app.py

# app.py - Mobile-First Implementation
import gradio as gr
import uuid
import logging
import traceback
from typing import Optional, Tuple, List, Dict, Any
import os

# Configure comprehensive logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler(),
        logging.FileHandler('app.log')
    ]
)
logger = logging.getLogger(__name__)

# Try to import orchestration components
orchestrator = None
orchestrator_available = False

# Import Process Flow Visualization
try:
    from process_flow_visualizer import (
        create_process_flow_tab,
        update_process_flow_visualization,
        clear_flow_history,
        export_flow_data
    )
    process_flow_available = True
    logger.info("✓ Process Flow Visualization available")
except ImportError as e:
    logger.warning(f"Process Flow Visualization not available: {e}")
    process_flow_available = False

try:
    logger.info("Attempting to import orchestration components...")
    import sys
    sys.path.insert(0, '.')
    sys.path.insert(0, 'src')
    
    from src.agents.intent_agent import create_intent_agent
    from src.agents.synthesis_agent import create_synthesis_agent
    from src.agents.safety_agent import create_safety_agent
    from src.agents.skills_identification_agent import create_skills_identification_agent
    from llm_router import LLMRouter
    from orchestrator_engine import MVPOrchestrator
    from context_manager import EfficientContextManager
    from config import settings
    
    logger.info("✓ Successfully imported orchestration components")
    orchestrator_available = True
except ImportError as e:
    logger.warning(f"Could not import orchestration components: {e}")
    logger.info("Will use placeholder mode")

try:
    from spaces import GPU
    SPACES_GPU_AVAILABLE = True
    logger.info("HF Spaces GPU available")
except ImportError:
    # Not running on HF Spaces or spaces module not available
    SPACES_GPU_AVAILABLE = False
    GPU = None
    logger.info("Running without HF Spaces GPU")

def create_mobile_optimized_interface():
    """Create the mobile-optimized Gradio interface and return demo with components"""
    
    # Store components for wiring
    interface_components = {}
    
    with gr.Blocks(
        title="AI Research Assistant MVP",
        theme=gr.themes.Soft(
            primary_hue="blue",
            secondary_hue="gray",
            font=("Inter", "system-ui", "sans-serif")
        ),
        css="""
        /* Mobile-first responsive CSS */
        .mobile-container {
            max-width: 100vw;
            margin: 0 auto;
            padding: 0 12px;
        }
        
        /* Touch-friendly button sizing */
        .gradio-button {
            min-height: 44px !important;
            min-width: 44px !important;
            font-size: 16px !important; /* Prevents zoom on iOS */
        }
        
        /* Mobile-optimized chat interface */
        .chatbot-container {
            height: 60vh !important;
            max-height: 60vh !important;
            overflow-y: auto !important;
            -webkit-overflow-scrolling: touch !important;
        }
        
        /* Mobile input enhancements */
        .textbox-input {
            font-size: 16px !important; /* Prevents zoom */
            min-height: 44px !important;
            padding: 12px !important;
        }
        
        /* Responsive grid adjustments */
        @media (max-width: 768px) {
            .gradio-row {
                flex-direction: column !important;
                gap: 8px !important;
            }
            
            .gradio-column {
                width: 100% !important;
            }
            
            .chatbot-container {
                height: 50vh !important;
            }
        }
        
        /* Dark mode support */
        @media (prefers-color-scheme: dark) {
            body {
                background: #1a1a1a;
                color: #ffffff;
            }
        }
        
        /* Hide scrollbars but maintain functionality */
        .chatbot-container::-webkit-scrollbar {
            width: 4px;
        }
        
        /* Loading states */
        .loading-indicator {
            display: flex;
            align-items: center;
            justify-content: center;
            padding: 20px;
        }
        
        /* Mobile menu enhancements */
        .accordion-content {
            max-height: 200px !important;
            overflow-y: auto !important;
        }
        
        /* Skills Tags Styling */
        #skills_tags_container {
            padding: 8px 12px;
            background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%);
            border-radius: 8px;
            border: 1px solid #dee2e6;
            margin: 8px 0;
            min-height: 40px;
            display: flex;
            flex-wrap: wrap;
            align-items: center;
            gap: 6px;
        }
        
