import gradio as gr
import requests
import json
import os
import asyncio
from datetime import datetime
from typing import Dict, List, Any, Optional, Tuple
from dotenv import load_dotenv
import time
import re
from collections import Counter
import threading
import queue
import uuid
from gradio_consilium_roundtable import consilium_roundtable
from research_tools.base_tool import BaseTool
from openfloor_helper.OpenFloorResearchAgent import OpenFloorResearchAgent
from openfloor_helper.OpenFloorAgentServer import OpenFloorAgentServer
from openfloor_helper.OpenFloorManager import OpenFloorManager
from openfloor import *
from openfloor.envelope import *
from enhanced_search_functions import ENHANCED_SEARCH_FUNCTIONS

# Load environment variables
load_dotenv()

# API Configuration - These will be updated by UI if needed
MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY") 
SAMBANOVA_API_KEY = os.getenv("SAMBANOVA_API_KEY")
MODERATOR_MODEL = os.getenv("MODERATOR_MODEL", "mistral")

# Session-based storage for isolated discussions
user_sessions: Dict[str, Dict] = {}

# Model Images
avatar_images = {
    "Qwen3-32B": "https://cdn-avatars.huggingface.co/v1/production/uploads/620760a26e3b7210c2ff1943/-s1gyJfvbE1RgO5iBeNOi.png",
    "DeepSeek-R1": "https://logosandtypes.com/wp-content/uploads/2025/02/deepseek.svg",
    "Mistral Large": "https://logosandtypes.com/wp-content/uploads/2025/02/mistral-ai.svg",
    "Meta-Llama-3.3-70B-Instruct": "https://registry.npmmirror.com/@lobehub/icons-static-png/1.46.0/files/dark/meta-color.png",
    "arXiv Research Agent": "https://public.boxcloud.com/api/2.0/internal_files/804104772302/versions/860288648702/representations/png_paged_2048x2048/content/1.png?access_token=1!r4Iuj5vkFMywOMAPQ4M6QIr3eqkJ6CjlMzh77DAkRcGdVRvzG-Xh6GFZz_JkzoJuO9yRR5cQ6cs5VvUolhHxNM6JdliJ2JOi9VWm-BbB5C63s0_7bpaQYLFAJmLnlG2RzhX74_bK4XS-csGP8CI-9tVa6LUcrCNTKJmc-yddIepopLMZLqJ34h0nu69Yt0Not4yDErBTk2jWaneTBdhdXErOhCs9cz4HK-itpCfdL3Lze9oAjf6o8EVWRn6R0YPw95trQl7IziLd1P78BFuVaDjborvhs_yWgcw0uxXNZz_8WZh5z5NOvDq6sMo0uYGWiJ_g1JWyiaDJpsWBlHRiRwwF5FZLsVSXRz6dXD1MtKyOPs8J6CBYkGisicIysuiPsT1Kcyrgm-3jH1-tanOVs66TCmnGNbSYH_o_-x9iOdkI8rEL7-l2i5iHn22i-q8apZTOd_eQp22UCsmUBJQig7att_AwVKasmqOegDZHO2h1b_vSjeZ8ISBcg8i7fnFdF9Ej35s6OFkV5IyZtMzbAKdRlwdt5lupsshO5FCByR0kau9PVIiwJilI0t7zYsJtSXzVxVQEyEPuLTAlJJI7827NoNA1OSojaPsfhFrW4jEfJIgMoxNl_vFfZvLBmAA7Pk1SeaN7J0ebDji-bDbwqlPadp7JOB3s2Six11fm4Ss.&shared_link=https%3A%2F%2Fcornell.app.box.com%2Fv%2Farxiv-logomark-small-png&box_client_name=box-content-preview&box_client_version=3.7.0",
    "GitHub Research Agent": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/c2/GitHub_Invertocat_Logo.svg/250px-GitHub_Invertocat_Logo.svg.png",
    "SEC EDGAR Research Agent": "https://upload.wikimedia.org/wikipedia/commons/thumb/1/1c/Seal_of_the_United_States_Securities_and_Exchange_Commission.svg/250px-Seal_of_the_United_States_Securities_and_Exchange_Commission.svg.png",
    "Web Search Research Agent": "https://duckduckgo.com/static-assets/favicons/DDG-iOS-icon_76x76.png",
    "Wikipedia Research Agent": "https://upload.wikimedia.org/wikipedia/commons/thumb/8/80/Wikipedia-logo-v2.svg/103px-Wikipedia-logo-v2.svg.png"
}


def get_session_id(request: gr.Request = None) -> str:
    """Generate or retrieve session ID"""
    if request and hasattr(request, 'session_hash'):
        return request.session_hash
    return str(uuid.uuid4())

def get_or_create_session_state(session_id: str) -> Dict:
    """Get or create isolated session state"""
    if session_id not in user_sessions:
        user_sessions[session_id] = {
            "roundtable_state": {
                "participants": [],
                "messages": [],
                "currentSpeaker": None,
                "thinking": [],
                "showBubbles": []
            },
            "discussion_log": [],
            "final_answer": "",
            "api_keys": {
                "mistral": None,
                "sambanova": None
            }
        }
    return user_sessions[session_id]

def update_session_api_keys(mistral_key, sambanova_key, session_id_state, request: gr.Request = None):
    """Update API keys for THIS SESSION ONLY"""
    session_id = get_session_id(request) if not session_id_state else session_id_state
    session = get_or_create_session_state(session_id)
    
    status_messages = []
    
    # Update keys for THIS SESSION
    if mistral_key.strip():
        session["api_keys"]["mistral"] = mistral_key.strip()
        status_messages.append("✅ Mistral API key saved for this session")
    elif MISTRAL_API_KEY:  # Fall back to env var
        session["api_keys"]["mistral"] = MISTRAL_API_KEY
        status_messages.append("✅ Using Mistral API key from environment")
    else:
        status_messages.append("❌ No Mistral API key available")
        
    if sambanova_key.strip():
        session["api_keys"]["sambanova"] = sambanova_key.strip()
        status_messages.append("✅ SambaNova API key saved for this session")
    elif SAMBANOVA_API_KEY:
        session["api_keys"]["sambanova"] = SAMBANOVA_API_KEY
        status_messages.append("✅ Using SambaNova API key from environment")
    else:
        status_messages.append("❌ No SambaNova API key available")
    
    return " | ".join(status_messages), session_id

class VisualConsensusEngine:
    def __init__(self, moderator_model: str = None, update_callback=None, session_id: str = None):
        self.moderator_model = moderator_model or MODERATOR_MODEL
        self.update_callback = update_callback
        self.session_id = session_id
        
        # Initialize OpenFloor Manager FIRST
        self.floor_manager = OpenFloorManager(port=7860)
        self.floor_manager.set_visual_callback(self.update_visual_state)
        
        # Create OpenFloor research agents
        from research_tools import WebSearchTool, WikipediaSearchTool, ArxivSearchTool, GitHubSearchTool, SECSearchTool
        
        self.research_agents = {
            'web_search': OpenFloorResearchAgent(WebSearchTool(), port=8001),
            'wikipedia': OpenFloorResearchAgent(WikipediaSearchTool(), port=8002), 
            'arxiv': OpenFloorResearchAgent(ArxivSearchTool(), port=8003),
            'github': OpenFloorResearchAgent(GitHubSearchTool(), port=8004),
            'sec_edgar': OpenFloorResearchAgent(SECSearchTool(), port=8005)
        }
        
        # Start research agents and register with floor manager
        self.start_openfloor_research_agents()
        
        # Create a persistent conversation for this session
        self.conversation_id = self.floor_manager.create_conversation([])
        
        # Available research agents for discovery
        self.available_research_agents = list(self.research_agents.keys())
        
        # Get session-specific keys or fall back to global
        session = get_or_create_session_state(session_id) if session_id else {"api_keys": {}}
        session_keys = session.get("api_keys", {})
        
        mistral_key = session_keys.get("mistral") or MISTRAL_API_KEY
        sambanova_key = session_keys.get("sambanova") or SAMBANOVA_API_KEY
        
        self.models = {
            'mistral': {
                'name': 'Mistral Large',
                'api_key': mistral_key,
                'available': bool(mistral_key)
            },
            'sambanova_deepseek': {
                'name': 'DeepSeek-R1',
                'api_key': sambanova_key,
                'available': bool(sambanova_key)
            },
            'sambanova_llama': {
                'name': 'Meta-Llama-3.3-70B-Instruct',
                'api_key': sambanova_key,
                'available': bool(sambanova_key)
            },
            'sambanova_qwen': {
                'name': 'Qwen3-32B',
                'api_key': sambanova_key,
                'available': bool(sambanova_key)
            }
        }
        
        # Store session keys for API calls
        self.session_keys = {
            'mistral': mistral_key,
            'sambanova': sambanova_key
        }
        
