rpa_vision_v3/agent_chat/app.py

#!/usr/bin/env python3
"""
RPA Vision V3 - Agent Chat

Interface conversationnelle pour communiquer avec le système RPA.
Style "Spotlight/Alfred" - minimaliste et efficace.

Composants intégrés:
- IntentParser: Compréhension des intentions utilisateur
- ConfirmationLoop: Validation avant actions critiques
- ResponseGenerator: Réponses en langage naturel
- ConversationManager: Contexte multi-tour

Usage:
    python agent_chat/app.py

Puis ouvrir: http://localhost:5004

Auteur: Dom - Janvier 2026
"""

import asyncio
import json
import logging
import os
import sys
from pathlib import Path
from datetime import datetime
from typing import Dict, Any, List, Optional

import requests as http_requests  # Pour les appels au streaming server

from flask import Flask, render_template, request, jsonify
from flask_socketio import SocketIO, emit

# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent))

from core.workflow import SemanticMatcher, VariableManager

# Import des composants conversationnels
from .intent_parser import IntentParser, IntentType, get_intent_parser
from .confirmation import ConfirmationLoop, ConfirmationStatus, RiskLevel, get_confirmation_loop
from .response_generator import ResponseGenerator, get_response_generator
from .conversation_manager import ConversationManager, get_conversation_manager
from .autonomous_planner import AutonomousPlanner, get_autonomous_planner, ExecutionPlan

# GPU Resource Manager (optional)
try:
    from core.gpu import get_gpu_resource_manager, ExecutionMode
    GPU_AVAILABLE = True
except ImportError:
    GPU_AVAILABLE = False

# Execution components (optional - pour exécution réelle)
try:
    from core.execution import ActionExecutor, TargetResolver, ErrorHandler
    from core.execution.execution_loop import ExecutionLoop, ExecutionMode as ExecMode, ExecutionState
    from core.pipeline.workflow_pipeline import WorkflowPipeline
    from core.capture import ScreenCapturer
    EXECUTION_AVAILABLE = True
except ImportError as e:
    logger.warning(f"Composants d'exécution non disponibles: {e}")
    EXECUTION_AVAILABLE = False

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)
app.config['SECRET_KEY'] = 'rpa-vision-v3-secret'
socketio = SocketIO(app, cors_allowed_origins="*")

# Global state
matcher: Optional[SemanticMatcher] = None
gpu_manager = None
intent_parser: Optional[IntentParser] = None
confirmation_loop: Optional[ConfirmationLoop] = None
response_generator: Optional[ResponseGenerator] = None
conversation_manager: Optional[ConversationManager] = None
autonomous_planner: Optional[AutonomousPlanner] = None

# Execution components
workflow_pipeline = None
action_executor = None
execution_loop = None
screen_capturer = None

# URL du streaming server (Agent V1) pour l'exécution distante
STREAMING_SERVER_URL = os.environ.get(
    "RPA_STREAMING_URL", "http://localhost:5005"
)

execution_status = {
    "running": False,
    "workflow": None,
    "progress": 0,
    "message": "",
    "can_minimize": True
}
command_history: List[Dict[str, Any]] = []


def init_system():
    """Initialiser tous les composants du système."""
    global matcher, gpu_manager
    global intent_parser, confirmation_loop, response_generator, conversation_manager
    global autonomous_planner

    # 1. SemanticMatcher — multi-répertoires (P0-6) + matching LLM (P0-7)
    # Scan data/workflows/ + data/training/workflows/ + data/training/live_sessions/workflows/
    try:
        matcher = SemanticMatcher(
            workflows_dir=None,  # None = scan tous les répertoires par défaut
            use_llm=True,        # Matching sémantique via Ollama (P0-7)
            llm_model="qwen2.5:7b",
        )
        dirs_info = matcher.get_directories()
        dirs_summary = ", ".join(
            f"{d['path']}({d['workflow_count']})" for d in dirs_info if d['exists']
        )
        logger.info(
            f"✓ SemanticMatcher: {len(matcher.get_all_workflows())} workflows "
            f"[{dirs_summary}]"
        )
    except Exception as e:
        logger.error(f"✗ SemanticMatcher: {e}")
        matcher = None

    # 2. GPU Resource Manager
    if GPU_AVAILABLE:
        try:
            gpu_manager = get_gpu_resource_manager()
            logger.info("✓ GPU Resource Manager connected")
        except Exception as e:
            logger.warning(f"⚠ GPU Resource Manager: {e}")
            gpu_manager = None

    # 3. Composants conversationnels
    try:
        intent_parser = get_intent_parser(use_llm=True)  # LLM activé (Ollama)
        confirmation_loop = get_confirmation_loop()
        response_generator = get_response_generator()
        conversation_manager = get_conversation_manager()

        # Injecter les workflows dans l'intent_parser pour contexte LLM
        if matcher and intent_parser:
            workflows_for_llm = [
                {"name": wf.name, "description": wf.description, "tags": wf.tags}
                for wf in matcher.get_all_workflows()
            ]
            intent_parser.set_workflows(workflows_for_llm)

        logger.info("✓ Composants conversationnels initialisés (LLM activé)")
    except Exception as e:
        logger.error(f"✗ Composants conversationnels: {e}")
        # Fallback aux composants de base
        intent_parser = IntentParser(use_llm=False)
        confirmation_loop = ConfirmationLoop()
        response_generator = ResponseGenerator()
        conversation_manager = ConversationManager()

