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GraphBuilder construit maintenant des ScreenState enrichis (ui_elements + detected_text) au lieu de stubs vides, et associe les clics aux UIElement par proximité spatiale. Détails : - __init__ accepte ui_detector, screen_analyzer, enable_ui_enrichment, element_proximity_max_px (+ lazy resolver via singleton C1) - _create_screen_states délègue à ScreenAnalyzer.analyze() — remplace l'appel à _extract_text() qui n'existait plus depuis le Lot C (bug silencieux : OCR cassé en prod depuis ce jour, caught except) - _find_clicked_element : bbox contenant strict + fallback proximité ≤50px, préfère le plus petit bbox (form vs button) - _build_click_target_spec : TargetSpec(by_role, by_text, selection_policy="by_similarity") avec ancres dans context_hints (anchor_element_id, anchor_bbox, anchor_center) - _build_edges propage le ScreenState source aux builders d'action - WorkflowPipeline passe ui_detector + enable_ui_enrichment au builder Impact : matching prod 3-5x plus précis, TargetSpec ne sont plus des "unknown_element" génériques, UIConstraint.required_roles se remplit correctement via _extract_common_ui_elements (qui marchait depuis toujours mais sur des state.ui_elements vides). Tests e2e migrés vers enable_ui_enrichment=False (2.9s vs 67s) — ils valident le pipeline DBSCAN/edges, pas la détection UI réelle. 15 nouveaux tests, 178 tests passants au total (incluant Lots A-E). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1972 lines
76 KiB
Python
1972 lines
76 KiB
Python
"""
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GraphBuilder - Construction Automatique de Workflow Graphs
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Ce module implémente la construction automatique de graphes de workflows
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en analysant les sessions enregistrées et en détectant les patterns répétés.
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Architecture:
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1. Création de ScreenStates depuis RawSession
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2. Calcul de State Embeddings pour tous les états
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3. Détection de patterns via clustering DBSCAN
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4. Construction de WorkflowNodes depuis clusters
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5. Construction de WorkflowEdges depuis transitions
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Algorithme de Détection de Patterns:
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- Utilise DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
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- Métrique: similarité cosinus entre embeddings
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- Filtre les clusters avec moins de N répétitions
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- Calcule un prototype (moyenne) pour chaque cluster
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Example:
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>>> builder = GraphBuilder(min_pattern_repetitions=3)
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>>> workflow = builder.build_from_session(raw_session)
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>>> print(f"Workflow with {len(workflow.nodes)} nodes")
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"""
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import logging
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import os
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from typing import List, Dict, Optional, Tuple, Any
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from collections import defaultdict, Counter
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from datetime import datetime
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from pathlib import Path
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import numpy as np
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from sklearn.cluster import DBSCAN
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from core.models.raw_session import RawSession, Event
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from core.models.screen_state import (
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ScreenState, WindowContext, RawLevel, PerceptionLevel,
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ContextLevel, EmbeddingRef
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)
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from core.models.workflow_graph import (
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Workflow,
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WorkflowNode,
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WorkflowEdge,
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ScreenTemplate,
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Action,
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TargetSpec,
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EdgeConstraints,
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PostConditions,
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PostConditionCheck,
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WindowConstraint,
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TextConstraint,
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UIConstraint,
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EmbeddingPrototype,
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)
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from core.embedding.state_embedding_builder import StateEmbeddingBuilder
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from core.embedding.faiss_manager import FAISSManager
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from core.training.quality_validator import TrainingQualityValidator, QualityReport
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Filtrage des événements parasites (modificateurs seuls, etc.)
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# Appliqué dans _find_transition_events et _build_compound_action.
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# ---------------------------------------------------------------------------
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_MODIFIER_ONLY_KEYS = {
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"ctrl", "ctrl_l", "ctrl_r",
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"alt", "alt_l", "alt_r",
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"shift", "shift_l", "shift_r",
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"win", "cmd", "cmd_l", "cmd_r",
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"meta", "super", "super_l", "super_r",
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}
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def _is_modifier_only(keys: list) -> bool:
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"""Retourne True si la liste de touches ne contient que des modificateurs."""
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if not keys:
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return True
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return all(k.lower() in _MODIFIER_ONLY_KEYS for k in keys)
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def _is_parasitic_event(event: Event) -> bool:
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"""Retourne True si l'événement est parasite (modificateur seul, texte vide).
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Filtrage appliqué aux événements de session avant construction des edges.
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"""
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if event.type == "key_press":
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keys = event.data.get("keys", [])
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if not keys or _is_modifier_only(keys):
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return True
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elif event.type == "text_input":
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text = event.data.get("text", "")
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if not text:
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return True
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return False
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def _merge_consecutive_text_steps(steps: list) -> list:
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"""Fusionne les text_input consécutifs en un seul."""
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merged = []
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for step in steps:
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if (step.get("type") in ("text_input", "type")
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and merged
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and merged[-1].get("type") in ("text_input", "type")):
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merged[-1]["text"] = merged[-1].get("text", "") + step.get("text", "")
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else:
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merged.append(dict(step))
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return merged
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def _dedup_consecutive_combos(steps: list) -> list:
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"""Supprime les key_combo/key_press dupliqués consécutifs."""
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deduped = []
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for step in steps:
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if (step.get("type") in ("key_combo", "key_press")
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and deduped
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and deduped[-1].get("type") in ("key_combo", "key_press")
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and deduped[-1].get("keys") == step.get("keys")):
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continue
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deduped.append(step)
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return deduped
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def _filter_modifier_only_steps(steps: list) -> list:
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"""Supprime les steps key_combo/key_press avec uniquement des modificateurs."""
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return [
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s for s in steps
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if not (
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s.get("type") in ("key_combo", "key_press")
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and _is_modifier_only(s.get("keys", []))
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)
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]
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def _ensure_min_waits(steps: list, min_wait_ms: int = 300) -> list:
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"""Ajoute un wait entre les steps si aucun wait n'existe."""
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if not steps:
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return steps
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result = [steps[0]]
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for step in steps[1:]:
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if result[-1].get("type") != "wait" and step.get("type") != "wait":
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result.append({"type": "wait", "duration_ms": min_wait_ms})
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result.append(step)
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return result
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class GraphBuilder:
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"""
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Constructeur de graphes de workflows depuis sessions brutes.
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Cette classe analyse une RawSession pour construire automatiquement
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un Workflow avec ses nodes et edges en détectant les patterns répétés.
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Attributes:
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embedding_builder: Builder pour calculer les State Embeddings
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faiss_manager: Manager FAISS pour indexation (optionnel)
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min_pattern_repetitions: Nombre minimum de répétitions pour un pattern
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clustering_eps: Distance maximum entre points pour DBSCAN
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clustering_min_samples: Nombre minimum d'échantillons par cluster
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Example:
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>>> builder = GraphBuilder(min_pattern_repetitions=3)
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>>> workflow = builder.build_from_session(session, "Login Workflow")
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"""
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def __init__(
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self,
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embedding_builder: Optional[StateEmbeddingBuilder] = None,
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faiss_manager: Optional[FAISSManager] = None,
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quality_validator: Optional[TrainingQualityValidator] = None,
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min_pattern_repetitions: int = 2,
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clustering_eps: float = 0.08,
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clustering_min_samples: int = 2,
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enable_quality_validation: bool = True,
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ui_detector: Optional[Any] = None,
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screen_analyzer: Optional[Any] = None,
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enable_ui_enrichment: bool = True,
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element_proximity_max_px: float = 50.0,
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):
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"""
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Initialiser le GraphBuilder.
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Args:
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embedding_builder: Builder pour State Embeddings (créé si None)
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faiss_manager: Manager FAISS pour indexation (optionnel)
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quality_validator: Validateur de qualité (créé si None)
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min_pattern_repetitions: Nombre minimum de répétitions pour un pattern
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clustering_eps: Epsilon pour DBSCAN (distance max entre points)
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clustering_min_samples: Nombre minimum d'échantillons pour un cluster
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enable_quality_validation: Activer la validation de qualité
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ui_detector: UIDetector optionnel. Si fourni, sera utilisé par
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l'analyzer lazy-initialisé. Sinon, fallback sur le singleton
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partagé (`get_screen_analyzer()`).
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screen_analyzer: Instance ScreenAnalyzer à utiliser directement.
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Si None, lazy init via le singleton partagé C1.
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enable_ui_enrichment: Active l'enrichissement visuel des
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ScreenStates lors de `_create_screen_states` (OCR + UIDetector).
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False = comportement historique (ui_elements=[], detected_text=[]).
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element_proximity_max_px: Distance maximale (en pixels) entre un
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clic et le bbox le plus proche pour qu'un UIElement soit
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considéré comme cible. Au-delà, le clic reste sans ancre.
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"""
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self.embedding_builder = embedding_builder or StateEmbeddingBuilder()
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self.faiss_manager = faiss_manager
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self.quality_validator = quality_validator or TrainingQualityValidator()
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self.min_pattern_repetitions = min_pattern_repetitions
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self.clustering_eps = clustering_eps
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self.clustering_min_samples = clustering_min_samples
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self.enable_quality_validation = enable_quality_validation
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self.enable_ui_enrichment = enable_ui_enrichment
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self.element_proximity_max_px = element_proximity_max_px
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# UIDetector explicite (optionnel) — injecté dans l'analyzer lazy.
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self._ui_detector = ui_detector
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# Instance ScreenAnalyzer. Si fournie, on l'utilise telle quelle ;
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# sinon, on bascule sur le singleton partagé (lazy init).