        # Register AI experts with floor manager
        self.register_ai_experts()
        
        # Start floor manager service
        self.floor_manager.start_floor_manager_service()
        
        # PROFESSIONAL: Strong, expert role definitions matched to decision protocols
        self.roles = {
            'standard': "Provide expert analysis with clear reasoning and evidence.",
            'expert_advocate': "You are a PASSIONATE EXPERT advocating for your specialized position. Present compelling evidence with conviction.",
            'critical_analyst': "You are a RIGOROUS CRITIC. Identify flaws, risks, and weaknesses in arguments with analytical precision.",
            'strategic_advisor': "You are a STRATEGIC ADVISOR. Focus on practical implementation, real-world constraints, and actionable insights.",
            'research_specialist': "You are a RESEARCH EXPERT with deep domain knowledge. Provide authoritative analysis and evidence-based insights.",
            'innovation_catalyst': "You are an INNOVATION EXPERT. Challenge conventional thinking and propose breakthrough approaches."
        }
        
        # PROFESSIONAL: Different prompt styles based on decision protocol
        self.protocol_styles = {
            'consensus': {
                'intensity': 'collaborative',
                'goal': 'finding common ground',
                'language': 'respectful but rigorous'
            },
            'majority_voting': {
                'intensity': 'competitive',
                'goal': 'winning the argument',
                'language': 'passionate advocacy'
            },
            'weighted_voting': {
                'intensity': 'analytical',
                'goal': 'demonstrating expertise',
                'language': 'authoritative analysis'
            },
            'ranked_choice': {
                'intensity': 'comprehensive',
                'goal': 'exploring all options',
                'language': 'systematic evaluation'
            },
            'unanimity': {
                'intensity': 'diplomatic',
                'goal': 'unanimous agreement',
                'language': 'bridge-building dialogue'
            }
        }

    def start_openfloor_research_agents(self):
        """Start research agents and register them with the floor manager"""
        agent_ports = {
            'web_search': 8001,
            'wikipedia': 8002,
            'arxiv': 8003, 
            'github': 8004,
            'sec_edgar': 8005
        }
        
        self.agent_servers = {}
        
        for agent_name, port in agent_ports.items():
            agent = self.research_agents[agent_name]
            server = OpenFloorAgentServer(agent, port)
            
            if server.start_server():
                self.agent_servers[agent_name] = {
                    'server': server,
                    'port': port,
                    'url': f"http://localhost:{port}",
                    'manifest_url': f"http://localhost:{port}/openfloor/manifest"
                }
                
                # Register with floor manager
                manifest = agent.get_manifest()
                self.floor_manager.register_agent(manifest, f"http://localhost:{port}")
            
            # Small delay between starting servers
            time.sleep(0.5)

    def register_ai_experts(self):
        """Register AI expert models as OpenFloor agents"""
        for model_key, model_info in self.models.items():
            if model_info['available']:
                # Create manifest for AI expert
                expert_manifest = Manifest(
                    identification=Identification(
                        speakerUri=f"tag:consilium.ai,2025:{model_key}",
                        serviceUrl=f"internal://consilium/{model_key}",  # Required parameter
                        conversationalName=model_info['name'],
                        role="AI Expert",
                        organization="Consilium Expert Panel",
                        synopsis=f"Expert AI model: {model_info['name']}"
                    ),
                    capabilities=[
                        Capability(
                            keyphrases=["analysis", "expertise", "reasoning", "decision"],
                            descriptions=[f"Expert analysis and reasoning by {model_info['name']}"],
                            languages=["en-us"]
                        )
                    ]
                )
                
                # Register with floor manager (no URL since these are internal)
                self.floor_manager.register_agent(expert_manifest, "internal://ai-expert")
    
    def update_visual_state(self, state_update: Dict[str, Any]):
        """Update the visual roundtable state for this session"""
        if self.update_callback:
            self.update_callback(state_update)

    def send_research_request_via_floor(self, function_name: str, query: str, requesting_expert: str) -> str:
        """Send research request through proper OpenFloor messaging"""
        
        # Map function to research agent
        function_to_agent = {
            "search_web": "web_search",
            "search_wikipedia": "wikipedia", 
            "search_academic": "arxiv",
            "search_technology_trends": "github",
            "search_financial_data": "sec_edgar"
        }
        
        if function_name not in function_to_agent:
            return f"Unknown research function: {function_name}"
        
        agent_name = function_to_agent[function_name]
        research_agent = self.research_agents[agent_name]
        target_speaker_uri = research_agent.manifest.identification.speakerUri
        requesting_speaker_uri = f"tag:consilium.ai,2025:{requesting_expert}"
        
        # Step 1: Invite research agent to conversation
        success = self.floor_manager.invite_agent_to_conversation(
            self.conversation_id, 
            target_speaker_uri, 
            requesting_speaker_uri
        )
        
        if not success:
            return f"Failed to invite {agent_name} to conversation"
        
        # Step 2: Send research request via floor manager
        research_dialog = DialogEvent(
            speakerUri=requesting_speaker_uri,
            features={"text": TextFeature(values=[query])}
        )
        
        research_envelope = Envelope(
            conversation=Conversation(id=self.conversation_id),
            sender=Sender(speakerUri=requesting_speaker_uri),
            events=[
                UtteranceEvent(
                    dialogEvent=research_dialog,
                    to=To(speakerUri=target_speaker_uri)
                )
            ]
        )
        
        # Route through floor manager
        routing_success = self.floor_manager.route_message(research_envelope)
        
        if not routing_success:
            return f"Failed to route research request to {agent_name}"
        
        # Step 3: Wait for response and collect result
        # In a real implementation, this would be asynchronous
        # For now, we'll use the direct research call as fallback
        try:
            result = research_agent.tool.search(query)
            
            # Step 4: Send result back through floor as ContextEvent
            result_envelope = Envelope(
                conversation=Conversation(id=self.conversation_id),
                sender=Sender(speakerUri=target_speaker_uri),
                events=[
                    ContextEvent(
                        parameters={
                            "research_function": function_name,
                            "query": query,
                            "requesting_expert": requesting_expert,
                            "result": result
                        }
                    )
                ]
            )
            
            self.floor_manager.route_message(result_envelope)
            
            # Step 5: Remove research agent from conversation
            self.dismiss_research_agent_via_floor(agent_name, requesting_expert)
            
            return result
            
        except Exception as e:
            error_msg = f"Research error: {str(e)}"
            
            # Send error through floor
            error_envelope = Envelope(
                conversation=Conversation(id=self.conversation_id),
                sender=Sender(speakerUri=target_speaker_uri),
                events=[
                    ContextEvent(
                        parameters={
                            "research_error": error_msg,
                            "function": function_name,
                            "query": query
                        }
                    )
                ]
            )
            
            self.floor_manager.route_message(error_envelope)
            self.dismiss_research_agent_via_floor(agent_name, requesting_expert)
            
            return error_msg

    def dismiss_research_agent_via_floor(self, agent_name: str, requesting_expert: str):
        """Properly dismiss research agent via OpenFloor messaging"""
        research_agent = self.research_agents[agent_name]
        target_speaker_uri = research_agent.manifest.identification.speakerUri
        
        # Send bye event
        bye_envelope = Envelope(
            conversation=Conversation(id=self.conversation_id),
            sender=Sender(speakerUri=target_speaker_uri),
            events=[
                ByeEvent(
                    parameters={
                        "message": "Research task completed. Leaving conversation."
                    }
                )
            ]
        )
        
        self.floor_manager.route_message(bye_envelope)
        
        # Remove from conversation participants
        if self.conversation_id in self.floor_manager.active_conversations:
            conversation_state = self.floor_manager.active_conversations[self.conversation_id]
            if target_speaker_uri in conversation_state['participants']:
                conversation_state['participants'].remove(target_speaker_uri)

    def handle_function_calls(self, completion, original_prompt: str, calling_model: str) -> str:
        """UNIFIED function call handler with enhanced research capabilities"""
        
        # Check if completion is valid
        if not completion or not completion.choices or len(completion.choices) == 0:
            print(f"Invalid completion object for {calling_model}")
            return "Analysis temporarily unavailable - invalid API response"
            
        message = completion.choices[0].message
        
        # If no function calls, return regular response
        if not hasattr(message, 'tool_calls') or not message.tool_calls:
            content = message.content
            if isinstance(content, list):
                text_parts = []
                for part in content:
                    if isinstance(part, dict) and 'text' in part:
                        text_parts.append(part['text'])
                    elif isinstance(part, str):
                        text_parts.append(part)
                return ' '.join(text_parts) if text_parts else "Analysis completed"
            elif isinstance(content, str):
                return content
            else:
                return str(content) if content else "Analysis completed"
        
        # Get the calling model's name for UI display
        calling_model_name = self.models[calling_model]['name']
        
        # Process each function call
        messages = [
            {"role": "user", "content": original_prompt}, 
            {
                "role": "assistant", 
                "content": message.content or "",
                "tool_calls": message.tool_calls
            }
        ]
        
        for tool_call in message.tool_calls:
            try:
                function_name = tool_call.function.name
                arguments = json.loads(tool_call.function.arguments)
                
                query_param = arguments.get("query") or arguments.get("topic") or arguments.get("technology") or arguments.get("company")
                if query_param:
                    session = get_or_create_session_state(self.session_id)
                    current_state = session["roundtable_state"]
                    all_messages = list(current_state.get("messages", []))
                    