    # 4. Composants d'exécution réelle
    global workflow_pipeline, action_executor, execution_loop, screen_capturer
    if EXECUTION_AVAILABLE:
        try:
            # Pipeline de workflow (matching + actions)
            workflow_pipeline = WorkflowPipeline()
            logger.info("✓ WorkflowPipeline initialisé")

            # Capture d'écran
            screen_capturer = ScreenCapturer()
            logger.info("✓ ScreenCapturer initialisé")

            # Résolveur de cibles et gestionnaire d'erreurs
            target_resolver = TargetResolver()
            error_handler = ErrorHandler()

            # Exécuteur d'actions
            action_executor = ActionExecutor(
                target_resolver=target_resolver,
                error_handler=error_handler,
                verify_postconditions=True
            )
            logger.info("✓ ActionExecutor initialisé")

            # Boucle d'exécution (pour mode automatique)
            execution_loop = ExecutionLoop(
                pipeline=workflow_pipeline,
                action_executor=action_executor,
                screen_capturer=screen_capturer
            )
            logger.info("✓ ExecutionLoop initialisé")

        except Exception as e:
            logger.warning(f"⚠ Composants d'exécution partiels: {e}")
            # Mode dégradé: simulation uniquement
            workflow_pipeline = None
            action_executor = None
            execution_loop = None
    else:
        logger.info("ℹ Mode simulation (composants d'exécution non disponibles)")

    # 5. Autonomous Planner (Agent Libre)
    try:
        autonomous_planner = get_autonomous_planner(llm_model="qwen2.5:7b")

        # Configurer les callbacks pour l'exécution
        if screen_capturer:
            autonomous_planner.set_screen_capturer(screen_capturer.capture)

        def progress_callback(data):
            socketio.emit('agent_progress', data)

        autonomous_planner.set_progress_callback(progress_callback)

        logger.info(f"✓ AutonomousPlanner initialisé (LLM: {autonomous_planner.llm_available})")
    except Exception as e:
        logger.warning(f"⚠ AutonomousPlanner: {e}")
        autonomous_planner = None


# =============================================================================
# Routes Web
# =============================================================================

@app.route('/')
def index():
    """Page principale - nouvelle interface chat."""
    return render_template('chat.html')


@app.route('/classic')
def classic():
    """Ancienne interface (fallback)."""
    return render_template('command.html')


@app.route('/api/status')
def api_status():
    """Statut complet du système."""
    workflows_count = len(matcher.get_all_workflows()) if matcher else 0
    
    # GPU Status
    gpu_status = None
    if gpu_manager:
        try:
            status = gpu_manager.get_status()
            gpu_status = {
                "mode": status.execution_mode.value,
                "vlm_state": status.vlm_state.value,
                "vlm_model": status.vlm_model,
                "clip_device": status.clip_device,
                "degraded": status.degraded_mode
            }
            if status.vram:
                gpu_status["vram"] = {
                    "used_mb": status.vram.used_mb,
                    "total_mb": status.vram.total_mb,
                    "percent": round(status.vram.used_mb / status.vram.total_mb * 100, 1) if status.vram.total_mb > 0 else 0
                }
        except Exception as e:
            logger.warning(f"GPU status error: {e}")
    
    # Ollama Status
    ollama_status = None
    try:
        import requests
        response = requests.get("http://localhost:11434/api/tags", timeout=2)
        if response.status_code == 200:
            models = response.json().get('models', [])
            ollama_status = {
                "available": True,
                "models_count": len(models)
            }
    except:
        ollama_status = {"available": False}
    
    return jsonify({
        "status": "online",
        "workflows_count": workflows_count,
        "execution": execution_status,
        "gpu": gpu_status,
        "ollama": ollama_status
    })


@app.route('/api/workflows')
def api_workflows():
    """Liste des workflows (tous répertoires confondus)."""
    if not matcher:
        return jsonify({"workflows": [], "directories": []})

    workflows = []
    for wf in matcher.get_all_workflows():
        workflows.append({
            "id": wf.workflow_id,
            "name": wf.name,
            "description": wf.description,
            "tags": wf.tags,
            "source": wf.source_dir,
        })

    return jsonify({
        "workflows": workflows,
        "directories": matcher.get_directories(),
    })


@app.route('/api/workflows/refresh', methods=['POST'])
def api_workflows_refresh():
    """
    Forcer le rechargement des workflows depuis tous les répertoires.

    Utile après qu'un nouveau workflow a été appris par le StreamProcessor.
    """
    if not matcher:
        return jsonify({"success": False, "error": "SemanticMatcher non initialisé"})

    try:
        count = matcher.reload_workflows()

        # Re-injecter les workflows dans l'intent_parser (contexte LLM)
        if intent_parser:
            workflows_for_llm = [
                {"name": wf.name, "description": wf.description, "tags": wf.tags}
                for wf in matcher.get_all_workflows()
            ]
            intent_parser.set_workflows(workflows_for_llm)

        return jsonify({
            "success": True,
            "workflows_count": count,
            "directories": matcher.get_directories(),
        })
    except Exception as e:
        logger.error(f"Erreur rechargement workflows: {e}")
        return jsonify({"success": False, "error": str(e)})


