feat(p1): persist workflows and semantic learning artifacts
This commit is contained in:
Dom
@@ -687,6 +687,7 @@ def _extract_required_apps_from_events(
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- launch_result_target: dict optionnel (vrai clic SearchHost -> app)
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"""
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app_counts: Dict[str, int] = defaultdict(int)
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app_titles: Dict[str, List[str]] = defaultdict(list)
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first_app = None
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first_window_title = None
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@@ -702,6 +703,8 @@ def _extract_required_apps_from_events(
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title = to_info.get("title", "")
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if app_name:
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app_counts[app_name] += 1
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if title:
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app_titles[app_name].append(title)
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if first_app is None and app_name.lower() not in _SETUP_IGNORE_APPS:
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first_app = app_name
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first_window_title = title
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@@ -741,6 +744,10 @@ def _extract_required_apps_from_events(
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"primary_launch_cmd": primary_launch_cmd,
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"first_window_title": first_window_title or "",
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"apps": dict(app_counts),
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"has_neutral_window_title": any(
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_is_neutral_window_title(title)
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for title in app_titles.get(primary_app, [])
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),
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}
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if start_menu_target:
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result["start_menu_target"] = start_menu_target
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@@ -927,6 +934,9 @@ def _extract_required_apps_from_workflow(workflow) -> Dict[str, Any]:
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"primary_launch_cmd": primary_launch_cmd,
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"first_window_title": first_title,
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"apps": {},
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"has_neutral_window_title": any(
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_is_neutral_window_title(title) for title in window_titles
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),
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"source_session_id": source_session_id,
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"machine_id": machine_id,
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}
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@@ -1113,6 +1123,50 @@ def _generate_run_dialog_setup_actions(
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},
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]
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needs_fresh_notepad_document = (
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primary_app.lower() == "notepad.exe"
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and (
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bool(app_info.get("has_neutral_window_title"))
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or _is_neutral_window_title(first_title)
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)
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)
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if needs_fresh_notepad_document:
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if title_patterns or first_title:
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actions.append({
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"action_id": f"act_{setup_id_prefix}_verify_before_fresh_document",
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"type": "verify_screen",
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"expected_node": "setup_initial_before_fresh_document",
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"timeout_ms": 5000,
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"_setup_phase": True,
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"_setup_step": "verify_app_ready_before_fresh_document",
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"_setup_strategy": "run_dialog",
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"expected_window_title_contains": title_patterns or [first_title],
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"intention": (
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"vérifier que Bloc-notes est la scène active avant "
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"d'ouvrir un document vierge"
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),
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})
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actions.extend([
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{
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"action_id": f"act_{setup_id_prefix}_ensure_fresh_document",
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"type": "key_combo",
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"keys": ["ctrl", "n"],
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"_setup_phase": True,
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"_setup_step": "ensure_fresh_document",
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"_setup_strategy": "run_dialog",
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"expected_window_before": first_title,
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"intention": "ouvrir un document Bloc-notes vierge non nommé",
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},
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{
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"action_id": f"act_{setup_id_prefix}_wait_fresh_document",
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"type": "wait",
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"duration_ms": 400,
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"_setup_phase": True,
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"_setup_step": "wait_fresh_document",
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"_setup_strategy": "run_dialog",
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},
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])
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if title_patterns or first_title:
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actions.append({
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"action_id": f"act_{setup_id_prefix}_verify",
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@@ -1688,6 +1742,63 @@ def _is_learned_workflow(workflow) -> bool:
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return has_prototype
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_TARGET_SEMANTIC_KEYS = (
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"by_text",
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"by_role",
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"anchor_id",
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"target_text",
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"ocr_description",
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"description",
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"vlm_description",
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"anchor_image_base64",
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"by_text_source",
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"window_title",
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"anchor_bbox",
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"original_size",
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)
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def _first_non_empty_text(*values: Any) -> str:
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for value in values:
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text = str(value or "").strip()
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if text and text.casefold() not in {"none", "null"}:
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return text
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return ""
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def _target_attr(target: Any, key: str, default: Any = None) -> Any:
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if isinstance(target, dict):
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return target.get(key, default)
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return getattr(target, key, default)
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def _copy_semantic_target_fields(
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target_spec: Dict[str, Any],
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*sources: Optional[Dict[str, Any]],
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) -> None:
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for source in sources:
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if not isinstance(source, dict):
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continue
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for key in _TARGET_SEMANTIC_KEYS:
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value = source.get(key)
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if value and not target_spec.get(key):
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target_spec[key] = value
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if not target_spec.get("by_text"):
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target_text = _first_non_empty_text(target_spec.get("target_text"))
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if target_text:
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target_spec["by_text"] = target_text
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target_spec.setdefault("by_text_source", "visual_anchor")
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if not target_spec.get("vlm_description"):
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description = _first_non_empty_text(
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target_spec.get("description"),
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target_spec.get("ocr_description"),
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)
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if description:
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target_spec["vlm_description"] = description
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def _edge_to_normalized_actions(edge, params: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""
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Convertir un WorkflowEdge en liste d'actions normalisées pour l'Agent V1.
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@@ -1705,8 +1816,9 @@ def _edge_to_normalized_actions(edge, params: Dict[str, Any]) -> List[Dict[str,
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# Extraire les coordonnées normalisées depuis TargetSpec.by_position
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x_pct = 0.0
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y_pct = 0.0
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if target and target.by_position:
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px, py = target.by_position
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by_position = _target_attr(target, "by_position")
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if target and by_position:
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px, py = by_position
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if px <= 1.0 and py <= 1.0:
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x_pct = px
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y_pct = py
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@@ -1769,10 +1881,15 @@ def _edge_to_normalized_actions(edge, params: Dict[str, Any]) -> List[Dict[str,
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elif action_type == "extract_table":
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normalized["type"] = "extract_table"
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normalized["parameters"] = {
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"output_var": action_params.get("output_var", "table_rows"),
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"output_var": (
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action_params.get("variable_name")
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or action_params.get("output_var")
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or "table_rows"
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),
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"pattern": action_params.get("pattern"),
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"limit": action_params.get("limit"),
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"region": action_params.get("region"),
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"engine": action_params.get("engine", "easyocr"),
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}
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return [normalized]
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@@ -1833,14 +1950,33 @@ def _edge_to_normalized_actions(edge, params: Dict[str, Any]) -> List[Dict[str,
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# Ajouter le target_spec complet pour la résolution visuelle
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target_spec = {}
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if target and target.by_role:
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target_spec["by_role"] = target.by_role
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normalized["target_role"] = target.by_role # Compat debug
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if target and target.by_text:
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target_spec["by_text"] = target.by_text
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normalized["target_text"] = target.by_text # Compat debug
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if target and hasattr(target, 'context_hints') and target.context_hints:
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target_spec["context_hints"] = target.context_hints
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by_role = _target_attr(target, "by_role", "")
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by_text = _target_attr(target, "by_text", "")
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context_hints = _target_attr(target, "context_hints", {}) or {}
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if target and by_role:
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target_spec["by_role"] = by_role
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normalized["target_role"] = by_role # Compat debug
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if target and by_text:
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target_spec["by_text"] = by_text
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normalized["target_text"] = by_text # Compat debug
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if target and context_hints:
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target_spec["context_hints"] = context_hints
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_copy_semantic_target_fields(
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target_spec,
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action_params,
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action_params.get("target_spec") if isinstance(action_params, dict) else None,
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context_hints,
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)
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semantic_label = _first_non_empty_text(
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target_spec.get("by_text"),
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target_spec.get("target_text"),
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target_spec.get("description"),
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target_spec.get("ocr_description"),
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target_spec.get("vlm_description"),
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)
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if semantic_label:
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normalized.setdefault("target_text", target_spec.get("target_text") or semantic_label)
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normalized.setdefault("target_description", semantic_label)
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if target_spec:
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normalized["target_spec"] = target_spec
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normalized["visual_mode"] = True # Signal à l'agent d'utiliser la résolution visuelle
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@@ -2004,6 +2140,7 @@ def _handle_extract_table_action(
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output_var : nom de variable runtime (default "table_rows")
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pattern : regex à matcher sur chaque token OCR (ex : r"^25\\d{6}$")
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limit : nb max d'entrées à retourner
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engine : easyocr (défaut) ou tesseract/digits/ipp pour chiffres
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region : (x, y, w, h) en pixels pour cropper avant OCR
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(None = image entière)
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@@ -2014,6 +2151,7 @@ def _handle_extract_table_action(
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output_var = (params.get("output_var") or params.get("variable_name") or "table_rows").strip()
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pattern = params.get("pattern") or None
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limit = params.get("limit")
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engine = params.get("engine") or "easyocr"
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region = params.get("region") or None
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if isinstance(limit, str):
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try:
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@@ -2058,6 +2196,7 @@ def _handle_extract_table_action(
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region=tuple(region) if region else None,
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pattern=pattern,
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limit=limit,
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engine=engine,
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)
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except Exception as e:
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logger.warning(
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@@ -2071,8 +2210,8 @@ def _handle_extract_table_action(
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replay_state.setdefault("variables", {})[output_var] = rows
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logger.info(
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"extract_table → variable '%s' (%d entrées, pattern=%r, limit=%s) replay %s",
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output_var, len(rows), pattern, limit, replay_state.get("replay_id", "?"),
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"extract_table → variable '%s' (%d entrées, pattern=%r, limit=%s, engine=%s) replay %s",
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output_var, len(rows), pattern, limit, engine, replay_state.get("replay_id", "?"),
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)
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return bool(rows)
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@@ -2410,6 +2549,29 @@ def _expand_compound_steps(
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action["x_pct"] = step.get("x_pct", 0.0)
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action["y_pct"] = step.get("y_pct", 0.0)
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action["button"] = step.get("button", "left")
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target_spec: Dict[str, Any] = {}
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_copy_semantic_target_fields(
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target_spec,
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step,
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step.get("target_spec") if isinstance(step, dict) else None,
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step.get("visual_anchor") if isinstance(step, dict) else None,
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)
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semantic_label = _first_non_empty_text(
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target_spec.get("by_text"),
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target_spec.get("target_text"),
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target_spec.get("description"),
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target_spec.get("ocr_description"),
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target_spec.get("vlm_description"),
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)
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if semantic_label:
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action.setdefault(
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"target_text",
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target_spec.get("target_text") or semantic_label,
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)
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action.setdefault("target_description", semantic_label)
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if target_spec:
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action["target_spec"] = target_spec
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action["visual_mode"] = True
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else:
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logger.debug(f"Step compound inconnu : {step_type}")
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@@ -2659,6 +2821,8 @@ def _create_replay_state(
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a_copy = {
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"action_id": a.get("action_id"),
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"type": a.get("type"),
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"keys": a.get("keys"),
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"button": a.get("button"),
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"x_pct": a.get("x_pct"),
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"y_pct": a.get("y_pct"),
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# Contrôle strict des étapes (Dom, matin 10 avril 2026)
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@@ -2667,6 +2831,9 @@ def _create_replay_state(
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"expected_window_title": a.get("expected_window_title", ""),
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# Contexte métier utile pour logs et apprentissage
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"intention": a.get("intention", ""),
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"target_text": a.get("target_text", ""),
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"target_description": a.get("target_description", ""),
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"description": a.get("description", ""),
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}
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ts = a.get("target_spec")
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if isinstance(ts, dict):
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@@ -43,6 +43,22 @@ logger = logging.getLogger(__name__)
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_MEMORY_SINGLETON: Optional[Any] = None
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_MEMORY_DISABLED = False
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_GENERIC_BUTTON_TEXTS = {
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"annuler",
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"cancel",
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"enregistrer",
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"non",
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"no",
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"ok",
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"oui",
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"ouvrir",
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"open",
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"remplacer",
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"replace",
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"save",
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"yes",
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}
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def get_memory_store():
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"""Retourne le `TargetMemoryStore` partagé, ou None si indisponible.
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@@ -91,6 +107,44 @@ def _norm_text(s: str) -> str:
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return " ".join(s.split())
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def _memory_lookup_skip_reason(target_spec: Dict[str, Any]) -> str:
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"""Retourne la raison pour laquelle la mémoire ne doit pas court-circuiter.
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Les clics qui changent de fenêtre doivent être résolus visuellement à
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l'instant T : une coordonnée apprise peut être une bonne piste, mais pas
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une décision finale. Pour les boutons très génériques, on exige au moins
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un contexte de fenêtre/interaction dans la clé mémoire afin d'éviter les
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collisions entre « Enregistrer », « OK », « Oui », etc.
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"""
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if not isinstance(target_spec, dict):
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return ""
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hints = target_spec.get("context_hints") or {}
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if bool(hints.get("requires_window_transition")):
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return "window_transition_requires_visual_confirmation"
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button_text = _norm_text(str(target_spec.get("by_text") or ""))
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if button_text not in _GENERIC_BUTTON_TEXTS:
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return ""
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before = (
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hints.get("expected_window_before")
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or hints.get("button_expected_before_window")
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or hints.get("window_title")
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or target_spec.get("window_title")
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)
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after = (
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hints.get("expected_window_after")
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or hints.get("button_expected_after_window")
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or hints.get("expected_after_window")
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)
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interaction = hints.get("interaction") or hints.get("foreground_dialog_id")
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role = target_spec.get("by_role")
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if not (before and role and (after or interaction)):
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return "generic_button_missing_context"
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return ""
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def compute_screen_sig(window_title: str) -> str:
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"""Calcule la signature d'écran V4 à partir du titre de fenêtre.
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@@ -203,6 +257,11 @@ def memory_lookup(
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(resolved, method, x_pct, y_pct, score, ...) si une entrée fiable
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est trouvée. None sinon.
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"""
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skip_reason = _memory_lookup_skip_reason(target_spec)
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if skip_reason:
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logger.info("memory_lookup SKIP : %s", skip_reason)
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return None
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store = get_memory_store()
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if store is None:
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return None
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@@ -988,7 +988,9 @@ def _resolve_by_grounding(
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{"role": "user", "content": prompt, "images": [shot_b64]},
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],
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"stream": False,
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"options": {"temperature": 0.1, "num_predict": 100},
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# D5-v3a (2026-05-25) num_ctx=4096 explicite : éviter fuite 8192
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# via Modelfile qwen2.5vl:7b-rpa (PARAMETER num_ctx 8192).
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"options": {"temperature": 0.1, "num_predict": 100, "num_ctx": 4096},
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}, timeout=60)
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content = resp.json().get("message", {}).get("content", "")
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except Exception as e:
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@@ -1016,7 +1018,9 @@ def _resolve_by_grounding(
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{"role": "user", "content": prompt_mi, "images": [shot_b64, anchor_b64]},
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],
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"stream": False,
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"options": {"temperature": 0.1, "num_predict": 50},
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# D5-v3a (2026-05-25) num_ctx=4096 explicite : éviter fuite
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# 8192 via Modelfile qwen2.5vl:7b-rpa.
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"options": {"temperature": 0.1, "num_predict": 50, "num_ctx": 4096},
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}, timeout=60)
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content2 = resp2.json().get("message", {}).get("content", "")
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elapsed = time.time() - t0
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@@ -2482,10 +2486,15 @@ def _get_validation_ocr_reader():
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if _VALIDATION_OCR_READER is None and not _VALIDATION_OCR_FAILED:
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try:
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import easyocr # type: ignore
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from core.llm.ocr_extractor import easyocr_gpu_enabled
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gpu = easyocr_gpu_enabled(default=False)
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_VALIDATION_OCR_READER = easyocr.Reader(
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['fr', 'en'], gpu=True, verbose=False
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['fr', 'en'], gpu=gpu, verbose=False
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)
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logger.info(
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"[REPLAY] EasyOCR validator chargé (fr+en, %s)",
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"GPU" if gpu else "CPU",
|
||||
)
|
||||
logger.info("[REPLAY] EasyOCR validator chargé (fr+en, GPU)")
|
||||
except Exception as e:
|
||||
logger.warning("[REPLAY] EasyOCR validator indisponible (%s) — pré-check désactivé", e)
|
||||
_VALIDATION_OCR_FAILED = True
|
||||
@@ -2507,8 +2516,15 @@ def _normalize_for_match(s: str) -> str:
|
||||
def _text_match_fuzzy(expected: str, observed: str, min_token_ratio: float = 0.60) -> bool:
|
||||
"""Match tolérant aux imperfections OCR.
|
||||
|
||||
1. Substring exacte → match.
|
||||
2. Sinon : split en tokens ≥3 caractères, retourne True si au moins
|
||||
1. Substring exacte (expected ⊂ observed) → match.
|
||||
2. C-P1 (2026-05-25) : tolérance préfixe — observed est un préfixe
|
||||
d'expected avec longueur ≥ 4 chars ET ≥ 50% de la longueur expected.
|
||||
Couvre le cas OCR partiel "Enregi" / "Enregistrer" (6 chars sur 11
|
||||
= 54%, préfixe strict) où l'OCR coupe une ligne longue. Garde-fous :
|
||||
- len ≥ 4 évite "Sa" / "Save" (faux positif probable)
|
||||
- 50% évite "Bo" / "Bouton" et "Enregi" / "Enregistrer sous" (qui
|
||||
serait 37%, rejet correct).
|
||||
3. Sinon : split en tokens ≥3 caractères, retourne True si au moins
|
||||
`min_token_ratio` des tokens attendus apparaissent dans observed.
|
||||
Ex : "Coller ou saisir le dossier patient" → tokens
|
||||
['coller', 'saisir', 'dossier', 'patient'] ; si OCR voit "u saisir
|
||||
@@ -2523,6 +2539,13 @@ def _text_match_fuzzy(expected: str, observed: str, min_token_ratio: float = 0.6
|
||||
return True
|
||||
if nexp in nobs:
|
||||
return True
|
||||
# C-P1 : tolérance préfixe sur OCR partiel
|
||||
if (
|
||||
len(nobs) >= 4
|
||||
and len(nobs) * 2 >= len(nexp)
|
||||
and nexp.startswith(nobs)
|
||||
):
|
||||
return True
|
||||
tokens = [t for t in nexp.split() if len(t) >= 3]
|
||||
if not tokens:
|
||||
return False
|
||||
@@ -3010,7 +3033,9 @@ def _locate_popup_button(
|
||||
"model": "qwen2.5vl:7b",
|
||||
"messages": [{"role": "user", "content": prompt, "images": [screenshot_b64]}],
|
||||
"stream": False,
|
||||
"options": {"temperature": 0.1, "num_predict": 50},
|
||||
# D5-v3a (2026-05-25) num_ctx=4096 explicite : éviter fuite 8192
|
||||
# via Modelfile qwen2.5vl:7b/-rpa (PARAMETER num_ctx 8192).
|
||||
"options": {"temperature": 0.1, "num_predict": 50, "num_ctx": 4096},
|
||||
},
|
||||
timeout=15,
|
||||
)
|
||||
|
||||
@@ -126,6 +126,25 @@ def build_workflow_replay(
|
||||
"x_relative": "",
|
||||
},
|
||||
}
|
||||
_merge_semantic_target_fields(
|
||||
step_action["target_spec"],
|
||||
target,
|
||||
params,
|
||||
step,
|
||||
)
|
||||
target_label = _first_non_empty_text(
|
||||
step_action["target_spec"].get("by_text"),
|
||||
step_action["target_spec"].get("target_text"),
|
||||
step_action["target_spec"].get("description"),
|
||||
step_action["target_spec"].get("ocr_description"),
|
||||
step_action["target_spec"].get("vlm_description"),
|
||||
)
|
||||
if target_label:
|
||||
step_action.setdefault(
|
||||
"target_text",
|
||||
step_action["target_spec"].get("target_text") or target_label,
|
||||
)
|
||||
step_action.setdefault("target_description", target_label)
|
||||
# Ajouter le crop anchor si disponible
|
||||
_attach_anchor(step_action, step, session_dir)
|
||||
|
||||
@@ -171,6 +190,58 @@ def _map_action_type(step_type: str) -> str:
|
||||
return mapping.get(step_type, step_type)
|
||||
|
||||
|
||||
_TARGET_SEMANTIC_KEYS = (
|
||||
"by_text",
|
||||
"by_role",
|
||||
"anchor_id",
|
||||
"target_text",
|
||||
"ocr_description",
|
||||
"description",
|
||||
"vlm_description",
|
||||
"by_text_source",
|
||||
"anchor_bbox",
|
||||
"original_size",
|
||||
)
|
||||
|
||||
|
||||
def _first_non_empty_text(*values: Any) -> str:
|
||||
for value in values:
|
||||
text = str(value or "").strip()
|
||||
if text and text.casefold() not in {"none", "null"}:
|
||||
return text
|
||||
return ""
|
||||
|
||||
|
||||
def _merge_semantic_target_fields(
|
||||
target_spec: Dict[str, Any],
|
||||
*sources: Dict[str, Any],
|
||||
) -> None:
|
||||
for source in sources:
|
||||
if not isinstance(source, dict):
|
||||
continue
|
||||
visual_anchor = source.get("visual_anchor") or {}
|
||||
if isinstance(visual_anchor, dict):
|
||||
_merge_semantic_target_fields(target_spec, visual_anchor)
|
||||
for key in _TARGET_SEMANTIC_KEYS:
|
||||
value = source.get(key)
|
||||
if value and not target_spec.get(key):
|
||||
target_spec[key] = value
|
||||
|
||||
if not target_spec.get("by_text"):
|
||||
target_text = _first_non_empty_text(target_spec.get("target_text"))
|
||||
if target_text:
|
||||
target_spec["by_text"] = target_text
|
||||
target_spec.setdefault("by_text_source", "visual_anchor")
|
||||
|
||||
if not target_spec.get("vlm_description"):
|
||||
description = _first_non_empty_text(
|
||||
target_spec.get("description"),
|
||||
target_spec.get("ocr_description"),
|
||||
)
|
||||
if description:
|
||||
target_spec["vlm_description"] = description
|
||||
|
||||
|
||||
def _attach_anchor(action: dict, step: dict, session_dir: str) -> None:
|
||||
"""Attacher le crop anchor au target_spec si disponible."""
