================================
+# HUGGINGFACE CACHE SETUP - Avoid Permission Errors
+# ============================================================================
+# Use /tmp for all HuggingFace operations (writable at runtime)
+HF_CACHE = "/tmp/huggingface"
+os.makedirs(f"{HF_CACHE}/hub", exist_ok=True)
+os.makedirs(f"{HF_CACHE}/modules", exist_ok=True)
+os.makedirs(f"{HF_CACHE}/transformers", exist_ok=True)
+
+# Configure HuggingFace cache locations
+os.environ['HF_HOME'] = HF_CACHE
+os.environ['HF_HUB_CACHE'] = f"{HF_CACHE}/hub"
+os.environ['HF_MODULES_CACHE'] = f"{HF_CACHE}/modules"
+os.environ['TRANSFORMERS_CACHE'] = f"{HF_CACHE}/transformers"
+os.environ['HF_HUB_ENABLE_HF_TRANSFER'] = '1'  # Faster downloads
+
+# Matplotlib cache (avoid permission warnings)
+os.environ['MPLCONFIGDIR'] = "/tmp/matplotlib"
+os.makedirs("/tmp/matplotlib", exist_ok=True)
+
+# ============================================================================
+# CORE DEPENDENCIES
+# ============================================================================
+import torch
+import gradio as gr
+from dotenv import load_dotenv
+
+# Agent architecture
+from agents import (
+    ToolDecisionAgent,
+    PromptRoutingAgents,
+    ThinkingAgents,
+    ResponseAgent,
+)
+
+# Lazy-loading model (optional pre-warm)
+from model_manager import get_model
+
+# State management
+from state_manager import (
+    GlobalStateManager,
+    LogicalExpressions,
+)
+
+# Prompt library
+from prompt_library import (
+    CORE_IDENTITY,
+    VAUGE_INPUT,
+    USER_UNDERSTANDING,
+    GENERAL_FORMATTING,
+    LATEX_FORMATTING,
+    GUIDING_TEACHING,
+    STRUCTURE_PRACTICE_QUESTIONS,
+    PRACTICE_QUESTION_FOLLOWUP,
+    TOOL_USE_ENHANCEMENT,
+)
+
+# LangGraph imports
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.prebuilt import ToolNode
+
+# LangChain Core
+from langchain_core.tools import tool
+from langchain_core.messages import HumanMessage, SystemMessage, AIMessage, ToolMessage, BaseMessage
+
+# Tool for graphing
+from graph_tool import generate_plot
+
+
+# ============================================================================
+# LIGHTEVAL FOR METRICS
+# ============================================================================
+try:
+    from lighteval.logging.evaluation_tracker import EvaluationTracker
+    from lighteval.models.transformers.transformers_model import TransformersModel
+    from lighteval.metrics.metrics_sample import BertScore, ROUGE
+    from lighteval.tasks.requests import Doc
+    LIGHTEVAL_AVAILABLE = True
+except ImportError:
+    LIGHTEVAL_AVAILABLE = False
+    logging.warning("LightEval not available - metrics tracking limited")
+
+# ============================================================================
+# CONFIGURATION
+# ============================================================================
+# Suppress warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+# Load environment
+load_dotenv(".env")
+HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+# Debug and runtime settings
+DEBUG_STATE = os.getenv("DEBUG_STATE", "false").lower() == "true"
+CURRENT_YEAR = datetime.now().year
+
+
+# ============================================================================
+# LOGGING SETUP
+# ============================================================================
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+def log_step(step_name: str, start_time: Optional[float] = None) -> float:
+    """
+    Log a pipeline step with timestamp and duration.
+    
+    Args:
+        step_name: Name of the step
+        start_time: Start time from previous call (if completing a step)
+        
+    Returns:
+        Current time for next call
+    """
+    now = time.time()
+    timestamp = datetime.now().strftime("%H:%M:%S.%f")[:-3]
+    
+    if start_time:
+        duration = now - start_time
+        logger.info(f"[{timestamp}] COMPLETED: {step_name} ({duration:.2f}s)")
+    else:
+        logger.info(f"[{timestamp}] STARTING: {step_name}")
+    
+    return now
+
+# ============================================================================
+# MODEL INFORMATION
+# ============================================================================
+print("="*60)
+print("MIMIR - Using Llama-3.2-3B-Instruct")
+print("  Model: meta-llama/Llama-3.2-3B-Instruct")
+print("  Memory: ~1GB (4-bit quantized)")
+print("  Context: 128K tokens")
+print("  Architecture: Single unified model")
+print("="*60)
+
+# ============================================================================
+# GLOBAL INITIALIZATION
+# ============================================================================
+
+logger.info("="*60)
+logger.info("INITIALIZING MIMIR APPLICATION")
+logger.info("="*60)
+
+init_start = log_step("Global Initialization")
+
+# Initialize state management
+global_state_manager = GlobalStateManager()
+logical_expressions = LogicalExpressions()
+logger.info("State management initialized")
+
+# Initialize agents (lazy loading - models load on first use)
+tool_agent = ToolDecisionAgent()
+routing_agents = PromptRoutingAgents()
+thinking_agents = ThinkingAgents()
+response_agent = ResponseAgent()
+logger.info("Agents initialized (using shared get_shared_llama)")
+
+# Pre-warm shared Qwen3-Claude (optional - happens on first agent call anyway)
+logger.info("Shared Qwen3-Claude agent ready (loads on first use)")
+
+log_step("Global Initialization", init_start)
+
+
+# ============================================================================
+# ANALYTICS & DATABASE FUNCTIONS
+# ============================================================================
+
+def get_trackio_database_path(project_name: str) -> Optional[str]:
+    """Get path to metrics SQLite database"""
+    possible_paths = [
+        f"./{project_name}.db",
+        f"./trackio_data/{project_name}.db",
+        f"./.trackio/{project_name}.db",
+        "./mimir_metrics.db"
+    ]
+    
+    for path in possible_paths:
+        if os.path.exists(path):
+            return path
+    
+    return None
+
+
+def get_project_statistics_with_nulls(cursor, project_name: str) -> Dict:
+    """Query metrics database for project statistics"""
+    try:
+        stats = {}
+        
+        # Total conversations
+        try:
+            cursor.execute("""
+                SELECT COUNT(DISTINCT run_id) as total_runs
+                FROM metrics 
+                WHERE project_name = ?