@app.route('/api/search', methods=['POST'])
def api_search():
    """Rechercher des workflows."""
    data = request.json
    query = data.get('query', '')
    
    if not matcher or not query:
        return jsonify({"matches": []})
    
    matches = matcher.find_workflows(query, limit=5, min_confidence=0.2)
    
    results = []
    for m in matches:
        results.append({
            "workflow_id": m.workflow_id,
            "workflow_name": m.workflow_name,
            "confidence": m.confidence,
            "extracted_params": m.extracted_params,
            "match_reason": m.match_reason
        })
    
    return jsonify({"matches": results})


@app.route('/api/execute', methods=['POST'])
def api_execute():
    """Exécuter une commande."""
    global execution_status
    
    data = request.json
    command = data.get('command', '')
    params = data.get('params', {})
    
    if not matcher or not command:
        return jsonify({"success": False, "error": "Invalid command"})
    
    # Trouver le workflow
    match = matcher.find_workflow(command, min_confidence=0.2)
    
    if not match:
        return jsonify({
            "success": False,
            "error": "Aucun workflow correspondant trouvé"
        })
    
    # Combiner les paramètres
    all_params = {**match.extracted_params, **params}
    
    # Enregistrer dans l'historique
    command_history.append({
        "timestamp": datetime.now().isoformat(),
        "command": command,
        "workflow": match.workflow_name,
        "params": all_params,
        "status": "started"
    })
    
    # Mettre à jour le statut
    execution_status = {
        "running": True,
        "workflow": match.workflow_name,
        "progress": 0,
        "message": "Démarrage..."
    }
    
    # Notifier via WebSocket
    socketio.emit('execution_started', {
        "workflow": match.workflow_name,
        "params": all_params
    })
    
    # Exécuter le workflow en arrière-plan
    socketio.start_background_task(execute_workflow, match, all_params)
    
    return jsonify({
        "success": True,
        "workflow": match.workflow_name,
        "params": all_params,
        "confidence": match.confidence
    })


@app.route('/api/history')
def api_history():
    """Historique des commandes."""
    return jsonify({"history": command_history[-20:]})


@app.route('/api/chat', methods=['POST'])
def api_chat():
    """
    Endpoint conversationnel principal.

    Utilise le flux complet:
    1. IntentParser: Analyse l'intention
    2. ConversationManager: Gère le contexte multi-tour
    3. ConfirmationLoop: Valide les actions sensibles
    4. ResponseGenerator: Génère la réponse
    """
    data = request.json
    message = data.get('message', '').strip()
    session_id = data.get('session_id')

    if not message:
        return jsonify({"error": "Message vide"}), 400

    # 1. Obtenir ou créer la session
    session = conversation_manager.get_or_create_session(session_id=session_id)

    # 2. Parser l'intention
    intent = intent_parser.parse(message)

    # 3. Résoudre les références anaphoriques (ex: "le même", "celui-ci")
    intent = conversation_manager.resolve_references(session, intent)

    # 4. Construire le contexte
    context = conversation_manager.get_context_summary(session)
    context["execution_status"] = execution_status

    # 5. Traiter selon le type d'intention
    result = {}
    action_taken = None

    if intent.intent_type == IntentType.CONFIRM:
        # Confirmer une action en attente
        pending = conversation_manager.get_pending_confirmation(session)
        if pending:
            confirmation_loop.confirm(pending.id)
            conversation_manager.clear_pending_confirmation(session)
            result = {"confirmed": True, "workflow": pending.workflow_name}
            action_taken = "confirmed"

            # Lancer l'exécution
            socketio.start_background_task(
                execute_workflow_from_confirmation, pending, session.session_id
            )
        else:
            result = {"confirmed": False}

    elif intent.intent_type == IntentType.DENY:
        # Refuser une action en attente
        pending = conversation_manager.get_pending_confirmation(session)
        if pending:
            confirmation_loop.deny(pending.id)
            conversation_manager.clear_pending_confirmation(session)
        result = {"denied": True}
        action_taken = "denied"

    elif intent.intent_type == IntentType.EXECUTE:
        # Exécuter un workflow
        if matcher and intent.workflow_hint:
            match = matcher.find_workflow(intent.workflow_hint, min_confidence=0.2)

            if match:
                # Évaluer le risque
                risk = confirmation_loop.evaluate_risk(
                    match.workflow_name,
                    {**match.extracted_params, **intent.parameters}
                )

                if confirmation_loop.requires_confirmation(risk):
                    # Créer une demande de confirmation
                    conf = confirmation_loop.create_confirmation_request(
                        workflow_name=match.workflow_name,
                        parameters={**match.extracted_params, **intent.parameters},
                        action_type="execute",
                        risk_level=risk
                    )
                    conversation_manager.set_pending_confirmation(session, conf)

                    # Générer la réponse de confirmation
                    response = response_generator.generate_confirmation_request(conf)
                    result = {"needs_confirmation": True, "confirmation": conf.to_dict()}
                    action_taken = "confirmation_requested"

                else:
                    # Exécuter directement
                    all_params = {**match.extracted_params, **intent.parameters}
                    result = {
                        "success": True,
                        "workflow": match.workflow_name,
                        "params": all_params,
                        "confidence": match.confidence
                    }
                    action_taken = "executed"

                    socketio.start_background_task(execute_workflow, match, all_params)
            else:
                result = {"not_found": True, "query": intent.workflow_hint}
        else:
            result = {"error": "Pas de workflow spécifié"}