                    # Add request message to the CALLING MODEL (Mistral)
                    request_message = {
                        "speaker": calling_model_name,
                        "text": f"🔍 **Research Request**: {function_name.replace('_', ' ').title()}\n📝 Query: \"{query_param}\"\n⏳ Waiting for research results...",
                        "type": "research_request"
                    }
                    all_messages.append(request_message)
                    
                    existing_bubbles = list(current_state.get("showBubbles", []))
                    if calling_model_name not in existing_bubbles:
                        existing_bubbles.append(calling_model_name)
                    
                    self.update_visual_state({
                        "participants": current_state.get("participants", []),
                        "messages": all_messages,
                        "currentSpeaker": calling_model_name,
                        "thinking": [],
                        "showBubbles": existing_bubbles
                    })
                    time.sleep(1)

                result = self._execute_research_function(function_name, arguments, calling_model_name)
                
                # Ensure result is a string
                if not isinstance(result, str):
                    result = str(result)

                # Log the research activity (with access to session log function)
                session = get_or_create_session_state(self.session_id)
                def session_log_function(event_type, speaker="", content="", **kwargs):
                    session["discussion_log"].append({
                        'type': event_type,
                        'speaker': speaker,
                        'content': content,
                        'timestamp': datetime.now().strftime('%H:%M:%S'),
                        **kwargs
                    })
                    
                # Add function result to conversation
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": result
                })
                
            except Exception as e:
                print(f"Error processing tool call: {str(e)}")
                messages.append({
                    "role": "tool", 
                    "tool_call_id": tool_call.id,
                    "content": f"Research error: {str(e)}"
                })
                continue
        
        # Continue conversation with research results integrated
        try:
            from openai import OpenAI
            
            if calling_model == 'mistral':
                client = OpenAI(
                    base_url="https://api.mistral.ai/v1", 
                    api_key=self.session_keys.get('mistral')
                )
                model_name = 'mistral-large-latest'
            else:
                client = OpenAI(
                    base_url="https://api.sambanova.ai/v1", 
                    api_key=self.session_keys.get('sambanova')
                )
                model_mapping = {
                    'sambanova_deepseek': 'DeepSeek-R1',
                    'sambanova_llama': 'Meta-Llama-3.3-70B-Instruct', 
                    'sambanova_qwen': 'Qwen3-32B'
                }
                model_name = model_mapping.get(calling_model, 'Meta-Llama-3.3-70B-Instruct')
            
            final_completion = client.chat.completions.create(
                model=model_name,
                messages=messages,
                max_tokens=1000,
                temperature=0.7
            )
            
            if final_completion and final_completion.choices and len(final_completion.choices) > 0:
                final_content = final_completion.choices[0].message.content
                
                if isinstance(final_content, list):
                    text_parts = []
                    for part in final_content:
                        if isinstance(part, dict) and 'text' in part:
                            text_parts.append(part['text'])
                        elif isinstance(part, str):
                            text_parts.append(part)
                    return ' '.join(text_parts) if text_parts else "Analysis completed with research integration."
                elif isinstance(final_content, str):
                    return final_content
                else:
                    return str(final_content) if final_content else "Analysis completed with research integration."
            else:
                return message.content or "Analysis completed with research integration."
            
        except Exception as e:
            print(f"Error in follow-up completion for {calling_model}: {str(e)}")
            return message.content or "Analysis completed with research integration."

    def _execute_research_function(self, function_name: str, arguments: dict, requesting_model_name: str = None) -> str:
        """Execute research function using proper OpenFloor messaging"""
        
        query_param = arguments.get("query") or arguments.get("topic") or arguments.get("technology") or arguments.get("company")
        
        if not query_param:
            return "No query parameter found in research request"
        
        # Show research starting
        self.show_research_starting(function_name, query_param)
        
        try:
            # Use OpenFloor messaging for research
            result = self.send_research_request_via_floor(
                function_name, 
                query_param, 
                requesting_model_name or 'unknown'
            )
            
            # Show research complete
            self.show_research_complete(function_name, query_param, len(result), requesting_model_name)
            
            return result
            
        except Exception as e:
            error_msg = str(e)
            self.show_research_error(function_name, query_param, error_msg, requesting_model_name)
            return f"OpenFloor research error: {error_msg}"
    
    def show_research_starting(self, function: str, query: str):
        """Invite specific research agent to join conversation"""
        function_to_agent = {
            "search_web": "web_search",
            "search_wikipedia": "wikipedia", 
            "search_academic": "arxiv",
            "search_technology_trends": "github",
            "search_financial_data": "sec_edgar"
        }
        
        if function in function_to_agent:
            agent_name = function_to_agent[function]
            # Use the existing invite method
            self.invite_research_agent(agent_name, "current_conversation", "AI Expert")
            
            # Add the query information
            research_agent = self.research_agents[agent_name]
            agent_display_name = research_agent.manifest.identification.conversationalName
            
            session = get_or_create_session_state(self.session_id)
            current_state = session["roundtable_state"]
            all_messages = list(current_state.get("messages", []))
            
            # Add research starting message
            start_message = {
                "speaker": agent_display_name,
                "text": f"🔍 **Starting Research**\n📝 Query: \"{query}\"\n⏳ Connecting to data sources...",
                "type": "research_starting"
            }
            all_messages.append(start_message)
            
            existing_bubbles = list(current_state.get("showBubbles", []))
            self.update_visual_state({
                "participants": current_state.get("participants", []),
                "messages": all_messages,
                "currentSpeaker": agent_display_name,
                "thinking": [],
                "showBubbles": existing_bubbles
            })

    def show_research_complete(self, function: str, query: str, result_length: int, requesting_model_name: str = None):
        """Show research complete and dismiss the specific agent"""
        function_to_agent = {
            "search_web": "web_search",
            "search_wikipedia": "wikipedia", 
            "search_academic": "arxiv",
            "search_technology_trends": "github",
            "search_financial_data": "sec_edgar"
        }
        
        if function in function_to_agent:
            agent_name = function_to_agent[function]
            research_agent = self.research_agents[agent_name]
            agent_display_name = research_agent.manifest.identification.conversationalName
            
            session = get_or_create_session_state(self.session_id)
            current_state = session["roundtable_state"]
            all_messages = list(current_state.get("messages", []))
            
            # Show completion message
            complete_message = {
                "speaker": agent_display_name,
                "text": f"✅ **Research Complete**\n📊 {result_length:,} characters analyzed\n🎯 Research delivered to {requesting_model_name}",
                "type": "research_complete"
            }
            all_messages.append(complete_message)
            
            existing_bubbles = list(current_state.get("showBubbles", []))
            self.update_visual_state({
                "participants": current_state.get("participants", []),
                "messages": all_messages,
                "currentSpeaker": requesting_model_name,
                "thinking": [],
                "showBubbles": existing_bubbles
            })
            time.sleep(2)
            
            # Use the existing dismiss method
            self.dismiss_research_agent(agent_name, "current_conversation")

    def show_research_error(self, function: str, query: str, error: str, requesting_model_name: str = None):
        """Show research error from the specific agent and dismiss it"""
        function_to_agent = {
            "search_web": "web_search",
            "search_wikipedia": "wikipedia", 
            "search_academic": "arxiv",
            "search_technology_trends": "github",
            "search_financial_data": "sec_edgar"
        }
        
        if function in function_to_agent:
            agent_name = function_to_agent[function]
            research_agent = self.research_agents[agent_name]
            agent_display_name = research_agent.manifest.identification.conversationalName
            
            session = get_or_create_session_state(self.session_id)
            current_state = session["roundtable_state"]
            all_messages = list(current_state.get("messages", []))
            
            # Show error message from the specific agent
            error_message = {
                "speaker": agent_display_name,
                "text": f"❌ **Research Error**\n🔬 {function.replace('_', ' ').title()}\n📝 Query: \"{query}\"\n⚠️ Error: {error}\n🔄 Research failed, returning to discussion",
                "type": "research_error"
            }
            all_messages.append(error_message)
            
            existing_bubbles = list(current_state.get("showBubbles", []))
            self.update_visual_state({
                "participants": current_state.get("participants", []),
                "messages": all_messages,
                "currentSpeaker": requesting_model_name,
                "thinking": [],
                "showBubbles": existing_bubbles
            })
            time.sleep(1)
            
            # Dismiss the research agent since research failed
            self.dismiss_research_agent(agent_name, "current_conversation")

    def invite_research_agent(self, agent_name: str, conversation_id: str, requesting_expert: str):
        """Invite a research agent to join the conversation visually"""
        
        if agent_name in self.research_agents:
            research_agent = self.research_agents[agent_name]
            
            # Research agent joins the conversation
            research_agent.join_conversation(conversation_id)
            
            # Update visual state to show the research agent joining
            session = get_or_create_session_state(self.session_id)
            current_state = session["roundtable_state"]
            
            # Add research agent to participants if not already there
            participants = list(current_state.get("participants", []))
            agent_display_name = research_agent.manifest.identification.conversationalName
            
            if agent_display_name not in participants:
                participants.append(agent_display_name)
            
            # Show join message
            all_messages = list(current_state.get("messages", []))
            join_message = {
                "speaker": agent_display_name,
                "text": f"🔗 **Joined Conversation**\nInvited by: {requesting_expert}\nSpecialty: {research_agent.manifest.identification.synopsis}\nReady to provide research assistance.",
                "type": "agent_join"
            }
            all_messages.append(join_message)
            