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self._screen_analyzer = screen_analyzer
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logger.info(
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f"GraphBuilder initialized: "
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f"min_repetitions={min_pattern_repetitions}, "
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f"eps={clustering_eps}, "
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f"min_samples={clustering_min_samples}, "
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f"quality_validation={enable_quality_validation}, "
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f"ui_enrichment={enable_ui_enrichment}"
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)
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# ------------------------------------------------------------------
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# Résolution paresseuse du ScreenAnalyzer (singleton C1 par défaut)
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# ------------------------------------------------------------------
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def _get_screen_analyzer(self):
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"""
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Retourner l'instance ScreenAnalyzer à utiliser.
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Priorité :
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1. Instance injectée via le constructeur (`screen_analyzer=…`).
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2. Singleton partagé `get_screen_analyzer()` (C1) — évite le double
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chargement GPU quand ExecutionLoop et stream_processor tournent.
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3. En dernier recours (import circulaire, tests), création locale.
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"""
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if self._screen_analyzer is not None:
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return self._screen_analyzer
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try:
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from core.pipeline import get_screen_analyzer
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self._screen_analyzer = get_screen_analyzer(
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ui_detector=self._ui_detector,
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)
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return self._screen_analyzer
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except Exception as e:
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logger.warning(
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f"Impossible d'obtenir le ScreenAnalyzer singleton "
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f"({e}); fallback sur une instance locale."
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)
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try:
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from core.pipeline.screen_analyzer import ScreenAnalyzer
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self._screen_analyzer = ScreenAnalyzer(
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ui_detector=self._ui_detector,
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)
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return self._screen_analyzer
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except Exception as e2:
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logger.error(
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f"Impossible d'instancier ScreenAnalyzer: {e2}. "
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"Enrichissement UI désactivé."
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)
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return None
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def build_from_session(
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self,
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session: RawSession,
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workflow_name: Optional[str] = None,
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precomputed_states: Optional[List["ScreenState"]] = None,
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precomputed_embeddings: Optional[List] = None,
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sequential: bool = False,
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) -> Workflow:
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"""
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Construire un Workflow complet depuis une RawSession.
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Processus:
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1. Créer ScreenStates depuis screenshots (ou utiliser precomputed_states)
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2. Calculer embeddings pour chaque état (ou réutiliser precomputed_embeddings)
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3. Détecter patterns via clustering (ou mode séquentiel)
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4. Construire nodes depuis clusters
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5. Construire edges depuis transitions
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Args:
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session: Session brute à analyser
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workflow_name: Nom du workflow (généré si None)
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precomputed_states: ScreenStates déjà analysés (streaming).
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Si fourni, saute l'étape 1 (pas de re-analyse via ScreenAnalyzer).
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precomputed_embeddings: Embeddings déjà calculés (streaming).
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Si fourni et de la bonne longueur (= len(screen_states)),
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saute l'étape 2 (pas de recalcul CLIP).
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sequential: Si True, crée un node par état d'écran (pas de
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clustering DBSCAN). Approprié pour les enregistrements
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single-pass d'un workflow — chaque screenshot est une étape
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distincte avec ses actions associées.
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Returns:
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Workflow construit avec nodes et edges
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Raises:
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ValueError: Si la session est vide ou invalide
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"""
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if not precomputed_states and not session.screenshots:
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raise ValueError("Session has no screenshots and no precomputed states")
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logger.info(
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f"Building workflow from session {session.session_id} "
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f"with {len(precomputed_states or session.screenshots)} "
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f"{'precomputed states' if precomputed_states else 'screenshots'}"
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f"{' (mode séquentiel)' if sequential else ''}"
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)
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# Étape 1: Créer ScreenStates (ou réutiliser ceux pré-calculés)
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if precomputed_states:
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screen_states = precomputed_states
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logger.debug(f"Using {len(screen_states)} precomputed screen states")
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else:
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screen_states = self._create_screen_states(session)
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logger.debug(f"Created {len(screen_states)} screen states")
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# Étape 2: Calculer embeddings (ou réutiliser ceux pré-calculés)
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if precomputed_embeddings is not None and len(precomputed_embeddings) == len(screen_states):
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embeddings = [np.asarray(e, dtype=np.float32) for e in precomputed_embeddings]
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logger.debug(f"Using {len(embeddings)} precomputed embeddings (skip CLIP)")
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else:
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if precomputed_embeddings is not None:
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logger.warning(
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f"precomputed_embeddings length mismatch "
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f"({len(precomputed_embeddings)} vs {len(screen_states)} states), "
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f"recalculating..."
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)
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embeddings = self._compute_embeddings(screen_states)
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logger.debug(f"Computed {len(embeddings)} embeddings")
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# Étape 3: Détecter patterns ou mode séquentiel
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if sequential:
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# Mode séquentiel : chaque état d'écran est un node distinct.
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# Pas de clustering — essentiel pour les enregistrements single-pass
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# où l'on veut reproduire fidèlement la séquence des actions.
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clusters = {i: [i] for i in range(len(screen_states))}
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logger.info(
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f"Mode séquentiel: {len(clusters)} nodes (1 par état)"
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)
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else:
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clusters = self._detect_patterns(embeddings, screen_states)
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logger.info(f"Detected {len(clusters)} patterns")
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# Étape 4: Construire nodes
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nodes = self._build_nodes(clusters, screen_states, embeddings)
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logger.info(f"Built {len(nodes)} workflow nodes")
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# Étape 5: Construire edges (passer les embeddings pour éviter recalcul)
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edges = self._build_edges(
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nodes, screen_states, session, embeddings=embeddings,
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sequential=sequential,
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)
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logger.info(f"Built {len(edges)} workflow edges")
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# Créer Workflow
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from core.models.workflow_graph import WorkflowStats, SafetyRules, LearningConfig
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workflow = Workflow(
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workflow_id=workflow_name or f"workflow_{session.session_id}",
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name=workflow_name or "Unnamed Workflow",
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description="Auto-generated workflow",
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version=1,
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learning_state="OBSERVATION",
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created_at=datetime.now(),
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updated_at=datetime.now(),
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entry_nodes=[nodes[0].node_id] if nodes else [],
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end_nodes=[],
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nodes=nodes,
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edges=edges,
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safety_rules=SafetyRules(),
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stats=WorkflowStats(),
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learning=LearningConfig()
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)
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# Étape 6: Validation de qualité
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quality_report = None
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if self.enable_quality_validation and screen_states:
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quality_report = self._validate_workflow_quality(
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workflow, screen_states, embeddings, clusters
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)
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|
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# Stocker le rapport dans les métadonnées du workflow
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workflow.metadata = workflow.metadata or {}
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workflow.metadata['quality_report'] = quality_report.to_dict()
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|
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# Ajuster learning_state basé sur la qualité
|
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if quality_report.is_production_ready:
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workflow.learning_state = "AUTO_CANDIDATE"
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logger.info("Workflow qualité suffisante -> AUTO_CANDIDATE")
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else:
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workflow.learning_state = "OBSERVATION"
|
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logger.warning(
|
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f"Qualité insuffisante ({quality_report.overall_score:.3f}), "
|
||
f"workflow reste en OBSERVATION"
|
||
)
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||
|
||
logger.info(
|
||
f"Workflow '{workflow.name}' built successfully: "
|
||
f"{len(nodes)} nodes, {len(edges)} edges"
|
||
)
|
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|
||
return workflow
|
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|
||
def _validate_workflow_quality(
|
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self,
|
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workflow: Workflow,
|
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screen_states: List[ScreenState],
|
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embeddings: List[np.ndarray],
|
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clusters: Dict[int, List[int]]
|
||
) -> QualityReport:
|
||
"""
|
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Valider la qualité du workflow construit.
|
||
|
||
Args:
|
||
workflow: Workflow à valider
|
||
screen_states: États d'écran utilisés
|
||
embeddings: Embeddings calculés
|
||
clusters: Clusters détectés
|
||
|
||
Returns:
|
||
QualityReport avec métriques et recommandations
|
||
"""
|
||
logger.info(f"Validation qualité du workflow {workflow.workflow_id}")
|
||
|
||
# Préparer les données pour le validateur
|
||
embeddings_array = np.array(embeddings)
|
||
|
||
# Créer labels depuis les clusters
|
||
labels = np.full(len(embeddings), -1) # -1 = bruit
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||
for cluster_id, indices in clusters.items():
|
||
for idx in indices:
|
||
labels[idx] = cluster_id
|
||
|
||
# Valider avec le TrainingQualityValidator
|
||
report = self.quality_validator.validate_workflow(
|
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workflow=workflow,
|
||
observations=screen_states,
|
||
embeddings=embeddings_array,
|
||
labels=labels
|
||
)
|
||
|
||
logger.info(
|
||
f"Validation terminée: score={report.overall_score:.3f}, "
|
||
f"production_ready={report.is_production_ready}"
|
||
)
|
||
|
||
return report
|
||
|
||
def _create_screen_states(self, session: RawSession) -> List[ScreenState]:
|
||
"""
|
||
Créer ScreenStates enrichis depuis les screenshots de la session.
|
||
|
||
Pour chaque screenshot:
|
||
1. Trouver l'événement associé pour le contexte de fenêtre
|
||
2. Créer les 4 niveaux du ScreenState
|
||
3. Optionnellement détecter les éléments UI
|
||
|
||
Args:
|
||
session: Session brute
|
||
|
||
Returns:
|
||
Liste de ScreenStates enrichis
|
||
"""
|
||
screen_states = []
|
||
|
||
# Créer un mapping screenshot_id -> événement
|
||
screenshot_to_event = {}
|
||
for event in session.events:
|
||
if event.screenshot_id:
|
||
screenshot_to_event[event.screenshot_id] = event
|
||
|
||
# Récupérer (une seule fois) l'analyzer partagé si l'enrichissement est actif.
|
||
# Le singleton C1 garantit qu'on ne recharge pas UIDetector/CLIP inutilement.
|
||
analyzer = None
|
||
if self.enable_ui_enrichment:
|
||
analyzer = self._get_screen_analyzer()
|
||
|
||
# Cache partagé (C1) : réutiliser les analyses si même screenshot est
|
||
# repassé plusieurs fois (peu fréquent en construction, utile en tests).
|
||
try:
|
||
from core.pipeline import get_screen_state_cache
|
||
|
||
state_cache = get_screen_state_cache()
|
||
except Exception as e:
|
||
logger.debug(f"ScreenStateCache indisponible ({e}); aucun cache utilisé.")