        .skill-tag {
            display: inline-block;
            background: linear-gradient(135deg, #007bff 0%, #0056b3 100%);
            color: white;
            padding: 4px 8px;
            border-radius: 12px;
            font-size: 12px;
            font-weight: 500;
            margin: 2px;
            box-shadow: 0 2px 4px rgba(0,123,255,0.2);
            transition: all 0.2s ease;
            cursor: pointer;
        }
        
        .skill-tag:hover {
            transform: translateY(-1px);
            box-shadow: 0 4px 8px rgba(0,123,255,0.3);
        }
        
        .skill-tag.high-confidence {
            background: linear-gradient(135deg, #28a745 0%, #1e7e34 100%);
        }
        
        .skill-tag.medium-confidence {
            background: linear-gradient(135deg, #ffc107 0%, #e0a800 100%);
            color: #212529;
        }
        
        .skill-tag.low-confidence {
            background: linear-gradient(135deg, #6c757d 0%, #495057 100%);
        }
        
        .skills-header {
            font-size: 11px;
            color: #6c757d;
            margin-right: 8px;
            font-weight: 600;
        }
        
        /* Dark mode support for skills */
        @media (prefers-color-scheme: dark) {
            #skills_tags_container {
                background: linear-gradient(135deg, #2d3748 0%, #1a202c 100%);
                border-color: #4a5568;
            }
            
            .skills-header {
                color: #a0aec0;
            }
        }
        """
    ) as demo:
        
        # Session Management (Mobile-Optimized)
        with gr.Column(elem_classes="mobile-container"):
            gr.Markdown("""
            # 🧠 Research Assistant
            *Academic AI with transparent reasoning*
            """)
            
            # Session Header Bar (Mobile-Friendly)
            with gr.Row():
                session_info = gr.Textbox(
                    label="Session ID",
                    value=str(uuid.uuid4())[:8],  # Shortened for mobile
                    max_lines=1,
                    show_label=False,
                    container=False,
                    scale=3
                )
                interface_components['session_info'] = session_info
                
                new_session_btn = gr.Button(
                    "🔄 New",
                    size="sm",
                    variant="secondary",
                    scale=1,
                    min_width=60
                )
                interface_components['new_session_btn'] = new_session_btn
                
                menu_toggle = gr.Button(
                    "⚙️",
                    size="sm",
                    variant="secondary",
                    scale=1,
                    min_width=60
                )
                interface_components['menu_toggle'] = menu_toggle
            
            # Main Chat Area (Mobile-Optimized)
            with gr.Tabs() as main_tabs:
                with gr.TabItem("💬 Chat", id="chat_tab"):
                    chatbot = gr.Chatbot(
                        label="",
                        show_label=False,
                        height="60vh",
                        elem_classes="chatbot-container",
                        type="messages"
                    )
                    interface_components['chatbot'] = chatbot
                    
                    # Skills Identification Display (between chat and input)
                    with gr.Row(visible=False, elem_id="skills_display") as skills_display_row:
                        skills_tags = gr.HTML(
                            value="",
                            show_label=False,
                            elem_id="skills_tags_container"
                        )
                        interface_components['skills_tags'] = skills_tags
                    
                    # Mobile Input Area
                    with gr.Row():
                        message_input = gr.Textbox(
                            placeholder="Ask me anything...",
                            show_label=False,
                            max_lines=3,
                            container=False,
                            scale=4,
                            autofocus=True
                        )
                        interface_components['message_input'] = message_input
                        
                        send_btn = gr.Button(
                            "↑ Send",
                            variant="primary",
                            scale=1,
                            min_width=80
                        )
                        interface_components['send_btn'] = send_btn
                
                # Technical Details Tab (Collapsible for Mobile)
                with gr.TabItem("🔍 Details", id="details_tab"):
                    with gr.Accordion("Reasoning Chain", open=False):
                        reasoning_display = gr.JSON(
                            label="",
                            show_label=False
                        )
                        interface_components['reasoning_display'] = reasoning_display
                    
                    with gr.Accordion("Agent Performance", open=False):
                        performance_display = gr.JSON(
                            label="",
                            show_label=False
                        )
                        interface_components['performance_display'] = performance_display
                    
                    with gr.Accordion("Session Context", open=False):
                        context_display = gr.JSON(
                            label="",
                            show_label=False
                        )
                        interface_components['context_display'] = context_display
                
                # Process Flow Tab (if available)
                if process_flow_available:
                    process_flow_tab = create_process_flow_tab(interface_components)
                    interface_components['process_flow_tab'] = process_flow_tab
            