+            """, (project_name,))
+            result = cursor.fetchone()
+            stats["total_conversations"] = result["total_runs"] if result and result["total_runs"] > 0 else None
+        except sqlite3.Error:
+            stats["total_conversations"] = None
+        
+        # Average response time
+        try:
+            cursor.execute("""
+                SELECT AVG(CAST(value AS FLOAT)) as avg_response_time
+                FROM metrics 
+                WHERE project_name = ? AND metric_name = 'response_time'
+            """, (project_name,))
+            result = cursor.fetchone()
+            if result and result["avg_response_time"] is not None:
+                stats["avg_session_length"] = round(result["avg_response_time"], 2)
+            else:
+                stats["avg_session_length"] = None
+        except sqlite3.Error:
+            stats["avg_session_length"] = None
+        
+        # Success rate
+        try:
+            cursor.execute("""
+                SELECT 
+                    COUNT(*) as total_responses,
+                    SUM(CASE WHEN CAST(value AS FLOAT) > 3.5 THEN 1 ELSE 0 END) as successful_responses
+                FROM metrics 
+                WHERE project_name = ? AND metric_name = 'quality_score'
+            """, (project_name,))
+            result = cursor.fetchone()
+            if result and result["total_responses"] > 0:
+                success_rate = (result["successful_responses"] / result["total_responses"]) * 100
+                stats["success_rate"] = round(success_rate, 1)
+            else:
+                stats["success_rate"] = None
+        except sqlite3.Error:
+            stats["success_rate"] = None
+            
+        return stats
+        
+    except sqlite3.Error as e:
+        logger.error(f"Database error: {e}")
+        return {"total_conversations": None, "avg_session_length": None, "success_rate": None}
+
+
+def get_recent_interactions_with_nulls(cursor, project_name: str, limit: int = 10) -> List:
+    """Query for recent interactions"""
+    try:
+        cursor.execute("""
+            SELECT 
+                m1.timestamp,
+                m2.value as response_time,
+                m3.value as prompt_mode,
+                m4.value as tools_used,
+                m5.value as quality_score,
+                m6.value as adapter_used,
+                m1.run_id
+            FROM metrics m1
+            LEFT JOIN metrics m2 ON m1.run_id = m2.run_id AND m2.metric_name = 'response_time'
+            LEFT JOIN metrics m3 ON m1.run_id = m3.run_id AND m3.metric_name = 'prompt_mode'
+            LEFT JOIN metrics m4 ON m1.run_id = m4.run_id AND m4.metric_name = 'tools_used'
+            LEFT JOIN metrics m5 ON m1.run_id = m5.run_id AND m5.metric_name = 'quality_score'
+            LEFT JOIN metrics m6 ON m1.run_id = m6.run_id AND m6.metric_name = 'active_adapter'
+            WHERE m1.project_name = ? AND m1.metric_name = 'conversation_start'
+            ORDER BY m1.timestamp DESC
+            LIMIT ?
+        """, (project_name, limit))
+        
+        results = cursor.fetchall()
+        recent_data = []
+        
+        for row in results:
+            recent_data.append([
+                row["timestamp"][:16] if row["timestamp"] else None,
+                float(row["response_time"]) if row["response_time"] is not None else None,
+                row["prompt_mode"] if row["prompt_mode"] else None,
+                bool(int(row["tools_used"])) if row["tools_used"] is not None else None,
+                float(row["quality_score"]) if row["quality_score"] is not None else None,
+                row["adapter_used"] if row["adapter_used"] else None
+            ])
+            
+        return recent_data
+        
+    except sqlite3.Error as e:
+        logger.error(f"Database error: {e}")
+        return []
+
+
+def create_dashboard_html_with_nulls(project_name: str, project_stats: Dict) -> str:
+    """Create dashboard HTML with enhanced agent-based metrics"""
+    def format_stat(value, suffix="", no_data_text="No data"):
+        if value is None:
+            return f'<span style="color: #999; font-style: italic;">{no_data_text}</span>'
+        return f"{value}{suffix}"
+    
+    def format_large_stat(value, suffix="", no_data_text="--"):
+        if value is None:
+            return f'<span style="color: #ccc;">{no_data_text}</span>'
+        return f"{value}{suffix}"
+    
+    # Get evaluation metrics from global state
+    try:
+        eval_summary = global_state_manager.get_evaluation_summary()
+        cache_status = global_state_manager.get_cache_status()
+        
+        project_stats["ml_educational_quality"] = eval_summary['aggregate_metrics']['avg_educational_quality']
+        project_stats["user_satisfaction"] = eval_summary['aggregate_metrics']['user_satisfaction_rate']
+        project_stats["active_sessions"] = cache_status['total_conversation_sessions']
+        
+    except Exception as e:
+        logger.warning(f"Could not get global state metrics: {e}")
+        project_stats["ml_educational_quality"] = None
+        project_stats["user_satisfaction"] = None
+        project_stats["active_sessions"] = None
+    
+    # Status determination
+    success_rate = project_stats.get("success_rate")
+    if success_rate is not None:
+        if success_rate >= 80:
+            status_color = "#4CAF50"
+            status_text = "Excellent"
+        elif success_rate >= 60:
+            status_color = "#FF9800"
+            status_text = "Good"
+        else:
+            status_color = "#F44336"
+            status_text = "Needs Improvement"
+    else:
+        status_color = "#999"
+        status_text = "No data"
+    
+    # Agent-based metrics section
+    agent_metrics_section = f"""
+    <div style="margin: 15px 0; padding: 10px; background: #f0f8ff; border-radius: 4px; border-left: 4px solid #007bff;">
+        <strong>🚀 Agent Performance (Qwen3-Claude Single Model):</strong>
+        Educational Quality: {format_stat(project_stats.get('ml_educational_quality'), '', 'N/A')} | 
+        User Satisfaction: {format_stat(project_stats.get('user_satisfaction'), '%' if project_stats.get('user_satisfaction') else '', 'N/A')} | 