    elif intent.intent_type == IntentType.LIST:
        # Lister les workflows avec métadonnées enrichies
        if matcher:
            workflows = [
                {
                    "name": wf.name,
                    "description": wf.description or "Workflow appris automatiquement",
                    "tags": wf.tags[:3] if wf.tags else [],
                    "id": wf.workflow_id,
                }
                for wf in matcher.get_all_workflows()
            ]
            result = {"workflows": workflows}
        else:
            result = {"workflows": []}
        action_taken = "listed"

    elif intent.intent_type == IntentType.STATUS:
        result = {"execution": execution_status}
        action_taken = "status_checked"

    elif intent.intent_type == IntentType.CANCEL:
        if execution_status.get("running"):
            execution_status["running"] = False
            execution_status["message"] = "Annulé"
            result = {"cancelled": True}
        else:
            result = {"cancelled": False}
        action_taken = "cancelled"

    elif intent.intent_type == IntentType.HISTORY:
        result = {"history": command_history[-10:]}
        action_taken = "history_shown"

    elif intent.intent_type == IntentType.QUERY:
        # Rechercher des infos sur le workflow demandé
        topic = intent.workflow_hint or intent.raw_query
        if matcher and topic:
            # Tenter de trouver le workflow correspondant
            match = matcher.find_workflow(topic, min_confidence=0.3)
            if match:
                help_text = matcher.get_workflow_help(match.workflow_id)
                result = {"answer": help_text}
            else:
                # Peut-être une question générale sur les workflows dispo
                all_wf = matcher.get_all_workflows()
                if all_wf:
                    wf_list = "\n".join(
                        f"• **{wf.name}**: {wf.description or 'Pas de description'}"
                        for wf in all_wf[:10]
                    )
                    result = {
                        "answer": f"Je n'ai pas trouvé de workflow '{topic}', "
                        f"mais voici les workflows disponibles :\n{wf_list}"
                    }
                else:
                    result = {}
        else:
            result = {}
        action_taken = "query_answered"

    elif intent.intent_type == IntentType.HELP:
        result = {}
        action_taken = "help_shown"

    elif intent.clarification_needed:
        result = {"clarification_needed": True}
        action_taken = "clarification_requested"

    # 6. Générer la réponse (si pas déjà fait pour confirmation)
    if action_taken != "confirmation_requested":
        response = response_generator.generate(intent, context, result)

    # 7. Enregistrer le tour dans la conversation
    conversation_manager.add_turn(
        session=session,
        user_message=message,
        intent=intent,
        response=response.message,
        action_taken=action_taken,
        result=result
    )

    # 8. Retourner la réponse
    return jsonify({
        "session_id": session.session_id,
        "intent": intent.to_dict(),
        "response": response.to_dict(),
        "result": result,
        "context": {
            "current_workflow": session.context.current_workflow,
            "has_pending_confirmation": session.context.pending_confirmation is not None
        }
    })


def execute_workflow_from_confirmation(confirmation, session_id):
    """Exécuter un workflow après confirmation."""
    global execution_status

    if not matcher:
        return

    # Trouver le workflow
    match = matcher.find_workflow(confirmation.workflow_name, min_confidence=0.1)
    if not match:
        return

    # Utiliser les paramètres confirmés (ou modifiés)
    params = confirmation.modified_parameters or confirmation.parameters

    # IMPORTANT: Marquer l'exécution comme active
    execution_status["running"] = True
    execution_status["workflow"] = confirmation.workflow_name
    execution_status["progress"] = 0
    execution_status["message"] = "Démarrage..."

    execute_workflow(match, params)


@app.route('/api/gpu/<action>', methods=['POST'])
def api_gpu_action(action):
    """Contrôler le GPU Resource Manager."""
    if not gpu_manager:
        return jsonify({"success": False, "error": "GPU Manager non disponible"})
    
    async def do_action():
        if action == "load-vlm":
            return await gpu_manager.ensure_vlm_loaded()
        elif action == "unload-vlm":
            return await gpu_manager.ensure_vlm_unloaded()
        elif action == "recording":
            await gpu_manager.set_execution_mode(ExecutionMode.RECORDING)
            return True
        elif action == "autopilot":
            await gpu_manager.set_execution_mode(ExecutionMode.AUTOPILOT)
            return True
        elif action == "idle":
            await gpu_manager.set_execution_mode(ExecutionMode.IDLE)
            return True
        return False
    
    try:
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        result = loop.run_until_complete(do_action())
        loop.close()
        
        return jsonify({"success": result, "action": action})
    except Exception as e:
        return jsonify({"success": False, "error": str(e)})


@app.route('/api/llm/status')
def api_llm_status():
    """Statut du LLM (Ollama)."""
    status = {
        "enabled": intent_parser.use_llm if intent_parser else False,
        "available": intent_parser.llm_available if intent_parser else False,
        "model": intent_parser.llm_model if intent_parser else None,
        "endpoint": intent_parser.llm_endpoint if intent_parser else None,
        "workflows_loaded": len(intent_parser._workflows_cache) if intent_parser else 0
    }

    # Lister les modèles Ollama disponibles
    try:
        import requests
        response = requests.get("http://localhost:11434/api/tags", timeout=2)
        if response.status_code == 200:
            models = response.json().get('models', [])
            status["available_models"] = [m["name"] for m in models]
    except:
        status["available_models"] = []

    return jsonify(status)


@app.route('/api/llm/model', methods=['POST'])
def api_llm_set_model():
    """Changer le modèle LLM."""
    data = request.json
    model = data.get('model')

    if not model:
        return jsonify({"success": False, "error": "Modèle non spécifié"})

    if intent_parser:
        intent_parser.llm_model = model
        intent_parser._check_llm_availability()
        return jsonify({
            "success": True,
            "model": model,
            "available": intent_parser.llm_available
        })

    return jsonify({"success": False, "error": "IntentParser non initialisé"})


# =============================================================================
# API Agent Libre (Autonomous Mode)
# =============================================================================

@app.route('/api/agent/plan', methods=['POST'])
def api_agent_plan():
    """
    Génère un plan d'exécution pour une tâche en langage naturel.