|
||
state_cache = None
|
||
|
||
enriched_count = 0
|
||
for i, screenshot in enumerate(session.screenshots):
|
||
# Trouver l'événement associé
|
||
event = screenshot_to_event.get(screenshot.screenshot_id)
|
||
|
||
# Construire WindowContext depuis l'événement (si dispo)
|
||
screen_env = session.environment.get("screen", {})
|
||
screen_res = screen_env.get("primary_resolution", [1920, 1080])
|
||
if event and event.window:
|
||
window = WindowContext(
|
||
app_name=event.window.app_name,
|
||
window_title=event.window.title,
|
||
screen_resolution=screen_res,
|
||
workspace="main",
|
||
monitor_index=screen_env.get("monitor_index", 0),
|
||
dpi_scale=screen_env.get("dpi_scale", 100),
|
||
monitors=screen_env.get("monitors"),
|
||
os_theme=session.environment.get("os_theme", "unknown"),
|
||
os_language=session.environment.get("os_language", "unknown"),
|
||
)
|
||
else:
|
||
window = WindowContext(
|
||
app_name="unknown",
|
||
window_title="Unknown",
|
||
screen_resolution=screen_res,
|
||
workspace="main",
|
||
monitor_index=screen_env.get("monitor_index", 0),
|
||
dpi_scale=screen_env.get("dpi_scale", 100),
|
||
monitors=screen_env.get("monitors"),
|
||
os_theme=session.environment.get("os_theme", "unknown"),
|
||
os_language=session.environment.get("os_language", "unknown"),
|
||
)
|
||
|
||
# Chemin absolu du screenshot
|
||
screenshot_absolute_path = (
|
||
f"data/training/sessions/{session.session_id}/"
|
||
f"{session.session_id}/{screenshot.relative_path}"
|
||
)
|
||
screenshot_path = Path(screenshot_absolute_path)
|
||
|
||
# Timestamp
|
||
if isinstance(screenshot.captured_at, str):
|
||
timestamp = datetime.fromisoformat(
|
||
screenshot.captured_at.replace('Z', '+00:00')
|
||
)
|
||
else:
|
||
timestamp = screenshot.captured_at
|
||
|
||
# ------------------------------------------------------------
|
||
# Enrichissement visuel : déléguer au ScreenAnalyzer partagé
|
||
# ------------------------------------------------------------
|
||
# L'analyzer renvoie un ScreenState complet avec :
|
||
# - raw (image + file_size)
|
||
# - perception (OCR + embedding ref)
|
||
# - ui_elements (détection UIDetector)
|
||
# On récupère ces niveaux et on rebâtit un état final avec le
|
||
# WindowContext et les metadata issus de la session brute (les
|
||
# données "metier" que l'analyzer ignore).
|
||
# ------------------------------------------------------------
|
||
detected_text: List[str] = []
|
||
text_method = "none"
|
||
ui_elements: List = []
|
||
raw = RawLevel(
|
||
screenshot_path=str(screenshot_path),
|
||
capture_method="mss",
|
||
file_size_bytes=(
|
||
screenshot_path.stat().st_size
|
||
if screenshot_path.exists()
|
||
else 0
|
||
),
|
||
)
|
||
|
||
if analyzer is not None and screenshot_path.exists():
|
||
try:
|
||
# Construire l'info fenêtre pour donner le contexte à
|
||
# l'UIDetector (certains détecteurs s'en servent pour
|
||
# filtrer hors-fenêtre).
|
||
window_info = {
|
||
"app_name": window.app_name,
|
||
"title": window.window_title,
|
||
"screen_resolution": list(window.screen_resolution or []),
|
||
}
|
||
|
||
analyzed = analyzer.analyze(
|
||
str(screenshot_path),
|
||
window_info=window_info,
|
||
enable_ocr=True,
|
||
enable_ui_detection=True,
|
||
session_id=session.session_id,
|
||
)
|
||
detected_text = list(analyzed.perception.detected_text or [])
|
||
text_method = (
|
||
analyzed.perception.text_detection_method or "none"
|
||
)
|
||
ui_elements = list(analyzed.ui_elements or [])
|
||
# Garder les métriques OCR/UI si présentes (debug)
|
||
analyzer_metadata = dict(analyzed.metadata or {})
|
||
raw = analyzed.raw # conserver file_size réel mesuré
|
||
if ui_elements:
|
||
enriched_count += 1
|
||
except Exception as e:
|
||
logger.warning(
|
||
f"Enrichissement visuel échoué pour {screenshot_path}: {e}. "
|
||
"Fallback sur ScreenState minimal."
|
||
)
|
||
analyzer_metadata = {"analyzer_error": str(e)}
|
||
else:
|
||
analyzer_metadata = {}
|
||
if self.enable_ui_enrichment and not screenshot_path.exists():
|
||
logger.debug(
|
||
f"Screenshot introuvable: {screenshot_path} "
|
||
"— ui_elements restera vide"
|
||
)
|
||
|
||
# PerceptionLevel : vector_id calculé de façon déterministe.
|
||
perception = PerceptionLevel(
|
||
embedding=EmbeddingRef(
|
||
provider="openclip_ViT-B-32",
|
||
vector_id=(
|
||
f"data/embeddings/screens/"
|
||
f"{session.session_id}_state_{i:04d}.npy"
|
||
),
|
||
dimensions=512,
|
||
),
|
||
detected_text=detected_text,
|
||
text_detection_method=text_method,
|
||
confidence_avg=0.85 if detected_text else 0.0,
|
||
)
|
||
|
||
# ContextLevel (métier)
|
||
context = ContextLevel(
|
||
current_workflow_candidate=None,
|
||
workflow_step=i,
|
||
user_id=session.user.get("id", "unknown"),
|
||
tags=(
|
||
list(session.context.get("tags", []))
|
||
if isinstance(session.context.get("tags"), list)
|
||
else []
|
||
),
|
||
business_variables={},
|
||
)
|
||
|
||
# Metadata : on garde le lien événement/session + éventuels
|
||
# compteurs remontés par l'analyzer.
|
||
metadata = {
|
||
"screenshot_id": screenshot.screenshot_id,
|
||
"event_type": event.type if event else None,
|
||
"event_time": event.t if event else None,
|
||
}
|
||
# Propager les indicateurs utiles de l'analyzer sans écraser la base.
|
||
for key in ("ocr_ms", "ui_ms", "analyzer_error"):
|
||
if key in analyzer_metadata:
|
||
metadata[key] = analyzer_metadata[key]
|
||
|
||
state = ScreenState(
|
||
screen_state_id=f"{session.session_id}_state_{i:04d}",
|
||
timestamp=timestamp,
|
||
session_id=session.session_id,
|
||
window=window,
|
||
raw=raw,
|
||
perception=perception,
|
||
context=context,
|
||
metadata=metadata,
|
||
ui_elements=ui_elements,
|
||
)
|
||
|
||
screen_states.append(state)
|
||
|
||
logger.info(
|
||
f"Created {len(screen_states)} enriched screen states "
|
||
f"({enriched_count} avec UI détectée, "
|
||
f"ui_enrichment={self.enable_ui_enrichment})"
|
||
)
|
||
return screen_states
|
||
|
||
def _compute_embeddings(
|
||
self, screen_states: List[ScreenState]
|
||
) -> List[np.ndarray]:
|
||
"""
|
||
Calculer State Embeddings pour tous les états.
|
||
|
||
Utilise StateEmbeddingBuilder pour générer des embeddings
|
||
multi-modaux (image + texte + UI). Ajoute optionnellement
|
||
les embeddings à l'index FAISS.
|
||
|
||
Args:
|
||
screen_states: Liste de ScreenStates
|
||
|
||
Returns:
|
||
Liste de vecteurs d'embeddings (numpy arrays)
|
||
"""
|
||
embeddings = []
|
||
|
||
for state in screen_states:
|
||
# Construire embedding
|
||
state_embedding = self.embedding_builder.build(state)
|
||
vector = state_embedding.get_vector()
|
||
embeddings.append(vector)
|
||
|
||
# Ajouter à FAISS si disponible
|
||
if self.faiss_manager:
|
||
self.faiss_manager.add_embedding(
|
||
state.screen_state_id,
|
||
vector,
|
||
{"state_id": state.screen_state_id},
|
||
)
|
||
|
||
return embeddings
|
||
|
||
def _detect_patterns(
|
||
self,
|
||
embeddings: List[np.ndarray],
|
||
screen_states: List[ScreenState],
|
||
) -> Dict[int, List[int]]:
|
||
"""
|
||
Détecter patterns répétés via clustering hybride.
|
||
|
||
Algorithme:
|
||
1. Pré-clustering par titre de fenêtre (discriminant fort)
|
||
2. DBSCAN au sein de chaque groupe de titres (affine par vision)
|
||
3. Filtrer clusters trop petits
|
||
|
||
Le titre de fenêtre est le signal le plus discriminant entre écrans.
|
||
CLIP/embeddings seul ne distingue pas assez les screenshots d'apps différentes
|
||
(distance cosine max ~0.12 entre Notepad et Explorer).