+        Active Sessions: {format_stat(project_stats.get('active_sessions'), '', 'N/A')}
+    </div>
+    """
+    
+    dashboard_html = f'''
+    <div style="text-align: center; padding: 20px; border: 1px solid #ddd; border-radius: 8px; background: #f9f9f9;">
+        <h3>📊 {project_name} Analytics</h3>
+        
+        <div style="display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 15px; margin: 20px 0;">
+            <div style="padding: 15px; background: white; border-radius: 6px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+                <div style="font-size: 24px; font-weight: bold; color: #2196F3;">{format_large_stat(project_stats.get('total_conversations'))}</div>
+                <div style="color: #666; font-size: 12px;">Total Sessions</div>
+            </div>
+            <div style="padding: 15px; background: white; border-radius: 6px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+                <div style="font-size: 24px; font-weight: bold; color: #FF9800;">{format_large_stat(project_stats.get('avg_session_length'), 's' if project_stats.get('avg_session_length') else '')}</div>
+                <div style="color: #666; font-size: 12px;">Avg Response Time</div>
+            </div>
+            <div style="padding: 15px; background: white; border-radius: 6px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+                <div style="font-size: 24px; font-weight: bold; color: {status_color};">{format_large_stat(success_rate, '%' if success_rate else '')}</div>
+                <div style="color: #666; font-size: 12px;">Success Rate ({status_text})</div>
+            </div>
+        </div>
+        
+        {agent_metrics_section}
+        
+        <div style="margin: 15px 0; padding: 10px; background: #fff3cd; border-radius: 4px; font-size: 14px;">
+            <strong>Model:</strong> {format_stat(project_stats.get('model_type'), no_data_text='Unknown')} | 
+            <strong>Last Updated:</strong> {project_stats.get('last_updated', 'Unknown')}
+        </div>
+    </div>
+    '''
+    
+    return dashboard_html
+
+
+def calculate_response_quality(response: str) -> float:
+    """Calculate response quality score"""
+    try:
+        length_score = min(len(response) / 200, 1.0)
+        educational_keywords = ['learn', 'understand', 'concept', 'example', 'practice']
+        keyword_score = sum(1 for keyword in educational_keywords if keyword in response.lower()) / len(educational_keywords)
+        
+        if len(response) < 20:
+            return 2.0
+        elif len(response) > 2000:
+            return 3.5
+        
+        base_score = 2.5 + (length_score * 1.5) + (keyword_score * 1.0)
+        return min(max(base_score, 1.0), 5.0)
+    except:
+        return 3.0
+
+
+def evaluate_educational_quality_with_tracking(user_query: str, response: str, thread_id: str = None, session_id: str = None):
+    """Educational quality evaluation with state tracking using LightEval"""
+    start_time = time.time()
+    
+    try:
+        # Educational indicators
+        educational_indicators = {
+            'has_examples': 'example' in response.lower(),
+            'structured_explanation': '##' in response or '1.' in response,
+            'appropriate_length': 100 < len(response) < 1500,
+            'encourages_learning': any(phrase in response.lower() 
+                for phrase in ['practice', 'try', 'consider', 'think about']),
+            'uses_latex': '$' in response,
+            'has_clear_sections': response.count('\n\n') >= 2
+        }
+        
+        educational_score = sum(educational_indicators.values()) / len(educational_indicators)
+        semantic_quality = min(len(response) / 500, 1.0)
+        response_time = time.time() - start_time
+        
+        # Use LightEval if available
+        if LIGHTEVAL_AVAILABLE:
+            try:
+                doc = Doc(
+                    task_name=f"turn_{thread_id or session_id}",
+                    query=user_query,
+                    choices=[response],
+                    gold_index=-1,
+                    specific_output=response
+                )
+                
+                bert_score = BertScore().compute(doc)
+                semantic_quality = bert_score if bert_score else semantic_quality
+                
+            except Exception as lighteval_error:
+                logger.warning(f"LightEval computation failed: {lighteval_error}")
+        
+        metrics = {
+            'semantic_quality': semantic_quality,
+            'educational_score': educational_score,
+            'response_time': response_time,
+            'indicators': educational_indicators
+        }
+        
+        # Track in global state
+        global_state_manager.add_educational_quality_score(
+            user_query=user_query,
+            response=response,
+            metrics=metrics,
+            session_id=session_id
+        )
+        
+        logger.info(f"Educational quality evaluated: {educational_score:.3f}")
+        return metrics
+        
+    except Exception as e:
+        logger.error(f"Educational quality evaluation failed: {e}")
+        return {'educational_score': 0.5, 'semantic_quality': 0.5, 'response_time': 0.0}
+
+def log_metrics_to_database(project_name: str, run_id: str, metrics: Dict):
+    """Log metrics to SQLite database for dashboard"""
+    try:
+        db_path = get_trackio_database_path(project_name)
+        
+        if db_path is None:
+            db_path = "./mimir_metrics.db"
+        
+        conn = sqlite3.connect(db_path)
+        cursor = conn.cursor()
+        
+        # Create metrics table if not exists
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS metrics (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                project_name TEXT,
+                run_id TEXT,
+                metric_name TEXT,
+                value TEXT,
+                timestamp TEXT
+            )
+        """)
+        
+        # Insert metrics
+        timestamp = datetime.now().isoformat()
+        for metric_name, metric_value in metrics.items():
+            cursor.execute("""
+                INSERT INTO metrics (project_name, run_id, metric_name, value, timestamp)
+                VALUES (?, ?, ?, ?, ?)