    Le mode "Agent Libre" permet d'exécuter des tâches sans workflow pré-enregistré.
    Le LLM (Qwen) décompose la demande en étapes d'actions.
    """
    if not autonomous_planner:
        return jsonify({"error": "Agent autonome non disponible"}), 503

    data = request.json
    user_request = data.get('request', '').strip()

    if not user_request:
        return jsonify({"error": "Requête vide"}), 400

    try:
        # Contexte optionnel (écran actuel, etc.)
        context = data.get('context', {})

        # Générer le plan
        plan = autonomous_planner.plan(user_request, context)

        return jsonify({
            "success": True,
            "plan": {
                "task": plan.task_description,
                "steps": [
                    {
                        "step": s.step_number,
                        "action": s.action_type.value,
                        "description": s.description,
                        "target": s.target,
                        "params": s.parameters,
                        "expected_result": s.expected_result
                    }
                    for s in plan.steps
                ],
                "estimated_seconds": plan.estimated_duration_seconds,
                "risk_level": plan.risk_level,
                "requires_confirmation": plan.requires_confirmation
            },
            "llm_available": autonomous_planner.llm_available
        })

    except Exception as e:
        logger.error(f"Agent plan error: {e}")
        return jsonify({"error": str(e)}), 500


@app.route('/api/agent/execute', methods=['POST'])
def api_agent_execute():
    """
    Exécute un plan d'agent autonome.

    Attend un objet plan (généré par /api/agent/plan) et l'exécute étape par étape.
    """
    if not autonomous_planner:
        return jsonify({"error": "Agent autonome non disponible"}), 503

    data = request.json
    plan_data = data.get('plan')

    if not plan_data:
        return jsonify({"error": "Plan manquant"}), 400

    try:
        # Reconstruire le plan depuis les données
        from .autonomous_planner import PlannedAction, ActionType

        steps = []
        for step_data in plan_data.get('steps', []):
            action_type_str = step_data.get('action', 'click')
            action_type_map = {
                'open_app': ActionType.OPEN_APP,
                'open_url': ActionType.OPEN_URL,
                'click': ActionType.CLICK,
                'type_text': ActionType.TYPE_TEXT,
                'hotkey': ActionType.HOTKEY,
                'scroll': ActionType.SCROLL,
                'wait': ActionType.WAIT,
                'screenshot': ActionType.SCREENSHOT
            }

            steps.append(PlannedAction(
                step_number=step_data.get('step', len(steps) + 1),
                action_type=action_type_map.get(action_type_str, ActionType.CLICK),
                description=step_data.get('description', ''),
                target=step_data.get('target'),
                parameters=step_data.get('params', {}),
                expected_result=step_data.get('expected_result')
            ))

        plan = ExecutionPlan(
            task_description=plan_data.get('task', ''),
            steps=steps,
            estimated_duration_seconds=plan_data.get('estimated_seconds', 30),
            risk_level=plan_data.get('risk_level', 'low')
        )

        # Exécuter en arrière-plan
        socketio.start_background_task(execute_agent_plan, plan)

        return jsonify({
            "success": True,
            "message": "Exécution démarrée",
            "steps_count": len(steps)
        })

    except Exception as e:
        logger.error(f"Agent execute error: {e}")
        return jsonify({"error": str(e)}), 500


@app.route('/api/agent/status')
def api_agent_status():
    """Statut de l'agent autonome."""
    return jsonify({
        "available": autonomous_planner is not None,
        "llm_available": autonomous_planner.llm_available if autonomous_planner else False,
        "llm_model": autonomous_planner.llm_model if autonomous_planner else None
    })


def execute_agent_plan(plan: ExecutionPlan):
    """Exécute un plan d'agent en arrière-plan."""
    import asyncio

    try:
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)

        results = loop.run_until_complete(autonomous_planner.execute_plan(plan))

        loop.close()

        # Envoyer le résultat final
        success_count = sum(1 for r in results if r.success)
        total = len(results)

        socketio.emit('execution_completed', {
            "success": success_count == total,
            "workflow": plan.task_description,
            "message": f"{success_count}/{total} étapes réussies",
            "results": [
                {
                    "step": r.action.step_number,
                    "success": r.success,
                    "message": r.message
                }
                for r in results
            ]
        })

    except Exception as e:
        logger.error(f"Agent execution error: {e}")
        socketio.emit('execution_completed', {
            "success": False,
            "workflow": plan.task_description,
            "message": f"Erreur: {str(e)}"
        })


@app.route('/api/help')
def api_help():
    """Aide et mode d'emploi."""
    help_content = {
        "title": "RPA Vision V3 - Mode d'emploi",
        "sections": [
            {
                "title": "🎯 Commandes en langage naturel",
                "content": """
Tapez simplement ce que vous voulez faire en français ou anglais.
Le système trouvera automatiquement le workflow correspondant.