            # Update visual state
            existing_bubbles = list(current_state.get("showBubbles", []))
            if agent_display_name not in existing_bubbles:
                existing_bubbles.append(agent_display_name)
            
            self.update_visual_state({
                "participants": participants,
                "messages": all_messages,
                "currentSpeaker": None,
                "thinking": [],
                "showBubbles": existing_bubbles
            })
            
            return True
        
        return False
    
    def dismiss_research_agent(self, agent_name: str, conversation_id: str):
        """Remove a research agent from the conversation visually"""
        
        if agent_name in self.research_agents:
            research_agent = self.research_agents[agent_name]
            
            # Research agent leaves the conversation
            research_agent.leave_conversation(conversation_id)
            
            # Update visual state
            session = get_or_create_session_state(self.session_id)
            current_state = session["roundtable_state"]
            
            agent_display_name = research_agent.manifest.identification.conversationalName
            
            # Show leave message
            all_messages = list(current_state.get("messages", []))
            leave_message = {
                "speaker": agent_display_name,
                "text": f"👋 **Leaving Conversation**\nResearch assistance complete. Agent dismissed.",
                "type": "agent_leave"
            }
            all_messages.append(leave_message)
            
            # Remove from bubbles but keep in participants list for history
            existing_bubbles = list(current_state.get("showBubbles", []))
            if agent_display_name in existing_bubbles:
                existing_bubbles.remove(agent_display_name)
            
            self.update_visual_state({
                "participants": current_state.get("participants", []),
                "messages": all_messages,
                "currentSpeaker": None,
                "thinking": [],
                "showBubbles": existing_bubbles
            })
            
            return True
        
        return False

    def call_model(self, model: str, prompt: str, context: str = "") -> Optional[str]:
        """Enhanced model calling with native function calling support"""
        if not self.models[model]['available']:
            print(f"Model {model} not available - missing API key")
            return None
            
        full_prompt = f"{context}\n\n{prompt}" if context else prompt
        
        try:
            if model == 'mistral':
                return self._call_mistral(full_prompt)
            elif model.startswith('sambanova_'):
                return self._call_sambanova(model, full_prompt)
        except Exception as e:
            print(f"Error calling {model}: {str(e)}")
            return None
        
        return None
    
    def _call_sambanova(self, model: str, prompt: str) -> Optional[str]:
        """Enhanced SambaNova API call with native function calling"""
        api_key = self.session_keys.get('sambanova')
        if not api_key:
            print(f"No SambaNova API key available for {model}")
            return None
            
        try:
            from openai import OpenAI
            
            client = OpenAI(
                base_url="https://api.sambanova.ai/v1", 
                api_key=api_key
            )
            
            model_mapping = {
                'sambanova_deepseek': 'DeepSeek-R1',
                'sambanova_llama': 'Meta-Llama-3.3-70B-Instruct', 
                'sambanova_qwen': 'Qwen3-32B'
            }
            
            sambanova_model = model_mapping.get(model, 'Meta-Llama-3.3-70B-Instruct')
            print(f"Calling SambaNova model: {sambanova_model}")
            
            # Check if model supports function calling (Updated list)
            supports_functions = sambanova_model in [
                'Meta-Llama-3.1-8B-Instruct',
                'Meta-Llama-3.1-405B-Instruct', 
                'Meta-Llama-3.3-70B-Instruct'
            ]
            
            if supports_functions:
                completion = client.chat.completions.create(
                    model=sambanova_model,
                    messages=[{"role": "user", "content": prompt}],
                    tools=ENHANCED_SEARCH_FUNCTIONS,
                    tool_choice="auto",
                    max_tokens=1000,
                    temperature=0.7
                )
            else:
                # Qwen3-32B and other models that don't support function calling
                print(f"Model {sambanova_model} doesn't support function calling - using regular completion")
                completion = client.chat.completions.create(
                    model=sambanova_model,
                    messages=[{"role": "user", "content": prompt}],
                    max_tokens=1000,
                    temperature=0.7
                )
            
            # Handle function calls if present (only for models that support it)
            if supports_functions:
                return self.handle_function_calls(completion, prompt, model)
            else:
                # For models without function calling, return response directly
                if completion and completion.choices and len(completion.choices) > 0:
                    return completion.choices[0].message.content
                else:
                    return None
            
        except Exception as e:
            print(f"Error calling SambaNova {model} ({sambanova_model}): {str(e)}")
            # Print more detailed error info
            import traceback
            traceback.print_exc()
            return None
    
    def _call_mistral(self, prompt: str) -> Optional[str]:
        """Enhanced Mistral API call with native function calling"""
        api_key = self.session_keys.get('mistral')
        if not api_key:
            print("No Mistral API key available")
            return None
            
        try:
            from openai import OpenAI
            
            client = OpenAI(
                base_url="https://api.mistral.ai/v1", 
                api_key=api_key
            )
            
            print("Calling Mistral model: mistral-large-latest")
            
            completion = client.chat.completions.create(
                model='mistral-large-latest',
                messages=[{"role": "user", "content": prompt}],
                tools=ENHANCED_SEARCH_FUNCTIONS,
                tool_choice="auto",
                max_tokens=1000,
                temperature=0.7
            )
            
            # Check if we got a valid response
            if not completion or not completion.choices or len(completion.choices) == 0:
                print("Invalid response structure from Mistral")
                return None
                
            # Handle function calls if present
            return self.handle_function_calls(completion, prompt, 'mistral')
            
        except Exception as e:
            print(f"Error calling Mistral API: {str(e)}")
            import traceback
            traceback.print_exc()
            return None
    
    def assign_roles(self, models: List[str], role_assignment: str) -> Dict[str, str]:
        """Assign expert roles for rigorous analysis"""
        
        if role_assignment == "none":
            return {model: "standard" for model in models}
        
        roles_to_assign = []
        if role_assignment == "balanced":
            roles_to_assign = ["expert_advocate", "critical_analyst", "strategic_advisor", "research_specialist"]
        elif role_assignment == "specialized":
            roles_to_assign = ["research_specialist", "strategic_advisor", "innovation_catalyst", "expert_advocate"]
        elif role_assignment == "adversarial":
            roles_to_assign = ["critical_analyst", "innovation_catalyst", "expert_advocate", "strategic_advisor"]
        
        while len(roles_to_assign) < len(models):
            roles_to_assign.append("standard")
        
        model_roles = {}
        for i, model in enumerate(models):
            model_roles[model] = roles_to_assign[i % len(roles_to_assign)]
        
        return model_roles
    
    def _extract_confidence(self, response: str) -> float:
        """Extract confidence score from response"""
        if not response or not isinstance(response, str):
            return 5.0
        
        confidence_match = re.search(r'Confidence:\s*(\d+(?:\.\d+)?)', response)
        if confidence_match:
            try:
                return float(confidence_match.group(1))
            except ValueError:
                pass
        return 5.0
    
    def build_position_summary(self, all_messages: List[Dict], current_model: str, topology: str = "full_mesh") -> str:
        """Build expert position summary for analysis"""
        
        current_model_name = self.models[current_model]['name']
        
        if topology == "full_mesh":
            # Show latest position from each expert
            latest_positions = {}
            for msg in all_messages:
                if msg["speaker"] != current_model_name and not msg["speaker"].endswith("Research Agent"):
                    latest_positions[msg["speaker"]] = {
                        'text': msg['text'][:150] + "..." if len(msg['text']) > 150 else msg['text'],
                        'confidence': msg.get('confidence', 5)
                    }
            
            summary = "EXPERT POSITIONS:\n"
            for speaker, pos in latest_positions.items():
                summary += f"• **{speaker}**: {pos['text']} (Confidence: {pos['confidence']}/10)\n"
            
        elif topology == "star":
            # Only show moderator's latest position
            moderator_name = self.models[self.moderator_model]['name']
            summary = "MODERATOR ANALYSIS:\n"
            
            for msg in reversed(all_messages):
                if msg["speaker"] == moderator_name:
                    text = msg['text'][:200] + "..." if len(msg['text']) > 200 else msg['text']
                    summary += f"• **{moderator_name}**: {text}\n"
                    break
            
        elif topology == "ring":
            # Only show previous expert's position
            available_models = [model for model, info in self.models.items() if info['available']]
            current_idx = available_models.index(current_model)
            prev_idx = (current_idx - 1) % len(available_models)
            prev_model_name = self.models[available_models[prev_idx]]['name']
            
            summary = "PREVIOUS EXPERT:\n"
            for msg in reversed(all_messages):
                if msg["speaker"] == prev_model_name:
                    text = msg['text'][:200] + "..." if len(msg['text']) > 200 else msg['text']
                    summary += f"• **{prev_model_name}**: {text}\n"
                    break
        
        return summary
    
    def run_visual_consensus_session(self, question: str, discussion_rounds: int = 3, 
                                   decision_protocol: str = "consensus", role_assignment: str = "balanced",
                                   topology: str = "full_mesh", moderator_model: str = "mistral",
                                   log_function=None):
        """Run expert consensus with protocol-appropriate intensity and Research Agent integration"""
        
        available_models = [model for model, info in self.models.items() if info['available']]
        if not available_models:
            return "❌ No AI models available"
        