+            """, (project_name, run_id, metric_name, str(metric_value), timestamp))
+        
+        conn.commit()
+        conn.close()
+        
+        logger.info(f"Logged {len(metrics)} metrics to database")
+        
+    except Exception as e:
+        logger.error(f"Failed to log metrics to database: {e}")
+
+
+def sync_trackio_with_global_state():
+    """Sync metrics database with global state manager data"""
+    try:
+        eval_summary = global_state_manager.get_evaluation_summary()
+        
+        # Log to database (agent-based metrics only)
+        metrics = {
+            "educational_quality_avg": eval_summary['aggregate_metrics']['avg_educational_quality'],
+            "user_satisfaction": eval_summary['aggregate_metrics']['user_satisfaction_rate'],
+            "total_evaluations": sum(eval_summary['total_evaluations'].values())
+        }
+        
+        log_metrics_to_database("Mimir", str(uuid.uuid4()), metrics)
+        
+        logger.info("Synced global state metrics to database")
+        
+    except Exception as e:
+        logger.error(f"Failed to sync metrics to database: {e}")
+
+
+def refresh_analytics_data_persistent():
+    """Refresh analytics data with global state persistence"""
+    project_name = "Mimir"
+    
+    try:
+        analytics_state = global_state_manager.get_analytics_state()
+        last_refresh = analytics_state.get('last_refresh')
+        
+        # If refreshed within last 30 seconds, return cached
+        if last_refresh and (datetime.now() - last_refresh).seconds < 30:
+            logger.info("Using cached analytics data (recent refresh)")
+            return (
+                analytics_state['project_stats'],
+                analytics_state['recent_interactions'],
+                analytics_state['dashboard_html']
+            )
+        
+        db_path = get_trackio_database_path(project_name)
+        
+        if db_path is None:
+            logger.warning("No metrics database found")
+            project_stats = {
+                "total_conversations": None,
+                "avg_session_length": None, 
+                "success_rate": None,
+                "model_type": "Qwen3-4B-Claude GGUF (Q6_K - Single Model)",
+                "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            }
+            
+            dashboard_html = create_dashboard_html_with_nulls(project_name, project_stats)
+            recent_interactions = []
+            
+            global_state_manager.update_analytics_state(
+                project_stats=project_stats,
+                recent_interactions=recent_interactions,
+                dashboard_html=dashboard_html
+            )
+            
+            return project_stats, recent_interactions, dashboard_html
+        
+        conn = sqlite3.connect(db_path)
+        conn.row_factory = sqlite3.Row
+        cursor = conn.cursor()
+        
+        project_stats = get_project_statistics_with_nulls(cursor, project_name)
+        project_stats["model_type"] = "Qwen3-4B-Claude GGUF (Q6_K - Single Model)"
+        project_stats["last_updated"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        
+        recent_data = get_recent_interactions_with_nulls(cursor, project_name, limit=10)
+        dashboard_html = create_dashboard_html_with_nulls(project_name, project_stats)
+        
+        conn.close()
+        
+        global_state_manager.update_analytics_state(
+            project_stats=project_stats,
+            recent_interactions=recent_data,
+            dashboard_html=dashboard_html
+        )
+        
+        logger.info("Analytics data refreshed and cached successfully")
+        return project_stats, recent_data, dashboard_html
+        
+    except Exception as e:
+        logger.error(f"Error refreshing analytics: {e}")
+        
+        error_stats = {
+            "error": str(e),
+            "total_conversations": None,
+            "avg_session_length": None,
+            "success_rate": None,
+            "model_type": "Error",
+            "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        }
+        
+        error_html = f"""
+        <div style="text-align: center; padding: 40px; border: 2px dashed #f44336; border-radius: 8px; background: #ffebee;">
+            <h3 style="color: #f44336;">⚠️ Analytics Error</h3>
+            <p>Could not load analytics data: {str(e)[:100]}</p>
+        </div>
+        """
+        
+        global_state_manager.update_analytics_state(
+            project_stats=error_stats,
+            recent_interactions=[],
+            dashboard_html=error_html,
+            error_state=str(e)
+        )
+        
+        return error_stats, [], error_html
+
+
+def export_metrics_json_persistent():
+    """Export metrics as JSON file"""
+    try:
+        project_stats, recent_data, _ = refresh_analytics_data_persistent()
+        
+        export_data = {
+            "project": "Mimir",
+            "export_timestamp": datetime.now().isoformat(),
+            "statistics": project_stats,
+            "recent_interactions": recent_data
+        }
+        
+        filename = f"mimir_metrics_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+        
+        with open(filename, 'w') as f:
+            json.dump(export_data, f, indent=2, default=str)
+        
+        global_state_manager.add_export_record("JSON", filename, success=True)
+        
+        logger.info(f"Metrics exported to {filename}")
+        gr.Info(f"Metrics exported successfully to {filename}")
+        
+    except Exception as e:
+        global_state_manager.add_export_record("JSON", "failed", success=False)
+        logger.error(f"Export failed: {e}")
+        gr.Warning(f"Export failed: {str(e)}")
+
+
+def export_metrics_csv_persistent():
+    """Export metrics as CSV file"""
+    try:
+        import csv
+        
+        _, recent_data, _ = refresh_analytics_data_persistent()
+        
+        filename = f"mimir_metrics_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
+        
+        with open(filename, 'w', newline='') as f:
+            writer = csv.writer(f)
+            writer.writerow(["Timestamp", "Response Time", "Mode", "Tools Used", "Quality Score", "Adapter"])
+            
+            for row in recent_data:
+                writer.writerow(row)
+        
+        global_state_manager.add_export_record("CSV", filename, success=True)
+        
+        logger.info(f"Metrics exported to {filename}")
+        gr.Info(f"Metrics exported successfully to {filename}")
+        
+    except Exception as e:
+        global_state_manager.add_export_record("CSV", "failed", success=False)
+        logger.error(f"Export failed: {e}")
+        gr.Warning(f"Export failed: {str(e)}")
+
+
+def load_analytics_state():
+    """Load analytics state from global manager"""
+    analytics_state = global_state_manager.get_analytics_state()
+    
+    project_stats = analytics_state['project_stats']
+    recent_interactions = analytics_state['recent_interactions']
