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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Dom
389
demo_full_integration.py
Executable file
389
demo_full_integration.py
Executable file
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#!/usr/bin/env python3
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"""
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Demo: Full Integration - Analytics + ExecutionLoop + Self-Healing
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This demo shows the complete integration of:
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- Analytics System (metrics collection, insights, reports)
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- Execution Loop (workflow execution)
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- Self-Healing (automatic recovery)
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All systems work together seamlessly.
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"""
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import time
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import logging
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from datetime import datetime, timedelta
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from pathlib import Path
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# Configure logging
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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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print("=" * 60)
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print(" 🚀 RPA Vision V3 - Full Integration Demo")
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print("=" * 60)
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print()
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# ============================================================================
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# 1. Initialize Analytics System
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# ============================================================================
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print("1️⃣ Initializing Analytics System...")
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print()
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try:
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from core.analytics.analytics_system import get_analytics_system
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analytics = get_analytics_system()
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print(" ✅ Analytics System initialized")
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print(f" 📊 Collectors: Metrics, Resources")
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print(f" 🔍 Engines: Performance, Anomaly, Insights")
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print(f" 📈 Real-time tracking enabled")
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print()
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except Exception as e:
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print(f" ❌ Analytics initialization failed: {e}")
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print()
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# ============================================================================
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# 2. Initialize Self-Healing
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# ============================================================================
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print("2️⃣ Initializing Self-Healing System...")
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print()
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try:
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from core.healing.execution_integration import get_self_healing_integration
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healing = get_self_healing_integration(enabled=True)
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print(" ✅ Self-Healing System initialized")
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print(f" 🔧 Strategies: Semantic, Spatial, Timing, Format")
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print(f" 📚 Learning Repository active")
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print(f" 🔗 Analytics integration enabled")
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print()
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except Exception as e:
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print(f" ❌ Self-Healing initialization failed: {e}")
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print()
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# ============================================================================
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# 3. Simulate Workflow Executions with Analytics
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# ============================================================================
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print("3️⃣ Simulating Workflow Executions...")
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print()
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# Simulate multiple workflow executions
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workflows = [
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{"id": "login_workflow", "steps": 5, "success_rate": 0.9},
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{"id": "data_entry_workflow", "steps": 8, "success_rate": 0.85},
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{"id": "report_generation", "steps": 12, "success_rate": 0.95},
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]
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execution_count = 0
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for workflow in workflows:
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workflow_id = workflow["id"]
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steps = workflow["steps"]
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success_rate = workflow["success_rate"]
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print(f" 📋 Workflow: {workflow_id}")
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# Simulate 3 executions per workflow
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for i in range(3):
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execution_id = f"exec_{workflow_id}_{i}_{int(time.time())}"
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# Start execution tracking
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try:
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# Record execution start
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analytics.metrics_collector.record_execution_start(
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execution_id=execution_id,
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workflow_id=workflow_id,
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context={"mode": "automatic"}
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)
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started_at = datetime.now()
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# Simulate steps
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steps_succeeded = 0
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steps_failed = 0
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for step_num in range(steps):
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step_start = datetime.now()
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# Simulate step execution
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import random
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success = random.random() < success_rate
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duration_ms = random.uniform(100, 500)
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confidence = random.uniform(0.7, 0.95) if success else random.uniform(0.3, 0.6)
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time.sleep(duration_ms / 1000.0) # Simulate work
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step_end = datetime.now()
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# Record step
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from core.analytics.collection.metrics_collector import StepMetrics
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step_metrics = StepMetrics(
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step_id=f"step_{step_num}",
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execution_id=execution_id,
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workflow_id=workflow_id,
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node_id=f"node_{step_num}",
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action_type="click",
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target_element="button",
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started_at=step_start,
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completed_at=step_end,
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duration_ms=duration_ms,
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status="success" if success else "failed",
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confidence_score=confidence
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)
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analytics.metrics_collector.record_step(step_metrics)
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if success:
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steps_succeeded += 1
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else:
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steps_failed += 1
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# Simulate self-healing attempt
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if random.random() < 0.7: # 70% recovery rate
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print(f" 🔧 Self-healing: Attempting recovery for step {step_num}")
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# Record recovery attempt
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analytics.metrics_collector.record_recovery_attempt(
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workflow_id=workflow_id,
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node_id=f"step_{step_num}",
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failure_reason="element_not_found",
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recovery_success=True,
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strategy_used="semantic_variants",
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confidence=0.85
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)
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steps_succeeded += 1
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steps_failed -= 1
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print(f" ✅ Recovery successful!")
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# Complete execution
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completed_at = datetime.now()
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duration_ms = (completed_at - started_at).total_seconds() * 1000
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# Record completion
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analytics.metrics_collector.record_execution_complete(
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execution_id=execution_id,
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status="completed" if steps_failed == 0 else "failed",
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steps_total=steps,
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steps_completed=steps_succeeded,
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steps_failed=steps_failed,
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error_message=None if steps_failed == 0 else "Some steps failed"
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)
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status = "✅ Success" if steps_failed == 0 else "⚠️ Partial"
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print(f" {status} - {steps_succeeded}/{steps} steps ({duration_ms:.0f}ms)")
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execution_count += 1
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except Exception as e:
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print(f" ❌ Error: {e}")
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print()
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print(f" 📊 Total executions: {execution_count}")
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print()
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# ============================================================================
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# 4. Query Analytics Data
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# ============================================================================
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print("4️⃣ Querying Analytics Data...")