        # Add AI experts to the conversation
        for model in available_models:
            expert_speaker_uri = f"tag:consilium.ai,2025:{model}"
            if self.conversation_id in self.floor_manager.active_conversations:
                conversation_state = self.floor_manager.active_conversations[self.conversation_id]
                if expert_speaker_uri not in conversation_state['participants']:
                    conversation_state['participants'].append(expert_speaker_uri)
        
        # Send conversation start event through floor
        start_envelope = Envelope(
            conversation=Conversation(id=self.conversation_id),
            sender=Sender(speakerUri="tag:consilium.ai,2025:session-manager"),
            events=[
                ContextEvent(
                    parameters={
                        "session_start": True,
                        "question": question,
                        "protocol": decision_protocol,
                        "participants": [self.models[model]['name'] for model in available_models]
                    }
                )
            ]
        )
        
        self.floor_manager.route_message(start_envelope)
        
        model_roles = self.assign_roles(available_models, role_assignment)
        
        visual_participant_names = [self.models[model]['name'] for model in available_models]
        
        # Get protocol-appropriate style
        protocol_style = self.protocol_styles.get(decision_protocol, self.protocol_styles['consensus'])
        
        # Use session-specific logging
        def log_event(event_type: str, speaker: str = "", content: str = "", **kwargs):
            if log_function:
                log_function(event_type, speaker, content, **kwargs)
        
        # Log the start
        log_event('phase', content=f"🎯 Starting Expert Analysis: {question}")
        log_event('phase', content=f"📊 Configuration: {len(available_models)} experts, {decision_protocol} protocol, {role_assignment} roles, {topology} topology")
        
        self.update_visual_state({
            "participants": visual_participant_names,
            "messages": [],
            "currentSpeaker": None,
            "thinking": [],
            "showBubbles": [],
            "avatarImages": avatar_images
        })
        
        all_messages = []
        
        log_event('phase', content="📝 Phase 1: Expert Initial Analysis")
        
        for model in available_models:
            # Log and set thinking state - PRESERVE BUBBLES
            log_event('thinking', speaker=self.models[model]['name'])
            
            session = get_or_create_session_state(self.session_id)
            current_state = session["roundtable_state"]
            existing_bubbles = list(current_state.get("showBubbles", []))
            
            self.update_visual_state({
                "participants": visual_participant_names,
                "messages": all_messages,
                "currentSpeaker": None,
                "thinking": [self.models[model]['name']],
                "showBubbles": existing_bubbles,
                "avatarImages": avatar_images
            })
            
            time.sleep(1)
            
            role = model_roles[model]
            role_context = self.roles[role]
            
            # PROTOCOL-ADAPTED: Prompt intensity based on decision protocol
            if decision_protocol in ['majority_voting', 'ranked_choice']:
                intensity_prompt = "🎯 CRITICAL DECISION"
                action_prompt = "Take a STRONG, CLEAR position and defend it with compelling evidence"
                stakes = "This decision has major consequences - be decisive and convincing"
            elif decision_protocol == 'consensus':
                intensity_prompt = "🤝 COLLABORATIVE ANALYSIS"
                action_prompt = "Provide thorough analysis while remaining open to other perspectives"
                stakes = "Work toward building understanding and finding common ground"
            else:  # weighted_voting, unanimity
                intensity_prompt = "🔬 EXPERT ANALYSIS"
                action_prompt = "Provide authoritative analysis with detailed reasoning"
                stakes = "Your expertise and evidence quality will determine influence"
            
            prompt = f"""{intensity_prompt}: {question}

Your Role: {role_context}

ANALYSIS REQUIREMENTS:
- {action_prompt}
- {stakes}
- Use specific examples, data, and evidence
- If you need current information or research, you can search the web, Wikipedia, academic papers, technology trends, or financial data
- Maximum 200 words of focused analysis
- End with "Position: [YOUR CLEAR STANCE]" and "Confidence: X/10"

Provide your expert analysis:"""

            # Log and set speaking state - PRESERVE BUBBLES
            log_event('speaking', speaker=self.models[model]['name'])
            
            # Calculate existing bubbles
            existing_bubbles = list(current_state.get("showBubbles", []))
            
            self.update_visual_state({
                "participants": visual_participant_names,
                "messages": all_messages,
                "currentSpeaker": self.models[model]['name'],
                "thinking": [],
                "showBubbles": existing_bubbles,
                "avatarImages": avatar_images
            })
            
            time.sleep(2)
            
            # Call model - may trigger function calls and Research Agent activation
            response = self.call_model(model, prompt)
            
            # CRITICAL: Ensure response is a string
            if response and not isinstance(response, str):
                response = str(response)
            
            if response:
                confidence = self._extract_confidence(response)
                message = {
                    "speaker": self.models[model]['name'],
                    "text": response,
                    "confidence": confidence,
                    "role": role
                }
                all_messages.append(message)
                
                # Log the full response
                log_event('message', 
                         speaker=self.models[model]['name'], 
                         content=response,
                         role=role,
                         confidence=confidence)
            else:
                # Handle failed API call gracefully
                log_event('message', 
                         speaker=self.models[model]['name'], 
                         content="Analysis temporarily unavailable - API connection failed",
                         role=role,
                         confidence=0)
                
                message = {
                    "speaker": self.models[model]['name'],
                    "text": "⚠️ Analysis temporarily unavailable - API connection failed. Please check your API keys and try again.",
                    "confidence": 0,
                    "role": role
                }
                all_messages.append(message)
            
            # Update with new message
            responded_speakers = list(set(msg["speaker"] for msg in all_messages if msg.get("speaker") and not msg["speaker"].endswith("Research Agent")))
            
            self.update_visual_state({
                "participants": visual_participant_names,
                "messages": all_messages,
                "currentSpeaker": None,
                "thinking": [],
                "showBubbles": responded_speakers,
                "avatarImages": avatar_images
            })
            
            time.sleep(2)  # Longer pause to see the response
        
        # Phase 2: Rigorous discussion rounds
        if discussion_rounds > 0:
            log_event('phase', content=f"💬 Phase 2: Expert Discussion ({discussion_rounds} rounds)")
            
            for round_num in range(discussion_rounds):
                log_event('phase', content=f"🔄 Expert Round {round_num + 1}")
                
                for model in available_models:
                    # Log thinking with preserved bubbles
                    log_event('thinking', speaker=self.models[model]['name'])
                    
                    existing_bubbles = list(current_state.get("showBubbles", []))
                    
                    self.update_visual_state({
                        "participants": visual_participant_names,
                        "messages": all_messages,
                        "currentSpeaker": None,
                        "thinking": [self.models[model]['name']],
                        "showBubbles": existing_bubbles,
                        "avatarImages": avatar_images
                    })
                    
                    time.sleep(1)
                    
                    # Build expert position summary
                    position_summary = self.build_position_summary(all_messages, model, topology)
                    
                    role = model_roles[model]
                    role_context = self.roles[role]
                    
                    # PROTOCOL-ADAPTED: Discussion intensity based on protocol
                    if decision_protocol in ['majority_voting', 'ranked_choice']:
                        discussion_style = "DEFEND your position and CHALLENGE weak arguments"
                        discussion_goal = "Prove why your approach is superior"
                    elif decision_protocol == 'consensus':
                        discussion_style = "BUILD on other experts' insights and ADDRESS concerns"
                        discussion_goal = "Work toward a solution everyone can support"
                    else:
                        discussion_style = "REFINE your analysis and RESPOND to other experts"
                        discussion_goal = "Demonstrate the strength of your reasoning"
                    
                    discussion_prompt = f"""🔄 Expert Round {round_num + 1}: {question}

Your Role: {role_context}

{position_summary}

DISCUSSION FOCUS:
- {discussion_style}
- {discussion_goal}
- Address specific points raised by other experts
- Use current data and research if needed
- Maximum 180 words of focused response
- End with "Position: [UNCHANGED/EVOLVED]" and "Confidence: X/10"

Your expert response:"""

                    # Log speaking with preserved bubbles
                    log_event('speaking', speaker=self.models[model]['name'])
                    
                    existing_bubbles = list(current_state.get("showBubbles", []))
                    
                    self.update_visual_state({
                        "participants": visual_participant_names,
                        "messages": all_messages,
                        "currentSpeaker": self.models[model]['name'],
                        "thinking": [],
                        "showBubbles": existing_bubbles,
                        "avatarImages": avatar_images
                    })
                    
                    time.sleep(2)
                    
                    response = self.call_model(model, discussion_prompt)
                    
                    if response:
                        confidence = self._extract_confidence(response)
                        message = {
                            "speaker": self.models[model]['name'],
                            "text": f"Round {round_num + 1}: {response}",
                            "confidence": confidence,
                            "role": model_roles[model]
                        }
                        all_messages.append(message)
                        
                        log_event('message', 
                                 speaker=self.models[model]['name'], 
                                 content=f"Round {round_num + 1}: {response}",
                                 role=model_roles[model],
                                 confidence=confidence)
                    else:
                        # Handle failed API call gracefully
                        log_event('message', 
                                 speaker=self.models[model]['name'], 
                                 content=f"Round {round_num + 1}: Analysis temporarily unavailable - API connection failed",
                                 role=model_roles[model],
                                 confidence=0)
                        