            # Mobile Bottom Navigation
            with gr.Row(visible=False, elem_id="mobile_nav") as mobile_navigation:
                chat_nav_btn = gr.Button("💬 Chat", variant="secondary", size="sm", min_width=0)
                details_nav_btn = gr.Button("🔍 Details", variant="secondary", size="sm", min_width=0)
                if process_flow_available:
                    flow_nav_btn = gr.Button("🔄 Flow", variant="secondary", size="sm", min_width=0)
                settings_nav_btn = gr.Button("⚙️ Settings", variant="secondary", size="sm", min_width=0)
        
        # Settings Panel (Modal for Mobile)
        with gr.Column(visible=False, elem_id="settings_panel") as settings:
            interface_components['settings_panel'] = settings
            
            with gr.Accordion("Display Options", open=True):
                show_reasoning = gr.Checkbox(
                    label="Show reasoning chain",
                    value=True,
                    info="Display step-by-step reasoning"
                )
                interface_components['show_reasoning'] = show_reasoning
                
                show_agent_trace = gr.Checkbox(
                    label="Show agent execution trace",
                    value=False,
                    info="Display which agents processed your request"
                )
                interface_components['show_agent_trace'] = show_agent_trace
                
                compact_mode = gr.Checkbox(
                    label="Compact mode",
                    value=False,
                    info="Optimize for smaller screens"
                )
                interface_components['compact_mode'] = compact_mode
            
            with gr.Accordion("Performance Options", open=False):
                response_speed = gr.Radio(
                    choices=["Fast", "Balanced", "Thorough"],
                    value="Balanced",
                    label="Response Speed Preference"
                )
                interface_components['response_speed'] = response_speed
                
                cache_enabled = gr.Checkbox(
                    label="Enable context caching",
                    value=True,
                    info="Faster responses using session memory"
                )
                interface_components['cache_enabled'] = cache_enabled
            
            save_prefs_btn = gr.Button("Save Preferences", variant="primary")
            interface_components['save_prefs_btn'] = save_prefs_btn
        
        # Wire up the submit handler INSIDE the gr.Blocks context
        if 'send_btn' in interface_components and 'message_input' in interface_components and 'chatbot' in interface_components:
            # Store interface components globally for dynamic return values
            global _interface_components
            _interface_components = interface_components
            
            # Build outputs list dynamically
            outputs = _build_outputs_list(interface_components, process_flow_available)
            
            # Include session_info in inputs to pass session ID
            inputs = [interface_components['message_input'], interface_components['chatbot']]
            if 'session_info' in interface_components:
                inputs.append(interface_components['session_info'])
            
            interface_components['send_btn'].click(
                fn=chat_handler_fn,
                inputs=inputs,
                outputs=outputs
            )
            
            # Wire up New Session button
            if 'new_session_btn' in interface_components and 'session_info' in interface_components:
                interface_components['new_session_btn'].click(
                    fn=lambda: str(uuid.uuid4())[:8],
                    outputs=[interface_components['session_info']]
                )
            
            # Wire up Settings button to toggle settings panel
            if 'menu_toggle' in interface_components and 'settings_panel' in interface_components:
                def toggle_settings(visible):
                    return gr.update(visible=not visible)
                
                interface_components['menu_toggle'].click(
                    fn=toggle_settings,
                    inputs=[interface_components['settings_panel']],
                    outputs=[interface_components['settings_panel']]
                )
            
            # Wire up Save Preferences button
            if 'save_prefs_btn' in interface_components:
                def save_preferences(*args):
                    logger.info("Preferences saved")
                    gr.Info("Preferences saved successfully!")
                
                interface_components['save_prefs_btn'].click(
                    fn=save_preferences,
                    inputs=[
                        interface_components.get('show_reasoning', None),
                        interface_components.get('show_agent_trace', None),
                        interface_components.get('response_speed', None),
                        interface_components.get('cache_enabled', None)
                    ]
                )
            
            # Wire up Process Flow event handlers if available
            if process_flow_available:
                # Clear flow history button
                if 'clear_flow_btn' in interface_components:
                    interface_components['clear_flow_btn'].click(
                        fn=clear_flow_history,
                        outputs=[
                            interface_components.get('flow_display'),
                            interface_components.get('flow_stats'),
                            interface_components.get('performance_metrics'),
                            interface_components.get('intent_details'),
                            interface_components.get('synthesis_details'),
                            interface_components.get('safety_details')
                        ]
                    )
                