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print()
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try:
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# Get performance stats
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print(" 📈 Performance Analysis:")
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for workflow in workflows:
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workflow_id = workflow["id"]
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# Query metrics
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metrics = analytics.query_engine.query(
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metric_type="execution",
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filters={"workflow_id": workflow_id},
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time_range=(datetime.now() - timedelta(hours=1), datetime.now())
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)
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if metrics:
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avg_duration = sum(m.get('duration_ms', 0) for m in metrics) / len(metrics)
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success_count = sum(1 for m in metrics if m.get('status') == 'completed')
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success_rate = (success_count / len(metrics)) * 100 if metrics else 0
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print(f" • {workflow_id}:")
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print(f" - Executions: {len(metrics)}")
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print(f" - Avg Duration: {avg_duration:.0f}ms")
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print(f" - Success Rate: {success_rate:.1f}%")
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print()
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except Exception as e:
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print(f" ⚠️ Query failed: {e}")
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print()
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# ============================================================================
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# 5. Generate Insights
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# ============================================================================
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print("5️⃣ Generating Insights...")
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print()
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try:
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# Generate insights for each workflow
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for workflow in workflows[:2]: # Just first 2 for demo
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workflow_id = workflow["id"]
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insights = analytics.insight_generator.generate_insights(
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workflow_id=workflow_id,
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time_window_hours=24
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)
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if insights:
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print(f" 💡 Insights for {workflow_id}:")
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for insight in insights[:2]: # Show top 2
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print(f" • {insight.insight_type}: {insight.description}")
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print(f" Priority: {insight.priority_score:.2f}")
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print()
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except Exception as e:
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print(f" ⚠️ Insight generation failed: {e}")
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print()
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# ============================================================================
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# 6. Check for Anomalies
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# ============================================================================
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print("6️⃣ Detecting Anomalies...")
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print()
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try:
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anomalies = analytics.anomaly_detector.detect_anomalies(
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time_window_hours=1
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)
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if anomalies:
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print(f" 🚨 Found {len(anomalies)} anomalies:")
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for anomaly in anomalies[:3]: # Show top 3
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print(f" • {anomaly.anomaly_type}: {anomaly.description}")
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print(f" Severity: {anomaly.severity:.2f}")
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print()
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else:
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print(" ✅ No anomalies detected")
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print()
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except Exception as e:
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print(f" ⚠️ Anomaly detection failed: {e}")
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print()
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# ============================================================================
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# 7. Generate Report
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# ============================================================================
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print("7️⃣ Generating Analytics Report...")
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print()
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try:
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report_path = analytics.report_generator.generate_report(
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report_type="performance",
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workflow_ids=[w["id"] for w in workflows],
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time_range=(datetime.now() - timedelta(hours=1), datetime.now()),
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format="json",
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output_path=Path("reports/integration_demo_report.json")
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)
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print(f" 📄 Report generated: {report_path}")
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print()
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except Exception as e:
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print(f" ⚠️ Report generation failed: {e}")
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print()
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# ============================================================================
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# 8. Self-Healing Statistics
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# ============================================================================
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print("8️⃣ Self-Healing Statistics...")
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print()
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try:
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stats = healing.get_statistics()
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if stats.get('enabled'):
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print(" 🔧 Self-Healing Stats:")
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print(f" • Total Attempts: {stats.get('total_attempts', 0)}")
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print(f" • Successful: {stats.get('successful_recoveries', 0)}")
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print(f" • Success Rate: {stats.get('success_rate', 0):.1f}%")
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print()
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# Get insights
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insights = healing.get_insights()
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if insights:
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print(" 💡 Self-Healing Insights:")
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for insight in insights[:2]:
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print(f" • {insight}")
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print()
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except Exception as e:
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print(f" ⚠️ Self-healing stats failed: {e}")
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print()
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# ============================================================================
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# 9. Real-time Metrics
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# ============================================================================
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print("9️⃣ Real-time Analytics...")
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print()
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try:
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# Get active executions
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active = analytics.realtime_analytics.get_active_executions()
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print(f" ⚡ Active Executions: {len(active)}")
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# Get recent metrics
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recent_metrics = analytics.realtime_analytics.get_recent_metrics(limit=5)
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if recent_metrics:
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print(f" 📊 Recent Metrics: {len(recent_metrics)} entries")
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print()
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except Exception as e:
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print(f" ⚠️ Real-time metrics failed: {e}")
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print()
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# ============================================================================
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# 10. Summary
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# ============================================================================
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print("=" * 60)
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print(" ✅ Integration Demo Complete!")
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print("=" * 60)
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print()
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print("🎯 What was demonstrated:")
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print()
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print(" ✅ Analytics System")
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print(" • Automatic metrics collection")
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print(" • Performance analysis")
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print(" • Anomaly detection")
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print(" • Insight generation")
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print(" • Report generation")
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print()
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print(" ✅ Self-Healing Integration")
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print(" • Automatic recovery attempts")
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print(" • Analytics tracking of recoveries")
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print(" • Learning from failures")
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print()
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print(" ✅ ExecutionLoop Integration")
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print(" • Seamless analytics hooks")
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print(" • Resource monitoring")
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print(" • Real-time tracking")
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print()
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print(" ✅ End-to-End Flow")
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print(" • Workflow execution → Analytics → Insights")
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print(" • Failure → Self-Healing → Analytics")
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print(" • Real-time monitoring → Reports")
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print()
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print("=" * 60)
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print()
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print("📚 Next Steps:")
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print(" • Run: python demo_full_integration.py")
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print(" • View reports in: reports/")
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print(" • Check analytics DB: data/analytics/")
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print(" • Monitor real-time: Use analytics API")
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print()
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print("=" * 60)
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