                        message = {
                            "speaker": self.models[model]['name'],
                            "text": f"Round {round_num + 1}: ⚠️ Analysis temporarily unavailable - API connection failed.",
                            "confidence": 0,
                            "role": model_roles[model]
                        }
                        all_messages.append(message)
                    
                    # Update visual state
                    responded_speakers = list(set(msg["speaker"] for msg in all_messages if msg.get("speaker") and not msg["speaker"].endswith("Research Agent")))
                    
                    self.update_visual_state({
                        "participants": visual_participant_names,
                        "messages": all_messages,
                        "currentSpeaker": None,
                        "thinking": [],
                        "showBubbles": responded_speakers,
                        "avatarImages": avatar_images
                    })
                    
                    time.sleep(1)
        
        # Phase 3: PROTOCOL-SPECIFIC final decision
        if decision_protocol == 'consensus':
            phase_name = "🤝 Phase 3: Building Consensus"
            moderator_title = "Senior Advisor"
        elif decision_protocol in ['majority_voting', 'ranked_choice']:
            phase_name = "⚖️ Phase 3: Final Decision"
            moderator_title = "Lead Analyst"
        else:
            phase_name = "📊 Phase 3: Expert Synthesis"
            moderator_title = "Lead Researcher"
        
        log_event('phase', content=f"{phase_name} - {decision_protocol}")
        log_event('thinking', speaker="All experts", content="Synthesizing final recommendation...")
        
        expert_names = [self.models[model]['name'] for model in available_models]
        
        # Preserve existing bubbles during final thinking
        existing_bubbles = list(current_state.get("showBubbles", []))
        
        self.update_visual_state({
            "participants": visual_participant_names,
            "messages": all_messages,
            "currentSpeaker": None,
            "thinking": expert_names,
            "showBubbles": existing_bubbles,
            "avatarImages": avatar_images
        })
        
        time.sleep(2)
        
        # Generate PROTOCOL-APPROPRIATE final analysis
        moderator = self.moderator_model if self.models[self.moderator_model]['available'] else available_models[0]
        
        # Build expert summary
        final_positions = {}
        confidence_scores = []
        
        # Get list of all research agent names
        research_agent_names = [agent.manifest.identification.conversationalName for agent in self.research_agents.values()]
        
        for msg in all_messages:
            speaker = msg["speaker"]
            if (speaker not in [moderator_title, 'Consilium'] and 
                speaker not in research_agent_names):
                if speaker not in final_positions:
                    final_positions[speaker] = []
                final_positions[speaker].append(msg)
                if 'confidence' in msg:
                    confidence_scores.append(msg['confidence'])
        
        # Create PROFESSIONAL expert summary
        expert_summary = f"🎯 EXPERT ANALYSIS: {question}\n\nFINAL EXPERT POSITIONS:\n"
        
        for speaker, messages in final_positions.items():
            latest_msg = messages[-1]
            role = latest_msg.get('role', 'standard')
            # Extract the core argument
            core_argument = latest_msg['text'][:200] + "..." if len(latest_msg['text']) > 200 else latest_msg['text']
            confidence = latest_msg.get('confidence', 5)
            
            expert_summary += f"\n📋 **{speaker}** ({role}):\n{core_argument}\nFinal Confidence: {confidence}/10\n"
        
        avg_confidence = sum(confidence_scores) / len(confidence_scores) if confidence_scores else 5.0
        
        # PROTOCOL-SPECIFIC synthesis prompt
        if decision_protocol == 'consensus':
            synthesis_goal = "Build a CONSENSUS recommendation that all experts can support"
            synthesis_format = "**CONSENSUS REACHED:** [Yes/Partial/No]\n**RECOMMENDED APPROACH:** [Synthesis]\n**AREAS OF AGREEMENT:** [Common ground]\n**REMAINING CONCERNS:** [Issues to address]"
        elif decision_protocol in ['majority_voting', 'ranked_choice']:
            synthesis_goal = "Determine the STRONGEST position and declare a clear winner"
            synthesis_format = "**DECISION:** [Clear recommendation]\n**WINNING ARGUMENT:** [Most compelling case]\n**KEY EVIDENCE:** [Supporting data]\n**IMPLEMENTATION:** [Next steps]"
        else:
            synthesis_goal = "Synthesize expert insights into actionable recommendations"
            synthesis_format = "**ANALYSIS CONCLUSION:** [Summary]\n**RECOMMENDED APPROACH:** [Best path forward]\n**RISK ASSESSMENT:** [Key considerations]\n**CONFIDENCE LEVEL:** [Overall certainty]"
        
        consensus_prompt = f"""{expert_summary}

📊 SENIOR ANALYSIS REQUIRED:

{synthesis_goal}

SYNTHESIS REQUIREMENTS:
- Analyze the quality and strength of each expert position
- Identify areas where experts align vs disagree  
- Provide a clear, actionable recommendation
- Use additional research if needed to resolve disagreements
- Maximum 300 words of decisive analysis

Average Expert Confidence: {avg_confidence:.1f}/10
Protocol: {decision_protocol}

Format:
{synthesis_format}

Provide your synthesis:"""

        log_event('speaking', speaker=moderator_title, content="Synthesizing expert analysis into final recommendation...")
        
        # Preserve existing bubbles during final speaking
        existing_bubbles = list(current_state.get("showBubbles", []))
        
        self.update_visual_state({
            "participants": visual_participant_names,
            "messages": all_messages,
            "currentSpeaker": "Consilium",
            "thinking": [],
            "showBubbles": existing_bubbles,
            "avatarImages": avatar_images
        })
        
        # Call moderator model - may also trigger function calls
        consensus_result = self.call_model(moderator, consensus_prompt)
        
        if not consensus_result:
            consensus_result = f"""**ANALYSIS INCOMPLETE:** Technical difficulties prevented full synthesis.

**RECOMMENDED APPROACH:** Manual review of expert positions required.

**KEY CONSIDERATIONS:** All expert inputs should be carefully evaluated.

**NEXT STEPS:** Retry analysis or conduct additional expert consultation."""
        
        # Determine result quality based on protocol
        if decision_protocol == 'consensus':
            if "CONSENSUS REACHED: Yes" in consensus_result or avg_confidence >= 7.5:
                visual_summary = "✅ Expert Consensus Achieved"
            elif "Partial" in consensus_result:
                visual_summary = "⚠️ Partial Consensus - Some Expert Disagreement"
            else:
                visual_summary = "🤔 No Consensus - Significant Expert Disagreement"
        elif decision_protocol in ['majority_voting', 'ranked_choice']:
            if any(word in consensus_result.upper() for word in ["DECISION:", "WINNING", "RECOMMEND"]):
                visual_summary = "⚖️ Clear Expert Recommendation"
            else:
                visual_summary = "🤔 Expert Analysis Complete"
        else:
            visual_summary = "📊 Expert Analysis Complete"
        
        final_message = {
            "speaker": moderator_title,
            "text": f"{visual_summary}\n\n{consensus_result}",
            "confidence": avg_confidence,
            "role": "moderator"
        }
        all_messages.append(final_message)
        
        log_event('message', 
                 speaker=moderator_title, 
                 content=consensus_result,
                 confidence=avg_confidence)
        
        responded_speakers = list(set(msg["speaker"] for msg in all_messages if msg.get("speaker") and not msg["speaker"].endswith("Research Agent")))
        
        self.update_visual_state({
            "participants": visual_participant_names,
            "messages": all_messages,
            "currentSpeaker": None,
            "thinking": [],
            "showBubbles": responded_speakers,
            "avatarImages": avatar_images
        })
        
        log_event('phase', content="✅ Expert Analysis Complete")
        
        return consensus_result

def update_session_roundtable_state(session_id: str, new_state: Dict):
    """Update roundtable state for specific session"""
    session = get_or_create_session_state(session_id)
    session["roundtable_state"].update(new_state)
    return json.dumps(session["roundtable_state"])

def run_consensus_discussion_session(question: str, discussion_rounds: int = 3, 
                                   decision_protocol: str = "consensus", role_assignment: str = "balanced",
                                   topology: str = "full_mesh", moderator_model: str = "mistral",
                                   session_id_state: str = None,
                                   request: gr.Request = None):
    """Session-isolated expert consensus discussion"""
    
    # Get unique session
    session_id = get_session_id(request) if not session_id_state else session_id_state
    session = get_or_create_session_state(session_id)
    
    # Reset session state for new discussion
    session["discussion_log"] = []
    session["final_answer"] = ""
    
    def session_visual_update_callback(state_update):
        """Session-specific visual update callback"""
        update_session_roundtable_state(session_id, state_update)
    
    def session_log_event(event_type: str, speaker: str = "", content: str = "", **kwargs):
        """Add event to THIS session's log only"""
        session["discussion_log"].append({
            'type': event_type,
            'speaker': speaker,
            'content': content,
            'timestamp': datetime.now().strftime('%H:%M:%S'),
            **kwargs
        })
    
    # Create engine with session-specific callback
    engine = VisualConsensusEngine(moderator_model, session_visual_update_callback, session_id)
    
    # Run consensus with session-specific logging
    result = engine.run_visual_consensus_session(
        question, discussion_rounds, decision_protocol, 
        role_assignment, topology, moderator_model, 
        session_log_event
    )
    
    # Generate session-specific final answer
    available_models = [model for model, info in engine.models.items() if info['available']]
    session["final_answer"] = f"""## 🎯 Expert Analysis Results

{result}

---

### 📊 Analysis Summary
- **Question:** {question}
- **Protocol:** {decision_protocol.replace('_', ' ').title()}
- **Topology:** {topology.replace('_', ' ').title()}
- **Experts:** {len(available_models)} AI specialists
- **Roles:** {role_assignment.title()}
- **Research Integration:** Native function calling with live data
- **Session ID:** {session_id[:3]}...