+    dashboard_html = analytics_state['dashboard_html']
+    
+    if dashboard_html is None:
+        dashboard_html = """
+        <div style="text-align: center; padding: 40px; border: 2px dashed #ccc; border-radius: 8px; background: #f8f9fa;">
+            <h3>📊 Analytics Dashboard</h3>
+            <p>Click "Refresh Data" to load analytics.</p>
+        </div>
+        """
+    
+    return project_stats, recent_interactions, dashboard_html
+
+
+def get_global_state_debug_info():
+    """Get debug information about global state"""
+    cache_status = global_state_manager.get_cache_status()
+    
+    debug_info = {
+        "cache_status": cache_status,
+        "timestamp": datetime.now().isoformat(),
+        "sessions": global_state_manager.get_all_sessions()
+    }
+    
+    return debug_info
+
+
+# ============================================================================
+# POST-PROCESSING
+# ============================================================================
+
+class ResponsePostProcessor:
+    """Post-processing pipeline for educational responses"""
+    
+    def __init__(self, max_length: int = 1800, min_length: int = 10):
+        self.max_length = max_length
+        self.min_length = min_length
+        
+        self.logical_stop_patterns = [
+            r'\n\n---\n',
+            r'\n\n## Summary\b',
+            r'\n\nIn conclusion\b',
+            r'\n\nTo summarize\b',
+        ]
+    
+    def process_response(self, raw_response: str, user_query: str = "") -> str:
+        """Main post-processing pipeline"""
+        try:
+            cleaned = self._enhanced_token_cleanup(raw_response)
+            cleaned = self._truncate_intelligently(cleaned)
+            cleaned = self._enhance_readability(cleaned)
+            
+            if not self._passes_quality_check(cleaned):
+                return self._generate_fallback_response(user_query)
+            
+            return cleaned.strip()
+            
+        except Exception as e:
+            logger.error(f"Post-processing error: {e}")
+            return raw_response
+    
+    def _enhanced_token_cleanup(self, text: str) -> str:
+        """Remove model artifacts"""
+        artifacts = [
+            r'<\|.*?\|>',
+            r'###\s*$',
+            r'User:\s*$',
+            r'Assistant:\s*$',
+            r'\n\s*\n\s*\n+',
+        ]
+        
+        for pattern in artifacts:
+            text = re.sub(pattern, '', text, flags=re.MULTILINE)
+        
+        return text
+    
+    def _truncate_intelligently(self, text: str) -> str:
+        """Truncate at logical educational endpoints"""
+        for pattern in self.logical_stop_patterns:
+            match = re.search(pattern, text, re.IGNORECASE)
+            if match:
+                return text[:match.start()].strip()
+        
+        if len(text) <= self.max_length:
+            return text
+        
+        sentences = re.split(r'[.!?]+\s+', text)
+        truncated = ""
+        
+        for sentence in sentences:
+            test_length = len(truncated + sentence + ". ")
+            if test_length <= self.max_length:
+                truncated += sentence + ". "
+            else:
+                break
+        
+        return truncated.strip()
+    
+    def _enhance_readability(self, text: str) -> str:
+        """Format for better presentation"""
+        text = re.sub(r'([.!?])([A-Z])', r'\1 \2', text)
+        text = re.sub(r'\s{2,}', ' ', text)
+        text = re.sub(r'\n\s*[-*]\s*', '\n- ', text)
+        
+        return text
+    
+    def _passes_quality_check(self, text: str) -> bool:
+        """Final quality validation"""
+        if len(text.strip()) < self.min_length:
+            return False
+        
+        sentences = re.split(r'[.!?]+', text)
+        valid_sentences = [s for s in sentences if len(s.strip()) > 5]
+        
+        return len(valid_sentences) > 0
+    
+    def _generate_fallback_response(self, user_query: str) -> str:
+        """Generate safe fallback"""
+        return "I'd be happy to help you understand this better. Could you clarify what specific aspect you'd like me to focus on?"
+    
+    def process_and_stream_response(self, raw_response: str, user_query: str = ""):
+        """Process response then stream word-by-word"""
+        try:
+            processed_response = self.process_response(raw_response, user_query)
+            
+            words = processed_response.split()
+            current_output = ""
+            
+            for i, word in enumerate(words):
+                current_output += word
+                if i < len(words) - 1:
+                    current_output += " "
+                
+                yield current_output
+                time.sleep(0.015)
+                
+        except Exception as e:
+            logger.error(f"Stream processing error: {e}")
+            yield "I encountered an error processing the response."
+
+
+post_processor = ResponsePostProcessor()
+
+
+# ============================================================================
+# TOOL FUNCTIONS
+# ============================================================================
+
+@tool(return_direct=False)
+def Create_Graph_Tool(
+    data: dict,
+    plot_type: str,
+    title: str = "Generated Plot",
+    x_label: str = "",
+    y_label: str = "",
+    educational_context: str = ""
+) -> str:
+    """Generate educational graphs"""
+    tool_start = log_step("Create_Graph_Tool")
+    
+    try:
+        content, artifact = generate_plot(
+            data=data,
+            plot_type=plot_type,
+            title=title,
+            x_label=x_label,
+            y_label=y_label
+        )
+        
+        if "error" in artifact:
+            log_step("Create_Graph_Tool", tool_start)
+            return f'<p style="color:red;">Graph generation failed: {artifact["error"]}</p>'
+        
+        base64_image = artifact["base64_image"]
+        
+        context_html = ""
+        if educational_context:
+            context_html = f'<div style="margin: 10px 0; padding: 10px; background: #f8f9fa; border-left: 4px solid #007bff;">💡 {educational_context}</div>'
+        
+        result = f"""{context_html}
+<div style="text-align: center; margin: 20px 0;">
+    <img src="data:image/png;base64,{base64_image}" 
+         style="max-width: 100%; height: auto; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.1);" 
+         alt="{title}" />
+</div>"""
+        
+        log_step("Create_Graph_Tool", tool_start)
+        return result
+        
+    except Exception as e:
+        logger.error(f"Graph tool error: {e}")
+        log_step("Create_Graph_Tool", tool_start)
+        return f'<p style="color:red;">Error: {str(e)}</p>'
+
+
+# ============================================================================
+# MAIN ORCHESTRATION WORKFLOW
+# ============================================================================
+
+def orchestrate_turn(user_input: str, session_id: str = "default") -> str:
+    """
+    Main orchestration function implementing the redesign workflow.
