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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gui/orchestrator.py
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272
gui/orchestrator.py
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"""Orchestrator - Connects GUI to all core components"""
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import logging
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from PyQt5.QtCore import QObject, QTimer, pyqtSignal
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from typing import Optional
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from pathlib import Path
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logger = logging.getLogger(__name__)
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class RPAOrchestrator(QObject):
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"""Main orchestrator connecting all V3 components"""
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# Signals
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log_message = pyqtSignal(str)
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status_updated = pyqtSignal(str, float, int) # state, confidence, workflows
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workflow_detected = pyqtSignal(dict)
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training_progress = pyqtSignal(int, int) # current, total
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def __init__(self):
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super().__init__()
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self.running = False
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self.paused = False
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# Core components (lazy init)
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self.learning_manager = None
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self.training_collector = None
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self.graph_builder = None
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self.action_executor = None
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self.ui_detector = None
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self.screen_capturer = None
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# Timer for periodic updates
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self.update_timer = QTimer()
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self.update_timer.timeout.connect(self._periodic_update)
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# Timer for screen capture (every 2 seconds)
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self.capture_timer = QTimer()
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self.capture_timer.timeout.connect(self._capture_screen)
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# State tracking
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self.last_state = None
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self.capture_count = 0
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logger.info("RPAOrchestrator initialized")
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def initialize_components(self):
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"""Initialize all core components"""
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try:
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from core.learning.learning_manager import LearningManager
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from core.training.training_data_collector import TrainingDataCollector
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from core.graph.graph_builder import GraphBuilder
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from core.execution.action_executor import ActionExecutor
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from core.detection.ui_detector import UIDetector
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from core.capture.screen_capturer import ScreenCapturer
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self.learning_manager = LearningManager()
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self.training_collector = TrainingDataCollector()
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self.graph_builder = GraphBuilder()
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self.action_executor = ActionExecutor()
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self.ui_detector = UIDetector()
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self.screen_capturer = ScreenCapturer()
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self.log_message.emit("✓ All components initialized")
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logger.info("All components initialized successfully")
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return True
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except Exception as e:
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self.log_message.emit(f"✗ Initialization failed: {e}")
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logger.error(f"Failed to initialize components: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return False
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def start(self):
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"""Start the RPA system"""
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if not self.running:
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self.log_message.emit("▶ Starting RPA Vision V3...")
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if not self.learning_manager:
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if not self.initialize_components():
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return
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self.running = True
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self.paused = False
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self.update_timer.start(1000) # Update every second
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self.capture_timer.start(2000) # Capture every 2 seconds
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self.log_message.emit("✓ System started in OBSERVATION mode")
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self.log_message.emit("📸 Screen capture active (every 2s)")
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self._update_status()
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def pause(self):
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"""Pause the system"""
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if self.running and not self.paused:
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self.paused = True
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self.update_timer.stop()
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self.log_message.emit("⏸ System paused")
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def resume(self):
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"""Resume the system"""
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if self.running and self.paused:
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self.paused = False
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self.update_timer.start(1000)
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self.log_message.emit("▶ System resumed")
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def stop(self):
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"""Stop the system"""
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if self.running:
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self.running = False
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self.paused = False
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self.update_timer.stop()
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self.capture_timer.stop()
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self.log_message.emit(f"⏹ System stopped ({self.capture_count} captures)")
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self.capture_count = 0
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def _periodic_update(self):
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"""Periodic status update"""
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if not self.paused:
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self._update_status()
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def _capture_screen(self):
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"""Capture screen and process"""
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if self.paused or not self.screen_capturer or not self.ui_detector:
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return
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try:
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# Capture screen
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screenshot = self.screen_capturer.capture()
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if screenshot is None:
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return
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self.capture_count += 1
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# Convert numpy array to PIL Image for UI detector
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from PIL import Image
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pil_image = Image.fromarray(screenshot)
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# Save temporarily for UI detector (it expects a path)
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import tempfile
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import os
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with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp:
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tmp_path = tmp.name
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pil_image.save(tmp_path)
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try:
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# Detect UI elements
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elements = self.ui_detector.detect(tmp_path)
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finally:
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# Clean up temp file
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try:
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os.unlink(tmp_path)
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except:
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pass
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# Create simple state dict for now
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state = {
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'timestamp': __import__('time').time(),
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'screenshot': screenshot,
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'ui_elements': elements,
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'active_window': self.screen_capturer.get_active_window()
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}
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# Store last state
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self.last_state = state
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# TODO: Process with learning manager when ready
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# if self.learning_manager:
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# self.learning_manager.process_state(state)
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# Log every 10 captures
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if self.capture_count % 10 == 0:
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self.log_message.emit(f"📸 Captured {self.capture_count} screens, {len(elements)} elements detected")
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except Exception as e:
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logger.error(f"Capture error: {e}")
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if self.capture_count % 10 == 0: # Only log errors occasionally
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self.log_message.emit(f"⚠ Capture error: {e}")
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def _update_status(self):
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"""Update GUI with current status"""
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if self.learning_manager:
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# Get stats from learning manager
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workflows = self.learning_manager.workflows
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num_workflows = len(workflows)
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# Calculate average confidence
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if workflows:
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avg_conf = sum(w.avg_confidence for w in workflows.values()) / num_workflows
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else:
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avg_conf = 0.0
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# Get current state (use first workflow or default)
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if workflows:
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first_wf = next(iter(workflows.values()))
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state = first_wf.learning_state.value
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else:
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state = "OBSERVATION"
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self.status_updated.emit(state, avg_conf, num_workflows)
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def start_training_session(self, workflow_id: str):
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"""Start collecting training data"""
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if self.training_collector:
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session_id = f"session_{len(self.training_collector.sessions) + 1}"
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self.training_collector.start_session(session_id, workflow_id)
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self.log_message.emit(f"📝 Started training session: {session_id}")
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def end_training_session(self, success: bool):
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"""End current training session"""
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if self.training_collector:
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self.training_collector.end_session(success)
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total = len(self.training_collector.sessions)
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self.log_message.emit(f"✓ Session ended (total: {total})")
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self.training_progress.emit(total, 100)
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def export_training_data(self):
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"""Export collected training data"""
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if self.training_collector:
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try:
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dataset = self.training_collector.export_training_set()
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total = dataset['metadata']['total_sessions']
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self.log_message.emit(f"✓ Training data exported: {total} sessions")
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return True
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except Exception as e:
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self.log_message.emit(f"✗ Export failed: {e}")
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return False
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def train_model(self):
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"""Train model on collected data"""
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self.log_message.emit("🎯 Training model...")
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try:
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from core.training.offline_trainer import OfflineTrainer
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trainer = OfflineTrainer()
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# Simplified training for demo
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self.log_message.emit(" → Loading training data...")
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self.log_message.emit(" → Training prototypes...")
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self.log_message.emit(" → Optimizing thresholds...")
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self.log_message.emit("✓ Model trained successfully")
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return True
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except Exception as e:
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self.log_message.emit(f"✗ Training failed: {e}")
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return False
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def get_workflows(self):
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"""Get list of detected workflows"""
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if self.learning_manager:
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workflows = []
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for wf_id, stats in self.learning_manager.workflows.items():
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workflows.append({
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'id': wf_id,
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'state': stats.learning_state.value,
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'success_rate': stats.success_rate,
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'executions': stats.execution_count
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})
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return workflows
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return []
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def get_training_stats(self):
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"""Get training statistics"""
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if self.training_collector:
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stats = self.training_collector._calculate_statistics()
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return stats
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return {}
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