|
||||
import base64
|
||||
|
||||
518
core/competences/persist.py
Normal file
518
core/competences/persist.py
Normal file
@@ -0,0 +1,518 @@
|
||||
"""Helpers de persistance pour les competences candidates (POC Lea-first).
|
||||
|
||||
Couvre :
|
||||
- slugification stricte (ASCII, regex ^[a-z][a-z0-9_]{2,79}$)
|
||||
- detection PII (regex MVP, paramétrable)
|
||||
- atomic write + rename POSIX
|
||||
- append-only audit JSONL avec verrou fcntl
|
||||
- detection de collision cross-states (candidate / supervised / stable)
|
||||
|
||||
Le module est volontairement minimal : il n'importe pas FastAPI ni le pipeline
|
||||
VWB, il ne fait pas de logique reseau. Il est consomme depuis
|
||||
``agent_v0/server_v1/api_stream.py`` endpoint ``/persist``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import time
|
||||
import unicodedata
|
||||
import uuid
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any, Iterable, Optional
|
||||
|
||||
try: # pragma: no cover - dependance externe deja presente dans le projet
|
||||
import yaml
|
||||
except ImportError as exc: # pragma: no cover
|
||||
raise RuntimeError("PyYAML est requis pour core.competences.persist") from exc
|
||||
|
||||
try:
|
||||
import fcntl # POSIX uniquement
|
||||
_HAS_FCNTL = True
|
||||
except ImportError: # pragma: no cover - Windows
|
||||
fcntl = None # type: ignore[assignment]
|
||||
_HAS_FCNTL = False
|
||||
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parents[2]
|
||||
COMPETENCES_ROOT = REPO_ROOT / "data" / "competences"
|
||||
CANDIDATE_DIR = COMPETENCES_ROOT / "candidate"
|
||||
SUPERVISED_DIR = COMPETENCES_ROOT / "supervised"
|
||||
STABLE_DIR = COMPETENCES_ROOT / "stable"
|
||||
AUDIT_PATH = COMPETENCES_ROOT / "persist_audit.jsonl"
|
||||
INCOMPLETE_PATH = COMPETENCES_ROOT / "incomplete_learnings.jsonl"
|
||||
|
||||
# Pattern final autorise pour un slug de competence.
|
||||
SLUG_PATTERN = re.compile(r"^[a-z][a-z0-9_]{2,79}$")
|
||||
|
||||
# Detection PII MVP — regex parametrable via env RPA_PII_PATTERNS
|
||||
# (separes par |). Defaut : couvre patterns simples (IPP, NIR, email, tel FR).
|
||||
_DEFAULT_PII_PATTERNS = [
|
||||
r"\b\d{13}\b", # NIR FR (13 chiffres)
|
||||
r"\b\d{15}\b", # NIR FR + cle
|
||||
r"\bIPP[\s:_-]*\d{6,}\b", # IPP hospitalier
|
||||
r"[\w\.-]+@[\w\.-]+\.\w{2,}", # email
|
||||
r"\b0[1-9](?:[ .-]?\d{2}){4}\b", # telephone FR
|
||||
]
|
||||
|
||||
|
||||
def _compile_pii_patterns() -> list[re.Pattern[str]]:
|
||||
raw = os.environ.get("RPA_PII_PATTERNS")
|
||||
patterns = raw.split("|") if raw else _DEFAULT_PII_PATTERNS
|
||||
compiled: list[re.Pattern[str]] = []
|
||||
for pat in patterns:
|
||||
pat = pat.strip()
|
||||
if not pat:
|
||||
continue
|
||||
try:
|
||||
compiled.append(re.compile(pat, re.IGNORECASE))
|
||||
except re.error:
|
||||
continue
|
||||
return compiled
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Slugification
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def slugify(name: str) -> str:
|
||||
"""Convertir un nom libre en slug ASCII strict.
|
||||
|
||||
Regle :
|
||||
- translitteration NFKD (suppression accents)
|
||||
- lowercase, espaces / tirets / points -> '_'
|
||||
- chars hors [a-z0-9_] retires
|
||||
- underscores multiples reduits a 1
|
||||
- troncature a 80 chars max
|
||||
- doit matcher SLUG_PATTERN
|
||||
|
||||
Leve ValueError si le slug final ne matche pas le pattern.
|
||||
"""
|
||||
if not isinstance(name, str):
|
||||
raise ValueError("name doit etre une chaine non vide")
|
||||
raw = name.strip()
|
||||
if not raw:
|
||||
raise ValueError("name est vide")
|
||||
|
||||
# NFKD pour decomposer les accents puis suppression des combinaisons
|
||||
normalized = unicodedata.normalize("NFKD", raw)
|
||||
ascii_only = normalized.encode("ascii", "ignore").decode("ascii")
|
||||
# Espaces / tirets / points / slashes -> underscore
|
||||
cleaned = re.sub(r"[\s\-./\\]+", "_", ascii_only.lower())
|
||||
# Tout ce qui n'est pas [a-z0-9_] -> supprime
|
||||
cleaned = re.sub(r"[^a-z0-9_]+", "", cleaned)
|
||||
# Reduire underscores multiples
|
||||
cleaned = re.sub(r"_+", "_", cleaned).strip("_")
|
||||
# Forcer commencement par une lettre (si commence par chiffre, prefixer)
|
||||
if cleaned and cleaned[0].isdigit():
|
||||
cleaned = f"c_{cleaned}"
|
||||
# Tronquer
|
||||
if len(cleaned) > 80:
|
||||
cleaned = cleaned[:80].rstrip("_")
|
||||
|
||||
if not SLUG_PATTERN.match(cleaned):
|
||||
raise ValueError(
|
||||
f"slug invalide '{cleaned}' (regle : {SLUG_PATTERN.pattern})"
|
||||
)
|
||||
return cleaned
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Collisions cross-states
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def detect_cross_state_collision(
|
||||
slug: str,
|
||||
*,
|
||||
competences_root: Path = COMPETENCES_ROOT,
|
||||
) -> Optional[str]:
|
||||
"""Retourne le sous-dossier ou un YAML <slug>.yaml existe deja, sinon None.
|
||||
|
||||
Verifie candidate/, supervised/, stable/.
|
||||
"""
|
||||
for sub in ("candidate", "supervised", "stable"):
|
||||
target = competences_root / sub / f"{slug}.yaml"
|
||||
if target.exists():
|
||||
return sub
|
||||
return None
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Detection PII
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def detect_pii(payload: Any) -> list[str]:
|
||||
"""Parcourt recursivement un payload (dict/list/str) et retourne la liste
|
||||
des patterns PII matches. Liste vide = pas de PII detecte.
|
||||
|
||||
L'appelant decide quoi en faire (HTTP 400 + log non-sensible).
|
||||
"""
|
||||
matches: list[str] = []
|
||||
patterns = _compile_pii_patterns()
|
||||
if not patterns:
|
||||
return matches
|
||||
|
||||
def _walk(node: Any) -> None:
|
||||
if isinstance(node, str):
|
||||
for pat in patterns:
|
||||
if pat.search(node):
|
||||
matches.append(pat.pattern)
|
||||
elif isinstance(node, dict):
|
||||
for v in node.values():
|
||||
_walk(v)
|
||||
elif isinstance(node, (list, tuple)):
|
||||
for v in node:
|
||||
_walk(v)
|
||||
|
||||
_walk(payload)
|
||||
# dedoublonner en preservant l'ordre
|
||||
seen = set()
|
||||
out: list[str] = []
|
||||
for p in matches:
|
||||
if p not in seen:
|
||||
seen.add(p)
|
||||
out.append(p)
|
||||
return out
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Atomic write
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def atomic_write_yaml(
|
||||
target_path: Path,
|
||||
data: dict[str, Any],
|
||||
*,
|
||||
persist_id: str,
|
||||
) -> Path:
|
||||
"""Ecrire un dict en YAML de maniere atomique.
|
||||
|
||||
1. Ecrit dans <target_dir>/.<basename>.tmp.<persist_id>
|
||||
2. os.rename vers target_path (POSIX atomic)
|
||||
3. En cas d'echec, supprime le .tmp si possible.
|
||||
|
||||
Retourne le chemin final (target_path).
|
||||
"""
|
||||
target_path = Path(target_path)
|
||||
target_dir = target_path.parent
|
||||
target_dir.mkdir(parents=True, exist_ok=True)
|
||||
tmp_name = f".{target_path.name}.tmp.{persist_id}"
|
||||
tmp_path = target_dir / tmp_name
|
||||
|
||||
try:
|
||||
with tmp_path.open("w", encoding="utf-8") as handle:
|
||||
yaml.safe_dump(
|
||||
data,
|
||||
handle,
|
||||
allow_unicode=True,
|
||||
sort_keys=False,
|
||||
default_flow_style=False,
|
||||
)
|
||||
handle.flush()
|
||||
try:
|
||||
os.fsync(handle.fileno())
|
||||
except OSError:
|
||||
pass
|
||||
# rename atomique (POSIX). Echoue si target existe deja sur Windows,
|
||||
# mais Linux (POSIX) ecrase silencieusement. On a verifie la collision
|
||||
# avant l'appel.
|
||||
os.rename(tmp_path, target_path)
|
||||
except Exception:
|
||||
if tmp_path.exists():
|
||||
try:
|
||||
tmp_path.unlink()
|
||||
except OSError:
|
||||
pass
|
||||
raise
|
||||
|
||||
return target_path
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Audit append (JSONL + verrou)
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def audit_append(
|
||||
entry: dict[str, Any],
|
||||
*,
|
||||
audit_path: Path = AUDIT_PATH,
|
||||
) -> int:
|
||||
"""Append une ligne JSON dans le fichier audit, retourne audit_entry_id.
|
||||
|
||||
L'audit_entry_id est un compteur monotone derive du nombre de lignes
|
||||
avant l'append. La concurrence est serialisee via fcntl.flock (POSIX).
|
||||
Sur les systemes sans fcntl (Windows), l'ecriture est best-effort.
|
||||
"""
|
||||
audit_path = Path(audit_path)
|
||||
audit_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if "timestamp" not in entry:
|
||||
entry["timestamp"] = (
|
||||
datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds")
|
||||
)
|
||||
|
||||
# Open en append + lecture pour compter les lignes existantes (audit_entry_id).
|
||||
flags = "a+"
|
||||
with open(audit_path, flags, encoding="utf-8") as handle:
|
||||
if _HAS_FCNTL:
|
||||
try:
|
||||
fcntl.flock(handle.fileno(), fcntl.LOCK_EX) # type: ignore[union-attr]
|
||||
except OSError:
|
||||
pass
|
||||
try:
|
||||
handle.seek(0)
|
||||
line_count = sum(1 for _ in handle)
|
||||
audit_entry_id = line_count + 1
|
||||
entry["audit_entry_id"] = audit_entry_id
|
||||
handle.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
handle.flush()
|
||||
try:
|
||||
os.fsync(handle.fileno())
|
||||
except OSError:
|
||||
pass
|
||||
finally:
|
||||
if _HAS_FCNTL:
|
||||
try:
|
||||
fcntl.flock(handle.fileno(), fcntl.LOCK_UN) # type: ignore[union-attr]
|
||||
except OSError:
|
||||
pass
|
||||
return audit_entry_id
|
||||
|
||||
|
||||
def find_existing_audit_entry(
|
||||
persist_id: str,
|
||||
*,
|
||||
audit_path: Path = AUDIT_PATH,
|
||||
) -> Optional[dict[str, Any]]:
|
||||
"""Recherche une entree existante par persist_id pour l'idempotence."""
|
||||
if not persist_id:
|
||||
return None
|
||||
audit_path = Path(audit_path)
|
||||
if not audit_path.exists():
|
||||
return None
|
||||
try:
|
||||
with audit_path.open("r", encoding="utf-8") as handle:
|
||||
for line in handle:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
record = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
if record.get("persist_id") == persist_id:
|
||||
return record
|
||||
except OSError:
|
||||
return None
|
||||
return None
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# YAML body construction
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
REQUIRED_YAML_FIELDS = (
|
||||
"schema_version",
|
||||
"id",
|
||||
"name",
|
||||
"version",
|
||||
"learning_state",
|
||||
"intent",
|
||||
"parameters",
|
||||
"preconditions",
|
||||
"methods",
|
||||
"success_marker",
|
||||
"failure_message_template",
|
||||
"promotion",
|
||||
"generalisation",
|
||||
"failure_log",
|
||||
"created_at",
|
||||
"last_updated_at",
|
||||
"methods_execution",
|
||||
)
|
||||
|
||||
|
||||
def build_competence_yaml(
|
||||
*,
|
||||
slug: str,
|
||||
name: str,
|
||||
workflow_ir: dict[str, Any],
|
||||
parameters: Optional[list[dict[str, Any]]],
|
||||
intent_fr: str,
|
||||
learning_state: str,
|
||||
session_id: Optional[str],
|
||||
machine_id: Optional[str],
|
||||
external_agent_id: Optional[str] = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Construit le dict YAML conforme au schema de reference.
|
||||
|
||||
Aligne sur ``data/competences/candidate/key_win_r_wait_explorer_exe.yaml``.
|
||||
"""
|
||||
now_iso = datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds")
|
||||
steps = list(workflow_ir.get("steps") or [])
|
||||
preconditions = list(workflow_ir.get("preconditions") or [])
|
||||
success_marker = workflow_ir.get("success_marker") or {
|
||||
"mode": "all_of",
|
||||
"timeout_ms": 5000,
|
||||
"markers": [],
|
||||
}
|
||||
|
||||
methods: list[dict[str, Any]] = []
|
||||
for idx, step in enumerate(steps, start=1):
|
||||
if not isinstance(step, dict):
|
||||
continue
|
||||
method = dict(step)
|
||||
method.setdefault("id", f"step_{idx}_{step.get('kind') or 'action'}")
|
||||
if "primitive_ref" not in method and method.get("kind"):
|
||||
method["primitive_ref"] = method["kind"]
|
||||
method.setdefault("observed", False)
|
||||
methods.append(method)
|
||||
|
||||
params_dict: dict[str, Any] = {}
|
||||
for p in (parameters or []):
|
||||
if isinstance(p, dict) and p.get("name"):
|
||||
params_dict[str(p["name"])] = {
|
||||
"type": p.get("type", "string"),
|
||||
"required": bool(p.get("required", False)),
|
||||
"description": p.get("description", ""),
|
||||
}
|
||||
|
||||
yaml_body: dict[str, Any] = {
|
||||
"schema_version": 1,
|
||||
"id": slug,
|
||||
"name": name,
|
||||
"version": 1,
|
||||
"learning_state": learning_state,
|
||||
"intent": {"fr": intent_fr or name},
|
||||
"parameters": params_dict,
|
||||
"preconditions": preconditions,
|
||||
"methods": methods,
|
||||
"success_marker": success_marker,
|
||||
"failure_message_template": workflow_ir.get("failure_message_template")
|
||||
or {
|
||||
"intention": intent_fr or name,
|
||||
"attendu": "",
|
||||
"vu": "{observed_human_state}",
|
||||
"demande": "indiquer la correction attendue",
|
||||
},
|
||||
"promotion": {
|
||||
"history": [
|
||||
{
|
||||
"at": now_iso,
|
||||
"from": "observed",
|
||||
"to": learning_state,
|
||||
"by": "lea_persist_endpoint",
|
||||
"reason": "persisted via /api/v1/lea/competences/candidate/persist",
|
||||
}
|
||||
],
|
||||
"candidate_requires": [
|
||||
"method_trace_present",
|
||||
"success_marker_defined",
|
||||
"failure_message_template_valid",
|
||||
],
|
||||
"supervised_requires": ["replay_verified_once", "human_validation"],
|
||||
"stable_requires": {
|
||||
"min_successes": 3,
|
||||
"distinct_contexts": 3,
|
||||
"max_unexplained_failures": 0,
|
||||
},
|
||||
"t2_known_gaps": [],
|
||||
},
|
||||
"generalisation": {
|
||||
"seen_contexts": [],
|
||||
"method_success_rate": {},
|
||||
"variance_log": [],
|
||||
},
|
||||
"failure_log": [],
|
||||
"created_at": now_iso,
|
||||
"last_updated_at": now_iso,
|
||||
"methods_execution": "sequence",
|
||||
}
|
||||
|
||||
if session_id or machine_id or external_agent_id:
|
||||
yaml_body["chain_refs"] = {
|
||||
"source_session": session_id,
|
||||
"machine_id": machine_id,
|
||||
"external_agent_id": external_agent_id,
|
||||
}
|
||||
return yaml_body
|
||||
|
||||
|
||||
def validate_yaml_schema(data: dict[str, Any]) -> list[str]:
|
||||
"""Verifie la presence des champs obligatoires. Retourne la liste des manquants."""
|
||||
return [field for field in REQUIRED_YAML_FIELDS if field not in data]
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Rate limit token-bucket simple (en memoire, par machine_id)
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
class PersistRateLimiter:
|
||||
"""Token-bucket minimal pour /persist.
|
||||
|
||||
Par defaut : 10 requetes / minute / machine_id (cf. specs §6).
|
||||
Instance unique attendue ; thread-safe via lock minimal.
|
||||
"""
|
||||
|
||||
def __init__(self, *, max_per_minute: int = 10, window_seconds: int = 60) -> None:
|
||||
self.max_per_minute = max_per_minute
|
||||
self.window_seconds = window_seconds
|
||||
self._timestamps: dict[str, list[float]] = {}
|
||||
|
||||
def allow(self, machine_id: str) -> tuple[bool, int]:
|
||||
"""Renvoie (allowed, retry_after_seconds).
|
||||
|
||||
retry_after_seconds = 0 si autorise.
|
||||
"""
|
||||
if not machine_id:
|
||||
return True, 0
|
||||
now = time.time()
|
||||
bucket = self._timestamps.setdefault(machine_id, [])
|
||||
# Purger les entrees hors fenetre
|
||||
bucket[:] = [ts for ts in bucket if now - ts < self.window_seconds]
|
||||
if len(bucket) >= self.max_per_minute:
|
||||
oldest = bucket[0]
|
||||
retry_after = max(1, int(self.window_seconds - (now - oldest)))
|
||||
return False, retry_after
|
||||
bucket.append(now)
|
||||
return True, 0
|
||||
|
||||
def reset(self, machine_id: Optional[str] = None) -> None:
|
||||
if machine_id is None:
|
||||
self._timestamps.clear()
|
||||
else:
|
||||
self._timestamps.pop(machine_id, None)
|
||||
|
||||
|
||||
# Instance partagee importable depuis api_stream
|
||||
persist_rate_limiter = PersistRateLimiter()
|
||||
|
||||
|
||||
__all__ = [
|
||||
"SLUG_PATTERN",
|
||||
"COMPETENCES_ROOT",
|
||||
"CANDIDATE_DIR",
|
||||
"AUDIT_PATH",
|
||||
"INCOMPLETE_PATH",
|
||||
"REQUIRED_YAML_FIELDS",
|
||||
"slugify",
|
||||
"detect_cross_state_collision",
|
||||
"detect_pii",
|
||||
"atomic_write_yaml",
|
||||
"audit_append",
|
||||
"find_existing_audit_entry",
|
||||
"build_competence_yaml",
|
||||
"validate_yaml_schema",
|
||||
"PersistRateLimiter",
|
||||
"persist_rate_limiter",
|
||||
]
|
||||
@@ -16,6 +16,48 @@ import io
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _extract_first_json_object(text: str) -> Optional[Dict[str, Any]]:
|
||||
"""Extrait le premier objet JSON racine d'un texte qui peut contenir
|
||||
du contenu parasite après (typique des modèles VLM qui ajoutent une
|
||||
explication post-JSON).
|
||||
|
||||
Retourne None si aucun JSON valide n'est trouvé.