**Exemples :**
- "facturer le client Acme"
- "exporter le rapport en PDF"
- "créer une facture pour Client ABC"
- "facturer les clients de A à Z"
"""
            },
            {
                "title": "📋 Paramètres",
                "content": """
Les paramètres sont extraits automatiquement de votre commande.
Vous pouvez aussi les spécifier manuellement dans le formulaire.

**Paramètres courants :**
- `client` : Nom du client
- `format` : Format d'export (pdf, excel)
- `start`, `end` : Plage de valeurs
"""
            },
            {
                "title": "⌨️ Raccourcis clavier",
                "content": """
- `Entrée` : Exécuter la commande
- `Échap` : Annuler / Fermer
- `↑` / `↓` : Naviguer dans l'historique
- `Ctrl+M` : Minimiser l'interface
"""
            },
            {
                "title": "🔄 Pendant l'exécution",
                "content": """
L'interface peut être minimisée pendant l'exécution.
Le workflow s'exécute en arrière-plan.
Vous serez notifié à la fin de l'exécution.
"""
            }
        ]
    }
    
    return jsonify(help_content)


# =============================================================================
# WebSocket Events
# =============================================================================

@socketio.on('connect')
def handle_connect():
    """Client connecté."""
    logger.info("Client connected")
    emit('status', execution_status)


@socketio.on('disconnect')
def handle_disconnect():
    """Client déconnecté."""
    logger.info("Client disconnected")


@socketio.on('cancel_execution')
def handle_cancel():
    """Annuler l'exécution."""
    global execution_status
    execution_status["running"] = False
    execution_status["message"] = "Annulé"
    emit('execution_cancelled', {}, broadcast=True)


# =============================================================================
# Exécution de workflow
# =============================================================================

def _try_streaming_server_replay(workflow_id: str, params: Dict[str, Any]) -> Optional[Dict[str, Any]]:
    """
    Tenter d'exécuter un workflow via le streaming server (Agent V1).

    POST http://localhost:5005/api/v1/traces/stream/replay
    avec workflow_id et params.

    Returns:
        Réponse du serveur si succès, None si indisponible ou erreur.
    """
    try:
        resp = http_requests.post(
            f"{STREAMING_SERVER_URL}/api/v1/traces/stream/replay",
            json={
                "workflow_id": workflow_id,
                "session_id": f"chat_{datetime.now().strftime('%H%M%S')}",
                "params": params or {},
            },
            timeout=5,
        )
        if resp.status_code == 200:
            data = resp.json()
            logger.info(f"Workflow {workflow_id} envoyé au streaming server: {data}")
            return data
        else:
            logger.debug(
                f"Streaming server refus (HTTP {resp.status_code}): "
                f"{resp.text[:200]}"
            )
    except http_requests.ConnectionError:
        logger.debug("Streaming server non disponible (connexion refusée)")
    except http_requests.Timeout:
        logger.debug("Streaming server timeout")
    except Exception as e:
        logger.debug(f"Erreur streaming server: {e}")

    return None


def execute_workflow(match, params):
    """
    Exécuter un workflow — tente d'abord le streaming server,
    puis fallback sur l'exécution locale.
    """
    global execution_status

    import time

    # Tenter l'exécution via le streaming server (Agent V1 distant)
    replay_result = _try_streaming_server_replay(match.workflow_id, params)
    if replay_result:
        # Le streaming server a accepté le replay
        execution_status["running"] = True
        execution_status["workflow"] = match.workflow_name
        execution_status["progress"] = 50
        execution_status["message"] = "Envoyé au streaming server (Agent V1)"

        socketio.emit('execution_progress', {
            "progress": 50,
            "step": "Exécution via streaming server...",
            "current": 1,
            "total": 1,
        })

        finish_execution(
            match.workflow_name, True,
            f"Workflow envoyé au streaming server ({replay_result.get('status', 'ok')})"
        )
        return

    # Fallback : exécution locale
    logger.info("Streaming server indisponible, exécution locale")

    try:
        # Charger le workflow
        with open(match.workflow_path, 'r') as f:
            workflow_data = json.load(f)

        # Créer le VariableManager et injecter les paramètres
        var_manager = VariableManager()
        var_manager.set_variables(params)

        # Substituer les variables
        workflow_data = var_manager.substitute_dict(workflow_data)

        # Obtenir les étapes (edges)
        edges = workflow_data.get("edges", [])
        total_steps = len(edges) if edges else 1

        # Étape 1: Initialisation
        update_progress(10, "Initialisation", 1, total_steps + 2)
        time.sleep(0.3)

        # Étape 2: Préparation GPU (si disponible)
        if gpu_manager and GPU_AVAILABLE:
            update_progress(15, "Préparation GPU...", 2, total_steps + 2)
            try:
                loop = asyncio.new_event_loop()
                asyncio.set_event_loop(loop)
                loop.run_until_complete(gpu_manager.set_execution_mode(ExecutionMode.AUTOPILOT))
                loop.close()
            except Exception as e:
                logger.warning(f"GPU mode change failed: {e}")

        # Vérifier si l'exécution réelle est disponible
        use_real_execution = (
            EXECUTION_AVAILABLE and
            action_executor is not None and
            screen_capturer is not None
        )

        if use_real_execution:
            logger.info(f"🚀 Exécution RÉELLE du workflow: {match.workflow_name}")
            _execute_workflow_real(workflow_data, edges, total_steps, params)
        else:
            logger.info(f"🎭 Exécution SIMULÉE du workflow: {match.workflow_name}")
            _execute_workflow_simulated(edges, total_steps)