|
||
|
||
Args:
|
||
embeddings: Vecteurs d'embeddings
|
||
screen_states: ScreenStates correspondants
|
||
|
||
Returns:
|
||
Dictionnaire {cluster_id: [indices des états]}
|
||
"""
|
||
if len(embeddings) < self.min_pattern_repetitions:
|
||
logger.warning(
|
||
f"Not enough states ({len(embeddings)}) for pattern detection "
|
||
f"(minimum: {self.min_pattern_repetitions})"
|
||
)
|
||
return {}
|
||
|
||
# Étape 1 : Pré-clustering par titre de fenêtre (ou app_name)
|
||
title_groups = defaultdict(list)
|
||
for idx, state in enumerate(screen_states):
|
||
# Extraire le titre de fenêtre — le meilleur discriminant
|
||
title = ""
|
||
if hasattr(state, 'window') and state.window:
|
||
title = getattr(state.window, 'window_title', '') or ''
|
||
# Normaliser : garder le nom de l'app (partie avant le tiret)
|
||
# Ex: "Sans titre - Bloc-notes" → "Bloc-notes"
|
||
# Ex: "Google - Chrome" → "Chrome"
|
||
app_key = self._normalize_window_title(title) if title else "__no_title__"
|
||
title_groups[app_key].append(idx)
|
||
|
||
n_title_groups = len(title_groups)
|
||
logger.info(
|
||
f"Window title pre-clustering: {n_title_groups} groups "
|
||
f"({', '.join(f'{k}:{len(v)}' for k, v in title_groups.items())})"
|
||
)
|
||
|
||
# Étape 2 : Si les titres distinguent bien les écrans, utiliser directement
|
||
# Si un seul groupe (titres identiques ou absents), fallback DBSCAN pur
|
||
if n_title_groups <= 1:
|
||
return self._dbscan_clustering(embeddings, screen_states)
|
||
|
||
# Étape 3 : Chaque groupe de titres devient un cluster
|
||
# Optionnellement, DBSCAN affine les groupes avec >5 screenshots
|
||
final_clusters = {}
|
||
cluster_id = 0
|
||
noise_count = 0
|
||
|
||
for title_key, indices in title_groups.items():
|
||
if len(indices) < self.min_pattern_repetitions:
|
||
noise_count += len(indices)
|
||
continue
|
||
|
||
if len(indices) > 5:
|
||
# Sous-clustering DBSCAN pour les gros groupes
|
||
sub_embeddings = [embeddings[i] for i in indices]
|
||
sub_X = np.array(sub_embeddings)
|
||
|
||
sub_clustering = DBSCAN(
|
||
eps=self.clustering_eps,
|
||
min_samples=self.clustering_min_samples,
|
||
metric="cosine",
|
||
)
|
||
sub_labels = sub_clustering.fit_predict(sub_X)
|
||
|
||
sub_clusters = defaultdict(list)
|
||
for local_idx, label in enumerate(sub_labels):
|
||
if label == -1:
|
||
noise_count += 1
|
||
else:
|
||
sub_clusters[label].append(indices[local_idx])
|
||
|
||
for sub_indices in sub_clusters.values():
|
||
if len(sub_indices) >= self.min_pattern_repetitions:
|
||
final_clusters[cluster_id] = sub_indices
|
||
cluster_id += 1
|
||
else:
|
||
# Petit groupe → 1 cluster direct
|
||
final_clusters[cluster_id] = indices
|
||
cluster_id += 1
|
||
|
||
logger.info(
|
||
f"Hybrid clustering results: {len(final_clusters)} patterns "
|
||
f"(from {n_title_groups} title groups), {noise_count} noise points"
|
||
)
|
||
|
||
return final_clusters
|
||
|
||
def _normalize_window_title(self, title: str) -> str:
|
||
"""Normaliser un titre de fenêtre pour le clustering.
|
||
|
||
Extrait le nom de l'application (partie significative) en ignorant
|
||
le contenu variable (nom de fichier, URL, etc.).
|
||
|
||
Ex: "document.txt - Bloc-notes" → "Bloc-notes"
|
||
Ex: "Google - Chrome" → "Chrome"
|
||
Ex: "C:\\Users\\... - Explorateur" → "Explorateur"
|
||
"""
|
||
if not title:
|
||
return "__no_title__"
|
||
|
||
# Séparateurs courants dans les titres Windows
|
||
for sep in [" - ", " — ", " – ", " | "]:
|
||
if sep in title:
|
||
parts = title.split(sep)
|
||
# Le nom de l'app est généralement le dernier segment
|
||
return parts[-1].strip()
|
||
|
||
return title.strip()
|
||
|
||
def _dbscan_clustering(
|
||
self,
|
||
embeddings: List[np.ndarray],
|
||
screen_states: List[ScreenState],
|
||
) -> Dict[int, List[int]]:
|
||
"""Clustering DBSCAN pur (fallback quand les titres ne discriminent pas)."""
|
||
X = np.array(embeddings)
|
||
|
||
clustering = DBSCAN(
|
||
eps=self.clustering_eps,
|
||
min_samples=self.clustering_min_samples,
|
||
metric="cosine",
|
||
)
|
||
labels = clustering.fit_predict(X)
|
||
|
||
clusters = defaultdict(list)
|
||
noise_count = 0
|
||
|
||
for idx, label in enumerate(labels):
|
||
if label == -1:
|
||
noise_count += 1
|
||
else:
|
||
clusters[label].append(idx)
|
||
|
||
filtered_clusters = {
|
||
cluster_id: indices
|
||
for cluster_id, indices in clusters.items()
|
||
if len(indices) >= self.min_pattern_repetitions
|
||
}
|
||
|
||
logger.info(
|
||
f"DBSCAN clustering results: {len(filtered_clusters)} patterns, "
|
||
f"{noise_count} noise points, "
|
||
f"{len(clusters) - len(filtered_clusters)} small clusters filtered"
|
||
)
|
||
|
||
return filtered_clusters
|
||
|
||
def _build_nodes(
|
||
self,
|
||
clusters: Dict[int, List[int]],
|
||
screen_states: List[ScreenState],
|
||
embeddings: List[np.ndarray],
|
||
) -> List[WorkflowNode]:
|
||
"""
|
||
Construire WorkflowNodes depuis les clusters détectés.
|
||
|
||
Pour chaque cluster:
|
||
1. Calculer embedding prototype (moyenne normalisée)
|
||
2. Extraire contraintes depuis états du cluster
|
||
3. Créer ScreenTemplate
|
||
4. Créer WorkflowNode
|
||
|
||
Args:
|
||
clusters: Clusters détectés {cluster_id: [indices]}
|
||
screen_states: ScreenStates
|
||
embeddings: Embeddings
|
||
|
||
Returns:
|
||
Liste de WorkflowNodes
|
||
"""
|
||
nodes = []
|
||
|
||
for cluster_id, indices in clusters.items():
|
||
# Calculer embedding prototype (moyenne)
|
||
cluster_embeddings = [embeddings[i] for i in indices]
|
||
prototype = np.mean(cluster_embeddings, axis=0)
|
||
prototype = prototype / np.linalg.norm(prototype) # Normaliser
|
||
|
||
# Extraire contraintes depuis les états du cluster
|
||
cluster_states = [screen_states[i] for i in indices]
|
||
template = self._create_screen_template(cluster_states, prototype)
|
||
|
||
# Créer node
|
||
node = WorkflowNode(
|
||
node_id=f"node_{cluster_id:03d}",
|
||
name=f"State Pattern {cluster_id}",
|
||
description=f"Pattern auto-détecté ({len(indices)} observations)",
|
||
template=template,
|
||
metadata={
|
||
"observation_count": len(indices),
|
||
"_prototype_vector": prototype.tolist(),
|
||
},
|
||
)
|
||
|
||
nodes.append(node)
|
||
logger.debug(
|
||
f"Created node {node.node_id} with {len(indices)} observations"
|
||
)
|
||
|
||
return nodes
|
||
|
||
def _create_screen_template(
|
||
self,
|
||
states: List[ScreenState],
|
||
prototype_embedding: np.ndarray,
|
||
) -> ScreenTemplate:
|
||
"""
|
||
Créer un ScreenTemplate depuis un cluster d'états.
|
||
|
||
Extrait les contraintes communes à tous les états du cluster :
|
||
- window_title_pattern : titre de fenêtre commun
|
||
- required_text_patterns : textes présents dans la majorité des états
|
||
- required_ui_elements : rôles/types UI récurrents
|
||
|
||
Args:
|
||
states: États du cluster
|
||
prototype_embedding: Embedding prototype
|
||
|
||
Returns:
|
||
ScreenTemplate avec contraintes extraites
|
||
"""
|
||
# --- Extraction du titre de fenêtre commun ---
|
||
window_title_pattern = self._extract_window_pattern(states)
|
||
|
||
# --- Extraction des textes récurrents ---
|
||
required_text_patterns = self._extract_common_texts(states)
|
||
|
||
# --- Extraction des éléments UI récurrents ---
|
||
required_ui_elements = self._extract_common_ui_elements(states)
|
||
|
||
# Construire les sous-objets de contraintes
|
||
window_constraint = WindowConstraint(
|
||
title_pattern=window_title_pattern,
|
||
title_contains=window_title_pattern,
|
||
)
|
||
|
||
text_constraint = TextConstraint(
|
||
required_texts=required_text_patterns,
|
||
)
|
||
|
||
ui_roles = [
|
||
e.get("role", "") for e in required_ui_elements if e.get("role")
|
||
]
|
||
ui_constraint = UIConstraint(
|
||
required_roles=ui_roles,
|
||
)
|
||
|
||
embedding_proto = EmbeddingPrototype(
|
||
provider="openclip_ViT-B-32",
|
||
vector_id="", # Le vecteur est stocké dans node.metadata._prototype_vector
|
||
min_cosine_similarity=0.85,
|
||
sample_count=len(states),
|
||
)
|
||
|
||
return ScreenTemplate(
|
||
window=window_constraint,
|
||
text=text_constraint,
|
||
ui=ui_constraint,
|
||
embedding=embedding_proto,
|
||
)
|
||
|
||
def _extract_window_pattern(self, states: List[ScreenState]) -> Optional[str]:
|
||
"""Extraire un pattern de titre de fenêtre commun aux états du cluster."""