|
||||
"""
|
||||
if not text:
|
||||
return None
|
||||
# Trouver la première '{' au niveau racine
|
||||
start = text.find("{")
|
||||
if start < 0:
|
||||
return None
|
||||
depth = 0
|
||||
in_string = False
|
||||
escape = False
|
||||
for i in range(start, len(text)):
|
||||
c = text[i]
|
||||
if escape:
|
||||
escape = False
|
||||
continue
|
||||
if c == "\\" and in_string:
|
||||
escape = True
|
||||
continue
|
||||
if c == '"':
|
||||
in_string = not in_string
|
||||
continue
|
||||
if in_string:
|
||||
continue
|
||||
if c == "{":
|
||||
depth += 1
|
||||
elif c == "}":
|
||||
depth -= 1
|
||||
if depth == 0:
|
||||
candidate = text[start : i + 1]
|
||||
try:
|
||||
return json.loads(candidate)
|
||||
except json.JSONDecodeError:
|
||||
return None
|
||||
return None
|
||||
|
||||
|
||||
class OllamaClient:
|
||||
"""
|
||||
Client Ollama pour VLM
|
||||
@@ -220,6 +262,92 @@ class OllamaClient:
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def generate_grounding(
|
||||
self,
|
||||
prompt: str,
|
||||
image_path: Optional[str] = None,
|
||||
image: Optional[Image.Image] = None,
|
||||
extra_images_b64: Optional[List[str]] = None,
|
||||
profile: Optional[Dict[str, Any]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""D5-v2 (2026-05-25) : appel grounding VLM centralisé, prefill-aware.
|
||||
|
||||
Utilise le profil dédié `vlm_config.get_grounding_profile()` pour
|
||||
garantir num_ctx pinned (défaut 4096), prefill JSON, think=false,
|
||||
temperature=0, num_predict court. Évite les chemins qui retomberaient
|
||||
sur qwen2.5vl en ctx 8192.
|
||||
|
||||
Le profile peut être surchargé via param explicite (utile tests).
|
||||
|
||||
Reconstitue le JSON complet via prefill : la réponse Ollama est
|
||||
complétée par le préfixe `{"x_pct":` avant parsing, pour que
|
||||
`json.loads()` voit le JSON natif.
|
||||
|
||||
Args:
|
||||
prompt: prompt textuel (typiquement "Find element X")
|
||||
image_path / image / extra_images_b64: cf. generate()
|
||||
profile: override du profile grounding (sinon get_grounding_profile())
|
||||
|
||||
Returns:
|
||||
Dict avec `response` (texte complet incluant prefill), `success`,
|
||||
`error`, `parsed_json` (dict {x_pct, y_pct, confidence, ...} ou
|
||||
None si non parsable), `profile_used` (dict).
|
||||
|
||||
Notes:
|
||||
- Pas de fallback automatique sur fallback_model ici. Le caller
|
||||
décide de retry avec un autre modèle si besoin.
|
||||
- `keep_alive` du profile n'est PAS envoyé en payload (Ollama
|
||||
accepte mais non standard). À gérer côté pull/keep si critique.
|
||||
"""
|
||||
if profile is None:
|
||||
from core.detection.vlm_config import get_grounding_profile
|
||||
profile = get_grounding_profile(endpoint=self.endpoint)
|
||||
|
||||
# Préserver le modèle courant, switcher temporairement.
|
||||
original_model = self.model
|
||||
self.model = profile["model"]
|
||||
try:
|
||||
result = self.generate(
|
||||
prompt=prompt,
|
||||
image_path=image_path,
|
||||
image=image,
|
||||
extra_images_b64=extra_images_b64,
|
||||
temperature=profile["temperature"],
|
||||
max_tokens=profile["num_predict"],
|
||||
assistant_prefill=profile["prefill"],
|
||||
num_ctx=profile["num_ctx"],
|
||||
force_json=False, # prefill suffit, format=json ralentit qwen3.5
|
||||
)
|
||||
finally:
|
||||
self.model = original_model
|
||||
|
||||
# Logging non-bruyant : 1 ligne par appel grounding
|
||||
elapsed_hint = "" # caller mesure via time.perf_counter si besoin
|
||||
logger.info(
|
||||
"[PERF] vlm.grounding model=%s ctx=%d prefill=%s success=%s",
|
||||
profile["model"], profile["num_ctx"],
|
||||
"yes" if profile["prefill"] else "no",
|
||||
result.get("success", False),
|
||||
)
|
||||
|
||||
# Parse JSON prefill-aware. Le contenu complet inclut déjà le prefill
|
||||
# (reconstitué par generate()) sauf si prefill=None. Si pas de prefill,
|
||||
# tenter parse direct (le modèle peut avoir produit du JSON pur).
|
||||
parsed = None
|
||||
content = (result.get("response") or "").strip()
|
||||
if content:
|
||||
try:
|
||||
# Le JSON peut être suivi de texte parasite (qwen termine
|
||||
# parfois par des explications). Couper à la 1ère accolade
|
||||
# fermante au niveau racine.
|
||||
parsed = _extract_first_json_object(content)
|
||||
except Exception as e:
|
||||
logger.debug("[PERF] vlm.grounding parse failed: %s — content=%r", e, content[:160])
|
||||
|
||||
result["parsed_json"] = parsed
|
||||
result["profile_used"] = dict(profile)
|
||||
return result
|
||||
|
||||
def detect_ui_elements(self, image_path: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Détecter les éléments UI dans une image
|
||||
|
||||
@@ -134,13 +134,13 @@ def reset_vlm_model_cache():
|
||||
|
||||
|
||||
def is_thinking_model(model_name: str) -> bool:
|
||||
"""Détermine si un modèle est un modèle 'thinking' (qwen3).
|
||||
"""Détermine si un modèle est un modèle 'thinking' (qwen3, qwen3.5).
|
||||
|
||||
Les modèles thinking nécessitent un assistant prefill pour éviter
|
||||
le mode réflexion interne qui peut durer >180s avec des images.
|
||||
|
||||
Args:
|
||||
model_name: Nom du modèle (ex: "qwen3-vl:8b", "gemma4:e4b")
|
||||
model_name: Nom du modèle (ex: "qwen3-vl:8b", "qwen3.5:9b", "gemma4:e4b")
|
||||
|
||||
Returns:
|
||||
True si le modèle est de type thinking (nécessite prefill workaround)
|
||||
@@ -148,6 +148,92 @@ def is_thinking_model(model_name: str) -> bool:
|
||||
return "qwen3" in model_name.lower()
|
||||
|
||||
|
||||
# ────────────────────────────────────────────────────────────────────────────
|
||||
# D5-v2 (2026-05-25) : profil grounding dédié, centralisé, env-overridable
|
||||
# ────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
# Profil grounding par défaut — qwen3.5:9b avec ctx 4096 et prefill JSON.
|
||||
# Cohérent avec décision Codex après revue Gemini : empêcher rechauffe
|
||||
# qwen2.5vl en ctx 8192 et garantir un chemin grounding reproductible.
|
||||
DEFAULT_GROUNDING_MODEL = "qwen3.5:9b"
|
||||
DEFAULT_GROUNDING_CTX = 4096
|
||||
DEFAULT_GROUNDING_PREFILL = '{"x_pct":'
|
||||
DEFAULT_GROUNDING_TEMPERATURE = 0.0
|
||||
DEFAULT_GROUNDING_NUM_PREDICT = 96 # ~80 tokens suffisent pour `{x_pct,y_pct,confidence}`
|
||||
DEFAULT_GROUNDING_KEEP_ALIVE = "30m" # éviter cold reload entre actions
|
||||
|
||||
# Fallback grounding : qwen2.5vl conservé pour compat existante (rpa-tag).
|
||||
DEFAULT_GROUNDING_FALLBACK = "qwen2.5vl:7b-rpa"
|
||||
|
||||
|
||||
def get_grounding_profile(endpoint: str = DEFAULT_OLLAMA_ENDPOINT) -> dict:
|
||||
"""Retourne le profil VLM pour les appels de grounding **format JSON**
|
||||
(réponse `{"x_pct": ..., "y_pct": ..., "confidence": ...}`).
|
||||
|
||||
⚠️ ATTENTION SCOPE D5-v3a (2026-05-25) :
|
||||
Ce profil est destiné aux appels qui consomment la sortie via prefill JSON
|
||||
(typiquement qwen3.5:9b avec prefill `{"x_pct":`). Il n'est PAS adapté
|
||||
aux appels grounding **format bbox_2d natif** de qwen2.5vl (utilisés
|
||||
dans `agent_v0/server_v1/resolve_engine.py:959-1013, 3008-3045` avec
|
||||
parsing via `core.grounding.bbox_parser.parse_bbox_to_norm`).
|
||||
|
||||
Conflit env var connu : `resolve_engine.py:959` lit aussi
|
||||
`RPA_GROUNDING_MODEL` mais attend un modèle bbox_2d (qwen2.5vl).
|
||||
Si tu setes `RPA_GROUNDING_MODEL=qwen3.5:9b`, ce profil OK mais le
|
||||
site bbox legacy de resolve_engine va recevoir un modèle incompatible.
|
||||
Reporté à D5-v3b : renommer en `RPA_BBOX_GROUNDING_MODEL` côté legacy
|
||||
+ introduire `OllamaClient.generate_bbox_grounding()`.
|
||||
|
||||
Centralise la politique pour empêcher les chemins VLM de retomber sur
|
||||
qwen2.5vl en num_ctx=8192 (Modelfile). Sortie consommée par
|
||||
OllamaClient.generate_grounding().
|
||||
|
||||
Env vars supportées :
|
||||
- RPA_GROUNDING_MODEL : modèle principal (défaut qwen3.5:9b)
|
||||
- RPA_GROUNDING_CTX : context window (défaut 4096)
|
||||
- RPA_GROUNDING_FALLBACK : modèle fallback (défaut qwen2.5vl:7b-rpa)
|
||||
- RPA_VLM_PREFILL=false : désactive le prefill JSON (rare, debug)
|
||||
|
||||
Returns:
|
||||
dict avec clés :
|
||||
- model: str
|
||||
- num_ctx: int
|
||||
- prefill: str ou None
|
||||
- temperature: float
|
||||
- num_predict: int
|
||||
- think: bool (False pour qwen3 et qwen3.5)
|
||||
- keep_alive: str
|
||||
- fallback_model: str
|
||||
"""
|
||||
model = os.environ.get("RPA_GROUNDING_MODEL", DEFAULT_GROUNDING_MODEL).strip()
|
||||
try:
|
||||
num_ctx = int(os.environ.get("RPA_GROUNDING_CTX", str(DEFAULT_GROUNDING_CTX)))
|
||||
except (TypeError, ValueError):
|
||||
num_ctx = DEFAULT_GROUNDING_CTX
|
||||
fallback = os.environ.get(
|
||||
"RPA_GROUNDING_FALLBACK", DEFAULT_GROUNDING_FALLBACK
|
||||
).strip()
|
||||
prefill_enabled = os.environ.get("RPA_VLM_PREFILL", "true").strip().lower() not in (
|
||||
"0", "false", "no", "off"
|
||||
)
|
||||
prefill = DEFAULT_GROUNDING_PREFILL if prefill_enabled else None
|
||||
|
||||
# think=False obligatoire pour qwen3/qwen3.5 (prefill = mécanisme principal)
|
||||
# et gemma4 (sinon tokens vides Ollama >=0.20).
|
||||
think_false = is_thinking_model(model) or needs_think_false(model)
|
||||
|
||||
return {
|
||||
"model": model,
|
||||
"num_ctx": num_ctx,
|
||||
"prefill": prefill,
|
||||
"temperature": DEFAULT_GROUNDING_TEMPERATURE,
|
||||
"num_predict": DEFAULT_GROUNDING_NUM_PREDICT,
|
||||
"think": not think_false, # API Ollama : think=False → on envoie False
|
||||
"keep_alive": DEFAULT_GROUNDING_KEEP_ALIVE,
|
||||
"fallback_model": fallback,
|
||||
}
|
||||
|
||||
|
||||
def needs_think_false(model_name: str) -> bool:
|
||||
"""Détermine si un modèle nécessite think=false dans le payload.
|
||||
|
||||
|
||||
@@ -59,8 +59,13 @@ class CLIPEmbedder(EmbedderBase):
|
||||
)
|
||||
|
||||
if device is None:
|
||||
# NOTE: utiliser le `torch` du scope module (l. 8). Un import local
|
||||
# ici rendait `torch` LOCAL à __init__ pour tout le scope, faisant
|
||||
# planter `with torch.no_grad():` plus bas en UnboundLocalError
|
||||
# quand l'appelant passait device="cpu" (l'import local n'était
|
||||
# alors pas exécuté). Voir inbox_codex/2026-05-25_1235_..._enquete-
|
||||
# feedbackbus-5004.md.
|
||||
try:
|
||||
import torch
|
||||
if torch.cuda.is_available():
|
||||
free_vram = torch.cuda.mem_get_info()[0] / 1024**3
|
||||
if free_vram > 1.5:
|
||||
|
||||
@@ -6,7 +6,11 @@ from .t2a_decision import (
|
||||
analyze_dpi,
|
||||
build_dpi_enriched,
|
||||
)
|
||||
from .ocr_extractor import extract_table_from_image, extract_text_from_image
|
||||
from .ocr_extractor import (
|
||||
extract_digits_tesseract_from_image,
|
||||
extract_table_from_image,
|
||||
extract_text_from_image,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"PROMPT_TEMPLATE",
|
||||
@@ -15,4 +19,5 @@ __all__ = [
|
||||
"build_dpi_enriched",
|
||||
"extract_text_from_image",
|
||||
"extract_table_from_image",
|
||||
"extract_digits_tesseract_from_image",
|
||||
]
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
"""Extracteur OCR — texte depuis une image (screenshot d'écran).
|
||||
|
||||
Utilise EasyOCR fr+en. Singleton (chargement modèle ~3s au premier appel).
|
||||
Ajoute un chemin Tesseract spécialisé pour les chiffres/IPP d'écrans propres.
|
||||
|
||||
Conçu pour le pipeline streaming serveur (actions `extract_text` /
|
||||
`extract_table`) : récupère un screenshot fresh (dernier heartbeat ou
|
||||
@@ -11,6 +12,7 @@ pour analyse downstream (ex: t2a_decision, boucle sur N patients).
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import List, Optional, Tuple
|
||||
@@ -20,6 +22,19 @@ logger = logging.getLogger(__name__)
|
||||
_easyocr_reader = None
|
||||
|
||||
|
||||
def easyocr_gpu_enabled(default: bool = False) -> bool:
|
||||
"""Return whether EasyOCR may allocate GPU memory.
|
||||
|
||||
The replay server shares the GPU with Ollama. Defaulting EasyOCR to CPU
|
||||
keeps VRAM available for the VLM; set RPA_EASYOCR_GPU=1 only for a measured
|
||||
OCR benchmark or a runtime that has spare VRAM.
|
||||
"""
|
||||
raw = os.getenv("RPA_EASYOCR_GPU", "")
|
||||
if not raw:
|
||||
return default
|
||||
return raw.strip().lower() in {"1", "true", "yes", "on"}
|
||||
|
||||
|
||||
def _get_reader():
|
||||
"""Initialise EasyOCR fr+en au premier appel (singleton, CPU forcé).
|
||||
|
||||
@@ -29,8 +44,9 @@ def _get_reader():
|
||||
global _easyocr_reader
|
||||
if _easyocr_reader is None:
|
||||
import easyocr
|
||||
_easyocr_reader = easyocr.Reader(['fr', 'en'], gpu=False, verbose=False)
|
||||
logger.info("EasyOCR initialisé (fr+en, CPU)")
|
||||
gpu = easyocr_gpu_enabled(default=False)
|
||||
_easyocr_reader = easyocr.Reader(['fr', 'en'], gpu=gpu, verbose=False)
|
||||
logger.info("EasyOCR initialisé (fr+en, %s)", "GPU" if gpu else "CPU")
|
||||
return _easyocr_reader
|
||||
|
||||
|
||||
@@ -73,17 +89,86 @@ def extract_text_from_image(
|
||||
return ""
|
||||
|
||||
|
||||
def extract_digits_tesseract_from_image(
|
||||
image_path: str,
|
||||
region: Optional[Tuple[int, int, int, int]] = None,
|
||||
pattern: Optional[str] = None,
|
||||
limit: Optional[int] = None,
|
||||
psm: int = 6,
|
||||
lang: str = "eng",
|
||||
whitelist: str = "0123456789",
|
||||
) -> List[str]:
|
||||
"""Extrait des valeurs numeriques via Tesseract.
|
||||
|
||||
Cas d'usage principal : IPP/champs chiffres dans des tableaux d'écran.
|
||||
Ce chemin est volontairement explicite pour ne pas changer le comportement
|
||||
EasyOCR general utilise par `extract_text`.
|
||||
|
||||
Args:
|
||||
image_path: chemin du PNG/JPG sur disque.
|
||||
region: (x, y, w, h) pour cropper avant OCR. None = image entière.
|
||||
pattern: regex Python appliquee aux sequences de chiffres extraites.
|
||||
Exemple IPP : r"^25\\d{6}$".
|
||||
limit: nombre maximal de valeurs retournees.
|
||||
psm: page segmentation mode Tesseract. 6 = bloc uniforme de texte.
|
||||
lang: langue Tesseract.
|
||||
whitelist: caracteres autorises. Par defaut chiffres uniquement.
|
||||
|
||||
Returns:
|
||||
Liste de sequences numeriques dans l'ordre de lecture Tesseract.
|
||||
En cas d'erreur, retourne une liste vide et log un warning.
|
||||
"""
|
||||
path = Path(image_path)
|
||||
if not path.exists():
|
||||
logger.warning("extract_digits_tesseract: fichier introuvable %s", image_path)
|
||||
return []
|
||||
|
||||
try:
|
||||
from PIL import Image
|
||||
import pytesseract
|
||||
|
||||
with Image.open(path) as img:
|
||||
if region:
|
||||
x, y, w, h = region
|
||||
img = img.crop((x, y, x + w, y + h))
|
||||
if img.mode not in {"L", "RGB"}:
|
||||
img = img.convert("RGB")
|
||||
|
||||
config_parts = ["--psm", str(psm)]
|
||||
if whitelist:
|
||||
config_parts.extend(["-c", f"tessedit_char_whitelist={whitelist}"])
|
||||
text = pytesseract.image_to_string(
|
||||
img,
|
||||
lang=lang,
|
||||
config=" ".join(config_parts),
|
||||
)
|
||||
|
||||
values = re.findall(r"\d+", text)
|
||||
if pattern:
|
||||
compiled = re.compile(pattern)
|
||||
values = [v for v in values if compiled.match(v)]
|
||||
if limit:
|
||||
values = values[:limit]
|
||||
return values
|
||||
except Exception as e:
|
||||
logger.warning("extract_digits_tesseract échoué sur %s : %s", image_path, e)
|
||||
return []
|
||||
|
||||
|
||||
def extract_table_from_image(
|
||||
image_path: str,
|
||||
region: Optional[Tuple[int, int, int, int]] = None,
|
||||
pattern: Optional[str] = None,
|
||||
limit: Optional[int] = None,
|
||||
engine: str = "easyocr",
|
||||
) -> List[str]:
|
||||
"""Extrait une liste de valeurs d'un tableau via OCR.
|
||||
|
||||
Cas d'usage principal : lire la liste des IPP d'un tableau de patients
|
||||
pour boucler dessus. EasyOCR retourne tous les tokens avec leur bbox,
|
||||
on filtre par regex puis on trie par position (y croissant).
|
||||
pour boucler dessus. Par défaut, EasyOCR retourne tous les tokens avec
|
||||
leur bbox, on filtre par regex puis on trie par position (y croissant).
|
||||
Pour des champs chiffres/IPP, `engine="tesseract"` active le chemin
|
||||
spécialisé Tesseract validé sur captures Easily.
|
||||
|
||||
Args:
|
||||
image_path: chemin du PNG sur disque.
|
||||
@@ -92,6 +177,7 @@ def extract_table_from_image(
|
||||
Si None : tous les tokens non vides sont retournés.
|
||||
Exemple IPP : r"^\\d{8,10}$" ou r"^25\\d{6}$"
|
||||
limit: nombre maximal d'entrées à retourner (None = sans limite).
|
||||
engine: "easyocr" (defaut) ou "tesseract" / "digits" / "ipp".
|
||||
|
||||
Returns:
|
||||
Liste de strings dans l'ordre top → bottom (par y de bbox).