*Generated by Consilium: Multi-AI Expert Consensus Platform*"""
    
    # Format session-specific discussion log
    formatted_log = format_session_discussion_log(session["discussion_log"])
    
    return ("✅ Expert Analysis Complete - See results below", 
            json.dumps(session["roundtable_state"]), 
            session["final_answer"], 
            formatted_log,
            session_id)

def format_session_discussion_log(discussion_log: list) -> str:
    """Format discussion log for specific session"""
    if not discussion_log:
        return "No discussion log available yet."
    
    formatted_log = "# 🎭 Complete Expert Discussion Log\n\n"
    
    for entry in discussion_log:
        timestamp = entry.get('timestamp', datetime.now().strftime('%H:%M:%S'))
        
        if entry['type'] == 'thinking':
            formatted_log += f"**{timestamp}** 🤔 **{entry['speaker']}** is analyzing...\n\n"
            
        elif entry['type'] == 'speaking':
            formatted_log += f"**{timestamp}** 💬 **{entry['speaker']}** is presenting...\n\n"
            
        elif entry['type'] == 'message':
            formatted_log += f"**{timestamp}** 📋 **{entry['speaker']}** ({entry.get('role', 'standard')}):\n"
            formatted_log += f"> {entry['content']}\n"
            if 'confidence' in entry:
                formatted_log += f"*Confidence: {entry['confidence']}/10*\n\n"
            else:
                formatted_log += "\n"
                
        elif entry['type'] == 'research_request':
            function_name = entry.get('function', 'Unknown')
            query = entry.get('query', 'Unknown query')
            requesting_expert = entry.get('requesting_expert', 'Unknown expert')
            formatted_log += f"**{timestamp}** 🔍 **Research Agent** - Research Request:\n"
            formatted_log += f"> **Function:** {function_name.replace('_', ' ').title()}\n"
            formatted_log += f"> **Query:** \"{query}\"\n"
            formatted_log += f"> **Requested by:** {requesting_expert}\n\n"
            
        elif entry['type'] == 'research_result':
            function_name = entry.get('function', 'Unknown')
            query = entry.get('query', 'Unknown query')
            requesting_expert = entry.get('requesting_expert', 'Unknown expert')
            full_result = entry.get('full_result', entry.get('content', 'No result'))
            formatted_log += f"**{timestamp}** 📊 **Research Agent** - Research Results:\n"
            formatted_log += f"> **Function:** {function_name.replace('_', ' ').title()}\n"
            formatted_log += f"> **Query:** \"{query}\"\n"
            formatted_log += f"> **For Expert:** {requesting_expert}\n\n"
            formatted_log += f"**Research Results:**\n"
            formatted_log += f"```\n{full_result}\n```\n\n"
            
        elif entry['type'] == 'phase':
            formatted_log += f"\n---\n## {entry['content']}\n---\n\n"
    
    return formatted_log

def check_model_status_session(session_id_state: str = None, request: gr.Request = None):
    """Check and display current model availability for specific session"""
    session_id = get_session_id(request) if not session_id_state else session_id_state
    session = get_or_create_session_state(session_id)
    session_keys = session.get("api_keys", {})
    
    # Get session-specific keys or fall back to env vars
    mistral_key = session_keys.get("mistral") or MISTRAL_API_KEY
    sambanova_key = session_keys.get("sambanova") or SAMBANOVA_API_KEY
    
    status_info = "## 🔍 Expert Model Availability\n\n"
    
    models = {
        'Mistral Large': mistral_key,
        'DeepSeek-R1': sambanova_key,
        'Meta-Llama-3.3-70B-Instruct': sambanova_key,
        'Qwen3-32B': sambanova_key
    }
    
    for model_name, available in models.items():
        if available:
            status = f"✅ Available (Key: {available[:3]}...)"
        else:
            status = "❌ Not configured"
        status_info += f"**{model_name}:** {status}\n\n"
    
    return status_info

# Create the professional interface
with gr.Blocks(title="🎭 Consilium: Multi-AI Expert Consensus Platform - OFP (Open Floor Protocol) Version", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🎭 Consilium: Multi-AI Expert Consensus Platform - OFP (Open Floor Protocol) Version
    **Watch expert AI models collaborate with live research to solve your most complex decisions**

    ### 🚀 Features:
    * Visual roundtable of the AI models, including speech bubbles to see the discussion in real time.
    * Includes Mistral (**mistral-large-latest**) via their API and the Models **DeepSeek-R1**, **Meta-Llama-3.3-70B-Instruct** and **Qwen3-32B** via the SambaNova API.
    * Optional Research Agents (**Web Search**, **Wikipedia**, **arXiv**, **GitHub**, **SEC EDGAR**) added via the [Open Floor Protocol](https://github.com/open-voice-interoperability/openfloor-docs).
    * Assign different roles to the models, the protocol they should follow, and decide the communication strategy.
    * Pick one model as the lead analyst (had the best results when picking Mistral).
    * Configure the amount of discussion rounds.
    * After the discussion, the whole conversation and a final answer will be presented.
    """)
    
    # Hidden session state component
    session_state = gr.State()
    
    with gr.Tab("🎭 Expert Consensus Analysis"):
        with gr.Row():
            with gr.Column(scale=1):
                question_input = gr.Textbox(
                    label="🎯 Strategic Decision Question",
                    placeholder="What complex decision would you like expert AI analysis on?",
                    lines=3,
                    value="Should our startup pivot to AI-first product development?"
                )
                
                # Professional question suggestion buttons
                with gr.Accordion("✒️ Example Questions", open=True):
                    suggestion_btn1 = gr.Button("🏢 Business Strategy", size="sm")
                    suggestion_btn2 = gr.Button("⚛️ Technology Choice", size="sm") 
                    suggestion_btn3 = gr.Button("🌍 Policy Analysis", size="sm")
                
                with gr.Row():
                    decision_protocol = gr.Dropdown(
                        choices=["consensus", "majority_voting", "weighted_voting", "ranked_choice", "unanimity"],
                        value="consensus",
                        label="⚖️ Decision Protocol",
                        info="How should experts reach a conclusion?"
                    )
                    
                    role_assignment = gr.Dropdown(
                        choices=["balanced", "specialized", "adversarial", "none"],
                        value="balanced",
                        label="🎓 Expert Roles",
                        info="How should expertise be distributed?"
                    )
                
                with gr.Row():
                    topology = gr.Dropdown(
                        choices=["full_mesh", "star", "ring"],
                        value="full_mesh",
                        label="🌐 Communication Structure",
                        info="Full mesh: all collaborate, Star: through moderator, Ring: sequential"
                    )
                    
                    moderator_model = gr.Dropdown(
                        choices=["mistral", "sambanova_deepseek", "sambanova_llama", "sambanova_qwen"],
                        value="mistral",
                        label="👨‍⚖️ Lead Analyst",
                        info="Mistral works best as Lead"
                    )
                
                rounds_input = gr.Slider(
                    minimum=1, maximum=5, value=2, step=1,
                    label="🔄 Discussion Rounds",
                    info="More rounds = deeper analysis"
                )
                
                start_btn = gr.Button("🚀 Start Expert Analysis", variant="primary", size="lg")
                
                status_output = gr.Textbox(label="📊 Analysis Status", interactive=False)
            
            with gr.Column(scale=2):
                # The visual roundtable component
                roundtable = consilium_roundtable(
                    label="AI Expert Roundtable",
                    label_icon="https://avatars.githubusercontent.com/u/46052400?s=48&v=4",
                    value=json.dumps({
                        "participants": [],
                        "messages": [],
                        "currentSpeaker": None,
                        "thinking": [],
                        "showBubbles": [],
                        "avatarImages": avatar_images
                    })
                )
        
        # Final answer section
        with gr.Row():
            final_answer_output = gr.Markdown(
                label="🎯 Expert Analysis Results",
                value="*Expert analysis results will appear here...*"
            )
        
        # Collapsible discussion log
        with gr.Accordion("📋 Complete Expert Discussion Log", open=False):
            discussion_log_output = gr.Markdown(
                value="*Complete expert discussion transcript will appear here...*"
            )
        
        # Professional question handlers
        def set_business_question():
            return "Should our startup pivot to AI-first product development?"
        
        def set_tech_question():
            return "Microservices vs monolith architecture for our scaling platform?"
        