                # Export flow data button
                if 'export_flow_btn' in interface_components:
                    interface_components['export_flow_btn'].click(
                        fn=export_flow_data,
                        outputs=[gr.File(label="Download Flow Data")]
                    )
                
                # Share flow button (placeholder)
                if 'share_flow_btn' in interface_components:
                    interface_components['share_flow_btn'].click(
                        fn=lambda: gr.Info("Flow sharing feature coming soon!"),
                        outputs=[]
                    )
    
    return demo, interface_components

def setup_event_handlers(demo, event_handlers):
    """Setup event handlers for the interface"""
    
    # Find components by their labels or types
    components = {}
    for block in demo.blocks:
        if hasattr(block, 'label'):
            if block.label == 'Session ID':
                components['session_info'] = block
            elif hasattr(block, 'value') and 'session' in str(block.value).lower():
                components['session_id'] = block
    
    # Setup message submission handler
    try:
        # This is a simplified version - you'll need to adapt based on your actual component structure
        if hasattr(demo, 'submit'):
            demo.submit(
                fn=event_handlers.handle_message_submit,
                inputs=[components.get('message_input'), components.get('chatbot')],
                outputs=[components.get('message_input'), components.get('chatbot')]
            )
    except Exception as e:
        print(f"Could not setup event handlers: {e}")
        # Fallback to basic functionality
    
    return demo

def _generate_skills_html(identified_skills: List[Dict[str, Any]]) -> str:
    """Generate HTML for skills tags display"""
    if not identified_skills:
        return ""
    
    # Limit to top 8 skills for UI
    top_skills = identified_skills[:8]
    
    # Generate HTML with confidence-based styling
    skills_html = '<div class="skills-header">🎯 Relevant Skills:</div>'
    
    for skill in top_skills:
        skill_name = skill.get('skill', 'Unknown Skill')
        probability = skill.get('probability', 0.5)
        
        # Determine confidence class based on probability
        if probability >= 0.7:
            confidence_class = "high-confidence"
        elif probability >= 0.4:
            confidence_class = "medium-confidence"
        else:
            confidence_class = "low-confidence"
        
        # Create skill tag
        skills_html += f'<span class="skill-tag {confidence_class}" title="Probability: {probability:.1%}">{skill_name}</span>'
    
    return skills_html

def _update_skills_display(skills_html: str) -> Tuple[str, bool]:
    """Update skills display visibility and content"""
    if skills_html and len(skills_html.strip()) > 0:
        return skills_html, True  # Show skills display
    else:
        return "", False  # Hide skills display

async def process_message_async(message: str, history: Optional[List], session_id: str) -> Tuple[List, str, dict, dict, dict, str, str]:
    """
    Process message with full orchestration system
    Returns (updated_history, empty_string, reasoning_data, performance_data, context_data, session_id, skills_html)
    
    GUARANTEES:
    - Always returns a response (never None or empty)
    - Handles all error cases gracefully
    - Provides fallback responses at every level
    - Returns metadata for Details tab
    - Returns session_id to maintain session continuity
    - Returns skills HTML for display
    """
    global orchestrator
    
    try:
        logger.info(f"Processing message: {message[:100]}")
        logger.info(f"Session ID: {session_id}")
        logger.info(f"Orchestrator available: {orchestrator is not None}")
        
        if not message or not message.strip():
            logger.debug("Empty message received")
            return history if history else [], "", {}, {}, {}, session_id, ""
        
        if history is None:
            history = []
        
        new_history = list(history) if isinstance(history, list) else []
        
        # Add user message
        new_history.append({"role": "user", "content": message.strip()})
        
        # Initialize Details tab data
        reasoning_data = {}
        performance_data = {}
        context_data = {}
        skills_html = ""  # Initialize skills_html
        
        # GUARANTEE: Always get a response
        response = "Hello! I'm processing your request..."
        
        # Try to use orchestrator if available
        if orchestrator is not None:
            try:
                logger.info("Attempting full orchestration...")
                # Use orchestrator to process
                result = await orchestrator.process_request(
                    session_id=session_id,
                    user_input=message.strip()
                )
                