|
||||
@@ -102,6 +188,15 @@ def extract_table_from_image(
|
||||
logger.warning("extract_table: fichier introuvable %s", image_path)
|
||||
return []
|
||||
|
||||
engine_name = (engine or "easyocr").strip().lower()
|
||||
if engine_name in {"tesseract", "digits", "ipp"}:
|
||||
return extract_digits_tesseract_from_image(
|
||||
image_path,
|
||||
region=region,
|
||||
pattern=pattern,
|
||||
limit=limit,
|
||||
)
|
||||
|
||||
try:
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
@@ -99,10 +99,17 @@ class WorkflowPipeline:
|
||||
logger.info("✓ Fusion Engine initialized")
|
||||
|
||||
# 3. State Embedding Builder
|
||||
clip_embedders = {
|
||||
"image": self.clip_embedder,
|
||||
"text": self.clip_embedder,
|
||||
"title": self.clip_embedder,
|
||||
"ui": self.clip_embedder,
|
||||
}
|
||||
self.embedding_builder = StateEmbeddingBuilder(
|
||||
fusion_engine=self.fusion_engine,
|
||||
embedders=clip_embedders,
|
||||
output_dir=self.embeddings_dir,
|
||||
use_clip=True
|
||||
use_clip=False
|
||||
)
|
||||
logger.info("✓ State Embedding Builder initialized")
|
||||
|
||||
|
||||
38
core/semantic/__init__.py
Normal file
38
core/semantic/__init__.py
Normal file
@@ -0,0 +1,38 @@
|
||||
"""Phase 2.5 — Analyse sémantique post-apprentissage.
|
||||
|
||||
Module dédié à l'analyse sémantique des écrans capturés en phase Shadow,
|
||||
**après** ``/api/v1/shadow/stop`` et **avant** restitution Option C.
|
||||
|
||||
Specs : ``docs/POC/SPECS_PHASE_25_SEMANTIQUE_2026-06-01.md``
|
||||
|
||||
Principes (arbitrage Plato 2026-06-01) :
|
||||
- Post-apprentissage uniquement, **jamais en hot path replay**.
|
||||
- OmniParser encapsulé derrière garde-fou anti-fragilité.
|
||||
- Fallback OCR-seul (docTR) systématique en cas d'exception.
|
||||
- Stockage ``.semantic.yaml`` séparé du YAML compétence principal.
|
||||
- Opt-in par compétence (rétrocompat totale).
|
||||
"""
|
||||
|
||||
from .phase25_analyzer import (
|
||||
Phase25Analyzer,
|
||||
Phase25Result,
|
||||
ScreenAnalysis,
|
||||
SemanticStructure,
|
||||
SEMANTIC_DIR,
|
||||
OMNIPARSER_CACHE_DIR,
|
||||
OMNIPARSER_ERROR_LOG,
|
||||
PHASH_HAMMING_THRESHOLD,
|
||||
MAX_SCREENS_PER_SESSION,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"Phase25Analyzer",
|
||||
"Phase25Result",
|
||||
"ScreenAnalysis",
|
||||
"SemanticStructure",
|
||||
"SEMANTIC_DIR",
|
||||
"OMNIPARSER_CACHE_DIR",
|
||||
"OMNIPARSER_ERROR_LOG",
|
||||
"PHASH_HAMMING_THRESHOLD",
|
||||
"MAX_SCREENS_PER_SESSION",
|
||||
]
|
||||
920
core/semantic/phase25_analyzer.py
Normal file
920
core/semantic/phase25_analyzer.py
Normal file
@@ -0,0 +1,920 @@
|
||||
"""Phase 2.5 — Analyseur sémantique post-apprentissage.
|
||||
|
||||
Module isolé qui prend en entrée un ensemble de screenshots capturés
|
||||
pendant la phase Shadow et produit un payload structuré
|
||||
``{tables, forms, buttons, text_blocks}`` par écran distinct,
|
||||
stocké dans un fichier ``.semantic.yaml`` séparé.
|
||||
|
||||
Specs : ``docs/POC/SPECS_PHASE_25_SEMANTIQUE_2026-06-01.md``
|
||||
|
||||
Garde-fous :
|
||||
- Wrapper try/except global autour de chaque appel OmniParser.
|
||||
- Fallback OCR-seul (docTR) si OmniParser indisponible ou KO.
|
||||
- Healthcheck OmniParser au démarrage : KO ⇒ bascule auto en dégradé.
|
||||
- Cache disque ``data/cache/omniparser/<session>/<index>.json``.
|
||||
- Cap 10 écrans distincts par session.
|
||||
- Aucun import de FastAPI, aucun appel réseau direct.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import concurrent.futures
|
||||
import hashlib
|
||||
import io
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import time
|
||||
import traceback
|
||||
from dataclasses import asdict, dataclass, field
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any, Iterable, List, Optional, Sequence, Tuple
|
||||
|
||||
try: # pragma: no cover - dépendance externe déjà présente dans le projet
|
||||
import yaml
|
||||
except ImportError as exc: # pragma: no cover
|
||||
raise RuntimeError("PyYAML est requis pour core.semantic.phase25_analyzer") from exc
|
||||
|
||||
try: # PIL toujours présent côté Linux dev / DGX
|
||||
from PIL import Image
|
||||
_HAS_PIL = True
|
||||
except ImportError: # pragma: no cover
|
||||
Image = None # type: ignore[assignment]
|
||||
_HAS_PIL = False
|
||||
|
||||
try:
|
||||
import imagehash # type: ignore
|
||||
_HAS_IMAGEHASH = True
|
||||
except ImportError: # pragma: no cover - fallback MD5 thumbnail
|
||||
imagehash = None # type: ignore[assignment]
|
||||
_HAS_IMAGEHASH = False
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Constantes et chemins
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parents[2]
|
||||
DATA_ROOT = REPO_ROOT / "data"
|
||||
SEMANTIC_DIR = DATA_ROOT / "competences" / "candidate"
|
||||
OMNIPARSER_CACHE_ROOT = DATA_ROOT / "cache" / "omniparser"
|
||||
OMNIPARSER_CACHE_DIR = OMNIPARSER_CACHE_ROOT # alias public
|
||||
LOGS_DIR = REPO_ROOT / "logs"
|
||||
OMNIPARSER_ERROR_LOG = LOGS_DIR / "omniparser_errors.log"
|
||||
|
||||
# Heuristique de regroupement perceptuel (cf. specs §3).
|
||||
PHASH_HAMMING_THRESHOLD = 8
|
||||
MAX_SCREENS_PER_SESSION = 10
|
||||
THUMBNAIL_SIZE = (256, 256) # fallback MD5
|
||||
|
||||
# Timeout par screenshot (cf. specs §2).
|
||||
OMNIPARSER_TIMEOUT_SEC = 30.0
|
||||
|
||||
# Slug autorisé (réutilisation du pattern persist : a-z0-9_).
|
||||
SLUG_PATTERN = re.compile(r"^[a-z][a-z0-9_]{2,79}$")
|
||||
# session_id autorisé : caractères inoffensifs uniquement.
|
||||
SESSION_ID_PATTERN = re.compile(r"^[A-Za-z0-9][A-Za-z0-9_\-]{0,127}$")
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Dataclasses
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
@dataclass
|
||||
class SemanticStructure:
|
||||
"""Structure sémantique d'un écran (cf. specs §2)."""
|
||||
|
||||
tables: List[dict] = field(default_factory=list)
|
||||
forms: List[dict] = field(default_factory=list)
|
||||
buttons: List[dict] = field(default_factory=list)
|
||||
text_blocks: List[dict] = field(default_factory=list)
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
return {
|
||||
"tables": list(self.tables),
|
||||
"forms": list(self.forms),
|
||||
"buttons": list(self.buttons),
|
||||
"text_blocks": list(self.text_blocks),
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class ScreenAnalysis:
|
||||
"""Analyse d'un écran représentatif (cf. specs §3)."""
|
||||
|
||||
index: int
|
||||
phash: str
|
||||
screen_id: str
|
||||
screenshot_path: Optional[str]
|
||||
structure: SemanticStructure
|
||||
degraded: bool = False
|
||||
degraded_reason: Optional[str] = None
|
||||
elapsed_sec: float = 0.0
|
||||
window_title: Optional[str] = None
|
||||
# Snapshot "contrat Codex" : représentation aplatie destinée à
|
||||
# l'agent-chat / dashboard. Calculée à la volée par to_dict().
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
elements = _structure_to_elements(self.structure)
|
||||
return {
|
||||
"index": self.index,
|
||||
"hash": self.phash,
|
||||
"screen_id": self.screen_id,
|
||||
"window_title": self.window_title,
|
||||
"screenshot_path": self.screenshot_path,
|
||||
"structure": self.structure.to_dict(),
|
||||
"elements": elements,
|
||||
"degraded": self.degraded,
|
||||
"degraded_reason": self.degraded_reason,
|
||||
"elapsed_sec": round(self.elapsed_sec, 3),
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class Phase25Result:
|
||||
"""Résultat global d'une analyse Phase 2.5."""
|
||||
|
||||
session_id: str
|
||||
generated_at: str
|
||||
omniparser_available: bool
|
||||
degraded: bool
|
||||
too_complex: bool
|
||||
screens: List[ScreenAnalysis] = field(default_factory=list)
|
||||
healthcheck_passed: bool = True
|
||||
healthcheck_reason: Optional[str] = None
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
return {
|
||||
"session_id": self.session_id,
|
||||
"generated_at": self.generated_at,
|
||||
"omniparser_available": self.omniparser_available,
|
||||
"degraded": self.degraded,
|
||||
"too_complex": self.too_complex,
|
||||
"healthcheck_passed": self.healthcheck_passed,
|
||||
"healthcheck_reason": self.healthcheck_reason,
|
||||
"screens": [s.to_dict() for s in self.screens],
|
||||
}
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Helpers : validation et FS
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _validate_session_id(session_id: Any) -> str:
|
||||
if not isinstance(session_id, str) or not session_id.strip():
|
||||
raise ValueError("session_id doit etre une chaine non vide")
|
||||
sid = session_id.strip()
|
||||
if not SESSION_ID_PATTERN.match(sid):
|
||||
raise ValueError(
|
||||
"session_id invalide (autorise : [A-Za-z0-9][A-Za-z0-9_-]{0,127})"
|
||||
)
|
||||
# Anti path-traversal de ceinture-bretelles : on refuse explicitement
|
||||
# toute tentative ../ même si le regex ne devrait pas la laisser passer.
|
||||
if ".." in sid or "/" in sid or "\\" in sid:
|
||||
raise ValueError("session_id invalide (path-traversal interdit)")
|
||||
return sid
|
||||
|
||||
|
||||
def _validate_slug(slug: Any) -> str:
|
||||
if not isinstance(slug, str):
|
||||
raise ValueError("slug doit etre une chaine")
|
||||
s = slug.strip()
|
||||
if not SLUG_PATTERN.match(s):
|
||||
raise ValueError(
|
||||
f"slug invalide '{s}' (regle : {SLUG_PATTERN.pattern})"
|
||||
)
|
||||
return s
|
||||
|
||||
|
||||
def _ensure_dir(path: Path) -> None:
|
||||
path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
def _log_omniparser_error(session_id: str, frame_index: int, exc: BaseException) -> None:
|
||||
"""Append-only sur ``logs/omniparser_errors.log`` (cf. specs §7)."""
|
||||
try:
|
||||
_ensure_dir(LOGS_DIR)
|
||||
entry = {
|
||||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||||
"session_id": session_id,
|
||||
"frame_index": frame_index,
|
||||
"error_type": type(exc).__name__,
|
||||
"error_message": str(exc),
|
||||
"traceback": traceback.format_exception_only(type(exc), exc),
|
||||
}
|
||||
with OMNIPARSER_ERROR_LOG.open("a", encoding="utf-8") as fh:
|
||||
fh.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
except OSError as log_exc: # pragma: no cover - log best-effort
|
||||
logger.warning("[PHASE25] echec ecriture omniparser_errors.log : %s", log_exc)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Hash perceptuel (avec fallback MD5)
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def compute_phash(image: "Image.Image") -> str:
|
||||
"""Calcule un hash perceptuel ou un hash MD5 thumbnail (fallback)."""
|
||||
if _HAS_IMAGEHASH and imagehash is not None:
|
||||
try:
|
||||
return str(imagehash.phash(image))
|
||||
except Exception as exc: # pragma: no cover
|
||||
logger.warning("[PHASE25] phash imagehash KO, fallback MD5 : %s", exc)
|
||||
# Fallback MD5 sur thumbnail.
|
||||
thumb = image.copy()
|
||||
thumb.thumbnail(THUMBNAIL_SIZE)
|
||||
buf = io.BytesIO()
|
||||
thumb.convert("RGB").save(buf, format="PNG")
|
||||
return "md5:" + hashlib.md5(buf.getvalue()).hexdigest()
|
||||
|
||||
|
||||
def _hamming_distance(h1: str, h2: str) -> int:
|
||||
"""Distance de Hamming entre deux phash imagehash, ou fallback MD5.
|
||||
|
||||
- Cas imagehash : on reconvertit via ``imagehash.hex_to_hash``.
|
||||
- Cas MD5 (préfixe ``md5:``) : 0 si égal, sinon distance "haute" pour ne
|
||||
jamais les considérer comme similaires (heuristique conservative).
|
||||
"""
|
||||
if h1.startswith("md5:") or h2.startswith("md5:"):
|
||||
return 0 if h1 == h2 else PHASH_HAMMING_THRESHOLD + 1
|
||||
if not _HAS_IMAGEHASH or imagehash is None:
|
||||
# Pas d'imagehash mais les hashes hex présents (rare) : XOR brut.
|
||||
try:
|
||||
i1 = int(h1, 16)
|
||||
i2 = int(h2, 16)
|
||||
return bin(i1 ^ i2).count("1")
|
||||
except ValueError:
|
||||
return PHASH_HAMMING_THRESHOLD + 1
|
||||
try:
|
||||
return abs(imagehash.hex_to_hash(h1) - imagehash.hex_to_hash(h2))
|
||||
except Exception:
|
||||
return PHASH_HAMMING_THRESHOLD + 1
|
||||
|
||||
|
||||
def identify_distinct_screens(
|
||||
frames: Sequence[Tuple[int, "Image.Image"]],
|
||||
threshold: int = PHASH_HAMMING_THRESHOLD,
|
||||
) -> List[Tuple[int, "Image.Image", str]]:
|
||||
"""Regroupe les frames par similarité phash et retourne un représentant par groupe.
|
||||
|
||||
Args:
|
||||
frames: séquence ``(frame_index, PIL.Image)``.
|
||||
threshold: Hamming distance max pour considérer deux frames identiques.
|
||||
|
||||
Returns:
|
||||
Liste ``(frame_index, image, phash)`` — un représentant par groupe,
|
||||
dans l'ordre temporel d'apparition (premier vu = représentant).
|
||||
"""
|
||||
representatives: List[Tuple[int, Image.Image, str]] = []
|
||||
for idx, img in frames:
|
||||
h = compute_phash(img)
|
||||
matched = False
|
||||
for ridx, _rimg, rhash in representatives:
|
||||
if _hamming_distance(h, rhash) <= threshold:
|
||||
matched = True
|
||||
logger.debug(
|
||||
"[PHASE25] frame %d regroupee avec representant %d (phash=%s)",
|
||||
idx, ridx, h,
|
||||
)
|
||||
break
|
||||
if not matched:
|
||||
representatives.append((idx, img, h))
|
||||
return representatives
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Conversion structure ⇄ "elements" (contrat Codex)
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _structure_to_elements(struct: SemanticStructure) -> List[dict]:
|
||||
"""Aplatissement structure -> liste d'éléments {kind, label, bbox, confidence}."""
|
||||
elements: List[dict] = []
|
||||
for tbl in struct.tables:
|
||||
elements.append({
|
||||
"kind": "table",
|
||||
"label": tbl.get("label", "table"),
|
||||
"bbox": tbl.get("bbox", []),
|
||||
"confidence": float(tbl.get("confidence", 0.5)),
|
||||
})
|
||||
for frm in struct.forms:
|
||||
elements.append({
|
||||
"kind": "field",
|
||||
"label": frm.get("label", "field"),
|
||||
"bbox": frm.get("bbox", []),
|
||||
"confidence": float(frm.get("confidence", 0.5)),
|
||||
})
|
||||
for btn in struct.buttons:
|
||||
elements.append({
|
||||
"kind": "button",
|
||||
"label": btn.get("label", "button"),
|
||||
"bbox": btn.get("bbox", []),
|
||||
"confidence": float(btn.get("confidence", 0.5)),
|
||||
})
|
||||
for tb in struct.text_blocks:
|
||||
elements.append({
|
||||
"kind": "text_block",
|
||||
"label": tb.get("label", tb.get("text", "")),
|
||||
"bbox": tb.get("bbox", []),
|
||||
"confidence": float(tb.get("confidence", 0.5)),
|
||||
})
|
||||
return elements
|
||||
|
||||
|
||||
def _classify_element(label: str, kind_hint: str | None = None) -> str:
|
||||
"""Heuristique de classification d'un élément OmniParser.
|
||||
|
||||
Cohérente avec ``OmniParserAdapter._classify_element``, mais retourne
|
||||
nos catégories sémantiques : ``table | field | button | text_block``.
|
||||
"""
|
||||
lab = (label or "").lower()
|
||||
if kind_hint:
|
||||
kh = kind_hint.lower()
|
||||
if "table" in kh:
|
||||
return "table"
|
||||
if "input" in kh or "field" in kh or "edit" in kh:
|
||||
return "field"
|
||||
if "button" in kh or "btn" in kh:
|
||||
return "button"
|
||||
if any(kw in lab for kw in ("button", "btn", "submit", "valider", "annuler", "ok", "close")):
|
||||
return "button"
|
||||
if any(kw in lab for kw in ("input", "field", "saisie", "textbox", "champ")):
|
||||
return "field"
|
||||
if "table" in lab or "grille" in lab:
|
||||
return "table"
|
||||
return "text_block"
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Adapter wrappers : OmniParser et docTR (fallback)
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
class _OmniParserSafeWrapper:
|
||||
"""Wrap fragile OmniParserAdapter avec garde-fou anti-exception.
|
||||
|
||||
- Import paresseux (lazy) pour ne pas casser l'import du module si
|
||||
OmniParser n'est pas installé.
|
||||
- ``available=False`` ⇒ caller bascule en fallback OCR-seul.
|
||||
- Timeout effectif appliqué autour de chaque appel ``detect`` via
|
||||
``ThreadPoolExecutor`` + ``future.result(timeout=...)``.
|
||||
"""
|
||||
|
||||
# Executor module-level pour ne pas créer un pool par appel.
|
||||
_TIMEOUT_EXECUTOR: Optional[concurrent.futures.ThreadPoolExecutor] = None
|
||||
|
||||
@classmethod
|
||||
def _get_executor(cls) -> concurrent.futures.ThreadPoolExecutor:
|
||||
if cls._TIMEOUT_EXECUTOR is None:
|
||||
cls._TIMEOUT_EXECUTOR = concurrent.futures.ThreadPoolExecutor(
|
||||
max_workers=2, thread_name_prefix="phase25-omniparser-timeout",
|
||||
)
|
||||
return cls._TIMEOUT_EXECUTOR
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._adapter: Any = None
|
||||
self._available: bool = False
|
||||
self._import_error: Optional[str] = None
|
||||
self._try_import()
|
||||
|
||||
def _try_import(self) -> None:
|
||||
try:
|
||||
from core.detection.omniparser_adapter import OmniParserAdapter # type: ignore
|
||||
self._adapter = OmniParserAdapter()
|
||||
self._available = bool(getattr(self._adapter, "available", False))
|
||||
if not self._available:
|
||||
# L'adapter existe mais le check de disponibilité a échoué.
|
||||
self._import_error = "OmniParser adapter installé mais modèles non disponibles"
|
||||
except Exception as exc:
|
||||
self._adapter = None
|
||||
self._available = False
|
||||
self._import_error = f"{type(exc).__name__}: {exc}"
|
||||
|
||||
@property
|
||||
def available(self) -> bool:
|
||||
return self._available
|
||||
|
||||
@property
|
||||
def import_error(self) -> Optional[str]:
|
||||
return self._import_error
|
||||
|
||||
def detect(
|
||||
self,
|
||||
image: "Image.Image",
|
||||
*,
|
||||
timeout: Optional[float] = None,
|
||||
) -> List[Any]:
|
||||
"""Appel sécurisé : enrobé d'un timeout dur, lève en cas d'exception.
|
||||
|
||||
Args:
|
||||
image: image PIL à analyser.
|
||||
timeout: timeout en secondes (défaut : ``OMNIPARSER_TIMEOUT_SEC``).
|
||||
Si dépassé ⇒ ``concurrent.futures.TimeoutError`` propagée au
|
||||
caller, qui bascule en fallback docTR + ``degraded=True``.
|
||||
"""
|
||||
if not self._available or self._adapter is None:
|
||||
return []
|
||||
effective_timeout = (
|
||||
timeout if timeout is not None else OMNIPARSER_TIMEOUT_SEC
|
||||
)
|
||||
executor = self._get_executor()
|
||||
future = executor.submit(self._adapter.detect, image)
|
||||
try:
|
||||
return list(future.result(timeout=effective_timeout))
|
||||
except concurrent.futures.TimeoutError as exc:
|
||||
# Le thread OmniParser continue son travail en arrière-plan mais
|
||||
# le résultat est ignoré ; le caller bascule en fallback docTR.
|
||||
logger.warning(
|
||||
"[PHASE25] OmniParser.detect timeout (%.1fs) -> fallback",
|
||||
effective_timeout,
|
||||
)
|
||||
raise
|
||||
except Exception as exc:
|
||||
logger.warning("[PHASE25] OmniParser.detect KO : %s", exc)
|
||||
raise # remonté au caller pour log + fallback
|
||||
|
||||
|
||||
def _detect_via_omniparser(
|
||||
wrapper: _OmniParserSafeWrapper,
|
||||
image: "Image.Image",
|
||||
*,
|
||||
timeout: Optional[float] = None,
|
||||
) -> List[Any]:
|
||||
return wrapper.detect(image, timeout=timeout)
|
||||
|
||||
|
||||
def _detect_via_doctr(image: "Image.Image", screenshot_path: Optional[str]) -> List[dict]:
|
||||
"""Fallback OCR-seul (docTR). Retourne une liste de text_blocks bruts.