        # Finalisation
        if execution_status["running"]:
            update_progress(95, "Finalisation...", total_steps + 1, total_steps + 2)
            time.sleep(0.2)
            finish_execution(match.workflow_name, True, "Workflow terminé avec succès")

    except Exception as e:
        logger.error(f"Execution error: {e}")
        import traceback
        traceback.print_exc()
        finish_execution(match.workflow_name, False, f"Erreur: {str(e)}")


def _execute_workflow_real(workflow_data, edges, total_steps, params):
    """Exécution réelle avec ActionExecutor."""
    import time
    import tempfile
    import os
    from PIL import Image

    # Capturer l'écran initial
    update_progress(20, "Capture écran initial...", 2, total_steps + 2)

    try:
        screenshot_array = screen_capturer.capture()
        if screenshot_array is None:
            raise Exception("Capture retourne None")

        # Sauvegarder le screenshot dans un fichier temporaire
        temp_dir = Path("data/temp")
        temp_dir.mkdir(parents=True, exist_ok=True)
        screenshot_path = str(temp_dir / f"capture_{datetime.now().strftime('%H%M%S')}.png")

        # Convertir numpy array en image et sauvegarder
        img = Image.fromarray(screenshot_array)
        img.save(screenshot_path)
        logger.info(f"📸 Screenshot capturé: {screenshot_path}")
    except Exception as e:
        logger.warning(f"Capture écran échouée: {e}, utilisation mode dégradé")
        _execute_workflow_simulated(edges, total_steps)
        return

    # Créer le ScreenState complet pour l'exécution
    try:
        from core.models.screen_state import (
            ScreenState, WindowContext, RawLevel, PerceptionLevel,
            ContextLevel, EmbeddingRef
        )

        file_size = os.path.getsize(screenshot_path) if os.path.exists(screenshot_path) else 0

        screen_state = ScreenState(
            screen_state_id=f"agent_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            timestamp=datetime.now(),
            session_id="agent_chat",
            window=WindowContext(
                app_name="unknown",
                window_title="Unknown",
                screen_resolution=[1920, 1080],
                workspace="main"
            ),
            raw=RawLevel(
                screenshot_path=screenshot_path,
                capture_method="agent_chat",
                file_size_bytes=file_size
            ),
            perception=PerceptionLevel(
                embedding=EmbeddingRef(provider="", vector_id="", dimensions=512),
                detected_text=[],
                text_detection_method="none",
                confidence_avg=0.0
            ),
            context=ContextLevel(),
            ui_elements=[]
        )
        logger.info("✓ ScreenState complet créé")
    except Exception as e:
        logger.warning(f"Création ScreenState échouée: {e}, utilisation mode simulé")
        _execute_workflow_simulated(edges, total_steps)
        return

    # Exécuter chaque edge avec ActionExecutor
    success_count = 0
    fail_count = 0

    for i, edge in enumerate(edges):
        if not execution_status["running"]:
            logger.info("⏹️ Exécution annulée par l'utilisateur")
            break

        action = edge.get("action", {})
        action_type = action.get("type", "unknown")

        progress = int(20 + (i + 1) / total_steps * 70)
        step_name = f"Étape {i+1}/{total_steps}: {action_type}"

        update_progress(progress, step_name, i + 3, total_steps + 2)
        logger.info(f"▶️ Exécution: {step_name}")

        try:
            # Créer un objet Edge compatible avec ActionExecutor
            workflow_edge = _create_workflow_edge(edge, params)

            # Exécuter l'action réelle
            result = action_executor.execute_edge(workflow_edge, screen_state)

            if result.status.value == "success":
                success_count += 1
                logger.info(f"✅ {step_name} - Succès")

                # Recapturer l'écran après chaque action réussie
                try:
                    time.sleep(0.3)  # Petit délai pour laisser l'UI se mettre à jour
                    new_screenshot = screen_capturer.capture()
                    if new_screenshot is not None:
                        new_path = str(temp_dir / f"capture_{datetime.now().strftime('%H%M%S_%f')}.png")
                        Image.fromarray(new_screenshot).save(new_path)
                        screen_state.raw.screenshot_path = new_path
                        screen_state.raw.file_size_bytes = os.path.getsize(new_path)
                except Exception as recapture_err:
                    logger.debug(f"Recapture échouée: {recapture_err}")
                    pass  # Continuer même si la recapture échoue
            else:
                fail_count += 1
                logger.warning(f"⚠️ {step_name} - {result.status.value}: {result.message}")

                # Continuer malgré l'échec (mode best-effort)
                if fail_count >= 3:
                    logger.error("❌ Trop d'échecs, arrêt de l'exécution")
                    break

        except Exception as e:
            fail_count += 1
            logger.error(f"❌ Erreur lors de {step_name}: {e}")

            if fail_count >= 3:
                logger.error("❌ Trop d'erreurs, arrêt de l'exécution")
                break

    logger.info(f"📊 Résultat: {success_count} succès, {fail_count} échecs sur {total_steps} étapes")


def _execute_workflow_simulated(edges, total_steps):
    """Exécution simulée (fallback)."""
    import time

    for i, edge in enumerate(edges):
        if not execution_status["running"]:
            break

        action = edge.get("action", {})
        action_type = action.get("type", "unknown")

        progress = int(20 + (i + 1) / total_steps * 70)
        step_name = f"Étape {i+1}: {action_type} (simulé)"

        update_progress(progress, step_name, i + 3, total_steps + 2)