+    
+    OPTIMIZED: Uses single Qwen3-Claude GGUF (loads once, all agents share)
+    
+    Steps:
+    1. Reset prompt state
+    2. Process user input (history)
+    3. Tool decision (Qwen3-Claude)
+    4. Regex checks
+    5. Agent execution (Qwen3-Claude)
+    6. Thinking agents (Qwen3-Claude)
+    7. Prompt assembly
+    8. Response generation (Qwen3-Claude)
+    9. Post-processing
+    10. Metrics tracking (background thread)
+    """
+    turn_start = log_step("orchestrate_turn")
+    run_id = str(uuid.uuid4())
+    
+    try:
+        # ====================================================================
+        # STEP 1: RESET PROMPT STATE
+        # ====================================================================
+        step_start = log_step("Step 1: Reset prompt state")
+        global_state_manager.reset_prompt_state()
+        prompt_state = global_state_manager.get_prompt_state_manager()
+        log_step("Step 1: Reset prompt state", step_start)
+        
+        # ====================================================================
+        # STEP 2: USER INPUT PROCESSING
+        # ====================================================================
+        step_start = log_step("Step 2: Process user input")
+        
+        # Get conversation history
+        conversation_state = global_state_manager.get_conversation_state(session_id)
+        recent_history = conversation_state['conversation_state'][-8:] if conversation_state['conversation_state'] else []
+        
+        # Format history for agents
+        recent_history_formatted = "\n".join([
+            f"{msg['role']}: {msg['content'][:100]}" 
+            for msg in recent_history
+        ]) if recent_history else "No previous conversation"
+        
+        log_step("Step 2: Process user input", step_start)
+        
+        # ====================================================================
+        # STEP 3: TOOL DECISION ENGINE (Qwen3-Claude)
+        # ====================================================================
+        step_start = log_step("Step 3: Tool decision")
+        tool_decision_result = tool_agent.should_use_visualization(user_input)
+        
+        tool_img_output = ""
+        tool_context = ""
+        
+        if tool_decision_result:
+            logger.info("Tool decision: YES - visualization needed")
+            prompt_state.update("TOOL_USE_ENHANCEMENT", True)
+        else:
+            logger.info("Tool decision: NO - no visualization needed")
+        
+        log_step("Step 3: Tool decision", step_start)
+        
+        # ====================================================================
+        # STEP 4: REGEX LOGICAL EXPRESSIONS
+        # ====================================================================
+        step_start = log_step("Step 4: Regex checks")
+        logical_expressions.apply_all_checks(user_input, prompt_state)
+        log_step("Step 4: Regex checks", step_start)
+        
+        # ====================================================================
+        # STEP 5: SEQUENTIAL AGENT EXECUTION (Qwen3-Claude)
+        # ====================================================================
+        step_start = log_step("Step 5: Routing agents")
+        
+        # Use unified process() method that handles all 4 routing agents
+        response_prompts_str, thinking_prompts_str = routing_agents.process(
+            user_input=user_input,
+            tool_used=(tool_decision_result and bool(tool_img_output))
+        )
+        
+        # Update prompt state with response prompts
+        if response_prompts_str:
+            for prompt_name in response_prompts_str.split('\n'):
+                if prompt_name.strip():
+                    prompt_state.update(prompt_name.strip(), True)
+                    logger.info(f"Response prompt activated: {prompt_name.strip()}")
+        
+        # Store thinking prompts for Step 6 (will be processed by ThinkingAgents)
+        thinking_prompts_from_routing = thinking_prompts_str.split('\n') if thinking_prompts_str else []
+        for prompt_name in thinking_prompts_from_routing:
+            if prompt_name.strip():
+                logger.info(f"Thinking prompt queued: {prompt_name.strip()}")
+        
+        log_step("Step 5: Routing agents", step_start)
+        
+        # ====================================================================
+        # STEP 6: THINKING AGENT PROCESSING (Qwen3-Claude)
+        # ====================================================================
+        step_start = log_step("Step 6: Thinking agents")
+        
+        # Use thinking prompts identified by routing agents in Step 5
+        thinking_prompts_list = []
+        
+        # Add thinking prompts from routing agents
+        for prompt_name in thinking_prompts_from_routing:
+            if prompt_name.strip():
+                thinking_prompts_list.append(prompt_name.strip())
+                prompt_state.update(prompt_name.strip(), True)
+        
+        # Additional heuristic: Add MATH_THINKING if LATEX_FORMATTING is active
+        # (This ensures math thinking is triggered even if routing agents didn't detect it)
+        if prompt_state.is_active("LATEX_FORMATTING") and "MATH_THINKING" not in thinking_prompts_list:
+            thinking_prompts_list.append("MATH_THINKING")
+            prompt_state.update("MATH_THINKING", True)
+        
+        # Execute thinking agents if any are active
+        thinking_context = ""
+        if thinking_prompts_list:
+            thinking_prompts_string = '\n'.join(thinking_prompts_list)
+            logger.info(f"Active thinking agents: {thinking_prompts_list}")
+            
+            think_start = log_step("Thinking agents execution")
+            thinking_context = thinking_agents.process(
+                user_input=user_input,
+                conversation_history=recent_history_formatted,
+                thinking_prompts=thinking_prompts_string,
+                tool_img_output=tool_img_output,
+                tool_context=tool_context
+            )
+            log_step("Thinking agents execution", think_start)
+        
+        log_step("Step 6: Thinking agents", step_start)
+        
+        # ====================================================================
+        # STEP 7: RESPONSE PROMPT ASSEMBLY
+        # ====================================================================
+        step_start = log_step("Step 7: Prompt assembly")
+        
+        # Get active response prompts
+        response_prompt_names = prompt_state.get_active_response_prompts()
+        
+        # Build prompt segments
+        prompt_segments = [CORE_IDENTITY]
+        
+        prompt_map = {
+            "VAUGE_INPUT": VAUGE_INPUT,
+            "USER_UNDERSTANDING": USER_UNDERSTANDING,
+            "GENERAL_FORMATTING": GENERAL_FORMATTING,
+            "LATEX_FORMATTING": LATEX_FORMATTING,
+            "GUIDING_TEACHING": GUIDING_TEACHING,
+            "STRUCTURE_PRACTICE_QUESTIONS": STRUCTURE_PRACTICE_QUESTIONS,
+            "PRACTICE_QUESTION_FOLLOWUP": PRACTICE_QUESTION_FOLLOWUP,
+            "TOOL_USE_ENHANCEMENT": TOOL_USE_ENHANCEMENT,
+        }
+        
+        for prompt_name in response_prompt_names:
+            if prompt_name in prompt_map:
+                prompt_segments.append(prompt_map[prompt_name])
+        
+        prompt_segments_text = "\n\n".join(prompt_segments)
+        
+        logger.info(f"Active prompts: {response_prompt_names}")
+        log_step("Step 7: Prompt assembly", step_start)
+        
+        # ====================================================================
+        # STEP 8: FINAL PROMPT CONSTRUCTION
+        # ====================================================================
+        step_start = log_step("Step 8: Final prompt construction")
+        
+        # Knowledge cutoff
+        knowledge_cutoff = f"""
+
+The current year is {CURRENT_YEAR}. Your knowledge cutoff date is October 2023. If the user asks about recent events or dynamic facts, inform them you may not have the most up-to-date information and suggest referencing direct sources."""
+        
+        complete_prompt = f"""
+{prompt_segments_text}
+
+If tools were used, context and output will be here. Ignore if empty:
+Image output: {tool_img_output}
+Image context: {tool_context}
+
+Conversation history, if available:
+{recent_history_formatted}
+
+Consider any context available to you:
+{thinking_context}
+
+Here is the user's current query:
+{user_input}
+
+{knowledge_cutoff}
+"""
+        
+        log_step("Step 8: Final prompt construction", step_start)
+        
+        # ====================================================================
+        # STEP 9: RESPONSE GENERATION (Phi3)
+        # ====================================================================
+        step_start = log_step("Step 9: Response generation")
+        raw_response = response_agent.invoke(complete_prompt)
+        log_step("Step 9: Response generation", step_start)
+        
+        # ====================================================================
+        # STEP 10: POST-PROCESSING
+        # ====================================================================
+        step_start = log_step("Step 10: Post-processing")
+        processed_response = post_processor.process_response(raw_response, user_input)
+        log_step("Step 10: Post-processing", step_start)
+        
+        # ====================================================================
+        # STEP 11: METRICS TRACKING (BACKGROUND THREAD - NON-BLOCKING)
+        # ====================================================================
+        step_start = log_step("Step 11: Metrics tracking")
+        
+        def track_metrics_async():
+            """Run metrics tracking in background to avoid blocking"""
+            try:
+                logger.info("[Background] Starting metrics tracking...")