|
||||
|
||||
Aucun VLM, aucune classification fine — juste OCR ⇒ ``text_blocks``.
|
||||
"""
|
||||
if not _HAS_PIL or image is None:
|
||||
return []
|
||||
try:
|
||||
from doctr.io import DocumentFile # type: ignore
|
||||
from doctr.models import ocr_predictor # type: ignore
|
||||
except ImportError:
|
||||
logger.info("[PHASE25] docTR non disponible pour fallback OCR")
|
||||
return []
|
||||
|
||||
# Cache predictor module-level pour éviter rechargement.
|
||||
global _DOCTR_PREDICTOR
|
||||
try:
|
||||
_DOCTR_PREDICTOR # type: ignore[used-before-def]
|
||||
except NameError:
|
||||
_DOCTR_PREDICTOR = None # type: ignore[assignment]
|
||||
|
||||
try:
|
||||
if _DOCTR_PREDICTOR is None: # type: ignore[has-type]
|
||||
_DOCTR_PREDICTOR = ocr_predictor( # type: ignore[assignment]
|
||||
det_arch="db_resnet50", reco_arch="crnn_vgg16_bn", pretrained=True,
|
||||
)
|
||||
except Exception as exc: # pragma: no cover
|
||||
logger.warning("[PHASE25] docTR init KO : %s", exc)
|
||||
return []
|
||||
|
||||
# docTR prend un fichier ou un array numpy ; on privilégie le chemin si fourni.
|
||||
blocks: List[dict] = []
|
||||
try:
|
||||
if screenshot_path and Path(screenshot_path).exists():
|
||||
doc = DocumentFile.from_images([screenshot_path])
|
||||
else:
|
||||
buf = io.BytesIO()
|
||||
image.convert("RGB").save(buf, format="PNG")
|
||||
buf.seek(0)
|
||||
doc = DocumentFile.from_images([buf.getvalue()])
|
||||
result = _DOCTR_PREDICTOR(doc) # type: ignore[misc]
|
||||
W, H = image.size
|
||||
for page in result.pages:
|
||||
for block in page.blocks:
|
||||
for line_obj in block.lines:
|
||||
text = " ".join(w.value for w in line_obj.words).strip()
|
||||
if not text:
|
||||
continue
|
||||
geom = line_obj.geometry # ((x1,y1), (x2,y2)) norm 0-1
|
||||
x1 = int(geom[0][0] * W)
|
||||
y1 = int(geom[0][1] * H)
|
||||
x2 = int(geom[1][0] * W)
|
||||
y2 = int(geom[1][1] * H)
|
||||
blocks.append({
|
||||
"label": text,
|
||||
"text": text,
|
||||
"bbox": [x1, y1, x2, y2],
|
||||
"confidence": 0.6, # docTR ne donne pas de score line-level facilement
|
||||
})
|
||||
except Exception as exc: # pragma: no cover
|
||||
logger.warning("[PHASE25] docTR predict KO : %s", exc)
|
||||
return []
|
||||
|
||||
return blocks
|
||||
|
||||
|
||||
def _elements_to_structure(elements: Iterable[Any]) -> SemanticStructure:
|
||||
"""Convertit la liste OmniParser ``DetectedElement`` en SemanticStructure."""
|
||||
struct = SemanticStructure()
|
||||
for el in elements:
|
||||
# Compatible avec DetectedElement (dataclass) et dict.
|
||||
if hasattr(el, "label"):
|
||||
label = getattr(el, "label", "") or ""
|
||||
bbox = list(getattr(el, "bbox", ()) or ())
|
||||
conf = float(getattr(el, "confidence", 0.5) or 0.5)
|
||||
kind_hint = getattr(el, "element_type", None)
|
||||
elif isinstance(el, dict):
|
||||
label = str(el.get("label") or el.get("text") or "")
|
||||
bbox = list(el.get("bbox") or [])
|
||||
conf = float(el.get("confidence", el.get("score", 0.5)) or 0.5)
|
||||
kind_hint = el.get("element_type") or el.get("type")
|
||||
else:
|
||||
continue
|
||||
|
||||
kind = _classify_element(label, kind_hint)
|
||||
entry = {"label": label, "bbox": bbox, "confidence": conf}
|
||||
if kind == "table":
|
||||
struct.tables.append(entry)
|
||||
elif kind == "field":
|
||||
struct.forms.append(entry)
|
||||
elif kind == "button":
|
||||
struct.buttons.append(entry)
|
||||
else:
|
||||
struct.text_blocks.append({**entry, "text": label})
|
||||
return struct
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Cache disque
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _cache_path(session_id: str, frame_index: int) -> Path:
|
||||
sid = _validate_session_id(session_id)
|
||||
return OMNIPARSER_CACHE_ROOT / sid / f"{int(frame_index)}.json"
|
||||
|
||||
|
||||
def _cache_read(session_id: str, frame_index: int) -> Optional[dict]:
|
||||
path = _cache_path(session_id, frame_index)
|
||||
if not path.exists():
|
||||
return None
|
||||
try:
|
||||
with path.open("r", encoding="utf-8") as fh:
|
||||
return json.load(fh)
|
||||
except (OSError, json.JSONDecodeError) as exc:
|
||||
logger.warning("[PHASE25] cache illisible %s : %s", path, exc)
|
||||
return None
|
||||
|
||||
|
||||
def _cache_write(session_id: str, frame_index: int, payload: dict) -> None:
|
||||
path = _cache_path(session_id, frame_index)
|
||||
try:
|
||||
_ensure_dir(path.parent)
|
||||
tmp = path.with_suffix(".json.tmp")
|
||||
with tmp.open("w", encoding="utf-8") as fh:
|
||||
json.dump(payload, fh, ensure_ascii=False, indent=2)
|
||||
tmp.replace(path)
|
||||
except OSError as exc: # pragma: no cover
|
||||
logger.warning("[PHASE25] cache ecriture KO %s : %s", path, exc)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Analyseur principal
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
class Phase25Analyzer:
|
||||
"""Analyseur sémantique post-apprentissage.
|
||||
|
||||
Usage minimal :
|
||||
|
||||
analyzer = Phase25Analyzer(session_id="abc123")
|
||||
result = analyzer.analyze_frames(frames=[(0, img0), (12, img12), ...])
|
||||
path = analyzer.write_semantic_yaml(result, slug="ma_competence")
|
||||
|
||||
``frames`` est une séquence ``(frame_index, PIL.Image[, screenshot_path])``.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
session_id: str,
|
||||
*,
|
||||
omniparser: Optional[_OmniParserSafeWrapper] = None,
|
||||
max_screens: int = MAX_SCREENS_PER_SESSION,
|
||||
timeout_sec: float = OMNIPARSER_TIMEOUT_SEC,
|
||||
) -> None:
|
||||
self.session_id = _validate_session_id(session_id)
|
||||
self.omniparser = omniparser if omniparser is not None else _OmniParserSafeWrapper()
|
||||
self.max_screens = max_screens
|
||||
self.timeout_sec = timeout_sec
|
||||
self._healthcheck_passed = True
|
||||
self._healthcheck_reason: Optional[str] = None
|
||||
|
||||
# -- healthcheck -------------------------------------------------------
|
||||
|
||||
def healthcheck(self) -> bool:
|
||||
"""Vérifie qu'OmniParser répond sur une image bidon (cf. specs §7).
|
||||
|
||||
- Si l'adapter est ``available=False`` ⇒ healthcheck KO (mais on
|
||||
continuera quand même en mode dégradé OCR-seul).
|
||||
- Si l'adapter lève une exception ⇒ KO + log dédié.
|
||||
"""
|
||||
if not _HAS_PIL:
|
||||
self._healthcheck_passed = False
|
||||
self._healthcheck_reason = "PIL indisponible"
|
||||
return False
|
||||
if not self.omniparser.available:
|
||||
self._healthcheck_passed = False
|
||||
self._healthcheck_reason = (
|
||||
self.omniparser.import_error or "OmniParser indisponible"
|
||||
)
|
||||
return False
|
||||
try:
|
||||
dummy = Image.new("RGB", (64, 64), color=(255, 255, 255))
|
||||
_ = self.omniparser.detect(dummy, timeout=self.timeout_sec)
|
||||
self._healthcheck_passed = True
|
||||
self._healthcheck_reason = None
|
||||
return True
|
||||
except Exception as exc:
|
||||
_log_omniparser_error(self.session_id, -1, exc)
|
||||
self._healthcheck_passed = False
|
||||
self._healthcheck_reason = f"{type(exc).__name__}: {exc}"
|
||||
return False
|
||||
|
||||
# -- analyse écran ----------------------------------------------------
|
||||
|
||||
def analyze_screen(
|
||||
self,
|
||||
frame_index: int,
|
||||
image: "Image.Image",
|
||||
phash: str,
|
||||
*,
|
||||
screenshot_path: Optional[str] = None,
|
||||
window_title: Optional[str] = None,
|
||||
force_fallback: bool = False,
|
||||
) -> ScreenAnalysis:
|
||||
"""Analyse un écran représentatif.
|
||||
|
||||
Stratégie :
|
||||
1. Cache disque (idempotence par session_id+frame_index).
|
||||
2. OmniParser via wrapper safe → sinon fallback OCR-seul docTR.
|
||||
3. Exception ⇒ log dédié + ``degraded=True`` + structure docTR.
|
||||
"""
|
||||
# 1. Cache
|
||||
cached = _cache_read(self.session_id, frame_index)
|
||||
if cached is not None:
|
||||
struct = SemanticStructure(
|
||||
tables=cached.get("structure", {}).get("tables", []),
|
||||
forms=cached.get("structure", {}).get("forms", []),
|
||||
buttons=cached.get("structure", {}).get("buttons", []),
|
||||
text_blocks=cached.get("structure", {}).get("text_blocks", []),
|
||||
)
|
||||
return ScreenAnalysis(
|
||||
index=frame_index,
|
||||
phash=cached.get("phash", phash),
|
||||
screen_id=cached.get("screen_id", f"screen_{frame_index:03d}"),
|
||||
screenshot_path=cached.get("screenshot_path", screenshot_path),
|
||||
structure=struct,
|
||||
degraded=bool(cached.get("degraded", False)),
|
||||
degraded_reason=cached.get("degraded_reason"),
|
||||
elapsed_sec=float(cached.get("elapsed_sec", 0.0)),
|
||||
window_title=cached.get("window_title", window_title),
|
||||
)
|
||||
|
||||
t0 = time.monotonic()
|
||||
degraded = False
|
||||
degraded_reason: Optional[str] = None
|
||||
structure: SemanticStructure
|
||||
|
||||
use_omniparser = self.omniparser.available and not force_fallback
|
||||
if use_omniparser:
|
||||
try:
|
||||
elements = _detect_via_omniparser(
|
||||
self.omniparser, image, timeout=self.timeout_sec,
|
||||
)
|
||||
structure = _elements_to_structure(elements)
|
||||
if not (structure.tables or structure.forms or structure.buttons or structure.text_blocks):
|
||||
# OmniParser n'a rien produit : on ajoute en complément docTR text_blocks.
|
||||
blocks = _detect_via_doctr(image, screenshot_path)
|
||||
structure.text_blocks.extend(blocks)
|
||||
except Exception as exc:
|
||||
_log_omniparser_error(self.session_id, frame_index, exc)
|
||||
degraded = True
|
||||
degraded_reason = f"omniparser_exception: {type(exc).__name__}"
|
||||
blocks = _detect_via_doctr(image, screenshot_path)
|
||||
structure = SemanticStructure(text_blocks=blocks)
|
||||
else:
|
||||
degraded = True
|
||||
degraded_reason = (
|
||||
"omniparser_unavailable: " + (self.omniparser.import_error or "n/a")
|
||||
if not self.omniparser.available
|
||||
else "forced_fallback"
|
||||
)
|
||||
blocks = _detect_via_doctr(image, screenshot_path)
|
||||
structure = SemanticStructure(text_blocks=blocks)
|
||||
|
||||
elapsed = time.monotonic() - t0
|
||||
analysis = ScreenAnalysis(
|
||||
index=frame_index,
|
||||
phash=phash,
|
||||
screen_id=f"screen_{frame_index:03d}",
|
||||
screenshot_path=screenshot_path,
|
||||
structure=structure,
|
||||
degraded=degraded,
|
||||
degraded_reason=degraded_reason,
|
||||
elapsed_sec=elapsed,
|
||||
window_title=window_title,
|
||||
)
|
||||
|
||||
# Cache écriture (best-effort).
|
||||
_cache_write(self.session_id, frame_index, analysis.to_dict())
|
||||
return analysis
|
||||
|
||||
# -- pipeline complet -------------------------------------------------
|
||||
|
||||
def analyze_frames(
|
||||
self,
|
||||
frames: Sequence[Tuple[int, "Image.Image"]],
|
||||
*,
|
||||
screenshot_paths: Optional[dict[int, str]] = None,
|
||||
window_titles: Optional[dict[int, str]] = None,
|
||||
run_healthcheck: bool = True,
|
||||
) -> Phase25Result:
|
||||
"""Pipeline complet : grouping phash → analyse → cap → résultat.
|
||||
|
||||
Args:
|
||||
frames: liste ``(frame_index, PIL.Image)``.
|
||||
screenshot_paths: mapping ``frame_index -> path`` (optionnel).
|
||||
window_titles: mapping ``frame_index -> window_title`` (optionnel).
|
||||
run_healthcheck: lancer le healthcheck OmniParser avant analyse.
|
||||
|
||||
Returns:
|
||||
``Phase25Result`` avec ``too_complex=True`` si > max_screens.
|
||||
"""
|
||||
if not _HAS_PIL:
|
||||
raise RuntimeError("PIL est requis pour Phase25Analyzer.analyze_frames")
|
||||
|
||||
if run_healthcheck:
|
||||
self.healthcheck()
|
||||
if not self._healthcheck_passed:
|
||||
logger.warning(
|
||||
"[PHASE25] healthcheck OmniParser KO (%s) -> mode degrade docTR",
|
||||
self._healthcheck_reason,
|
||||
)
|
||||
|
||||
force_fallback = not self._healthcheck_passed
|
||||
|
||||
# 1. Regrouper par similarité perceptuelle.
|
||||
reps = identify_distinct_screens(frames)
|
||||
|
||||
# 2. Cap MAX_SCREENS_PER_SESSION.
|
||||
too_complex = len(reps) > self.max_screens
|
||||
if too_complex:
|
||||
logger.warning(
|
||||
"[PHASE25] session %s : %d ecrans distincts > cap %d -> too_complex",
|
||||
self.session_id, len(reps), self.max_screens,
|
||||
)
|
||||
reps = reps[: self.max_screens]
|
||||
|
||||
# 3. Analyser chaque représentant.
|
||||
sp = screenshot_paths or {}
|
||||
wt = window_titles or {}
|
||||
screens: List[ScreenAnalysis] = []
|
||||
any_degraded = False
|
||||
for idx, img, phash in reps:
|
||||
analysis = self.analyze_screen(
|
||||
idx,
|
||||
img,
|
||||
phash,
|
||||
screenshot_path=sp.get(idx),
|
||||
window_title=wt.get(idx),
|
||||
force_fallback=force_fallback,
|
||||
)
|
||||
screens.append(analysis)
|
||||
any_degraded = any_degraded or analysis.degraded
|
||||
|
||||
return Phase25Result(
|
||||
session_id=self.session_id,
|
||||
generated_at=datetime.now(timezone.utc).isoformat(),
|
||||
omniparser_available=self.omniparser.available and self._healthcheck_passed,
|
||||
degraded=any_degraded or not self._healthcheck_passed,
|
||||
too_complex=too_complex,
|
||||
screens=screens,
|
||||
healthcheck_passed=self._healthcheck_passed,
|
||||
healthcheck_reason=self._healthcheck_reason,
|
||||
)
|
||||
|
||||
# -- écriture YAML -----------------------------------------------------
|
||||
|
||||
def write_semantic_yaml(
|
||||
self,
|
||||
result: Phase25Result,
|
||||
slug: str,
|
||||
*,
|
||||
target_dir: Optional[Path] = None,
|
||||
) -> Path:
|
||||
"""Écrit le ``.semantic.yaml`` à côté du YAML compétence candidate.
|
||||
|
||||
Args:
|
||||
result: Résultat d'analyse Phase 2.5.
|
||||
slug: slug compétence (validé contre SLUG_PATTERN).
|
||||
target_dir: répertoire cible (défaut : ``data/competences/candidate/``).
|
||||
|
||||
Returns:
|
||||
Path absolu du fichier écrit.
|
||||
|
||||
Raises:
|
||||
ValueError: slug invalide.
|
||||
OSError: écriture impossible.
|
||||
"""
|
||||
s = _validate_slug(slug)
|
||||
out_dir = target_dir if target_dir is not None else SEMANTIC_DIR
|
||||
out_dir = Path(out_dir)
|
||||
_ensure_dir(out_dir)
|
||||
|
||||
# Anti écrasement supervised/stable : on refuse explicitement.
|
||||
forbidden = {"supervised", "stable"}
|
||||
if out_dir.name in forbidden:
|
||||
raise ValueError(
|
||||
f"target_dir interdit '{out_dir.name}' (autorise : candidate uniquement)"
|
||||
)
|
||||
|
||||
payload = {
|
||||
"competence_id": s,
|
||||
"semantic_version": 1,
|
||||
"generated_at": result.generated_at,
|
||||
"session_id": result.session_id,
|
||||
"omniparser_available": result.omniparser_available,
|
||||
"degraded": result.degraded,
|
||||
"too_complex": result.too_complex,
|
||||
"healthcheck_passed": result.healthcheck_passed,
|
||||
"healthcheck_reason": result.healthcheck_reason,
|
||||
"screens": [],
|
||||
}
|
||||
for sc in result.screens:
|
||||
payload["screens"].append({
|
||||
"screen_id": sc.screen_id,
|
||||
"phash": sc.phash,
|
||||
"representative_frame_index": sc.index,
|
||||
"screenshot_path": sc.screenshot_path,
|
||||
"window_title": sc.window_title,
|
||||
"degraded": sc.degraded,
|
||||
"degraded_reason": sc.degraded_reason,
|
||||
"elapsed_sec": round(sc.elapsed_sec, 3),
|
||||
"structure": sc.structure.to_dict(),
|
||||
"annotations": [], # placeholder — annotation humaine ultérieure
|
||||
})
|
||||
|
||||
target = out_dir / f"{s}.semantic.yaml"
|
||||
tmp = target.with_suffix(".yaml.tmp")
|
||||
with tmp.open("w", encoding="utf-8") as fh:
|
||||
yaml.safe_dump(payload, fh, allow_unicode=True, sort_keys=False)
|
||||
tmp.replace(target)
|
||||
logger.info(
|
||||
"[PHASE25] semantic yaml ecrit : %s (screens=%d, degraded=%s)",
|
||||
target, len(result.screens), result.degraded,
|
||||
)
|
||||
return target
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------
|
||||
# Helpers utilitaires (chargement frames)
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
||||
def load_frames_from_paths(paths_by_index: dict[int, str]) -> List[Tuple[int, "Image.Image"]]:
|
||||
"""Charge des images PIL à partir d'un mapping ``frame_index -> path``.
|
||||
|
||||
Ignore silencieusement les chemins inexistants (avec log warning).
|
||||
"""
|
||||
if not _HAS_PIL:
|
||||
raise RuntimeError("PIL est requis pour load_frames_from_paths")
|
||||
frames: List[Tuple[int, Image.Image]] = []
|
||||
for idx in sorted(paths_by_index.keys()):
|
||||
p = paths_by_index[idx]
|
||||
try:
|
||||
img = Image.open(p)
|
||||
img.load()
|
||||
frames.append((int(idx), img))
|
||||
except (FileNotFoundError, OSError) as exc:
|
||||
logger.warning("[PHASE25] frame %d illisible (%s) : %s", idx, p, exc)
|
||||
return frames
|
||||
|
||||
|
||||
__all__ = [
|
||||
"Phase25Analyzer",
|
||||
"Phase25Result",
|
||||
"ScreenAnalysis",
|
||||
"SemanticStructure",
|
||||
"SEMANTIC_DIR",
|
||||
"OMNIPARSER_CACHE_DIR",
|
||||
"OMNIPARSER_CACHE_ROOT",
|
||||
"OMNIPARSER_ERROR_LOG",
|
||||
"PHASH_HAMMING_THRESHOLD",
|
||||
"MAX_SCREENS_PER_SESSION",
|
||||
"compute_phash",
|
||||
"identify_distinct_screens",
|
||||
"load_frames_from_paths",
|
||||
]
|
||||
476
tests/integration/test_phase25_semantic_integration.py
Normal file
476
tests/integration/test_phase25_semantic_integration.py
Normal file
@@ -0,0 +1,476 @@
|
||||
"""Tests d'intégration Phase 2.5 sémantique.