                # Extract response from result with multiple fallback checks
                if isinstance(result, dict):
                    # Extract the text response (not the dict)
                    response = (
                        result.get('response') or 
                        result.get('final_response') or 
                        result.get('safety_checked_response') or
                        result.get('original_response') or
                        str(result.get('result', ''))
                    )
                    
                    # Extract metadata for Details tab with enhanced reasoning chain
                    reasoning_data = result.get('metadata', {}).get('reasoning_chain', {
                        "chain_of_thought": {},
                        "alternative_paths": [],
                        "uncertainty_areas": [],
                        "evidence_sources": [],
                        "confidence_calibration": {}
                    })
                    
                    # Ensure we have the enhanced structure even if orchestrator didn't provide it
                    if not reasoning_data.get('chain_of_thought'):
                        reasoning_data = {
                            "chain_of_thought": {
                                "step_1": {
                                    "hypothesis": "Processing user request",
                                    "evidence": [f"User input: {message[:50]}..."],
                                    "confidence": 0.7,
                                    "reasoning": "Basic request processing"
                                }
                            },
                            "alternative_paths": [],
                            "uncertainty_areas": [],
                            "evidence_sources": [],
                            "confidence_calibration": {"overall_confidence": 0.7}
                        }
                    
                    performance_data = {
                        "agent_trace": result.get('agent_trace', []),
                        "processing_time": result.get('metadata', {}).get('processing_time', 0),
                        "token_count": result.get('metadata', {}).get('token_count', 0),
                        "confidence_score": result.get('confidence_score', 0.7),
                        "agents_used": result.get('metadata', {}).get('agents_used', [])
                    }
                    
                    context_data = {
                        "interaction_id": result.get('interaction_id', 'unknown'),
                        "session_id": session_id,
                        "timestamp": result.get('timestamp', ''),
                        "warnings": result.get('metadata', {}).get('warnings', [])
                    }
                    
                    # Extract skills data for UI display
                    skills_html = ""
                    skills_result = result.get('metadata', {}).get('skills_result', {})
                    if skills_result and skills_result.get('identified_skills'):
                        skills_html = _generate_skills_html(skills_result['identified_skills'])
                else:
                    response = str(result) if result else "Processing complete."
                
                # Final safety check - ensure response is not empty
                # Handle both string and dict types
                if isinstance(response, dict):
                    response = str(response.get('content', response))
                if not response or (isinstance(response, str) and len(response.strip()) == 0):
                    response = f"I understand you said: '{message}'. I'm here to assist you!"
                
                logger.info(f"Orchestrator returned response (length: {len(response)})")
                
            except Exception as orch_error:
                logger.error(f"Orchestrator error: {orch_error}", exc_info=True)
                # Fallback response with error info and enhanced reasoning
                response = f"I'm experiencing some technical difficulties. Your message was: '{message[:100]}...' Please try again or rephrase your question."
                reasoning_data = {
                    "chain_of_thought": {
                        "step_1": {
                            "hypothesis": "System encountered an error during processing",
                            "evidence": [f"Error: {str(orch_error)[:100]}..."],
                            "confidence": 0.3,
                            "reasoning": "Orchestrator failure - fallback mode activated"
                        }
                    },
                    "alternative_paths": [],
                    "uncertainty_areas": [
                        {
                            "aspect": "System reliability",
                            "confidence": 0.3,
                            "mitigation": "Error handling and graceful degradation"
                        }
                    ],
                    "evidence_sources": [],
                    "confidence_calibration": {"overall_confidence": 0.3, "error_mode": True}
                }
        else:
            # System initialization message with enhanced reasoning
            logger.info("Orchestrator not yet available")
            response = f"Hello! I received your message about: '{message}'.\n\nThe orchestration system is initializing. Your question is important and I'll provide a comprehensive answer shortly."
            reasoning_data = {
                "chain_of_thought": {
                    "step_1": {
                        "hypothesis": "System is initializing and not yet ready",
                        "evidence": ["Orchestrator not available", f"User input: {message[:50]}..."],
                        "confidence": 0.5,
                        "reasoning": "System startup phase - components loading"
                    }
                },
                "alternative_paths": [],
                "uncertainty_areas": [
                    {
                        "aspect": "System readiness",
                        "confidence": 0.5,
                        "mitigation": "Graceful initialization message"
                    }
                ],
                "evidence_sources": [],
                "confidence_calibration": {"overall_confidence": 0.5, "initialization_mode": True}
            }
            skills_html = ""  # Initialize skills_html for orchestrator not available case
        
