feat(agent_chat): Ajouter mode Agent Libre avec planification LLM
- Nouveau module autonomous_planner.py pour planification intelligente - Utilise Qwen via Ollama pour décomposer les tâches en actions - Actions supportées: open_url, click, type_text, hotkey, scroll, wait - Intégration OWL-v2 et VLM pour détection visuelle intelligente - Nouvelle interface chat conversationnelle (chat.html) - Prompt LLM générique adaptable à toute demande - Endpoints API: /api/agent/plan, /api/agent/execute, /api/agent/status Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Dom
@@ -41,6 +41,7 @@ from .intent_parser import IntentParser, IntentType, get_intent_parser
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from .confirmation import ConfirmationLoop, ConfirmationStatus, RiskLevel, get_confirmation_loop
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from .response_generator import ResponseGenerator, get_response_generator
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from .conversation_manager import ConversationManager, get_conversation_manager
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from .autonomous_planner import AutonomousPlanner, get_autonomous_planner, ExecutionPlan
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# GPU Resource Manager (optional)
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try:
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@@ -74,6 +75,7 @@ intent_parser: Optional[IntentParser] = None
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confirmation_loop: Optional[ConfirmationLoop] = None
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response_generator: Optional[ResponseGenerator] = None
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conversation_manager: Optional[ConversationManager] = None
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autonomous_planner: Optional[AutonomousPlanner] = None
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# Execution components
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workflow_pipeline = None
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@@ -95,6 +97,7 @@ def init_system():
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"""Initialiser tous les composants du système."""
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global matcher, gpu_manager
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global intent_parser, confirmation_loop, response_generator, conversation_manager
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global autonomous_planner
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# 1. SemanticMatcher
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try:
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@@ -178,6 +181,24 @@ def init_system():
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else:
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logger.info("ℹ Mode simulation (composants d'exécution non disponibles)")
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# 5. Autonomous Planner (Agent Libre)
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try:
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autonomous_planner = get_autonomous_planner(llm_model="qwen2.5:7b")
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# Configurer les callbacks pour l'exécution
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if screen_capturer:
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autonomous_planner.set_screen_capturer(screen_capturer.capture)
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def progress_callback(data):
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socketio.emit('agent_progress', data)
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autonomous_planner.set_progress_callback(progress_callback)
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logger.info(f"✓ AutonomousPlanner initialisé (LLM: {autonomous_planner.llm_available})")
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except Exception as e:
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logger.warning(f"⚠ AutonomousPlanner: {e}")
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autonomous_planner = None
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# =============================================================================
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# Routes Web
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@@ -185,7 +206,13 @@ def init_system():
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@app.route('/')
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def index():
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"""Page principale."""
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"""Page principale - nouvelle interface chat."""
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return render_template('chat.html')
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@app.route('/classic')
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def classic():
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"""Ancienne interface (fallback)."""
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return render_template('command.html')
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@@ -614,6 +641,174 @@ def api_llm_set_model():
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return jsonify({"success": False, "error": "IntentParser non initialisé"})
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# =============================================================================
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# API Agent Libre (Autonomous Mode)
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# =============================================================================
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@app.route('/api/agent/plan', methods=['POST'])
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def api_agent_plan():
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"""
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Génère un plan d'exécution pour une tâche en langage naturel.
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Le mode "Agent Libre" permet d'exécuter des tâches sans workflow pré-enregistré.
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Le LLM (Qwen) décompose la demande en étapes d'actions.
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"""
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if not autonomous_planner:
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return jsonify({"error": "Agent autonome non disponible"}), 503
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data = request.json
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user_request = data.get('request', '').strip()
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if not user_request:
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return jsonify({"error": "Requête vide"}), 400
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try:
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# Contexte optionnel (écran actuel, etc.)
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context = data.get('context', {})
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# Générer le plan
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plan = autonomous_planner.plan(user_request, context)
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return jsonify({
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"success": True,
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"plan": {
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"task": plan.task_description,
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"steps": [
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{
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"step": s.step_number,
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"action": s.action_type.value,
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"description": s.description,
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"target": s.target,
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"params": s.parameters,
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"expected_result": s.expected_result
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}
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for s in plan.steps
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],
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"estimated_seconds": plan.estimated_duration_seconds,
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"risk_level": plan.risk_level,
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"requires_confirmation": plan.requires_confirmation
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},
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"llm_available": autonomous_planner.llm_available
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})
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except Exception as e:
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logger.error(f"Agent plan error: {e}")
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return jsonify({"error": str(e)}), 500
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@app.route('/api/agent/execute', methods=['POST'])
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def api_agent_execute():
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"""
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Exécute un plan d'agent autonome.
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Attend un objet plan (généré par /api/agent/plan) et l'exécute étape par étape.
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"""
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if not autonomous_planner:
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return jsonify({"error": "Agent autonome non disponible"}), 503
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data = request.json
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plan_data = data.get('plan')
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if not plan_data:
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return jsonify({"error": "Plan manquant"}), 400
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try:
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# Reconstruire le plan depuis les données
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from .autonomous_planner import PlannedAction, ActionType
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steps = []
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for step_data in plan_data.get('steps', []):
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action_type_str = step_data.get('action', 'click')
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action_type_map = {
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'open_app': ActionType.OPEN_APP,
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'open_url': ActionType.OPEN_URL,
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'click': ActionType.CLICK,
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'type_text': ActionType.TYPE_TEXT,
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'hotkey': ActionType.HOTKEY,
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'scroll': ActionType.SCROLL,
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'wait': ActionType.WAIT,
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'screenshot': ActionType.SCREENSHOT
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}
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steps.append(PlannedAction(
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step_number=step_data.get('step', len(steps) + 1),
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action_type=action_type_map.get(action_type_str, ActionType.CLICK),
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description=step_data.get('description', ''),
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target=step_data.get('target'),
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parameters=step_data.get('params', {}),
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expected_result=step_data.get('expected_result')
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))
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plan = ExecutionPlan(
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task_description=plan_data.get('task', ''),
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steps=steps,
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estimated_duration_seconds=plan_data.get('estimated_seconds', 30),
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risk_level=plan_data.get('risk_level', 'low')
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)
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# Exécuter en arrière-plan
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socketio.start_background_task(execute_agent_plan, plan)
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return jsonify({
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"success": True,
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"message": "Exécution démarrée",
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"steps_count": len(steps)
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})
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except Exception as e:
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logger.error(f"Agent execute error: {e}")
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return jsonify({"error": str(e)}), 500
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@app.route('/api/agent/status')
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def api_agent_status():
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"""Statut de l'agent autonome."""
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return jsonify({
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"available": autonomous_planner is not None,
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"llm_available": autonomous_planner.llm_available if autonomous_planner else False,
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"llm_model": autonomous_planner.llm_model if autonomous_planner else None
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})
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def execute_agent_plan(plan: ExecutionPlan):
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"""Exécute un plan d'agent en arrière-plan."""
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import asyncio
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try:
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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results = loop.run_until_complete(autonomous_planner.execute_plan(plan))
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loop.close()
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# Envoyer le résultat final
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success_count = sum(1 for r in results if r.success)
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total = len(results)
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socketio.emit('execution_completed', {
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"success": success_count == total,
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"workflow": plan.task_description,
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"message": f"{success_count}/{total} étapes réussies",
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"results": [
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{
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"step": r.action.step_number,
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"success": r.success,
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"message": r.message
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}
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for r in results
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]
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})
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except Exception as e:
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logger.error(f"Agent execution error: {e}")
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socketio.emit('execution_completed', {
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"success": False,
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"workflow": plan.task_description,
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"message": f"Erreur: {str(e)}"
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})
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@app.route('/api/help')
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def api_help():
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"""Aide et mode d'emploi."""
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