+                
+                # Track educational quality
+                quality_metrics = evaluate_educational_quality_with_tracking(
+                    user_query=user_input,
+                    response=processed_response,
+                    thread_id=run_id,
+                    session_id=session_id
+                )
+                
+                # Log metrics to database
+                metrics_to_log = {
+                    "conversation_start": datetime.now().isoformat(),
+                    "response_time": time.time() - turn_start,
+                    "quality_score": calculate_response_quality(processed_response),
+                    "educational_score": quality_metrics['educational_score'],
+                    "prompt_mode": ",".join(response_prompt_names),
+                    "tools_used": 1 if prompt_state.is_active("TOOL_USE_ENHANCEMENT") else 0,
+                    "thinking_agents": ",".join(thinking_prompts_list) if thinking_prompts_list else "none",
+                    "active_adapter": response_agent.model_type if response_agent.model_loaded else "not_loaded"
+                }
+                
+                log_metrics_to_database("Mimir", run_id, metrics_to_log)
+                logger.info("[Background] ✓ Metrics tracking completed")
+                
+            except Exception as metrics_error:
+                logger.warning(f"[Background] Metrics tracking failed: {metrics_error}")
+        
+        # Start background thread (daemon=True so it doesn't block shutdown)
+        metrics_thread = threading.Thread(
+            target=track_metrics_async,
+            daemon=True,
+            name="MetricsTracking"
+        )
+        metrics_thread.start()
+        
+        log_step("Step 11: Metrics tracking", step_start)
+        logger.info("✓ Metrics tracking started in background - continuing immediately")
+        
+        log_step("orchestrate_turn", turn_start)
+        return processed_response
+        
+    except Exception as e:
+        logger.error(f"Orchestration error: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+        log_step("orchestrate_turn", turn_start)
+        return f"I encountered an error: {str(e)}"
+
+
+# ============================================================================
+# GRADIO CALLBACK FUNCTIONS (FIXED STATE MANAGEMENT)
+# ============================================================================
+
+def get_loading_animation_base64():
+    """Load animated GIF as base64"""
+    try:
+        with open("loading_animation.gif", "rb") as gif_file:
+            gif_data = gif_file.read()
+            gif_base64 = base64.b64encode(gif_data).decode('utf-8')
+            return f"data:image/gif;base64,{gif_base64}"
+    except FileNotFoundError:
+        logger.warning("loading_animation.gif not found")
+        return None
+
+
+def remove_loading_animations(chat_history):
+    """Remove loading animations from chat"""
+    return [msg for msg in chat_history if not (
+        msg.get("role") == "assistant" and 
+        "loading-animation" in str(msg.get("content", ""))
+    )]
+
+
+def add_user_message(message, chat_history, conversation_state):
+    """
+    Add user message with proper state management.
+    ✅ FIXED: Returns updated states to Gradio components.
+    """
+    callback_start = log_step("add_user_message")
+    
+    if not message.strip():
+        log_step("add_user_message", callback_start)
+        return "", chat_history, conversation_state
+    
+    # Get current state from global manager
+    current_state = global_state_manager.get_conversation_state()
+    chat_history = current_state['chat_history']
+    conversation_state = current_state['conversation_state']
+    
+    # Add to both states
+    conversation_state.append({"role": "user", "content": message})
+    chat_history.append({"role": "user", "content": message})
+    
+    # Update global state
+    global_state_manager.update_conversation_state(chat_history, conversation_state)
+    
+    log_step("add_user_message", callback_start)
+    
+    # ✅ CRITICAL: Return updated states to Gradio
+    return "", chat_history, conversation_state
+
+
+def add_loading_animation(chat_history, conversation_state):
+    """
+    Add loading animation with proper state management.
+    ✅ FIXED: Returns updated states to Gradio components.
+    """
+    callback_start = log_step("add_loading_animation")
+    
+    # Get current state from global manager
+    current_state = global_state_manager.get_conversation_state()
+    chat_history = current_state['chat_history']
+    conversation_state = current_state['conversation_state']
+    
+    if not conversation_state:
+        log_step("add_loading_animation", callback_start)
+        return chat_history, conversation_state
+    
+    # Remove any existing loading animations
+    chat_history = remove_loading_animations(chat_history)
+    
+    # Add loading animation
+    gif_data = get_loading_animation_base64()
+    if gif_data:
+        loading_html = f'<div class="loading-animation" style="display: flex; align-items: center; justify-content: center; padding: 0.5px;"><img src="{gif_data}" alt="Thinking..." style="height: 64px; width: auto; max-width: 80px;" /></div>'
+    else:
+        loading_html = '<div class="loading-animation" style="display: flex; align-items: center; justify-content: center; padding: 0.5px;"><div style="width: 64px; height: 64px;"></div></div>'
+    
+    chat_history.append({"role": "assistant", "content": loading_html})
+    
+    # Update global state
+    global_state_manager.update_conversation_state(chat_history, conversation_state)
+    
+    log_step("add_loading_animation", callback_start)
+    
+    # ✅ CRITICAL: Return updated states to Gradio
+    return chat_history, conversation_state
+
+
+def generate_response(chat_history, conversation_state):
+    """
+    Generate response using orchestration with proper streaming.
+    ✅ FIXED: Loading animation stays until first chunk, then streams properly.