        # Add assistant response
        new_history.append({"role": "assistant", "content": response})
        logger.info("✓ Message processing complete")
        
        return new_history, "", reasoning_data, performance_data, context_data, session_id, skills_html
        
    except Exception as e:
        # FINAL FALLBACK: Always return something to user with enhanced reasoning
        logger.error(f"Error in process_message_async: {e}", exc_info=True)
        
        # Create error history with helpful message
        error_history = list(history) if history else []
        error_history.append({"role": "user", "content": message})
        
        # User-friendly error message
        error_message = (
            f"I encountered a technical issue processing your message: '{message[:50]}...'. "
            f"Please try rephrasing your question or contact support if this persists."
        )
        error_history.append({"role": "assistant", "content": error_message})
        
        # Enhanced reasoning for error case
        reasoning_data = {
            "chain_of_thought": {
                "step_1": {
                    "hypothesis": "Critical system error occurred",
                    "evidence": [f"Exception: {str(e)[:100]}...", f"User input: {message[:50]}..."],
                    "confidence": 0.2,
                    "reasoning": "System error handling - final fallback activated"
                }
            },
            "alternative_paths": [],
            "uncertainty_areas": [
                {
                    "aspect": "System stability",
                    "confidence": 0.2,
                    "mitigation": "Error logging and user notification"
                }
            ],
            "evidence_sources": [],
            "confidence_calibration": {"overall_confidence": 0.2, "critical_error": True}
        }
        
        return error_history, "", reasoning_data, {}, {}, session_id, ""

# Global variable to store interface components for dynamic return values
_interface_components = {}

def _build_outputs_list(interface_components: dict, process_flow_available: bool) -> list:
    """
    Build outputs list dynamically based on available interface components
    """
    outputs = [interface_components['chatbot'], interface_components['message_input']]
    
    # Add Details tab components
    if 'reasoning_display' in interface_components:
        outputs.append(interface_components['reasoning_display'])
    if 'performance_display' in interface_components:
        outputs.append(interface_components['performance_display'])
    if 'context_display' in interface_components:
        outputs.append(interface_components['context_display'])
    if 'session_info' in interface_components:
        outputs.append(interface_components['session_info'])
    if 'skills_tags' in interface_components:
        outputs.append(interface_components['skills_tags'])
    
    # Add Process Flow outputs if available
    if process_flow_available:
        if 'flow_display' in interface_components:
            outputs.append(interface_components['flow_display'])
        if 'flow_stats' in interface_components:
            outputs.append(interface_components['flow_stats'])
        if 'performance_metrics' in interface_components:
            outputs.append(interface_components['performance_metrics'])
        if 'intent_details' in interface_components:
            outputs.append(interface_components['intent_details'])
        if 'synthesis_details' in interface_components:
            outputs.append(interface_components['synthesis_details'])
        if 'safety_details' in interface_components:
            outputs.append(interface_components['safety_details'])
    
    return outputs

def _build_dynamic_return_values(result: tuple, skills_content: str, interface_components: dict, process_flow_available: bool = False, flow_updates: dict = None) -> tuple:
    """
    Build return values dynamically based on available interface components
    This ensures the return values match the outputs list exactly
    """
    return_values = []
    
    # Base components (always present)
    return_values.extend([
        result[0],  # chatbot (history)
        result[1],  # message_input (empty_string)
    ])
    
    # Add Details tab components
    if 'reasoning_display' in interface_components:
        return_values.append(result[2])  # reasoning_data
    if 'performance_display' in interface_components:
        return_values.append(result[3])  # performance_data
    if 'context_display' in interface_components:
        return_values.append(result[4])  # context_data
    if 'session_info' in interface_components:
        return_values.append(result[5])  # session_id
    if 'skills_tags' in interface_components:
        return_values.append(skills_content)  # skills_content
    
    # Add Process Flow outputs if available
    if process_flow_available:
        if 'flow_display' in interface_components:
            return_values.append(flow_updates.get("flow_display", "") if flow_updates else "")
        if 'flow_stats' in interface_components:
            return_values.append(flow_updates.get("flow_stats", {}) if flow_updates else {})
        if 'performance_metrics' in interface_components:
            return_values.append(flow_updates.get("performance_metrics", {}) if flow_updates else {})
        if 'intent_details' in interface_components:
            return_values.append(flow_updates.get("intent_details", {}) if flow_updates else {})
        if 'synthesis_details' in interface_components:
            return_values.append(flow_updates.get("synthesis_details", {}) if flow_updates else {})
        if 'safety_details' in interface_components:
            return_values.append(flow_updates.get("safety_details", {}) if flow_updates else {})
    
    return tuple(return_values)

def process_message(message: str, history: Optional[List], session_id: Optional[str] = None) -> tuple:
    """
    Synchronous wrapper for async processing
    Returns dynamic tuple based on available interface components
    """
    import asyncio
    