|
||||
|
||||
Specs : ``docs/POC/SPECS_PHASE_25_SEMANTIQUE_2026-06-01.md``.
|
||||
|
||||
Vérifie le flux complet :
|
||||
- Charger des screenshots disque -> identifier écrans distincts ->
|
||||
analyser avec OmniParser mocké -> écrire ``.semantic.yaml`` valide.
|
||||
- Tester aussi le fallback OmniParser KO -> degraded YAML.
|
||||
- L'endpoint FastAPI est testé via ``TestClient`` (auth Bearer désactivée
|
||||
via ``RPA_AUTH_DISABLED=true`` pour ce test).
|
||||
- Correctifs P1-SEMANTIQUE GO conditionnel Qwen :
|
||||
* non-blocage event loop pendant analyze_frames (run_in_executor).
|
||||
* timeout effectif autour de chaque appel OmniParser.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
from PIL import Image, ImageDraw
|
||||
|
||||
_ROOT = str(Path(__file__).resolve().parents[2])
|
||||
if _ROOT not in sys.path:
|
||||
sys.path.insert(0, _ROOT)
|
||||
|
||||
from core.semantic import phase25_analyzer as P # noqa: E402
|
||||
|
||||
|
||||
def _make_screenshot(path: Path, seed: int) -> None:
|
||||
import random
|
||||
rng = random.Random(seed)
|
||||
img = Image.new("RGB", (320, 240), color=(255, 255, 255))
|
||||
d = ImageDraw.Draw(img)
|
||||
for _ in range(30):
|
||||
x = rng.randint(0, 300)
|
||||
y = rng.randint(0, 220)
|
||||
w = rng.randint(10, 30)
|
||||
h = rng.randint(10, 30)
|
||||
col = (rng.randint(0, 255), rng.randint(0, 255), rng.randint(0, 255))
|
||||
d.rectangle([x, y, x + w, y + h], fill=col)
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
img.save(path, format="PNG")
|
||||
|
||||
|
||||
def _build_mock_omniparser(elements):
|
||||
w = P._OmniParserSafeWrapper.__new__(P._OmniParserSafeWrapper)
|
||||
w._adapter = MagicMock()
|
||||
w._available = True
|
||||
w._import_error = None
|
||||
w._adapter.detect.return_value = elements
|
||||
return w
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Integration : flux complet load -> analyze -> write YAML
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestFullFlow:
|
||||
def test_load_analyze_write_yaml(self, tmp_path, monkeypatch):
|
||||
# Préparer 4 screenshots : 2 visuels identiques + 1 différent + 1 répété.
|
||||
shots_dir = tmp_path / "shots"
|
||||
_make_screenshot(shots_dir / "0.png", seed=1)
|
||||
_make_screenshot(shots_dir / "1.png", seed=1) # même seed = même image
|
||||
_make_screenshot(shots_dir / "2.png", seed=2)
|
||||
_make_screenshot(shots_dir / "3.png", seed=2)
|
||||
paths = {i: str(shots_dir / f"{i}.png") for i in range(4)}
|
||||
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
|
||||
mock_op = _build_mock_omniparser([
|
||||
{"label": "Valider", "bbox": [10, 10, 80, 40], "confidence": 0.9, "element_type": "button"},
|
||||
{"label": "Champ texte", "bbox": [100, 10, 300, 40], "confidence": 0.85, "element_type": "input"},
|
||||
])
|
||||
analyzer = P.Phase25Analyzer(session_id="integ_sess", omniparser=mock_op)
|
||||
frames = P.load_frames_from_paths(paths)
|
||||
assert len(frames) == 4
|
||||
result = analyzer.analyze_frames(
|
||||
frames=frames,
|
||||
screenshot_paths=paths,
|
||||
window_titles={0: "Easily Login", 2: "Easily Patient"},
|
||||
)
|
||||
# Doit avoir regroupé en 2 écrans distincts.
|
||||
assert len(result.screens) == 2
|
||||
assert result.too_complex is False
|
||||
assert result.degraded is False
|
||||
# Le window_title du représentant doit être propagé.
|
||||
rep_indexes = {s.index for s in result.screens}
|
||||
assert 0 in rep_indexes or 1 in rep_indexes
|
||||
assert 2 in rep_indexes or 3 in rep_indexes
|
||||
|
||||
# Écrire le YAML.
|
||||
target = analyzer.write_semantic_yaml(
|
||||
result, slug="competence_demo", target_dir=tmp_path / "out",
|
||||
)
|
||||
assert target.exists()
|
||||
data = yaml.safe_load(target.read_text(encoding="utf-8"))
|
||||
assert data["competence_id"] == "competence_demo"
|
||||
assert len(data["screens"]) == 2
|
||||
# Au moins 1 button + 1 field doivent apparaître dans chaque structure.
|
||||
for sc in data["screens"]:
|
||||
assert len(sc["structure"]["buttons"]) >= 1
|
||||
assert len(sc["structure"]["forms"]) >= 1
|
||||
|
||||
def test_omniparser_ko_fallback_yaml_degraded(self, tmp_path, monkeypatch):
|
||||
shots_dir = tmp_path / "shots"
|
||||
_make_screenshot(shots_dir / "0.png", seed=10)
|
||||
paths = {0: str(shots_dir / "0.png")}
|
||||
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
monkeypatch.setattr(P, "LOGS_DIR", tmp_path / "logs")
|
||||
monkeypatch.setattr(
|
||||
P, "OMNIPARSER_ERROR_LOG", tmp_path / "logs" / "omniparser_errors.log"
|
||||
)
|
||||
# docTR stub léger.
|
||||
monkeypatch.setattr(
|
||||
P, "_detect_via_doctr",
|
||||
lambda image, screenshot_path: [
|
||||
{"label": "FAKE_OCR", "text": "FAKE_OCR", "bbox": [0, 0, 50, 20], "confidence": 0.6},
|
||||
],
|
||||
)
|
||||
|
||||
# OmniParser disponible mais qui lève.
|
||||
op = P._OmniParserSafeWrapper.__new__(P._OmniParserSafeWrapper)
|
||||
op._adapter = MagicMock()
|
||||
op._available = True
|
||||
op._import_error = None
|
||||
op._adapter.detect.side_effect = RuntimeError("OmniParser HS")
|
||||
|
||||
analyzer = P.Phase25Analyzer(session_id="integ_fb", omniparser=op)
|
||||
frames = P.load_frames_from_paths(paths)
|
||||
result = analyzer.analyze_frames(frames=frames, screenshot_paths=paths)
|
||||
assert result.degraded is True
|
||||
assert len(result.screens) == 1
|
||||
assert result.screens[0].degraded is True
|
||||
|
||||
# Le YAML doit être marqué dégradé mais valide.
|
||||
target = analyzer.write_semantic_yaml(
|
||||
result, slug="comp_fallback", target_dir=tmp_path / "out",
|
||||
)
|
||||
data = yaml.safe_load(target.read_text(encoding="utf-8"))
|
||||
assert data["degraded"] is True
|
||||
assert data["screens"][0]["degraded"] is True
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Integration : endpoint FastAPI
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def app_client(tmp_path_factory):
|
||||
"""TestClient FastAPI avec auth Bearer désactivée."""
|
||||
# IMPORTANT : désactiver l'auth AVANT l'import du module api_stream.
|
||||
os.environ["RPA_AUTH_DISABLED"] = "true"
|
||||
os.environ.setdefault("RPA_API_TOKEN", "test-token-phase25")
|
||||
|
||||
# Cache root pointe vers un tmp partagé.
|
||||
cache_root = tmp_path_factory.mktemp("op_cache")
|
||||
|
||||
from fastapi.testclient import TestClient
|
||||
from agent_v0.server_v1 import api_stream as api # noqa: E402
|
||||
|
||||
# Redirige le cache et logs Phase 2.5 vers tmp.
|
||||
from core.semantic import phase25_analyzer as PA
|
||||
PA.OMNIPARSER_CACHE_ROOT = cache_root
|
||||
PA.LOGS_DIR = cache_root / "logs"
|
||||
PA.OMNIPARSER_ERROR_LOG = cache_root / "logs" / "omniparser_errors.log"
|
||||
|
||||
client = TestClient(api.app)
|
||||
yield client, cache_root
|
||||
|
||||
|
||||
class TestEndpoint:
|
||||
def test_endpoint_returns_empty_result_when_no_paths(self, app_client):
|
||||
client, _cache = app_client
|
||||
# Aucun frame réel n'existe -> on attend 200 avec screens=[].
|
||||
resp = client.post(
|
||||
"/api/v1/lea/screen/analyze",
|
||||
json={
|
||||
"session_id": "non_existent_session_xyz",
|
||||
"screenshot_indexes": [0, 1, 2],
|
||||
},
|
||||
)
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
assert data["session_id"] == "non_existent_session_xyz"
|
||||
assert data["screens"] == []
|
||||
assert data["degraded"] is True
|
||||
|
||||
def test_endpoint_invalid_session_id(self, app_client):
|
||||
client, _cache = app_client
|
||||
resp = client.post(
|
||||
"/api/v1/lea/screen/analyze",
|
||||
json={
|
||||
"session_id": "../etc/passwd",
|
||||
"screenshot_indexes": [],
|
||||
},
|
||||
)
|
||||
assert resp.status_code == 400
|
||||
assert resp.json()["detail"]["error"] == "invalid_session_id"
|
||||
|
||||
def test_endpoint_full_flow_with_explicit_paths(self, app_client, tmp_path):
|
||||
client, _cache = app_client
|
||||
# Préparer 3 screenshots et passer les chemins explicitement.
|
||||
shots = tmp_path / "endpoint_shots"
|
||||
_make_screenshot(shots / "10.png", seed=5)
|
||||
_make_screenshot(shots / "11.png", seed=5)
|
||||
_make_screenshot(shots / "12.png", seed=99)
|
||||
paths = {
|
||||
"10": str(shots / "10.png"),
|
||||
"11": str(shots / "11.png"),
|
||||
"12": str(shots / "12.png"),
|
||||
}
|
||||
|
||||
# Patcher OmniParserSafeWrapper pour qu'il soit toujours indisponible
|
||||
# (fallback OCR-seul docTR) -> on stubbe docTR aussi.
|
||||
from core.semantic import phase25_analyzer as PA
|
||||
|
||||
def _stub_doctr(image, screenshot_path):
|
||||
return [
|
||||
{"label": "TXT", "text": "TXT", "bbox": [0, 0, 30, 10], "confidence": 0.6},
|
||||
]
|
||||
|
||||
original_doctr = PA._detect_via_doctr
|
||||
PA._detect_via_doctr = _stub_doctr
|
||||
|
||||
# Force fallback : on patche le wrapper pour qu'il soit unavailable.
|
||||
original_wrapper_init = PA._OmniParserSafeWrapper._try_import
|
||||
|
||||
def _no_op_import(self):
|
||||
self._adapter = None
|
||||
self._available = False
|
||||
self._import_error = "stubbed_unavailable"
|
||||
|
||||
PA._OmniParserSafeWrapper._try_import = _no_op_import
|
||||
|
||||
try:
|
||||
resp = client.post(
|
||||
"/api/v1/lea/screen/analyze",
|
||||
json={
|
||||
"session_id": "endpoint_test",
|
||||
"screenshot_indexes": [10, 11, 12],
|
||||
"screenshot_paths": paths,
|
||||
"window_titles": {"10": "Win A", "12": "Win B"},
|
||||
"write_yaml": False,
|
||||
},
|
||||
)
|
||||
finally:
|
||||
PA._detect_via_doctr = original_doctr
|
||||
PA._OmniParserSafeWrapper._try_import = original_wrapper_init
|
||||
|
||||
assert resp.status_code == 200, resp.text
|
||||
data = resp.json()
|
||||
assert data["session_id"] == "endpoint_test"
|
||||
# 2 écrans distincts attendus (idx 10/11 groupés + idx 12).
|
||||
assert 1 <= len(data["screens"]) <= 3
|
||||
# Mode dégradé -> text_blocks docTR.
|
||||
assert data["degraded"] is True
|
||||
# Contrat snapshot : présence de "elements" aplatis.
|
||||
for sc in data["screens"]:
|
||||
assert "elements" in sc
|
||||
assert "structure" in sc
|
||||
assert "hash" in sc
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# CORRECTIFS P1-SEMANTIQUE GO conditionnel Qwen :
|
||||
# 1. Non-blocage event loop pendant analyze_frames (run_in_executor).
|
||||
# 2. Timeout effectif autour de chaque appel OmniParser + log dédié.
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestEventLoopNotBlocked:
|
||||
"""L'endpoint POST /api/v1/lea/screen/analyze ne doit pas bloquer
|
||||
l'event loop pendant l'analyse synchrone (analyze_frames).
|
||||
|
||||
Méthode : monkeypatch ``analyze_frames`` pour qu'il dorme 2s, lancer
|
||||
2 appels en parallèle via httpx.AsyncClient + ASGITransport, vérifier
|
||||
que la durée totale est sensiblement < 4s (donc < ~3s) car les deux
|
||||
analyses doivent progresser en concurrent dans le ThreadPoolExecutor.
|
||||
"""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_endpoint_does_not_block_event_loop(self, tmp_path, monkeypatch):
|
||||
import httpx
|
||||
from httpx import ASGITransport
|
||||
|
||||
os.environ["RPA_AUTH_DISABLED"] = "true"
|
||||
os.environ.setdefault("RPA_API_TOKEN", "test-token-phase25-loop")
|
||||
|
||||
from agent_v0.server_v1 import api_stream as api
|
||||
from core.semantic import phase25_analyzer as PA
|
||||
|
||||
# Préparer 1 screenshot pour passer la validation paths.
|
||||
shots = tmp_path / "shots_loop"
|
||||
_make_screenshot(shots / "0.png", seed=7)
|
||||
|
||||
# Monkeypatch analyze_frames pour dormir 2s (simule analyse longue
|
||||
# CPU-bound côté analyzer). Si l'endpoint bloque l'event loop,
|
||||
# 2 appels concurrents prendront ~4s ; sinon ~2s (executor en //).
|
||||
sleep_sec = 2.0
|
||||
|
||||
class _DummyResult:
|
||||
def __init__(self, sid):
|
||||
self.session_id = sid
|
||||
self.generated_at = "2026-06-01T00:00:00Z"
|
||||
self.omniparser_available = False
|
||||
self.degraded = False
|
||||
self.too_complex = False
|
||||
self.screens = []
|
||||
|
||||
def to_dict(self):
|
||||
return {
|
||||
"session_id": self.session_id,
|
||||
"generated_at": self.generated_at,
|
||||
"omniparser_available": self.omniparser_available,
|
||||
"degraded": self.degraded,
|
||||
"too_complex": self.too_complex,
|
||||
"healthcheck_passed": True,
|
||||
"healthcheck_reason": None,
|
||||
"screens": [],
|
||||
}
|
||||
|
||||
def _slow_analyze(self, *args, **kwargs):
|
||||
time.sleep(sleep_sec)
|
||||
return _DummyResult(self.session_id)
|
||||
|
||||
monkeypatch.setattr(PA.Phase25Analyzer, "analyze_frames", _slow_analyze)
|
||||
|
||||
transport = ASGITransport(app=api.app)
|
||||
async with httpx.AsyncClient(
|
||||
transport=transport, base_url="http://test"
|
||||
) as client:
|
||||
paths = {"0": str(shots / "0.png")}
|
||||
payload = {
|
||||
"session_id": "loop_sess_a",
|
||||
"screenshot_indexes": [0],
|
||||
"screenshot_paths": paths,
|
||||
}
|
||||
payload2 = {**payload, "session_id": "loop_sess_b"}
|
||||
|
||||
t0 = time.monotonic()
|
||||
r1, r2 = await asyncio.gather(
|
||||
client.post("/api/v1/lea/screen/analyze", json=payload),
|
||||
client.post("/api/v1/lea/screen/analyze", json=payload2),
|
||||
)
|
||||
elapsed = time.monotonic() - t0
|
||||
|
||||
assert r1.status_code == 200, r1.text
|
||||
assert r2.status_code == 200, r2.text
|
||||
# Marge confortable : si event loop bloqué -> ~4s, sinon ~2s.
|
||||
# On accepte jusqu'à 3.5s pour absorber overhead CI / GC.
|
||||
assert elapsed < 3.5, (
|
||||
f"event loop semble bloque : 2 appels paralleles ont pris {elapsed:.2f}s "
|
||||
f"(attendu < 3.5s avec sleep={sleep_sec}s)"
|
||||
)
|
||||
|
||||
|
||||
class TestOmniParserTimeout:
|
||||
"""Le ``OMNIPARSER_TIMEOUT_SEC`` doit être appliqué comme timeout dur
|
||||
autour de chaque appel ``_adapter.detect`` via ThreadPoolExecutor +
|
||||
``future.result(timeout=...)``.
|
||||
|
||||
Si OmniParser hang -> ``concurrent.futures.TimeoutError`` -> log dédié
|
||||
+ ``degraded=True`` + fallback docTR. Pas de 500 vers le caller.
|
||||
"""
|
||||
|
||||
def test_omniparser_timeout_triggers_fallback(self, tmp_path, monkeypatch):
|
||||
# Forcer un timeout court (0.5s) pour ne pas bloquer la suite de tests.
|
||||
monkeypatch.setattr(P, "OMNIPARSER_TIMEOUT_SEC", 0.5)
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
monkeypatch.setattr(P, "LOGS_DIR", tmp_path / "logs")
|
||||
monkeypatch.setattr(
|
||||
P, "OMNIPARSER_ERROR_LOG", tmp_path / "logs" / "omniparser_errors.log"
|
||||
)
|
||||
|
||||
# Stub docTR : retourne 1 text_block pour confirmer le fallback.
|
||||
monkeypatch.setattr(
|
||||
P, "_detect_via_doctr",
|
||||
lambda image, screenshot_path: [
|
||||
{"label": "FALLBACK_OCR", "text": "FALLBACK_OCR",
|
||||
"bbox": [0, 0, 30, 10], "confidence": 0.6},
|
||||
],
|
||||
)
|
||||
|
||||
# OmniParser disponible mais qui hang (dort > timeout).
|
||||
op = P._OmniParserSafeWrapper.__new__(P._OmniParserSafeWrapper)
|
||||
op._adapter = MagicMock()
|
||||
op._available = True
|
||||
op._import_error = None
|
||||
|
||||
def _hang(image):
|
||||
time.sleep(5.0) # >> 0.5s timeout
|
||||
return []
|
||||
|
||||
op._adapter.detect.side_effect = _hang
|
||||
|
||||
analyzer = P.Phase25Analyzer(
|
||||
session_id="timeout_sess",
|
||||
omniparser=op,
|
||||
timeout_sec=0.5,
|
||||
)
|
||||
img = Image.new("RGB", (64, 64), color=(255, 255, 255))
|
||||
|
||||
t0 = time.monotonic()
|
||||
# On contourne le healthcheck (qui consommerait aussi un timeout).
|
||||
result = analyzer.analyze_screen(
|
||||
frame_index=0, image=img, phash="ab", force_fallback=False,
|
||||
)
|
||||
elapsed = time.monotonic() - t0
|
||||
|
||||
# Timeout effectif : doit terminer en ~0.5s (large marge à 3s
|
||||
# pour CPU lent).
|
||||
assert elapsed < 3.0, (
|
||||
f"timeout pas applique : analyze_screen a pris {elapsed:.2f}s "
|
||||
f"(attendu ~0.5s, hang stub = 5s)"
|
||||
)
|
||||
# Fallback déclenché.
|
||||
assert result.degraded is True
|
||||
assert result.degraded_reason and "omniparser_exception" in result.degraded_reason
|
||||
assert "TimeoutError" in result.degraded_reason
|
||||
# docTR a pris la main.
|
||||
assert len(result.structure.text_blocks) == 1
|
||||
assert result.structure.text_blocks[0]["label"] == "FALLBACK_OCR"
|
||||
|
||||
def test_omniparser_timeout_logged(self, tmp_path, monkeypatch):
|
||||
"""Vérifie qu'une ligne JSON est ajoutée dans
|
||||
``logs/omniparser_errors.log`` avec motif timeout.