        # Simulation avec délai
        time.sleep(0.5)
        logger.info(f"🎭 Simulé: {step_name}")


def _create_workflow_edge(edge_dict, params):
    """Créer un objet WorkflowEdge depuis un dictionnaire."""
    from dataclasses import dataclass, field
    from typing import Optional, Dict, Any, List

    # Importer les vraies classes du système
    try:
        from core.models.workflow_graph import Action, TargetSpec, WorkflowEdge, EdgeConstraints, PostConditions, ActionType
        USE_REAL_CLASSES = True
    except ImportError:
        USE_REAL_CLASSES = False

    action_dict = edge_dict.get("action", {})
    target_dict = action_dict.get("target", {})

    # Substituer les paramètres dans le texte cible
    target_text = target_dict.get("text", "")
    if target_text and isinstance(target_text, str):
        for key, val in params.items():
            target_text = target_text.replace(f"{{{{{key}}}}}", str(val))  # {{key}}
            target_text = target_text.replace(f"${{{key}}}", str(val))  # ${key}
            target_text = target_text.replace(f"${key}", str(val))  # $key

    if USE_REAL_CLASSES:
        # Créer le TargetSpec avec les vraies classes
        target_spec = TargetSpec(
            by_role=target_dict.get("role"),
            by_text=target_text if target_text else target_dict.get("text"),
            by_position=target_dict.get("position"),
        )

        # Convertir le type d'action en ActionType Enum
        action_type_str = action_dict.get("type", "mouse_click")
        try:
            action_type = ActionType(action_type_str)
        except ValueError:
            # Fallback si le type n'existe pas dans l'enum
            action_type = ActionType.MOUSE_CLICK

        # Créer l'Action
        action = Action(
            type=action_type,
            target=target_spec,
            parameters=action_dict.get("parameters", {})
        )

        # Créer le WorkflowEdge
        return WorkflowEdge(
            edge_id=edge_dict.get("edge_id", edge_dict.get("id", f"edge_{id(edge_dict)}")),
            from_node=edge_dict.get("source", edge_dict.get("from_node", "")),
            to_node=edge_dict.get("target", edge_dict.get("to_node", "")),
            action=action,
            constraints=EdgeConstraints(),
            post_conditions=PostConditions()
        )
    else:
        # Fallback: classes simples
        @dataclass
        class SimpleTargetSpec:
            by_role: Optional[str] = None
            by_text: Optional[str] = None
            by_position: Optional[tuple] = None
            selection_policy: str = "first"
            fallback_strategy: str = "visual_similarity"
            embedding_ref: Optional[Any] = None
            context_hints: Dict[str, Any] = field(default_factory=dict)
            hard_constraints: Dict[str, Any] = field(default_factory=dict)
            weights: Dict[str, float] = field(default_factory=dict)

        @dataclass
        class SimpleAction:
            type: str
            target: SimpleTargetSpec
            parameters: Dict[str, Any] = field(default_factory=dict)

        @dataclass
        class SimpleWorkflowEdge:
            edge_id: str
            from_node: str
            to_node: str
            action: SimpleAction
            constraints: Any = None
            post_conditions: Any = None

        target_spec = SimpleTargetSpec(
            by_role=target_dict.get("role"),
            by_text=target_text if target_text else target_dict.get("text"),
        )

        action = SimpleAction(
            type=action_dict.get("type", "unknown"),
            target=target_spec,
            parameters=action_dict.get("parameters", {})
        )

        return SimpleWorkflowEdge(
            edge_id=edge_dict.get("edge_id", edge_dict.get("id", f"edge_{id(edge_dict)}")),
            from_node=edge_dict.get("source", edge_dict.get("from_node", "")),
            to_node=edge_dict.get("target", edge_dict.get("to_node", "")),
            action=action,
            constraints=None,
            post_conditions=None
        )


def update_progress(progress: int, message: str, current: int, total: int):
    """Mettre à jour la progression."""
    global execution_status
    
    execution_status["progress"] = progress
    execution_status["message"] = message
    
    socketio.emit('execution_progress', {
        "progress": progress,
        "step": message,
        "current": current,
        "total": total
    })


def finish_execution(workflow_name: str, success: bool, message: str):
    """Terminer l'exécution."""
    global execution_status
    
    execution_status["running"] = False
    execution_status["progress"] = 100 if success else 0
    execution_status["message"] = message
    
    # Mettre à jour l'historique
    if command_history:
        command_history[-1]["status"] = "completed" if success else "failed"
    
    socketio.emit('execution_completed', {
        "workflow": workflow_name,
        "success": success,
        "message": message
    })


# =============================================================================
# Main
# =============================================================================

if __name__ == '__main__':
    init_system()
    
    print("""
╔════════════════════════════════════════════════════════════╗
║       RPA Vision V3 - Interface de Commande                ║
║                                                            ║
║       🌐 http://localhost:5004                             ║
║                                                            ║
║       Ctrl+C pour arrêter                                  ║
╚════════════════════════════════════════════════════════════╝
""")
    
    socketio.run(app, host='127.0.0.1', port=5004, debug=False, allow_unsafe_werkzeug=True)