|
||
titles = [s.window.window_title for s in states if s.window.window_title]
|
||
if not titles:
|
||
return None
|
||
|
||
# Si tous les titres sont identiques, retourner directement
|
||
if len(set(titles)) == 1:
|
||
return titles[0]
|
||
|
||
# Trouver le préfixe commun le plus long
|
||
prefix = os.path.commonprefix(titles)
|
||
if len(prefix) >= 5:
|
||
return prefix.rstrip(" -–—|")
|
||
|
||
# Fallback: le titre le plus fréquent
|
||
from collections import Counter
|
||
most_common = Counter(titles).most_common(1)[0][0]
|
||
return most_common
|
||
|
||
def _extract_common_texts(
|
||
self, states: List[ScreenState], min_presence_ratio: float = 0.6
|
||
) -> List[str]:
|
||
"""
|
||
Extraire les textes présents dans la majorité des états du cluster.
|
||
|
||
Args:
|
||
states: États du cluster
|
||
min_presence_ratio: Proportion minimale de présence (0.6 = 60% des états)
|
||
"""
|
||
if not states:
|
||
return []
|
||
|
||
# Collecter les textes de chaque état
|
||
text_counts: Dict[str, int] = defaultdict(int)
|
||
states_with_text = 0
|
||
|
||
for state in states:
|
||
if hasattr(state.perception, 'detected_text') and state.perception.detected_text:
|
||
states_with_text += 1
|
||
seen_in_state = set()
|
||
for text in state.perception.detected_text:
|
||
normalized = text.strip().lower()
|
||
if len(normalized) >= 3 and normalized not in seen_in_state:
|
||
text_counts[normalized] += 1
|
||
seen_in_state.add(normalized)
|
||
|
||
if states_with_text == 0:
|
||
return []
|
||
|
||
# Garder les textes présents dans au moins min_presence_ratio des états
|
||
threshold = max(2, int(states_with_text * min_presence_ratio))
|
||
common_texts = [
|
||
text for text, count in text_counts.items()
|
||
if count >= threshold
|
||
]
|
||
|
||
# Limiter à 10 textes les plus fréquents
|
||
common_texts.sort(key=lambda t: text_counts[t], reverse=True)
|
||
return common_texts[:10]
|
||
|
||
def _extract_common_ui_elements(
|
||
self, states: List[ScreenState], min_presence_ratio: float = 0.5
|
||
) -> List[Dict[str, Any]]:
|
||
"""
|
||
Extraire les types/rôles d'éléments UI récurrents dans le cluster.
|
||
|
||
Retourne une liste de contraintes UI au format:
|
||
[{"type": "button", "role": "validate", "min_count": 1}, ...]
|
||
"""
|
||
if not states:
|
||
return []
|
||
|
||
# Compter les paires (type, role) dans chaque état
|
||
role_counts: Dict[str, int] = defaultdict(int)
|
||
type_counts: Dict[str, int] = defaultdict(int)
|
||
states_with_ui = 0
|
||
|
||
for state in states:
|
||
if state.ui_elements:
|
||
states_with_ui += 1
|
||
seen_roles = set()
|
||
seen_types = set()
|
||
for el in state.ui_elements:
|
||
el_type = getattr(el, 'type', 'unknown')
|
||
el_role = getattr(el, 'role', 'unknown')
|
||
|
||
if el_role != 'unknown' and el_role not in seen_roles:
|
||
role_counts[el_role] += 1
|
||
seen_roles.add(el_role)
|
||
|
||
if el_type != 'unknown' and el_type not in seen_types:
|
||
type_counts[el_type] += 1
|
||
seen_types.add(el_type)
|
||
|
||
if states_with_ui == 0:
|
||
return []
|
||
|
||
threshold = max(2, int(states_with_ui * min_presence_ratio))
|
||
|
||
constraints = []
|
||
|
||
# Ajouter les rôles récurrents
|
||
for role, count in role_counts.items():
|
||
if count >= threshold:
|
||
constraints.append({
|
||
"role": role,
|
||
"min_count": 1,
|
||
})
|
||
|
||
# Limiter à 8 contraintes
|
||
constraints.sort(key=lambda c: role_counts.get(c.get("role", ""), 0), reverse=True)
|
||
return constraints[:8]
|
||
|
||
# ------------------------------------------------------------------
|
||
# Association spatiale clic → UIElement
|
||
# ------------------------------------------------------------------
|
||
|
||
def _find_clicked_element(
|
||
self,
|
||
event: Event,
|
||
ui_elements: List[Any],
|
||
) -> Optional[Any]:
|
||
"""
|
||
Identifier l'UIElement cible d'un clic par proximité spatiale.
|
||
|
||
Règle :
|
||
1. Si un bbox contient strictement la position du clic → match.
|
||
2. Sinon, on prend le bbox le plus proche (distance euclidienne
|
||
au bord) sous réserve qu'il soit à <= `element_proximity_max_px`.
|
||
3. Sinon, aucun ancrage possible → None.
|
||
|
||
Cette association transforme un clic "aveugle" (coordonnées brutes)
|
||
en un clic "intelligent" (rôle + label), permettant au matcher de
|
||
retrouver l'élément même si la résolution ou la position change.
|
||
|
||
Args:
|
||
event: Événement `mouse_click` (avec `data["pos"] = [x, y]`).
|
||
ui_elements: Liste des UIElement détectés sur l'écran source.
|
||
|
||
Returns:
|
||
UIElement le plus pertinent, ou None si rien ne correspond.
|
||
"""
|
||
if not ui_elements:
|
||
return None
|
||
if not event or event.type != "mouse_click":
|
||
return None
|
||
|
||
pos = event.data.get("pos") if event.data else None
|
||
if not pos or len(pos) < 2:
|
||
return None
|
||
|
||
try:
|
||
click_x = float(pos[0])
|
||
click_y = float(pos[1])
|
||
except (TypeError, ValueError):
|
||
return None
|
||
|
||
best_contained = None
|
||
best_contained_area = None
|
||
best_near = None
|
||
best_near_distance = None
|
||
|
||
for element in ui_elements:
|
||
bbox = getattr(element, "bbox", None)
|
||
if bbox is None:
|
||
continue
|
||
|
||
# Extraction défensive des coordonnées (BBox Pydantic ou tuple)
|
||
try:
|
||
bx = int(getattr(bbox, "x", bbox[0]))
|
||
by = int(getattr(bbox, "y", bbox[1]))
|
||
bw = int(getattr(bbox, "width", bbox[2]))
|
||
bh = int(getattr(bbox, "height", bbox[3]))
|
||
except (AttributeError, IndexError, TypeError):
|
||
continue
|
||
|
||
# Cas 1 : la position est strictement dans le bbox.
|
||
if bx <= click_x <= bx + bw and by <= click_y <= by + bh:
|
||
# Sélectionner le plus petit bbox qui contient (élément le plus spécifique)
|
||
area = max(1, bw * bh)
|
||
if best_contained is None or area < best_contained_area:
|
||
best_contained = element
|
||
best_contained_area = area
|
||
continue
|
||
|
||
# Cas 2 : calculer la distance au bord le plus proche.
|
||
dx = max(bx - click_x, 0, click_x - (bx + bw))
|
||
dy = max(by - click_y, 0, click_y - (by + bh))
|
||
distance = (dx * dx + dy * dy) ** 0.5
|
||
|
||
if best_near is None or distance < best_near_distance:
|
||
best_near = element
|
||
best_near_distance = distance
|
||
|
||
if best_contained is not None:
|
||
return best_contained
|
||
|
||
if (
|
||
best_near is not None
|
||
and best_near_distance is not None
|
||
and best_near_distance <= self.element_proximity_max_px
|
||
):
|
||
return best_near
|
||
|
||
return None
|
||
|
||
# Patterns d'erreur courants pour la détection fail_fast
|
||
_ERROR_PATTERNS = [
|
||
"erreur", "error", "échec", "failed", "impossible",
|
||
"accès refusé", "access denied", "not found",
|
||
"timeout", "connexion perdue", "session expirée",
|
||
]
|
||
|
||
def _build_edges(
|
||
self,
|
||
nodes: List[WorkflowNode],
|
||
screen_states: List[ScreenState],
|
||
session: RawSession,
|
||
embeddings: Optional[List[np.ndarray]] = None,
|
||
sequential: bool = False,
|
||
) -> List[WorkflowEdge]:
|
||
"""
|
||
Construire WorkflowEdges depuis les transitions observées.
|
||
|
||
Algorithme:
|
||
1. Mapper chaque ScreenState vers son node (via embedding similarity)
|
||
En mode séquentiel, le mapping est direct (state i → node i).