+    """
+    callback_start = log_step("generate_response")
+    
+    # Get fresh state from global manager
+    current_state = global_state_manager.get_conversation_state()
+    chat_history = current_state['chat_history']
+    conversation_state = current_state['conversation_state']
+    
+    if not conversation_state:
+        log_step("generate_response", callback_start)
+        return chat_history, conversation_state
+    
+    # Get last user message
+    last_user_message = ""
+    for msg in reversed(conversation_state):
+        if msg["role"] == "user":
+            last_user_message = msg["content"]
+            break
+    
+    if not last_user_message:
+        log_step("generate_response", callback_start)
+        return chat_history, conversation_state
+    
+    try:
+        # ✅ DON'T remove loading animation yet - let it show during orchestration
+        
+        # Call orchestration (this takes time)
+        orch_start = log_step("orchestrate_turn call")
+        raw_response = orchestrate_turn(last_user_message)
+        log_step("orchestrate_turn call", orch_start)
+        
+        # Stream the processed response
+        first_chunk = True
+        for chunk in post_processor.process_and_stream_response(raw_response, last_user_message):
+            # ✅ Remove loading animation on FIRST chunk only
+            if first_chunk:
+                chat_history = remove_loading_animations(chat_history)
+                first_chunk = False
+            
+            # Update chat display
+            if chat_history and chat_history[-1]["role"] == "assistant":
+                chat_history[-1]["content"] = chunk
+            else:
+                chat_history.append({"role": "assistant", "content": chunk})
+            
+            # ✅ Yield to update UI during streaming
+            yield chat_history, conversation_state
+        
+        # Add final response to conversation state
+        final_response = chunk if 'chunk' in locals() else raw_response
+        conversation_state.append({"role": "assistant", "content": final_response})
+        
+        # Update global state with final conversation
+        global_state_manager.update_conversation_state(chat_history, conversation_state)
+        
+        # ✅ Final yield with complete states
+        yield chat_history, conversation_state
+        
+    except Exception as e:
+        logger.error(f"Response generation error: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+        
+        error_msg = f"I encountered an error: {str(e)}"
+        
+        # Clean up and show error
+        chat_history = remove_loading_animations(chat_history)
+        chat_history.append({"role": "assistant", "content": error_msg})
+        conversation_state.append({"role": "assistant", "content": error_msg})
+        
+        global_state_manager.update_conversation_state(chat_history, conversation_state)
+        yield chat_history, conversation_state
+    
+    log_step("generate_response", callback_start)
+
+
+def reset_conversation():
+    """
+    Reset conversation with global state persistence.
+    ✅ Returns empty states to Gradio components.
+    """
+    callback_start = log_step("reset_conversation")
+    global_state_manager.reset_conversation_state()
+    log_step("reset_conversation", callback_start)
+    return [], []
+
+
+def load_conversation_state():
+    """
+    Load conversation state from global manager.
+    ✅ Returns current states to Gradio components.
+    """
+    callback_start = log_step("load_conversation_state")
+    current_state = global_state_manager.get_conversation_state()
+    log_step("load_conversation_state", callback_start)
+    
+    # ✅ Extract and return both states
+    return current_state['chat_history'], current_state['conversation_state']
+
+
+# ============================================================================
+# MULTI-PAGE INTERFACE
+# ============================================================================
+def create_interface():
+    """Create multi-page Gradio interface"""
+    logger.info("Creating Gradio interface...")
+    
+    # Import page modules
+    import gradio_chatbot
+    import gradio_analytics
+    import gradio_prompt_testing  # NEW
+    
+    with gr.Blocks(title="Mimir - Educational AI Assistant") as demo:
+        navbar = gr.Navbar(
+            visible=True,
+            main_page_name="Mimir Chatbot",
+            value=[("Case Study", "https://github.com/Jdesiree112/Technical_Portfolio/tree/main/CaseStudy_Mimir")]
+        )
+        gradio_chatbot.demo.render()
+    
+    with demo.route("Analytics"):
+        navbar = gr.Navbar(
+            visible=True,
+            main_page_name="Mimir Chatbot",
+            value=[("Case Study", "https://github.com/Jdesiree112/Technical_Portfolio/tree/main/CaseStudy_Mimir")]
+        )
+        gradio_analytics.demo.render()
+    
+    with demo.route("Prompt Testing"):
+        navbar = gr.Navbar(
+            visible=True,
+            main_page_name="Mimir Chatbot",
+            value=[("Case Study", "https://github.com/Jdesiree112/Technical_Portfolio/tree/main/CaseStudy_Mimir")]
+        )
+        gradio_prompt_testing.demo.render()
+    
+    logger.info("Interface created successfully")
+    return demo
+
+
+# ============================================================================
+# MAIN EXECUTION
+# ============================================================================
+if __name__ == "__main__":
+    try:
+        logger.info("="*60)
+        logger.info("STARTING MAIN EXECUTION")
+        logger.info("="*60)
+        
+        # Warm up models first
+        logger.info("→ Importing compile_model...")
+        from compile_model import compile_all
+        
+        logger.info("→ Starting model compilation...")
+        compile_start = time.time()
+        compile_all()
+        compile_duration = time.time() - compile_start
+        logger.info(f"✓ Model compilation completed in {compile_duration:.2f}s")
+        
+        logger.info("="*60)
+        logger.info("MIMIR APPLICATION READY")
+        logger.info("="*60)
+        logger.info(f"LightEval available: {LIGHTEVAL_AVAILABLE}")
+        logger.info(f"Current year: {CURRENT_YEAR}")
+        logger.info(f"Single Qwen3-Claude model optimization: ENABLED ✅")
+        logger.info("="*60)
+        
+        # Create and launch interface
+        logger.info("→ Creating Gradio interface...")
+        interface_start = time.time()
+        interface = create_interface()
+        interface_duration = time.time() - interface_start
+        logger.info(f"✓ Interface created in {interface_duration:.2f}s")
+        
+        logger.info("→ Launching Gradio server on 0.0.0.0:7860...")
+        logger.info("→ Waiting for first user connection...")
+        
+        interface.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            share=False,
+            debug=True,
+            favicon_path="favicon.ico" if os.path.exists("favicon.ico") else None,
+            show_error=True,
+            ssr_mode=False,
+            quiet=False,
+            prevent_thread_lock=False,
+            max_threads=40
+        )
+        
+        logger.info("✓ Gradio server started successfully")
+        
+    except KeyboardInterrupt:
+        logger.info("Shutting down Mimir gracefully...")
+    except Exception as e:
+        logger.error("="*60)
+        logger.error("CRITICAL ERROR IN MAIN EXECUTION")
+        logger.error("="*60)
+        logger.error(f"Error type: {type(e).__name__}")
+        logger.error(f"Error message: {e}")
+        logger.error("="*60)
+        logger.error("Full traceback:")
+        import traceback
+        logger.error(traceback.format_exc())
+        logger.error("="*60)
+        raise