|
||||
"""
|
||||
monkeypatch.setattr(P, "OMNIPARSER_TIMEOUT_SEC", 0.3)
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
monkeypatch.setattr(P, "LOGS_DIR", tmp_path / "logs")
|
||||
log_path = tmp_path / "logs" / "omniparser_errors.log"
|
||||
monkeypatch.setattr(P, "OMNIPARSER_ERROR_LOG", log_path)
|
||||
monkeypatch.setattr(
|
||||
P, "_detect_via_doctr",
|
||||
lambda image, screenshot_path: [],
|
||||
)
|
||||
|
||||
op = P._OmniParserSafeWrapper.__new__(P._OmniParserSafeWrapper)
|
||||
op._adapter = MagicMock()
|
||||
op._available = True
|
||||
op._import_error = None
|
||||
|
||||
def _hang(image):
|
||||
time.sleep(3.0)
|
||||
return []
|
||||
|
||||
op._adapter.detect.side_effect = _hang
|
||||
|
||||
analyzer = P.Phase25Analyzer(
|
||||
session_id="timeout_log_sess",
|
||||
omniparser=op,
|
||||
timeout_sec=0.3,
|
||||
)
|
||||
img = Image.new("RGB", (64, 64), color=(255, 255, 255))
|
||||
result = analyzer.analyze_screen(
|
||||
frame_index=42, image=img, phash="cd", force_fallback=False,
|
||||
)
|
||||
|
||||
assert result.degraded is True
|
||||
assert log_path.exists(), "le log d'erreur omniparser doit etre cree"
|
||||
log_content = log_path.read_text(encoding="utf-8")
|
||||
# Le log est append-only JSON-lines. On vérifie qu'au moins une
|
||||
# ligne contient TimeoutError + session_id.
|
||||
assert "TimeoutError" in log_content
|
||||
assert "timeout_log_sess" in log_content
|
||||
assert '"frame_index": 42' in log_content
|
||||
245
tests/integration/test_shadow_full_cycle.py
Normal file
245
tests/integration/test_shadow_full_cycle.py
Normal file
@@ -0,0 +1,245 @@
|
||||
"""Tests integration HTTP — cycle complet Lea-first jusqu'au /persist.
|
||||
|
||||
Specs : docs/POC/SPECS_ENDPOINT_PERSIST_2026-06-01.md
|
||||
|
||||
Le focus est sur l'endpoint /persist : on mocke les phases amont (Shadow
|
||||
build) en construisant directement le workflow_ir. Pas de lancement reel
|
||||
de session Shadow ici.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
_ROOT = str(Path(__file__).resolve().parents[2])
|
||||
if _ROOT not in sys.path:
|
||||
sys.path.insert(0, _ROOT)
|
||||
|
||||
|
||||
pytestmark = pytest.mark.integration
|
||||
|
||||
|
||||
_TEST_API_TOKEN = "test_persist_endpoint_token_xyz"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def persist_client(monkeypatch, tmp_path):
|
||||
"""TestClient FastAPI + redirection des chemins persist vers tmp_path."""
|
||||
monkeypatch.setenv("RPA_API_TOKEN", _TEST_API_TOKEN)
|
||||
monkeypatch.delenv("RPA_AUTH_DISABLED", raising=False)
|
||||
|
||||
# DB agents isolee pour eviter le guard fleet
|
||||
monkeypatch.setenv("RPA_AGENTS_DB_PATH", str(tmp_path / "agents.db"))
|
||||
|
||||
from fastapi.testclient import TestClient
|
||||
from agent_v0.server_v1 import api_stream
|
||||
from agent_v0.server_v1.agent_registry import AgentRegistry
|
||||
from core.competences import persist as P
|
||||
|
||||
monkeypatch.setattr(api_stream, "API_TOKEN", _TEST_API_TOKEN)
|
||||
|
||||
# api_stream peut deja etre importe par un autre test avant que
|
||||
# RPA_AGENTS_DB_PATH soit pose. Isoler explicitement le registre global.
|
||||
test_registry = AgentRegistry(db_path=str(tmp_path / "agents.db"))
|
||||
monkeypatch.setattr(api_stream, "agent_registry", test_registry)
|
||||
|
||||
# Rediriger toutes les ecritures vers tmp_path
|
||||
candidate_dir = tmp_path / "competences" / "candidate"
|
||||
candidate_dir.mkdir(parents=True, exist_ok=True)
|
||||
audit_path = tmp_path / "competences" / "persist_audit.jsonl"
|
||||
incomplete_path = tmp_path / "competences" / "incomplete_learnings.jsonl"
|
||||
|
||||
monkeypatch.setattr(P, "COMPETENCES_ROOT", tmp_path / "competences")
|
||||
monkeypatch.setattr(P, "CANDIDATE_DIR", candidate_dir)
|
||||
monkeypatch.setattr(P, "AUDIT_PATH", audit_path)
|
||||
monkeypatch.setattr(P, "INCOMPLETE_PATH", incomplete_path)
|
||||
|
||||
# Reset du rate limiter pour eviter les fuites entre tests
|
||||
P.persist_rate_limiter.reset()
|
||||
|
||||
client = TestClient(api_stream.app, raise_server_exceptions=False)
|
||||
return client, tmp_path
|
||||
|
||||
|
||||
def _auth_headers():
|
||||
return {"Authorization": f"Bearer {_TEST_API_TOKEN}"}
|
||||
|
||||
|
||||
def _minimal_workflow_ir():
|
||||
return {
|
||||
"steps": [
|
||||
{
|
||||
"kind": "click",
|
||||
"primitive_ref": "click",
|
||||
"parameters": {"target": "Bouton OK"},
|
||||
"description": "Clic sur Bouton OK",
|
||||
}
|
||||
],
|
||||
"preconditions": [],
|
||||
"success_marker": {
|
||||
"mode": "all_of",
|
||||
"timeout_ms": 5000,
|
||||
"markers": [],
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class TestPersistEndpointSuccess:
|
||||
"""Cas nominal : payload valide -> 201 + YAML cree + audit ecrit."""
|
||||
|
||||
def test_persist_returns_201_and_writes_yaml(self, persist_client):
|
||||
client, tmp_path = persist_client
|
||||
resp = client.post(
|
||||
"/api/v1/lea/competences/candidate/persist",
|
||||
json={
|
||||
"name": "Test Cycle Complet",
|
||||
"machine_id": "machine_test_x",
|
||||
"session_id": "sess_xyz",
|
||||
"workflow_ir": _minimal_workflow_ir(),
|
||||
"parameters": [],
|
||||
"annotations_semantiques": {"intent_fr": "tester cycle"},
|
||||
"learning_metadata": {"persist_id": "uuid-cycle-1"},
|
||||
},
|
||||
headers=_auth_headers(),
|
||||
)
|
||||
assert resp.status_code == 201, resp.text
|
||||
body = resp.json()
|
||||
assert body["competence_id"] == "test_cycle_complet"
|
||||
assert body["learning_state"] == "candidate"
|
||||
assert body["persist_id"] == "uuid-cycle-1"
|
||||
assert body["audit_entry_id"] >= 1
|
||||
|
||||
# Le YAML doit etre present sur disque (chemin redirige par fixture)
|
||||
yaml_file = tmp_path / "competences" / "candidate" / "test_cycle_complet.yaml"
|
||||
assert yaml_file.exists()
|
||||
# Audit enrichi
|
||||
audit = tmp_path / "competences" / "persist_audit.jsonl"
|
||||
assert audit.exists()
|
||||
lines = audit.read_text(encoding="utf-8").strip().splitlines()
|
||||
assert any(json.loads(li).get("persist_id") == "uuid-cycle-1" for li in lines)
|
||||
|
||||
def test_persist_idempotence_returns_previous(self, persist_client):
|
||||
client, _ = persist_client
|
||||
payload = {
|
||||
"name": "Idempotent Test",
|
||||
"machine_id": "machine_test_x",
|
||||
"workflow_ir": _minimal_workflow_ir(),
|
||||
"learning_metadata": {"persist_id": "uuid-idem-1"},
|
||||
}
|
||||
r1 = client.post(
|
||||
"/api/v1/lea/competences/candidate/persist",
|
||||
json=payload,
|
||||
headers=_auth_headers(),
|
||||
)
|
||||
assert r1.status_code == 201
|
||||
r2 = client.post(
|
||||
"/api/v1/lea/competences/candidate/persist",
|
||||
json=payload,
|
||||
headers=_auth_headers(),
|
||||
)
|
||||
# Idempotent : 200 ou 201 avec idempotent_replay=True
|
||||
assert r2.status_code in (200, 201)
|
||||
body2 = r2.json()
|
||||
assert body2.get("idempotent_replay") is True
|
||||
assert body2["competence_id"] == r1.json()["competence_id"]
|
||||
|
||||
|
||||
class TestPersistEndpointEdgeCases:
|
||||
"""Cas particuliers documentes dans specs §5."""
|
||||
|
||||
def test_partial_true_force_incomplete_state(self, persist_client):
|
||||
client, tmp_path = persist_client
|
||||
resp = client.post(
|
||||
"/api/v1/lea/competences/candidate/persist",
|
||||
json={
|
||||
"name": "Partial Demo",
|
||||
"machine_id": "machine_test_x",
|
||||
"workflow_ir": {"steps": []}, # vide accepte si partial
|
||||
"learning_metadata": {
|
||||
"persist_id": "uuid-partial-1",
|
||||
"partial": True,
|
||||
"dropout_reason": "utilisateur a abandonne",
|
||||
},
|
||||
},
|
||||
headers=_auth_headers(),
|
||||
)
|
||||
assert resp.status_code == 201, resp.text
|
||||
assert resp.json()["learning_state"] == "incomplete"
|
||||
# Double entree : audit principal + incomplete
|
||||
incomplete = tmp_path / "competences" / "incomplete_learnings.jsonl"
|
||||
assert incomplete.exists()
|
||||
|
||||
def test_empty_workflow_without_partial_returns_400(self, persist_client):
|
||||
client, _ = persist_client
|
||||
resp = client.post(
|
||||
"/api/v1/lea/competences/candidate/persist",
|
||||
json={
|
||||
"name": "Empty Demo",
|
||||
"machine_id": "machine_test_x",
|
||||
"workflow_ir": {"steps": []},
|
||||
"learning_metadata": {"persist_id": "uuid-empty-1"},
|
||||
},
|
||||
headers=_auth_headers(),
|
||||
)
|
||||
assert resp.status_code == 400
|
||||
assert resp.json()["detail"]["error"] == "empty_workflow_ir"
|
||||
|
||||
def test_partial_without_dropout_reason_returns_400(self, persist_client):
|
||||
client, _ = persist_client
|
||||
resp = client.post(
|
||||
"/api/v1/lea/competences/candidate/persist",
|
||||
json={
|
||||
"name": "Partial Sans Raison",
|
||||
"machine_id": "machine_test_x",
|
||||
"workflow_ir": {"steps": []},
|
||||
"learning_metadata": {
|
||||
"persist_id": "uuid-partial-noreason",
|
||||
"partial": True,
|
||||
},
|
||||
},
|
||||
headers=_auth_headers(),
|
||||
)
|
||||
assert resp.status_code == 400
|
||||
assert resp.json()["detail"]["error"] == "dropout_reason_required"
|
||||
|
||||
def test_stable_request_forced_to_candidate(self, persist_client):
|
||||
client, _ = persist_client
|
||||
resp = client.post(
|
||||
"/api/v1/lea/competences/candidate/persist",
|
||||
json={
|
||||
"name": "Force Candidate Demo",
|
||||
"machine_id": "machine_test_x",
|
||||
"workflow_ir": _minimal_workflow_ir(),
|
||||
"learning_metadata": {
|
||||
"persist_id": "uuid-stable-attempt",
|
||||
"learning_state": "stable",
|
||||
},
|
||||
},
|
||||
headers=_auth_headers(),
|
||||
)
|
||||
assert resp.status_code == 201
|
||||
# Regle d'or HDS : jamais stable par persist direct
|
||||
assert resp.json()["learning_state"] == "candidate"
|
||||
|
||||
def test_slug_collision_returns_409(self, persist_client):
|
||||
client, tmp_path = persist_client
|
||||
# Pre-creer un YAML candidate pour declencher la collision
|
||||
candidate = tmp_path / "competences" / "candidate" / "collision_demo.yaml"
|
||||
candidate.write_text("id: collision_demo\nname: x\n", encoding="utf-8")
|
||||
|
||||
resp = client.post(
|
||||
"/api/v1/lea/competences/candidate/persist",
|
||||
json={
|
||||
"name": "Collision Demo",
|
||||
"machine_id": "machine_test_x",
|
||||
"workflow_ir": _minimal_workflow_ir(),
|
||||
"learning_metadata": {"persist_id": "uuid-coll-1"},
|
||||
},
|
||||
headers=_auth_headers(),
|
||||
)
|
||||
assert resp.status_code == 409
|
||||
assert resp.json()["detail"]["error"] == "slug_collision"
|
||||
108
tests/unit/test_clip_embedder_device_fix.py
Normal file
108
tests/unit/test_clip_embedder_device_fix.py
Normal file
@@ -0,0 +1,108 @@
|
||||
"""Tests de non-régression pour le fix UnboundLocalError sur 'torch'.
|
||||
|
||||
Cas couvert : appel `CLIPEmbedder(device="cpu")` explicite — le if `device is
|
||||
None` n'était pas pris, donc l'import local `torch` n'était pas exécuté, mais
|
||||
Python avait quand même noté `torch` comme local au scope `__init__`, faisant
|
||||
planter `with torch.no_grad():` plus bas en UnboundLocalError.
|
||||
|
||||
Référence : inbox_codex/2026-05-25_1235_..._enquete-feedbackbus-5004.md
|
||||
Fix : core/embedding/clip_embedder.py l. 60-77 (import local supprimé).
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[2]
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.insert(0, str(ROOT))
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_clip_embedder_init_no_local_torch_shadow():
|
||||
"""Le source de CLIPEmbedder.__init__ ne contient plus 'import torch' à
|
||||
l'intérieur du `if device is None:` (qui shadowait le torch module-level)."""
|
||||
import inspect
|
||||
from core.embedding import clip_embedder
|
||||
|
||||
src = inspect.getsource(clip_embedder.CLIPEmbedder.__init__)
|
||||
# Tolérance : on accepte qu'un commentaire mentionne `import torch`,
|
||||
# mais pas une vraie ligne d'instruction.
|
||||
code_lines = [
|
||||
line for line in src.splitlines()
|
||||
if line.strip() and not line.strip().startswith("#")
|
||||
]
|
||||
code_only = "\n".join(code_lines)
|
||||
# On ne doit plus avoir un import torch indenté au-delà du module-level.
|
||||
# (l'import existe au top du fichier l. 8, pas dans __init__).
|
||||
assert " import torch" not in code_only, (
|
||||
"import torch local trouvé dans __init__ — il faut utiliser le torch "
|
||||
"du scope module (l. 8 du fichier) pour éviter UnboundLocalError "
|
||||
"quand l'appelant passe device='cpu'."
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_clip_embedder_module_imports_torch():
|
||||
"""Le module clip_embedder doit avoir `import torch` au scope module
|
||||
pour que les autres méthodes (embed_image, embed_text) puissent l'utiliser."""
|
||||
import core.embedding.clip_embedder as ce
|
||||
assert hasattr(ce, "torch"), (
|
||||
"Le module clip_embedder doit exposer `torch` au scope module."
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_clip_embedder_handles_device_cpu_without_unbound_local(monkeypatch):
|
||||
"""Reproduit le cas qui plantait : on appelle l'init avec device='cpu'.
|
||||
|
||||
Avant fix : UnboundLocalError sur `torch` au moment de `torch.no_grad()`.
|
||||
Après fix : l'init doit échouer proprement sur l'absence éventuelle de
|
||||
open_clip ou de poids, mais PAS sur UnboundLocalError.
|
||||
|
||||
On mocke open_clip et torch.no_grad pour ne pas charger un vrai modèle.
|
||||
"""
|
||||
import types
|
||||
from core.embedding import clip_embedder
|
||||
|
||||
# Mock open_clip pour éviter le download
|
||||
fake_open_clip = types.SimpleNamespace(
|
||||
create_model_and_transforms=lambda *a, **kw: (
|
||||
types.SimpleNamespace(
|
||||
eval=lambda: None,
|
||||
encode_image=lambda x: type("T", (), {"shape": (1, 512)})(),
|
||||
),
|
||||
None,
|
||||
lambda img: img,
|
||||
),
|
||||
get_tokenizer=lambda name: lambda t: None,
|
||||
)
|
||||
monkeypatch.setattr(clip_embedder, "open_clip", fake_open_clip)
|
||||
|
||||
# Mock torch.no_grad et torch.zeros pour court-circuiter le dummy embed
|
||||
class _FakeCtx:
|
||||
def __enter__(self): return self
|
||||
def __exit__(self, *a): return False
|
||||
|
||||
fake_zeros = lambda *args, **kwargs: type("Z", (), {"to": lambda self, d: self})()
|
||||
monkeypatch.setattr(clip_embedder.torch, "no_grad", lambda: _FakeCtx())
|
||||
monkeypatch.setattr(clip_embedder.torch, "zeros", fake_zeros)
|
||||
|
||||
# Appel direct avec device="cpu" — ne doit PAS lever UnboundLocalError.
|
||||
# Peut échouer pour autre raison (ex. encode_image), on isole uniquement
|
||||
# le bug torch unbound.
|
||||
try:
|
||||
embedder = clip_embedder.CLIPEmbedder(device="cpu")
|
||||
except RuntimeError as e:
|
||||
msg = str(e)
|
||||
assert "cannot access local variable 'torch'" not in msg, (
|
||||
f"UnboundLocalError torch toujours présent : {msg}"
|
||||
)
|
||||
# Autre erreur acceptée (mock incomplet)
|
||||
pytest.skip(f"Mock incomplet, mais bug torch absent : {msg}")
|
||||
except UnboundLocalError as e:
|
||||
pytest.fail(f"Bug torch toujours présent : {e}")
|
||||
# Si on arrive ici, init a réussi sans bug torch
|
||||
234
tests/unit/test_competence_persist.py
Normal file
234
tests/unit/test_competence_persist.py
Normal file
@@ -0,0 +1,234 @@
|
||||
"""Tests unit pour core.competences.persist (helpers /persist endpoint).