|
||
2. Identifier les transitions (state_i -> state_j où node change)
|
||
3. Extraire l'action depuis l'événement entre les deux états
|
||
4. Créer WorkflowEdge avec action, pré-conditions et post-conditions
|
||
|
||
Les EdgeConstraints sont peuplées depuis le ScreenTemplate du node source :
|
||
- required_window_title, required_app_name, min_source_similarity
|
||
- required_texts (textes OCR fréquents dans le cluster source)
|
||
|
||
Les PostConditions sont peuplées depuis le ScreenTemplate du node cible :
|
||
- expected_window_title, expected_app_name, min_target_similarity
|
||
- expected_texts + forbidden_texts (patterns d'erreur courants)
|
||
|
||
Args:
|
||
nodes: WorkflowNodes construits
|
||
screen_states: ScreenStates
|
||
session: Session brute (pour événements)
|
||
embeddings: Embeddings pré-calculés (évite un recalcul dans _map_states_to_nodes)
|
||
sequential: Mode séquentiel — chaque paire consécutive = transition
|
||
|
||
Returns:
|
||
Liste de WorkflowEdges
|
||
"""
|
||
if not nodes or len(screen_states) < 2:
|
||
logger.warning("Not enough data to build edges")
|
||
return []
|
||
|
||
edges = []
|
||
edge_counts = defaultdict(int) # Pour compter les occurrences de chaque transition
|
||
|
||
# Index des nodes par ID pour accès rapide
|
||
node_by_id = {node.node_id: node for node in nodes}
|
||
|
||
# Étape 1: Mapper chaque état vers son node
|
||
if sequential:
|
||
# Mode séquentiel : mapping direct state[i] → node[i]
|
||
state_to_node = {}
|
||
for i, state in enumerate(screen_states):
|
||
if i < len(nodes):
|
||
state_to_node[state.screen_state_id] = nodes[i].node_id
|
||
logger.debug(
|
||
f"Mode séquentiel: {len(state_to_node)} states mappés directement"
|
||
)
|
||
else:
|
||
state_to_node = self._map_states_to_nodes(
|
||
screen_states, nodes, embeddings=embeddings
|
||
)
|
||
|
||
# Étape 2: Récupérer la résolution d'écran pour normaliser les coordonnées
|
||
screen_env = session.environment.get("screen", {})
|
||
screen_resolution = tuple(screen_env.get("primary_resolution", [1920, 1080]))
|
||
|
||
# Étape 3: Parcourir les transitions
|
||
for i in range(len(screen_states) - 1):
|
||
current_state = screen_states[i]
|
||
next_state = screen_states[i + 1]
|
||
|
||
current_node_id = state_to_node.get(current_state.screen_state_id)
|
||
next_node_id = state_to_node.get(next_state.screen_state_id)
|
||
|
||
# En mode séquentiel, chaque paire consécutive est une transition
|
||
# En mode clustering, uniquement si les nodes sont différents
|
||
if current_node_id and next_node_id and (
|
||
sequential or current_node_id != next_node_id
|
||
):
|
||
# Trouver TOUS les événements entre les deux états
|
||
transition_events = self._find_transition_events(
|
||
current_state, next_state, session.events
|
||
)
|
||
|
||
# Créer l'edge
|
||
edge_key = f"{current_node_id}_to_{next_node_id}"
|
||
edge_counts[edge_key] += 1
|
||
|
||
# Ne créer l'edge qu'une fois, mais compter les occurrences
|
||
if edge_counts[edge_key] == 1:
|
||
source_node = node_by_id.get(current_node_id)
|
||
target_node = node_by_id.get(next_node_id)
|
||
edge = self._create_edge(
|
||
current_node_id, next_node_id,
|
||
transition_events[-1] if transition_events else None,
|
||
edge_key,
|
||
source_node=source_node,
|
||
target_node=target_node,
|
||
all_events=transition_events,
|
||
screen_resolution=screen_resolution,
|
||
source_state=current_state,
|
||
)
|
||
edges.append(edge)
|
||
|
||
# Mettre à jour les stats des edges avec les comptages
|
||
for edge in edges:
|
||
edge_key = f"{edge.from_node}_to_{edge.to_node}"
|
||
edge.stats.execution_count = edge_counts[edge_key]
|
||
edge.stats.success_count = edge_counts[edge_key]
|
||
|
||
logger.info(f"Built {len(edges)} edges from {sum(edge_counts.values())} transitions")
|
||
return edges
|
||
|
||
def _map_states_to_nodes(
|
||
self,
|
||
screen_states: List[ScreenState],
|
||
nodes: List[WorkflowNode],
|
||
embeddings: Optional[List[np.ndarray]] = None,
|
||
) -> Dict[str, str]:
|
||
"""
|
||
Mapper chaque ScreenState vers le node le plus proche.
|
||
|
||
Utilise la similarité d'embedding pour trouver le meilleur match.
|
||
|
||
Args:
|
||
screen_states: Liste de ScreenStates à mapper.
|
||
nodes: WorkflowNodes cibles.
|
||
embeddings: Embeddings pré-calculés (même ordre que screen_states).
|
||
Si fourni, évite de recalculer via self.embedding_builder.build().
|
||
"""
|
||
state_to_node = {}
|
||
|
||
# Récupérer les embeddings des prototypes de nodes
|
||
node_prototypes = {}
|
||
for node in nodes:
|
||
# Priorité : vecteur en mémoire (metadata), sinon chargement depuis disque
|
||
proto_list = node.metadata.get("_prototype_vector")
|
||
if proto_list is not None:
|
||
node_prototypes[node.node_id] = np.array(proto_list, dtype=np.float32)
|
||
elif node.template and node.template.embedding and node.template.embedding.vector_id:
|
||
proto_path = Path(node.template.embedding.vector_id)
|
||
if proto_path.exists():
|
||
node_prototypes[node.node_id] = np.load(proto_path)
|
||
|
||
if not node_prototypes:
|
||
logger.warning("No node prototypes available for mapping")
|
||
return state_to_node
|
||
|
||
# Vérifier si les embeddings pré-calculés sont utilisables
|
||
use_precomputed = (
|
||
embeddings is not None and len(embeddings) == len(screen_states)
|
||
)
|
||
if use_precomputed:
|
||
logger.debug("_map_states_to_nodes: using precomputed embeddings")
|
||
|
||
# Pour chaque état, trouver le node le plus proche
|
||
for i, state in enumerate(screen_states):
|
||
try:
|
||
# Utiliser l'embedding pré-calculé ou recalculer
|
||
if use_precomputed:
|
||
state_vector = np.asarray(embeddings[i], dtype=np.float32)
|
||
else:
|
||
state_embedding = self.embedding_builder.build(state)
|
||
state_vector = state_embedding.get_vector()
|
||
|
||
# Trouver le node avec la meilleure similarité
|
||
best_node_id = None
|
||
best_similarity = -1
|
||
|
||
for node_id, prototype in node_prototypes.items():
|
||
similarity = np.dot(state_vector, prototype)
|
||
if similarity > best_similarity:
|
||
best_similarity = similarity
|
||
best_node_id = node_id
|
||
|
||
if best_node_id and best_similarity > 0.7: # Seuil minimum
|
||
state_to_node[state.screen_state_id] = best_node_id
|
||
|
||
except Exception as e:
|
||
logger.warning(f"Failed to map state {state.screen_state_id}: {e}")
|
||
|
||
return state_to_node
|
||
|
||
def _get_state_time(self, state: ScreenState, fallback: float = 0) -> float:
|
||
"""Extraire le timestamp d'un ScreenState.
|
||
|
||
Priorité :
|
||
1. metadata['event_time'] (set par _create_screen_states)
|
||
2. metadata['shot_timestamp'] (set par le reprocessing)
|
||
3. state.timestamp converti en epoch si c'est un datetime
|
||
4. fallback
|
||
|
||
Note : event_time peut être 0.0 (timestamps relatifs), donc on
|
||
vérifie `is not None` et non `> 0`.
|
||
"""
|
||
if state.metadata:
|
||
et = state.metadata.get("event_time")
|
||
if et is not None:
|
||
return float(et)
|
||
st = state.metadata.get("shot_timestamp")
|
||
if st is not None:
|
||
return float(st)
|
||
if state.timestamp:
|
||
try:
|
||
return state.timestamp.timestamp()
|
||
except (AttributeError, OSError):
|
||
pass
|
||
return fallback
|
||
|
||
def _find_transition_events(
|
||
self,
|
||
current_state: ScreenState,
|
||
next_state: ScreenState,
|
||
events: List[Event],
|
||
) -> List[Event]:
|
||
"""
|
||
Trouver TOUS les événements entre deux états (pas juste le dernier).
|
||
|
||
Collecte toutes les actions utilisateur (clics, frappes, saisie texte)
|
||
qui se sont produites entre les timestamps des deux ScreenStates.
|
||
C'est essentiel pour le replay : une transition peut nécessiter
|
||
plusieurs actions (ex: Win+R → taper "notepad" → Entrée).
|
||
|
||
Timestamps : utilise _get_state_time() qui supporte plusieurs
|
||
sources (event_time, shot_timestamp, datetime).
|
||
|
||
Args:
|
||
current_state: État source
|
||
next_state: État cible
|
||
events: Tous les événements de la session
|
||
|
||
Returns:
|
||
Liste ordonnée (par timestamp) de tous les événements d'action
|
||
entre les deux états. Peut être vide.
|
||
"""
|
||
current_time = self._get_state_time(current_state, fallback=0)
|
||
next_time = self._get_state_time(next_state, fallback=float('inf'))
|
||
|
||
action_events = []
|
||
for event in events:
|
||
if current_time <= event.t < next_time:
|
||
if event.type in ["mouse_click", "key_press", "text_input"]:
|
||
# Filtrer les événements parasites (modificateurs seuls, texte vide)
|
||
if not _is_parasitic_event(event):
|
||
action_events.append(event)
|
||
|
||
return action_events
|
||
|
||
def _find_transition_event(
|
||
self,
|
||
current_state: ScreenState,
|
||
next_state: ScreenState,
|
||
events: List[Event]
|
||
) -> Optional[Event]:
|
||
"""
|
||
Trouver l'événement qui a causé la transition entre deux états.
|
||
|
||
DEPRECATED: Utiliser _find_transition_events() pour obtenir TOUS les événements.
|
||
Conservé pour rétrocompatibilité.
|
||
"""
|
||
all_events = self._find_transition_events(current_state, next_state, events)
|
||
return all_events[-1] if all_events else None
|
||
|
||
def _create_edge(
|
||
self,
|
||
from_node: str,
|
||
to_node: str,
|
||
event: Optional[Event],
|
||
edge_id: str,
|
||
source_node: Optional[WorkflowNode] = None,
|
||
target_node: Optional[WorkflowNode] = None,
|
||
all_events: Optional[List[Event]] = None,
|
||
screen_resolution: Tuple[int, int] = (1920, 1080),
|
||
source_state: Optional[ScreenState] = None,
|
||
) -> WorkflowEdge:
|
||
"""
|
||
Créer un WorkflowEdge depuis une transition observée.