    # Use provided session_id or generate a new one
    if not session_id:
        session_id = str(uuid.uuid4())[:8]
    
    try:
        # Run async processing
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        result = loop.run_until_complete(process_message_async(message, history, session_id))
        
        # Extract skills_html from result and determine visibility
        skills_html = result[6]
        skills_content, skills_visible = _update_skills_display(skills_html)
        
        # Return dynamic values based on available components
        return _build_dynamic_return_values(result, skills_content, _interface_components, process_flow_available)
    except Exception as e:
        logger.error(f"Error in process_message: {e}", exc_info=True)
        error_history = list(history) if history else []
        error_history.append({"role": "user", "content": message})
        error_history.append({"role": "assistant", "content": f"Error: {str(e)}"})
        
        # Enhanced reasoning for sync error case
        reasoning_data = {
            "chain_of_thought": {
                "step_1": {
                    "hypothesis": "Synchronous processing error",
                    "evidence": [f"Sync error: {str(e)[:100]}...", f"User input: {message[:50]}..."],
                    "confidence": 0.2,
                    "reasoning": "Synchronous wrapper error handling"
                }
            },
            "alternative_paths": [],
            "uncertainty_areas": [
                {
                    "aspect": "Processing reliability",
                    "confidence": 0.2,
                    "mitigation": "Error logging and fallback response"
                }
            ],
            "evidence_sources": [],
            "confidence_calibration": {"overall_confidence": 0.2, "sync_error": True}
        }
        
        # Return dynamic values for error case
        error_result = (error_history, "", reasoning_data, {}, {}, session_id, "")
        return _build_dynamic_return_values(error_result, "", _interface_components, process_flow_available)

# Decorate the chat handler with GPU if available
if SPACES_GPU_AVAILABLE and GPU is not None:
    @GPU  # This decorator is detected by HF Spaces for ZeroGPU allocation
    def gpu_chat_handler(message, history, session_id=None):
        """Handle chat messages with GPU support"""
        # Use provided session_id or generate new one
        if not session_id:
            session_id = str(uuid.uuid4())[:8]
        result = process_message(message, history, session_id)
        # Return all 15 values directly
        return result
    chat_handler_fn = gpu_chat_handler
else:
    def chat_handler_wrapper(message, history, session_id=None):
        """Wrapper to handle session ID with Process Flow Visualization"""
        if not session_id:
            session_id = str(uuid.uuid4())[:8]
        result = process_message(message, history, session_id)
        # Extract skills_html from result and determine visibility
        skills_html = result[6]
        skills_content, skills_visible = _update_skills_display(skills_html)
        
        # Prepare process flow updates if available
        flow_updates = {}
        if process_flow_available:
            try:
                # Extract data for process flow visualization
                reasoning_data = result[2]
                performance_data = result[3]
                context_data = result[4]
                
                # Create dynamic agent results for visualization
                step_results = {}
                
                # Intent result
                step_results["intent_result"] = {
                    "primary_intent": reasoning_data.get("chain_of_thought", {}).get("step_1", {}).get("hypothesis", "unknown"),
                    "confidence_scores": {"overall": reasoning_data.get("confidence_calibration", {}).get("overall_confidence", 0.7)},
                    "secondary_intents": [],
                    "reasoning_chain": list(reasoning_data.get("chain_of_thought", {}).keys()),
                    "context_tags": ["general"],
                    "processing_time": performance_data.get("processing_time", 0.5),
                    "agent_id": "INTENT_REC_001"
                }
                
                # Skills result (if available)
                if "skills_result" in reasoning_data:  # Check if skills data is in reasoning_data
                    step_results["skills_result"] = reasoning_data["skills_result"]
                else:
                    step_results["skills_result"] = {
                        "identified_skills": [],
                        "confidence_score": 0.7,
                        "processing_time": performance_data.get("processing_time", 0.5) * 0.2,
                        "agent_id": "SKILLS_ID_001"
                    }
                