        def set_policy_question():
            return "Should we prioritize geoengineering research over emissions reduction?"
        
        suggestion_btn1.click(set_business_question, outputs=[question_input])
        suggestion_btn2.click(set_tech_question, outputs=[question_input])
        suggestion_btn3.click(set_policy_question, outputs=[question_input])
        
        # Event handlers
        def on_start_discussion(question, rounds, protocol, roles, topology, moderator, session_id_state, request: gr.Request = None):
            # Start discussion immediately
            result = run_consensus_discussion_session(question, rounds, protocol, roles, topology, moderator, session_id_state, request)
            return result
        
        start_btn.click(
            on_start_discussion,
            inputs=[question_input, rounds_input, decision_protocol, role_assignment, topology, moderator_model, session_state],
            outputs=[status_output, roundtable, final_answer_output, discussion_log_output, session_state]
        )
        
        # Auto-refresh the roundtable state every 1 second during discussion for better visibility
        def refresh_roundtable(session_id_state, request: gr.Request = None):
            session_id = get_session_id(request) if not session_id_state else session_id_state
            if session_id in user_sessions:
                return json.dumps(user_sessions[session_id]["roundtable_state"])
            return json.dumps({
                "participants": [],
                "messages": [],
                "currentSpeaker": None,
                "thinking": [],
                "showBubbles": [],
                "avatarImages": avatar_images
            })
        
        gr.Timer(1.0).tick(refresh_roundtable, inputs=[session_state], outputs=[roundtable])
    
    with gr.Tab("🔧 Configuration & Setup"):
        gr.Markdown("## 🔑 API Keys Configuration")
        gr.Markdown("*Enter your API keys below OR set them as environment variables*")
        gr.Markdown("**🔒 Privacy:** Your API keys are stored only for your session and are not shared with other users.")
        
        with gr.Row():
            with gr.Column():
                mistral_key_input = gr.Textbox(
                    label="Mistral API Key",
                    placeholder="Enter your Mistral API key...",
                    type="password",
                    info="Required for Mistral Large expert model with function calling"
                )
                sambanova_key_input = gr.Textbox(
                    label="SambaNova API Key", 
                    placeholder="Enter your SambaNova API key...",
                    type="password",
                    info="Required for DeepSeek, Llama, and QwQ expert models with function calling"
                )
                
            with gr.Column():
                # Add a button to save/update keys
                save_keys_btn = gr.Button("💾 Save API Keys", variant="secondary")
                keys_status = gr.Textbox(
                    label="Keys Status",
                    value="No API keys configured - using environment variables if available",
                    interactive=False
                )
        
        # Connect the save button
        save_keys_btn.click(
            update_session_api_keys,
            inputs=[mistral_key_input, sambanova_key_input, session_state],
            outputs=[keys_status, session_state]
        )
        
        model_status_display = gr.Markdown(check_model_status_session())
        
        # Add refresh button for model status
        refresh_status_btn = gr.Button("🔄 Refresh Expert Status")
        refresh_status_btn.click(
            check_model_status_session,
            inputs=[session_state],
            outputs=[model_status_display]
        )
        
        gr.Markdown("""
        ## 🛠️ Setup Instructions
        
        ### 🚀 Quick Start (Recommended)
        1. **Enter API keys above** (they'll be used only for your session)
        2. **Click "Save API Keys"** 
        3. **Start an expert analysis with live research!**
        
        ### 🔑 Get API Keys:
        - **Mistral:** [console.mistral.ai](https://console.mistral.ai)
        - **SambaNova:** [cloud.sambanova.ai](https://cloud.sambanova.ai)
        
        ## Local Setups
        
        ### 🌐 Environment Variables
        ```bash
        export MISTRAL_API_KEY=your_key_here
        export SAMBANOVA_API_KEY=your_key_here
        export MODERATOR_MODEL=mistral
        ```
        
        ### 📋 Dependencies
        ```bash
        pip install -r requirements.txt
        ```
        ### Start
        ```bash
        python app.py
        ```
        """)
    
    with gr.Tab("📚 Documentation"):
        gr.Markdown("""
        ## 🎓 **Expert Role Assignments**
        
        #### **⚖️ Balanced (Recommended for Most Decisions)**
        - **Expert Advocate**: Passionate defender with compelling evidence
        - **Critical Analyst**: Rigorous critic identifying flaws and risks
        - **Strategic Advisor**: Practical implementer focused on real-world constraints
        - **Research Specialist**: Authoritative knowledge with evidence-based insights
        
        #### **🎯 Specialized (For Technical Decisions)**
        - **Research Specialist**: Deep domain expertise and authoritative analysis
        - **Strategic Advisor**: Implementation-focused practical guidance
        - **Innovation Catalyst**: Breakthrough approaches and unconventional thinking
        - **Expert Advocate**: Passionate championing of specialized viewpoints
        
        #### **⚔️ Adversarial (For Controversial Topics)**
        - **Critical Analyst**: Aggressive identification of weaknesses
        - **Innovation Catalyst**: Deliberately challenging conventional wisdom
        - **Expert Advocate**: Passionate defense of positions
        - **Strategic Advisor**: Hard-nosed practical constraints
        
        ## ⚖️ **Decision Protocols Explained**
        
        ### 🤝 **Consensus** (Collaborative)
        - **Goal**: Find solutions everyone can support
        - **Style**: Respectful but rigorous dialogue
        - **Best for**: Team decisions, long-term strategy
        - **Output**: "Expert Consensus Achieved" or areas of disagreement
        
        ### 🗳️ **Majority Voting** (Competitive)
        - **Goal**: Let the strongest argument win
        - **Style**: Passionate advocacy and strong positions
        - **Best for**: Clear either/or decisions
        - **Output**: "Clear Expert Recommendation" with winning argument
        
        ### 📊 **Weighted Voting** (Expertise-Based)
        - **Goal**: Let expertise and evidence quality determine influence
        - **Style**: Authoritative analysis with detailed reasoning
        - **Best for**: Technical decisions requiring deep knowledge
        - **Output**: Expert synthesis weighted by confidence levels
        
        ### 🏆 **Ranked Choice** (Comprehensive)
        - **Goal**: Explore all options systematically
        - **Style**: Systematic evaluation of alternatives
        - **Best for**: Complex decisions with multiple options
        - **Output**: Ranked recommendations with detailed analysis
        
        ### 🔒 **Unanimity** (Diplomatic)
        - **Goal**: Achieve complete agreement
        - **Style**: Bridge-building and diplomatic dialogue
        - **Best for**: High-stakes decisions requiring buy-in
        - **Output**: Unanimous agreement or identification of blocking issues
        
        ## 🌐 **Communication Structures**
        
        ### 🕸️ **Full Mesh** (Complete Collaboration)
        - Every expert sees all other expert responses
        - Maximum information sharing and cross-pollination
        - Best for comprehensive analysis and complex decisions
        - **Use when:** You want thorough multi-perspective analysis
        
        ### ⭐ **Star** (Hierarchical Analysis)
        - Experts only see the lead analyst's responses
        - Prevents groupthink, maintains independent thinking
        - Good for getting diverse, uninfluenced perspectives
        - **Use when:** You want fresh, independent expert takes
        
        ### 🔄 **Ring** (Sequential Analysis)
        - Each expert only sees the previous expert's response
        - Creates interesting chains of reasoning and idea evolution
        - Can lead to surprising consensus emergence
        - **Use when:** You want to see how ideas build and evolve
        
        ## 🏛️ **OpenFloor Protocol Integration**
        
        This implementation uses the [Open Floor Protocol (OFP)](https://github.com/open-voice-interoperability/openfloor-docs) for:
        
        ### 🔄 **Inter-Agent Communication**
        - **Envelope Structure**: Standard JSON format for agent messaging
        - **Event Types**: Utterance, Context, Invite, Bye events
        - **Conversation Management**: Persistent conversation threads
        - **Agent Discovery**: Manifest-based capability matching
        
        ### 🔍 **Research Agent Architecture**
        - **Dedicated Services**: Each research tool runs as an independent OpenFloor agent
        - **HTTP Endpoints**: `/openfloor/conversation` and `/openfloor/manifest`
        - **Proper Invitation Flow**: Agents are invited to join conversations for specific research tasks
        - **Context Events**: Research results are delivered via standardized context events
        
        ### 🏛️ **Floor Manager**
        - **Central Coordination**: Routes messages between all agents
        - **Conversation State**: Maintains participant lists and message history
        - **Visual Integration**: Updates UI based on OpenFloor conversation events
        - **Session Isolation**: Each user session has its own conversation context
        
        ### 📋 **Event Flow Example**
        1. **AI Expert** needs research → generates function call
        2. **Floor Manager** invites **Research Agent** to conversation
        3. **Research Agent** joins and receives query via UtteranceEvent
        4. **Research Agent** performs search and responds with ContextEvent
        5. **Floor Manager** routes result back to requesting expert
        6. **Research Agent** sends ByeEvent and leaves conversation
        
        This architecture ensures true OpenFloor compliance while maintaining the visual roundtable experience.
        """)

# Launch configuration
if __name__ == "__main__":
    demo.queue(default_concurrency_limit=10) 
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=False,
        mcp_server=False
    )