|
||
|
||
Peuple les EdgeConstraints depuis le template du node source et
|
||
les PostConditions depuis le template du node cible.
|
||
|
||
Si plusieurs événements sont associés à la transition (all_events),
|
||
crée une action "compound" contenant toutes les étapes (steps),
|
||
ce qui permet au replay de reproduire fidèlement la séquence complète
|
||
(ex: Win+R → taper "notepad" → Entrée).
|
||
|
||
Args:
|
||
from_node: ID du node source
|
||
to_node: ID du node cible
|
||
event: Dernier événement de la transition (rétrocompatibilité)
|
||
edge_id: Identifiant de l'edge
|
||
source_node: Node source (pour extraire les pré-conditions)
|
||
target_node: Node cible (pour extraire les post-conditions)
|
||
all_events: TOUS les événements entre les deux états (si disponible)
|
||
screen_resolution: Résolution de référence pour normaliser les coordonnées
|
||
"""
|
||
# Si on a plusieurs événements, créer une action compound
|
||
events_to_use = all_events or ([event] if event else [])
|
||
|
||
# UIElements de l'écran source — sert à ancrer les clics sur un vrai
|
||
# élément UI (rôle, texte, bbox) plutôt que sur une coordonnée brute.
|
||
source_ui_elements = (
|
||
list(source_state.ui_elements)
|
||
if source_state and source_state.ui_elements
|
||
else []
|
||
)
|
||
|
||
if len(events_to_use) > 1:
|
||
action = self._build_compound_action(
|
||
events_to_use, screen_resolution,
|
||
source_ui_elements=source_ui_elements,
|
||
)
|
||
elif len(events_to_use) == 1:
|
||
action = self._build_single_action(
|
||
events_to_use[0],
|
||
source_ui_elements=source_ui_elements,
|
||
)
|
||
else:
|
||
action = Action(
|
||
type="unknown",
|
||
target=TargetSpec(
|
||
by_role="unknown",
|
||
selection_policy="first",
|
||
fallback_strategy="visual_similarity",
|
||
),
|
||
parameters={},
|
||
)
|
||
|
||
# ---------------------------------------------------------------
|
||
# Pré-conditions (EdgeConstraints) depuis le template du node source
|
||
# ---------------------------------------------------------------
|
||
constraints = self._build_edge_constraints(source_node)
|
||
|
||
# ---------------------------------------------------------------
|
||
# Post-conditions depuis le template du node cible
|
||
# ---------------------------------------------------------------
|
||
post_conditions = self._build_post_conditions(
|
||
to_node, source_node, target_node
|
||
)
|
||
|
||
# Créer l'edge
|
||
from core.models.workflow_graph import EdgeStats
|
||
|
||
# Metadata enrichie pour le diagnostic
|
||
edge_metadata = {
|
||
"auto_generated": True,
|
||
"event_count": len(events_to_use),
|
||
}
|
||
if events_to_use:
|
||
edge_metadata["created_from_event"] = events_to_use[-1].type
|
||
if len(events_to_use) > 1:
|
||
edge_metadata["event_types"] = [e.type for e in events_to_use]
|
||
else:
|
||
edge_metadata["created_from_event"] = None
|
||
|
||
return WorkflowEdge(
|
||
edge_id=edge_id,
|
||
from_node=from_node,
|
||
to_node=to_node,
|
||
action=action,
|
||
constraints=constraints,
|
||
post_conditions=post_conditions,
|
||
stats=EdgeStats(),
|
||
metadata=edge_metadata,
|
||
)
|
||
|
||
def _build_single_action(
|
||
self,
|
||
event: Event,
|
||
source_ui_elements: Optional[List[Any]] = None,
|
||
) -> Action:
|
||
"""
|
||
Construire une Action simple depuis un seul événement.
|
||
|
||
Pour un clic, si `source_ui_elements` est fourni, on tente d'ancrer
|
||
l'action sur l'UIElement le plus proche (par proximité spatiale).
|
||
Le TargetSpec devient alors discriminant :
|
||
- `by_role` = rôle sémantique de l'élément (ex: "primary_action")
|
||
- `by_text` = label détecté (ex: "Valider")
|
||
- `selection_policy` = "by_similarity" (laisse le matcher scorer)
|
||
- `context_hints["anchor_element_id"]` = traçabilité
|
||
- `context_hints["anchor_bbox"]` = invariant spatial debug
|
||
|
||
À défaut d'ancrage (pas d'UIElement ou clic hors de toute bbox
|
||
proche), on retombe sur `by_role="unknown_element"` (legacy).
|
||
"""
|
||
action_type = event.type
|
||
action_params: Dict[str, Any] = {}
|
||
target_spec: Optional[TargetSpec] = None
|
||
|
||
if action_type == "mouse_click":
|
||
action_params = {
|
||
"button": event.data.get("button", "left"),
|
||
"position": event.data.get("pos", [0, 0]),
|
||
"wait_after_ms": 500,
|
||
}
|
||
target_spec = self._build_click_target_spec(
|
||
event, source_ui_elements or []
|
||
)
|
||
|
||
elif action_type == "key_press":
|
||
action_params = {
|
||
"keys": event.data.get("keys", []),
|
||
"wait_after_ms": 200,
|
||
}
|
||
target_spec = TargetSpec(
|
||
by_role="keyboard_input",
|
||
selection_policy="first",
|
||
fallback_strategy="visual_similarity",
|
||
)
|
||
|
||
elif action_type == "text_input":
|
||
action_params = {
|
||
"text": event.data.get("text", ""),
|
||
"wait_after_ms": 300,
|
||
}
|
||
target_spec = TargetSpec(
|
||
by_role="text_field",
|
||
selection_policy="first",
|
||
fallback_strategy="visual_similarity",
|
||
)
|
||
else:
|
||
action_params = {}
|
||
target_spec = TargetSpec(
|
||
by_role="unknown",
|
||
selection_policy="first",
|
||
fallback_strategy="visual_similarity",
|
||
)
|
||
|
||
return Action(
|
||
type=action_type,
|
||
target=target_spec,
|
||
parameters=action_params,
|
||
)
|
||
|
||
def _build_click_target_spec(
|
||
self,
|
||
event: Event,
|
||
source_ui_elements: List[Any],
|
||
) -> TargetSpec:
|
||
"""
|
||
Construire un TargetSpec pour un clic, en essayant de l'ancrer à
|
||
un UIElement détecté sur l'écran source.
|
||
|
||
Retourne toujours un TargetSpec valide :
|
||
- ancré (role + text + context_hints) si un élément proche existe ;
|
||
- fallback `unknown_element` sinon (comportement historique).
|
||
"""
|
||
clicked = self._find_clicked_element(event, source_ui_elements)
|
||
|
||
if clicked is None:
|
||
return TargetSpec(
|
||
by_role="unknown_element",
|
||
selection_policy="first",
|
||
fallback_strategy="visual_similarity",
|
||
)
|
||
|
||
# Extraction défensive des attributs de l'élément.
|
||
role = getattr(clicked, "role", None) or "unknown_element"
|
||
label = getattr(clicked, "label", None) or None
|
||
element_id = getattr(clicked, "element_id", None)
|
||
|
||
# Contexte de traçabilité — `context_hints` est le seul dict libre
|
||
# disponible dans TargetSpec (pas de champ `metadata` dédié).
|
||
context_hints: Dict[str, Any] = {}
|
||
if element_id:
|
||
context_hints["anchor_element_id"] = str(element_id)
|
||
|
||
bbox = getattr(clicked, "bbox", None)
|
||
if bbox is not None:
|
||
try:
|
||
context_hints["anchor_bbox"] = {
|
||
"x": int(getattr(bbox, "x", bbox[0])),
|
||
"y": int(getattr(bbox, "y", bbox[1])),
|
||
"width": int(getattr(bbox, "width", bbox[2])),
|
||
"height": int(getattr(bbox, "height", bbox[3])),
|
||
}
|
||
except (AttributeError, IndexError, TypeError):
|
||
pass
|
||
|
||
# Center (utile comme ancre de fallback quand le matcher échoue)
|
||
center = getattr(clicked, "center", None)
|
||
if center is not None:
|
||
try:
|
||
context_hints["anchor_center"] = [int(center[0]), int(center[1])]
|
||
except (IndexError, TypeError):
|
||
pass
|
||
|
||
return TargetSpec(
|
||
by_role=role,
|
||
by_text=label,
|
||
selection_policy="by_similarity",
|
||
fallback_strategy="visual_similarity",
|
||
context_hints=context_hints,
|
||
)
|
||
|
||
def _build_compound_action(
|
||
self,
|
||
events: List[Event],
|
||
screen_resolution: Tuple[int, int] = (1920, 1080),
|
||
source_ui_elements: Optional[List[Any]] = None,
|
||
) -> Action:
|
||
"""
|
||
Construire une Action compound (multi-étapes) depuis plusieurs événements.
|
||
|
||
Quand l'utilisateur fait plusieurs actions entre deux changements d'écran
|
||
(ex: Win+R → taper "notepad" → Entrée), on les regroupe en une seule
|
||
action compound avec une liste de steps ordonnés.
|
||
|
||
Des waits sont insérés automatiquement entre les étapes quand le délai
|
||
réel dépasse 500ms (plafonné à 5000ms).
|
||
|
||
Args:
|
||
events: Liste ordonnée d'événements (>= 2)
|
||
screen_resolution: Résolution de référence (largeur, hauteur)
|
||
|
||
Returns:
|
||
Action de type "compound" avec steps dans parameters
|
||
"""
|
||
steps = []
|
||
ref_w, ref_h = screen_resolution
|
||
|
||
for i, event in enumerate(events):
|
||
# Insérer un wait si le délai entre deux événements est > 500ms
|
||
if i > 0:
|
||
delta_ms = int((event.t - events[i - 1].t) * 1000)
|
||
if delta_ms > 500:
|
||
steps.append({
|
||
"type": "wait",
|
||
"duration_ms": min(delta_ms, 5000),
|
||
})
|
||
|
||
if event.type == "mouse_click":
|
||
pos = event.data.get("pos", [0, 0])
|
||
steps.append({
|
||
"type": "mouse_click",
|
||
"position": list(pos),
|
||
"x_pct": round(pos[0] / ref_w, 6) if ref_w > 0 else 0.0,
|
||
"y_pct": round(pos[1] / ref_h, 6) if ref_h > 0 else 0.0,
|
||
"button": event.data.get("button", "left"),
|
||
"ref_width": ref_w,
|
||
"ref_height": ref_h,
|
||
})
|
||
elif event.type == "text_input":
|
||
steps.append({
|
||
"type": "text_input",
|
||
"text": event.data.get("text", ""),
|
||
})
|
||
elif event.type == "key_press":
|
||
keys = event.data.get("keys", [])
|
||
if keys:
|
||
steps.append({
|
||
"type": "key_press",
|
||
"keys": keys,
|
||
})
|
||
else:
|
||
# Type inconnu : stocker quand même pour ne rien perdre
|
||
steps.append({
|
||
"type": event.type,
|
||
"data": dict(event.data) if event.data else {},
|
||
})
|
||
|
||
# ---------------------------------------------------------------
|
||
# Nettoyage des steps parasites :
|
||
# 1. Supprimer les modificateurs seuls (ctrl, alt, shift, etc.)
|
||
# 2. Fusionner les text_input consécutifs (ex: "No","m",":"," " → "Nom: ")
|
||
# 3. Dédupliquer les key_combo consécutifs identiques
|
||
# 4. Garantir un wait minimum de 300ms entre chaque step
|
||
# ---------------------------------------------------------------
|
||
steps = _filter_modifier_only_steps(steps)
|
||
steps = _merge_consecutive_text_steps(steps)
|
||
steps = _dedup_consecutive_combos(steps)
|
||
steps = _ensure_min_waits(steps)
|
||
|
||
# La cible du compound = cible de la dernière action (le clic final, etc.)
|
||
last_event = events[-1]
|
||
if last_event.type == "mouse_click":
|
||
# On tente d'ancrer le clic final aux UIElements détectés,
|
||
# comme dans _build_single_action.
|
||
target_spec = self._build_click_target_spec(
|
||
last_event, source_ui_elements or []
|
||
)
|
||
elif last_event.type == "text_input":
|
||
target_spec = TargetSpec(
|
||
by_role="text_field",
|
||
selection_policy="first",
|
||
fallback_strategy="visual_similarity",
|
||
)
|
||
elif last_event.type == "key_press":
|
||
target_spec = TargetSpec(
|
||
by_role="keyboard_input",
|
||
selection_policy="first",
|
||
fallback_strategy="visual_similarity",
|
||
)
|
||
else:
|
||
target_spec = TargetSpec(
|
||
by_role="unknown",
|
||
selection_policy="first",
|
||
fallback_strategy="visual_similarity",
|
||
)
|
||
|
||
return Action(
|
||
type="compound",
|
||
target=target_spec,
|
||
parameters={
|
||
"steps": steps,
|
||
"step_count": len(steps),
|
||
"ref_width": ref_w,
|
||
"ref_height": ref_h,
|
||
},
|
||
)
|
||
|
||
def _build_edge_constraints(
|
||
self,
|
||
source_node: Optional[WorkflowNode],
|
||
) -> EdgeConstraints:
|
||
"""
|
||
Construire les EdgeConstraints (pré-conditions) depuis le node source.
|
||
|
||
Extrait du ScreenTemplate du node source :
|
||
- required_window_title : titre de fenêtre attendu
|
||
- required_app_name : nom de l'application
|
||
- min_source_similarity : seuil de similarité embedding
|
||
- window / text : contraintes WindowConstraint et TextConstraint
|
||
"""
|
||
if not source_node or not source_node.template:
|
||
return EdgeConstraints(
|
||
pre_conditions={},
|
||
required_confidence=0.8,
|
||
max_wait_time_ms=5000,
|
||
)
|
||
|
||
tpl = source_node.template
|
||
|
||
# Extraire le titre de fenêtre depuis le WindowConstraint
|
||
req_title = None
|
||
req_app = None
|
||
window_constraint = None
|
||
|
||
if tpl.window:
|
||
req_title = tpl.window.title_contains or tpl.window.title_pattern
|
||
req_app = tpl.window.process_name
|
||
# Copier la contrainte de fenêtre complète
|
||
window_constraint = WindowConstraint(
|
||
title_pattern=tpl.window.title_pattern,
|
||
title_contains=tpl.window.title_contains,
|
||
process_name=tpl.window.process_name,
|
||
)
|
||
|
||
# Extraire la contrainte de texte (required_texts du template)
|
||
text_constraint = None
|
||
if tpl.text and tpl.text.required_texts:
|
||
# Limiter à 5 textes les plus significatifs (>= 3 car, <= 100 car)
|
||
filtered = [
|
||
t for t in tpl.text.required_texts
|
||
if 3 <= len(t) <= 100
|
||
][:5]
|
||
if filtered:
|
||
text_constraint = TextConstraint(
|
||
required_texts=filtered,
|
||
)
|
||
|
||
# Seuil de similarité depuis l'embedding prototype
|
||
min_sim = 0.80
|
||
if tpl.embedding and tpl.embedding.min_cosine_similarity:
|
||
min_sim = tpl.embedding.min_cosine_similarity
|
||
|
||
return EdgeConstraints(
|
||
pre_conditions={},
|
||
required_confidence=min_sim,
|
||
max_wait_time_ms=5000,
|
||
window=window_constraint,
|
||
text=text_constraint,
|
||
min_source_similarity=min_sim,
|
||
required_app_name=req_app,
|
||
required_window_title=req_title,
|
||
)
|
||
|
||
def _build_post_conditions(
|
||
self,
|
||
to_node_id: str,
|
||
source_node: Optional[WorkflowNode],
|
||
target_node: Optional[WorkflowNode],
|
||
) -> PostConditions:
|
||
"""
|
||
Construire les PostConditions depuis le node cible.
|
||
|
||
Peuple :
|
||
- expected_window_title / expected_app_name depuis le template cible
|
||
- success checks : textes attendus (required_texts du template cible)
|
||
+ window_title_contains si le titre change
|
||
- fail_fast checks : patterns d'erreur courants (forbidden_texts)
|
||
- min_target_similarity depuis l'embedding prototype cible
|
||
"""
|
||
if not target_node or not target_node.template:
|
||
return PostConditions(
|
||
expected_node=to_node_id,
|
||
window_change_expected=False,
|
||
new_ui_elements_expected=[],
|
||
timeout_ms=10000,
|
||
)
|
||
|
||
tpl = target_node.template
|
||
|
||
# Extraire les infos du node cible
|
||
expected_title = None
|
||
expected_app = None
|
||
if tpl.window:
|
||
expected_title = tpl.window.title_contains or tpl.window.title_pattern
|
||
expected_app = tpl.window.process_name
|
||
|
||
# Seuil de similarité cible
|
||
min_sim = 0.80
|
||
if tpl.embedding and tpl.embedding.min_cosine_similarity:
|
||
min_sim = tpl.embedding.min_cosine_similarity
|
||
|
||
# Détecter si le titre de fenêtre change entre source et cible
|
||
window_change = False
|
||
if source_node and source_node.template and source_node.template.window:
|
||
src_title = source_node.template.window.title_contains or source_node.template.window.title_pattern
|
||
if src_title and expected_title and src_title != expected_title:
|
||
window_change = True
|
||
|
||
# --- Construire les checks de succès ---
|
||
success_checks = []
|
||
|
||
# Check sur le titre de fenêtre cible (si défini)
|
||
if expected_title:
|
||
success_checks.append(
|
||
PostConditionCheck(kind="window_title_contains", value=expected_title)
|
||
)
|
||
|
||
# Checks sur les textes attendus dans l'écran cible
|
||
if tpl.text and tpl.text.required_texts:
|
||
for text in tpl.text.required_texts[:5]:
|
||
if 3 <= len(text) <= 100:
|
||
success_checks.append(
|
||
PostConditionCheck(kind="text_present", value=text)
|
||
)
|
||
|
||
# --- Construire les checks fail_fast (détection d'erreurs) ---
|
||
fail_fast_checks = []
|
||
for pattern in self._ERROR_PATTERNS:
|
||
fail_fast_checks.append(
|
||
PostConditionCheck(kind="text_present", value=pattern)
|
||
)
|
||
|
||
return PostConditions(
|
||
success_mode="all",
|
||
timeout_ms=10000,
|
||
poll_ms=200,
|
||
success=success_checks,
|
||
fail_fast=fail_fast_checks,
|
||
retries=2,
|
||
backoff_ms=150,
|
||
expected_window_title=expected_title,
|
||
expected_app_name=expected_app,
|
||
min_target_similarity=min_sim,
|
||
expected_node=to_node_id,
|
||
window_change_expected=window_change,
|
||
new_ui_elements_expected=[],
|
||
)
|
||
|
||
|
||
def main():
|
||
"""Point d'entrée pour tests manuels."""
|
||
logging.basicConfig(
|
||
level=logging.INFO,
|
||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||
)
|
||
|
||
builder = GraphBuilder(min_pattern_repetitions=3)
|
||
logger.info(f"GraphBuilder initialized: {builder}")
|
||
logger.info("Ready to build workflows from sessions")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|