                # Synthesis result
                step_results["synthesis_result"] = {
                    "final_response": result[0][-1]["content"] if result[0] else "",
                    "draft_response": "",
                    "source_references": ["INTENT_REC_001"],
                    "coherence_score": 0.85,
                    "synthesis_method": "llm_enhanced",
                    "intent_alignment": {"intent_detected": step_results["intent_result"]["primary_intent"], "alignment_score": 0.8},
                    "processing_time": performance_data.get("processing_time", 0.5) - 0.15,
                    "agent_id": "RESP_SYNTH_001"
                }
                
                # Safety result
                step_results["safety_result"] = {
                    "original_response": result[0][-1]["content"] if result[0] else "",
                    "safety_checked_response": result[0][-1]["content"] if result[0] else "",
                    "warnings": [],
                    "safety_analysis": {
                        "toxicity_score": 0.1,
                        "bias_indicators": [],
                        "privacy_concerns": [],
                        "overall_safety_score": 0.9,
                        "confidence_scores": {"safety": 0.9}
                    },
                    "blocked": False,
                    "processing_time": 0.1,
                    "agent_id": "SAFETY_BIAS_001"
                }
                
                # Final response
                step_results["final_response"] = result[0][-1]["content"] if result[0] else ""
                
                # Update process flow visualization dynamically
                flow_updates = update_process_flow_visualization(
                    user_input=message,
                    session_id=session_id,
                    processing_time=performance_data.get("processing_time", 1.0),
                    **step_results
                )
            except Exception as e:
                logger.error(f"Error updating process flow: {e}")
                flow_updates = {}
        
        # Return dynamic values including process flow
        return _build_dynamic_return_values(result, skills_content, _interface_components, process_flow_available, flow_updates)
    chat_handler_fn = chat_handler_wrapper

# Initialize orchestrator on module load
def initialize_orchestrator():
    """Initialize the orchestration system with logging"""
    global orchestrator
    
    if not orchestrator_available:
        logger.info("Orchestrator components not available, skipping initialization")
        return
    
    try:
        logger.info("=" * 60)
        logger.info("INITIALIZING ORCHESTRATION SYSTEM")
        logger.info("=" * 60)
        
        # Get HF token
        hf_token = os.getenv('HF_TOKEN', '')
        if not hf_token:
            logger.warning("HF_TOKEN not found in environment")
        
        # Initialize LLM Router
        logger.info("Step 1/6: Initializing LLM Router...")
        llm_router = LLMRouter(hf_token)
        logger.info("✓ LLM Router initialized")
        
        # Initialize Agents
        logger.info("Step 2/6: Initializing Agents...")
        agents = {
            'intent_recognition': create_intent_agent(llm_router),
            'response_synthesis': create_synthesis_agent(llm_router),
            'safety_check': create_safety_agent(llm_router),
        }
        
        # Add skills identification agent
        skills_agent = create_skills_identification_agent(llm_router)
        agents['skills_identification'] = skills_agent
        logger.info("✓ Skills identification agent initialized")
        
        logger.info(f"✓ Initialized {len(agents)} agents")
        
        # Initialize Context Manager
        logger.info("Step 3/6: Initializing Context Manager...")
        context_manager = EfficientContextManager()
        logger.info("✓ Context Manager initialized")
        
        # Initialize Orchestrator
        logger.info("Step 4/6: Initializing Orchestrator...")
        orchestrator = MVPOrchestrator(llm_router, context_manager, agents)
        logger.info("✓ Orchestrator initialized")
        
        logger.info("=" * 60)
        logger.info("ORCHESTRATION SYSTEM READY")
        logger.info("=" * 60)
        
    except Exception as e:
        logger.error(f"Failed to initialize orchestrator: {e}", exc_info=True)
        orchestrator = None

# Try to initialize orchestrator
initialize_orchestrator()

if __name__ == "__main__":
    logger.info("=" * 60)
    logger.info("STARTING APP")
    logger.info("=" * 60)
    
    demo, components = create_mobile_optimized_interface()
    
    logger.info("✓ Interface created")
    logger.info(f"Orchestrator available: {orchestrator is not None}")
    
    # Launch the app
    logger.info("=" * 60)
    logger.info("LAUNCHING GRADIO APP")
    logger.info("=" * 60)
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )