v1.0 - Version stable: multi-PC, détection UI-DETR-1, 3 modes exécution
- Frontend v4 accessible sur réseau local (192.168.1.40) - Ports ouverts: 3002 (frontend), 5001 (backend), 5004 (dashboard) - Ollama GPU fonctionnel - Self-healing interactif - Dashboard confiance Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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examples/run_rpa.py
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163
examples/run_rpa.py
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#!/usr/bin/env python3
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"""
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Exemple d'utilisation du RPA Vision V3
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Ce script montre comment :
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1. Charger un workflow existant
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2. Démarrer l'exécution en mode supervisé
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3. Observer la progression
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4. Arrêter proprement
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Usage:
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python examples/run_rpa.py --workflow <workflow_id> --mode supervised
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"""
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import argparse
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import logging
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import time
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import sys
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from pathlib import Path
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# Ajouter le répertoire racine au path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from core.pipeline import WorkflowPipeline, create_pipeline
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from core.execution import ExecutionLoop, ExecutionMode, ExecutionState, create_execution_loop
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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logger = logging.getLogger(__name__)
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def on_step_complete(result):
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"""Callback appelé après chaque étape."""
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status = "✓" if result.success else "✗"
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logger.info(
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f"{status} Step: node={result.node_id}, "
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f"confidence={result.match_confidence:.2f}, "
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f"duration={result.duration_ms:.0f}ms"
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)
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def on_state_change(new_state):
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"""Callback appelé lors des changements d'état."""
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logger.info(f"State changed to: {new_state.value}")
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def on_error(error_type, exception):
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"""Callback appelé en cas d'erreur."""
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logger.error(f"Error [{error_type}]: {exception}")
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def confirmation_callback(message, action_info):
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"""Callback pour demander confirmation (mode supervisé)."""
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print(f"\n{'='*60}")
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print(f"ACTION REQUIRED: {message}")
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print(f"Details: {action_info}")
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print(f"{'='*60}")
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response = input("Execute? [y/N]: ").strip().lower()
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return response == 'y'
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def main():
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parser = argparse.ArgumentParser(description="RPA Vision V3 Runner")
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parser.add_argument("--workflow", "-w", required=True, help="Workflow ID to execute")
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parser.add_argument(
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"--mode", "-m",
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choices=["observation", "coaching", "supervised", "automatic"],
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default="supervised",
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help="Execution mode"
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)
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parser.add_argument("--data-dir", "-d", default="data", help="Data directory")
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parser.add_argument("--list", "-l", action="store_true", help="List available workflows")
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args = parser.parse_args()
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# Créer le pipeline
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logger.info("Initializing RPA Vision V3...")
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pipeline = create_pipeline(
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data_dir=args.data_dir,
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use_gpu=False,
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enable_ui_detection=True
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)
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# Lister les workflows si demandé
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if args.list:
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workflows = pipeline.list_workflows()
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if not workflows:
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print("No workflows found.")
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else:
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print("\nAvailable workflows:")
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for wf in workflows:
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print(f" - {wf['workflow_id']}: {wf['name']} ({wf['learning_state']})")
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return
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# Créer la boucle d'exécution
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loop = create_execution_loop(pipeline, capture_interval_ms=500)
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# Enregistrer les callbacks
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loop.on_step_complete(on_step_complete)
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loop.on_state_change(on_state_change)
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loop.on_error(on_error)
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# Déterminer le mode
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mode_map = {
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"observation": ExecutionMode.OBSERVATION,
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"coaching": ExecutionMode.COACHING,
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"supervised": ExecutionMode.SUPERVISED,
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"automatic": ExecutionMode.AUTOMATIC
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}
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mode = mode_map[args.mode]
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# Démarrer l'exécution
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logger.info(f"Starting workflow '{args.workflow}' in {args.mode} mode...")
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if mode == ExecutionMode.SUPERVISED:
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# En mode supervisé, utiliser le callback de confirmation
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loop.confirmation_callback = confirmation_callback
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success = loop.start(args.workflow, mode=mode)
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if not success:
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logger.error("Failed to start execution")
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return
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# Boucle principale - afficher la progression
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try:
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while loop.get_state() in [ExecutionState.RUNNING, ExecutionState.PAUSED, ExecutionState.WAITING_CONFIRMATION]:
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progress = loop.get_progress()
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# Afficher la progression
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print(
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f"\rProgress: {progress['steps_executed']} steps, "
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f"{progress['steps_succeeded']} succeeded, "
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f"{progress['steps_failed']} failed, "
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f"node: {progress['current_node'] or 'N/A'}",
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end="", flush=True
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)
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time.sleep(1)
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except KeyboardInterrupt:
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logger.info("\nStopping execution...")
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loop.stop()
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finally:
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# Afficher le résumé
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print("\n")
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final_progress = loop.get_progress()
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logger.info(f"Execution finished: {final_progress['status']}")
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logger.info(f" Steps executed: {final_progress['steps_executed']}")
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logger.info(f" Steps succeeded: {final_progress['steps_succeeded']}")
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logger.info(f" Steps failed: {final_progress['steps_failed']}")
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logger.info(f" Duration: {final_progress['duration_seconds']:.1f}s")
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# Nettoyer
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loop.cleanup()
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if __name__ == "__main__":
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main()
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