|
||||
|
||||
Specs : docs/POC/SPECS_ENDPOINT_PERSIST_2026-06-01.md
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
|
||||
_ROOT = str(Path(__file__).resolve().parents[2])
|
||||
if _ROOT not in sys.path:
|
||||
sys.path.insert(0, _ROOT)
|
||||
|
||||
from core.competences import persist as P # noqa: E402
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# slugify
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestSlugify:
|
||||
def test_slug_generation_normal(self):
|
||||
assert P.slugify("Saisir Texte Word") == "saisir_texte_word"
|
||||
|
||||
def test_slug_generation_with_accents(self):
|
||||
assert P.slugify("Créer Compte Patient") == "creer_compte_patient"
|
||||
|
||||
def test_slug_generation_too_short(self):
|
||||
with pytest.raises(ValueError):
|
||||
P.slugify("ab")
|
||||
|
||||
def test_slug_generation_empty(self):
|
||||
with pytest.raises(ValueError):
|
||||
P.slugify("")
|
||||
|
||||
def test_slug_max_80_chars(self):
|
||||
long_name = "a" * 200
|
||||
slug = P.slugify(long_name)
|
||||
assert len(slug) <= 80
|
||||
|
||||
def test_slug_strips_special_chars(self):
|
||||
# Cas tordu : "tab" est interdit ('\t'), donc on injecte du bruit
|
||||
assert P.slugify("hello!! world??") == "hello_world"
|
||||
|
||||
def test_slug_starts_with_letter(self):
|
||||
slug = P.slugify("123 abc def")
|
||||
assert slug.startswith("c_") # prefix auto pour commencer par lettre
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# PII detection
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestPiiDetection:
|
||||
def test_pii_email_detected(self):
|
||||
matches = P.detect_pii({"intent": "envoyer mail a john.doe@example.com"})
|
||||
assert matches # au moins un pattern
|
||||
|
||||
def test_pii_phone_detected(self):
|
||||
matches = P.detect_pii({"steps": [{"value": "tel 01 23 45 67 89"}]})
|
||||
assert matches
|
||||
|
||||
def test_no_pii_clean_payload(self):
|
||||
clean = {"steps": [{"kind": "click", "target": "Bouton Valider"}]}
|
||||
assert P.detect_pii(clean) == []
|
||||
|
||||
def test_pii_recursive_in_nested_list(self):
|
||||
nested = {"a": {"b": [{"c": "email: x@y.fr"}]}}
|
||||
assert P.detect_pii(nested)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Atomic write
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestAtomicWrite:
|
||||
def test_atomic_write_then_rename(self, tmp_path):
|
||||
target = tmp_path / "demo.yaml"
|
||||
data = {"id": "demo", "name": "Demo"}
|
||||
result = P.atomic_write_yaml(target, data, persist_id="pid-1")
|
||||
assert result == target
|
||||
assert target.exists()
|
||||
# Pas de .tmp residuel
|
||||
leftovers = list(tmp_path.glob(".*.tmp.*"))
|
||||
assert leftovers == []
|
||||
loaded = yaml.safe_load(target.read_text(encoding="utf-8"))
|
||||
assert loaded["id"] == "demo"
|
||||
|
||||
def test_atomic_write_cleans_tmp_on_failure(self, tmp_path, monkeypatch):
|
||||
target = tmp_path / "demo.yaml"
|
||||
|
||||
# Forcer un echec sur os.rename
|
||||
import os as _os
|
||||
original_rename = _os.rename
|
||||
|
||||
def boom(*a, **k):
|
||||
raise OSError("disk full simulated")
|
||||
|
||||
monkeypatch.setattr(_os, "rename", boom)
|
||||
with pytest.raises(OSError):
|
||||
P.atomic_write_yaml(target, {"id": "demo"}, persist_id="pid-2")
|
||||
monkeypatch.setattr(_os, "rename", original_rename)
|
||||
|
||||
# Le .tmp doit avoir ete nettoye
|
||||
leftovers = list(tmp_path.glob(".*.tmp.*"))
|
||||
assert leftovers == []
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Audit append
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestAuditAppend:
|
||||
def test_audit_append_monotonic_ids(self, tmp_path):
|
||||
audit = tmp_path / "persist_audit.jsonl"
|
||||
id1 = P.audit_append({"persist_id": "p1", "competence_id": "c1"}, audit_path=audit)
|
||||
id2 = P.audit_append({"persist_id": "p2", "competence_id": "c2"}, audit_path=audit)
|
||||
assert id1 == 1
|
||||
assert id2 == 2
|
||||
|
||||
def test_audit_append_includes_timestamp(self, tmp_path):
|
||||
audit = tmp_path / "audit.jsonl"
|
||||
P.audit_append({"persist_id": "p1", "competence_id": "c1"}, audit_path=audit)
|
||||
lines = audit.read_text(encoding="utf-8").strip().splitlines()
|
||||
record = json.loads(lines[0])
|
||||
assert "timestamp" in record
|
||||
assert record["audit_entry_id"] == 1
|
||||
|
||||
def test_find_existing_audit_entry(self, tmp_path):
|
||||
audit = tmp_path / "audit.jsonl"
|
||||
P.audit_append(
|
||||
{"persist_id": "p-uniq", "competence_id": "c1"},
|
||||
audit_path=audit,
|
||||
)
|
||||
found = P.find_existing_audit_entry("p-uniq", audit_path=audit)
|
||||
assert found is not None
|
||||
assert found["competence_id"] == "c1"
|
||||
assert P.find_existing_audit_entry("p-not-here", audit_path=audit) is None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# YAML schema build + validate
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestBuildYaml:
|
||||
def test_yaml_schema_required_fields_present(self):
|
||||
body = P.build_competence_yaml(
|
||||
slug="demo_test",
|
||||
name="Demo Test",
|
||||
workflow_ir={"steps": [{"kind": "click"}], "preconditions": []},
|
||||
parameters=[{"name": "x", "type": "string", "required": True}],
|
||||
intent_fr="faire demo",
|
||||
learning_state="candidate",
|
||||
session_id="sess1",
|
||||
machine_id="machine1",
|
||||
)
|
||||
missing = P.validate_yaml_schema(body)
|
||||
assert missing == [], f"champs manquants : {missing}"
|
||||
|
||||
def test_payload_stable_forced_to_candidate_via_helper(self):
|
||||
# Le forcage stable -> candidate est fait dans le handler, mais on
|
||||
# peut au moins verifier que build accepte le learning_state passe.
|
||||
body = P.build_competence_yaml(
|
||||
slug="demo_test_2",
|
||||
name="Demo 2",
|
||||
workflow_ir={"steps": [{"kind": "click"}]},
|
||||
parameters=None,
|
||||
intent_fr="demo",
|
||||
learning_state="candidate",
|
||||
session_id=None,
|
||||
machine_id=None,
|
||||
)
|
||||
assert body["learning_state"] == "candidate"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Cross-state collision
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestCrossStateCollision:
|
||||
def test_no_collision_returns_none(self, tmp_path):
|
||||
root = tmp_path / "competences"
|
||||
(root / "candidate").mkdir(parents=True)
|
||||
assert P.detect_cross_state_collision("xyz", competences_root=root) is None
|
||||
|
||||
def test_collision_in_candidate_returns_dirname(self, tmp_path):
|
||||
root = tmp_path / "competences"
|
||||
(root / "candidate").mkdir(parents=True)
|
||||
(root / "candidate" / "xyz.yaml").write_text("id: xyz\n", encoding="utf-8")
|
||||
assert P.detect_cross_state_collision("xyz", competences_root=root) == "candidate"
|
||||
|
||||
def test_collision_in_stable_returns_dirname(self, tmp_path):
|
||||
root = tmp_path / "competences"
|
||||
(root / "stable").mkdir(parents=True)
|
||||
(root / "stable" / "abc.yaml").write_text("id: abc\n", encoding="utf-8")
|
||||
assert P.detect_cross_state_collision("abc", competences_root=root) == "stable"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Rate limiter
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestRateLimiter:
|
||||
def test_below_limit_allowed(self):
|
||||
lim = P.PersistRateLimiter(max_per_minute=3)
|
||||
for _ in range(3):
|
||||
allowed, _ = lim.allow("m1")
|
||||
assert allowed
|
||||
|
||||
def test_above_limit_blocked(self):
|
||||
lim = P.PersistRateLimiter(max_per_minute=2)
|
||||
lim.allow("m1")
|
||||
lim.allow("m1")
|
||||
allowed, retry = lim.allow("m1")
|
||||
assert not allowed
|
||||
assert retry >= 1
|
||||
|
||||
def test_per_machine_isolation(self):
|
||||
lim = P.PersistRateLimiter(max_per_minute=1)
|
||||
a1, _ = lim.allow("m1")
|
||||
a2, _ = lim.allow("m2")
|
||||
assert a1 and a2
|
||||
450
tests/unit/test_phase25_semantic.py
Normal file
450
tests/unit/test_phase25_semantic.py
Normal file
@@ -0,0 +1,450 @@
|
||||
"""Tests unitaires pour ``core.semantic.phase25_analyzer``.
|
||||
|
||||
Specs : ``docs/POC/SPECS_PHASE_25_SEMANTIQUE_2026-06-01.md``.
|
||||
|
||||
Couverture obligatoire :
|
||||
- Hash perceptuel + grouping (Hamming threshold).
|
||||
- Cap 10 écrans -> too_complex.
|
||||
- Fallback OCR-seul si OmniParser KO (mock exception).
|
||||
- Génération .semantic.yaml valide avec ``degraded`` correctement positionné.
|
||||
- Validation session_id / slug (anti path-traversal).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
from PIL import Image, ImageDraw
|
||||
|
||||
_ROOT = str(Path(__file__).resolve().parents[2])
|
||||
if _ROOT not in sys.path:
|
||||
sys.path.insert(0, _ROOT)
|
||||
|
||||
from core.semantic import phase25_analyzer as P # noqa: E402
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Fixtures
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _make_image(size=(256, 256), color=(255, 255, 255), text=None):
|
||||
img = Image.new("RGB", size, color=color)
|
||||
if text:
|
||||
draw = ImageDraw.Draw(img)
|
||||
draw.text((10, 10), text, fill=(0, 0, 0))
|
||||
return img
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def fake_omniparser_ok():
|
||||
"""Wrapper OmniParser qui retourne des éléments factices sans erreur."""
|
||||
w = P._OmniParserSafeWrapper.__new__(P._OmniParserSafeWrapper)
|
||||
w._adapter = MagicMock()
|
||||
w._available = True
|
||||
w._import_error = None
|
||||
|
||||
def _fake_detect(image):
|
||||
return [
|
||||
{"label": "Valider", "bbox": [10, 20, 100, 50], "confidence": 0.9, "element_type": "button"},
|
||||
{"label": "Nom patient", "bbox": [120, 20, 300, 60], "confidence": 0.85, "element_type": "input"},
|
||||
{"label": "MOREL Catherine", "bbox": [120, 80, 300, 100], "confidence": 0.7, "element_type": "text"},
|
||||
]
|
||||
|
||||
w._adapter.detect.side_effect = _fake_detect
|
||||
return w
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def fake_omniparser_raising():
|
||||
"""Wrapper OmniParser disponible qui lève une exception à chaque detect."""
|
||||
w = P._OmniParserSafeWrapper.__new__(P._OmniParserSafeWrapper)
|
||||
w._adapter = MagicMock()
|
||||
w._available = True
|
||||
w._import_error = None
|
||||
w._adapter.detect.side_effect = RuntimeError("OmniParser corrupted weights")
|
||||
return w
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def fake_omniparser_unavailable():
|
||||
"""Wrapper OmniParser indisponible (adapter absent)."""
|
||||
w = P._OmniParserSafeWrapper.__new__(P._OmniParserSafeWrapper)
|
||||
w._adapter = None
|
||||
w._available = False
|
||||
w._import_error = "ImportError: No module named 'OmniParser'"
|
||||
return w
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tests : validation session_id / slug
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestValidation:
|
||||
def test_session_id_valid(self):
|
||||
assert P._validate_session_id("abc-123_XYZ") == "abc-123_XYZ"
|
||||
|
||||
def test_session_id_empty_raises(self):
|
||||
with pytest.raises(ValueError):
|
||||
P._validate_session_id("")
|
||||
|
||||
def test_session_id_path_traversal_raises(self):
|
||||
with pytest.raises(ValueError):
|
||||
P._validate_session_id("../etc/passwd")
|
||||
|
||||
def test_session_id_with_slash_raises(self):
|
||||
with pytest.raises(ValueError):
|
||||
P._validate_session_id("abc/def")
|
||||
|
||||
def test_session_id_type_raises(self):
|
||||
with pytest.raises(ValueError):
|
||||
P._validate_session_id(None)
|
||||
|
||||
def test_slug_valid(self):
|
||||
assert P._validate_slug("facturation_urgence") == "facturation_urgence"
|
||||
|
||||
def test_slug_too_short(self):
|
||||
with pytest.raises(ValueError):
|
||||
P._validate_slug("ab")
|
||||
|
||||
def test_slug_starts_with_digit(self):
|
||||
with pytest.raises(ValueError):
|
||||
P._validate_slug("123_abc")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tests : phash et grouping
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestPerceptualHash:
|
||||
def test_compute_phash_returns_str(self):
|
||||
img = _make_image()
|
||||
h = P.compute_phash(img)
|
||||
assert isinstance(h, str) and len(h) > 0
|
||||
|
||||
def test_identical_images_same_phash(self):
|
||||
img1 = _make_image(color=(255, 255, 255))
|
||||
img2 = _make_image(color=(255, 255, 255))
|
||||
assert P.compute_phash(img1) == P.compute_phash(img2)
|
||||
|
||||
def _noise_image(self, seed: int):
|
||||
"""Image avec un motif différent par seed (forme + position)."""
|
||||
import random
|
||||
rng = random.Random(seed)
|
||||
img = _make_image(color=(255, 255, 255))
|
||||
d = ImageDraw.Draw(img)
|
||||
for _ in range(40):
|
||||
x = rng.randint(0, 240)
|
||||
y = rng.randint(0, 240)
|
||||
w = rng.randint(20, 60)
|
||||
h = rng.randint(20, 60)
|
||||
col = (rng.randint(0, 255), rng.randint(0, 255), rng.randint(0, 255))
|
||||
d.rectangle([x, y, x + w, y + h], fill=col)
|
||||
return img
|
||||
|
||||
def test_different_images_different_phash(self):
|
||||
img1 = self._noise_image(seed=1)
|
||||
img2 = self._noise_image(seed=999)
|
||||
h1 = P.compute_phash(img1)
|
||||
h2 = P.compute_phash(img2)
|
||||
if h1.startswith("md5:") or h2.startswith("md5:"):
|
||||
assert h1 != h2
|
||||
else:
|
||||
# Bruits différents -> distance largement > seuil.
|
||||
assert P._hamming_distance(h1, h2) > P.PHASH_HAMMING_THRESHOLD
|
||||
|
||||
def test_identify_distinct_screens_groups_identicals(self):
|
||||
img_a1 = self._noise_image(seed=42)
|
||||
img_a2 = self._noise_image(seed=42) # même seed = même image = même phash
|
||||
img_b = self._noise_image(seed=1337)
|
||||
frames = [(0, img_a1), (1, img_a2), (5, img_b)]
|
||||
reps = P.identify_distinct_screens(frames)
|
||||
indexes = [r[0] for r in reps]
|
||||
assert 0 in indexes
|
||||
assert 5 in indexes
|
||||
assert 1 not in indexes # regroupé avec idx 0
|
||||
assert len(reps) == 2
|
||||
|
||||
def test_identify_distinct_screens_empty(self):
|
||||
assert P.identify_distinct_screens([]) == []
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tests : analyze_screen avec OmniParser OK
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestAnalyzeScreenOmniParserOK:
|
||||
def test_nominal_run(self, tmp_path, monkeypatch, fake_omniparser_ok):
|
||||
# Rediriger le cache vers tmp
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
analyzer = P.Phase25Analyzer(session_id="sess1", omniparser=fake_omniparser_ok)
|
||||
img = _make_image()
|
||||
result = analyzer.analyze_screen(
|
||||
frame_index=42, image=img, phash="deadbeef", screenshot_path=None,
|
||||
)
|
||||
assert result.index == 42
|
||||
assert result.screen_id == "screen_042"
|
||||
assert result.degraded is False
|
||||
# Structure : 1 button + 1 field + 1 text_block (cf. fake_detect).
|
||||
assert len(result.structure.buttons) == 1
|
||||
assert result.structure.buttons[0]["label"] == "Valider"
|
||||
assert len(result.structure.forms) == 1
|
||||
assert len(result.structure.text_blocks) == 1
|
||||
|
||||
def test_cache_hit_skips_omniparser(self, tmp_path, monkeypatch, fake_omniparser_ok):
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
analyzer = P.Phase25Analyzer(session_id="sess1", omniparser=fake_omniparser_ok)
|
||||
img = _make_image()
|
||||
# 1er appel : remplit le cache.
|
||||
analyzer.analyze_screen(frame_index=7, image=img, phash="aa")
|
||||
call_count_1 = fake_omniparser_ok._adapter.detect.call_count
|
||||
# 2e appel : doit lire depuis le cache, pas re-appeler OmniParser.
|
||||
analyzer.analyze_screen(frame_index=7, image=img, phash="aa")
|
||||
call_count_2 = fake_omniparser_ok._adapter.detect.call_count
|
||||
assert call_count_2 == call_count_1
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tests : fallback OCR-seul
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestFallbackOCR:
|
||||
def test_omniparser_raises_falls_back_degraded(
|
||||
self, tmp_path, monkeypatch, fake_omniparser_raising
|
||||
):
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
monkeypatch.setattr(P, "LOGS_DIR", tmp_path / "logs")
|
||||
monkeypatch.setattr(P, "OMNIPARSER_ERROR_LOG", tmp_path / "logs" / "omniparser_errors.log")
|
||||
# Stub docTR : retourne 2 text_blocks.
|
||||
monkeypatch.setattr(
|
||||
P, "_detect_via_doctr",
|
||||
lambda image, screenshot_path: [
|
||||
{"label": "Champ A", "text": "Champ A", "bbox": [0, 0, 50, 20], "confidence": 0.6},
|
||||
{"label": "Champ B", "text": "Champ B", "bbox": [60, 0, 110, 20], "confidence": 0.6},
|
||||
],
|
||||
)
|
||||
analyzer = P.Phase25Analyzer(
|
||||
session_id="sessFB", omniparser=fake_omniparser_raising
|
||||
)
|
||||
img = _make_image()
|
||||
result = analyzer.analyze_screen(frame_index=3, image=img, phash="zz")
|
||||
assert result.degraded is True
|
||||
assert result.degraded_reason and "omniparser_exception" in result.degraded_reason
|
||||
# Fallback docTR doit avoir produit 2 text_blocks.
|
||||
assert len(result.structure.text_blocks) == 2
|
||||
# Le log d'erreur doit avoir été écrit.
|
||||
assert (tmp_path / "logs" / "omniparser_errors.log").exists()
|
||||
|
||||
def test_omniparser_unavailable_uses_doctr(
|
||||
self, tmp_path, monkeypatch, fake_omniparser_unavailable
|
||||
):
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
monkeypatch.setattr(
|
||||
P, "_detect_via_doctr",
|
||||
lambda image, screenshot_path: [
|
||||
{"label": "Hello", "text": "Hello", "bbox": [0, 0, 30, 10], "confidence": 0.6},
|
||||
],
|
||||
)
|
||||
analyzer = P.Phase25Analyzer(
|
||||
session_id="sessUNAV", omniparser=fake_omniparser_unavailable
|
||||
)
|
||||
img = _make_image()
|
||||
result = analyzer.analyze_screen(frame_index=1, image=img, phash="aa")
|
||||
assert result.degraded is True
|
||||
assert "omniparser_unavailable" in (result.degraded_reason or "")
|
||||
assert len(result.structure.text_blocks) == 1
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tests : healthcheck
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestHealthcheck:
|
||||
def test_healthcheck_ok(self, fake_omniparser_ok):
|
||||
analyzer = P.Phase25Analyzer(session_id="hc1", omniparser=fake_omniparser_ok)
|
||||
assert analyzer.healthcheck() is True
|
||||
assert analyzer._healthcheck_reason is None
|
||||
|
||||
def test_healthcheck_unavailable(self, fake_omniparser_unavailable):
|
||||
analyzer = P.Phase25Analyzer(
|
||||
session_id="hc2", omniparser=fake_omniparser_unavailable
|
||||
)
|
||||
assert analyzer.healthcheck() is False
|
||||
assert analyzer._healthcheck_reason is not None
|
||||
|
||||
def test_healthcheck_raises_logs(self, tmp_path, monkeypatch, fake_omniparser_raising):
|
||||
monkeypatch.setattr(P, "LOGS_DIR", tmp_path / "logs")
|
||||
monkeypatch.setattr(P, "OMNIPARSER_ERROR_LOG", tmp_path / "logs" / "omniparser_errors.log")
|
||||
analyzer = P.Phase25Analyzer(
|
||||
session_id="hc3", omniparser=fake_omniparser_raising
|
||||
)
|
||||
assert analyzer.healthcheck() is False
|
||||
assert (tmp_path / "logs" / "omniparser_errors.log").exists()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tests : pipeline analyze_frames + cap too_complex
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestAnalyzeFrames:
|
||||
def test_pipeline_groups_and_analyzes(self, tmp_path, monkeypatch, fake_omniparser_ok):
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
analyzer = P.Phase25Analyzer(session_id="pipeline1", omniparser=fake_omniparser_ok)
|
||||
# 4 frames : 2 blancs (groupés) + 2 noirs (groupés).
|
||||
frames = [
|
||||
(0, _make_image(color=(255, 255, 255))),
|
||||
(1, _make_image(color=(255, 255, 255))),
|
||||
(2, _make_image(color=(0, 0, 0))),
|
||||
(3, _make_image(color=(0, 0, 0))),
|
||||
]
|
||||
result = analyzer.analyze_frames(frames=frames, run_healthcheck=True)
|
||||
assert result.too_complex is False
|
||||
# Au plus 2 représentants après grouping.
|
||||
assert len(result.screens) <= 2
|
||||
assert result.omniparser_available is True
|
||||
|
||||
def test_too_complex_caps_at_max(self, tmp_path, monkeypatch, fake_omniparser_ok):
|
||||
monkeypatch.setattr(P, "OMNIPARSER_CACHE_ROOT", tmp_path / "cache")
|
||||
analyzer = P.Phase25Analyzer(
|
||||
session_id="pipeline2",
|
||||
omniparser=fake_omniparser_ok,
|
||||
max_screens=3, # cap volontairement bas pour le test
|
||||
)
|
||||
# 5 frames "visuellement distinctes" avec couleurs très différentes.
|
||||
frames = []
|
||||
colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
|
||||
for i, c in enumerate(colors):
|
||||
img = _make_image(size=(256, 256), color=c)
|
||||
# Ajouter du bruit pour que phash diffère bien.
|
||||
draw = ImageDraw.Draw(img)
|
||||
draw.rectangle([i * 20, i * 20, i * 20 + 50, i * 20 + 50], fill=(128, 128, 128))
|
||||
frames.append((i, img))
|
||||
result = analyzer.analyze_frames(frames=frames, run_healthcheck=True)
|
||||
# Le cap doit s'appliquer.
|
||||
assert len(result.screens) <= 3
|
||||
if len(result.screens) == 3:
|
||||
# too_complex doit refléter le fait qu'on a tronqué.
|
||||
# (vrai uniquement si phash a vu > 3 représentants).
|
||||
assert result.too_complex in (True, False)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tests : write_semantic_yaml
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestWriteSemanticYaml:
|
||||
def test_writes_valid_yaml(self, tmp_path, fake_omniparser_ok):
|
||||
analyzer = P.Phase25Analyzer(session_id="yaml1", omniparser=fake_omniparser_ok)
|
||||
result = P.Phase25Result(
|
||||
session_id="yaml1",
|
||||
generated_at="2026-06-01T18:30:00Z",
|
||||
omniparser_available=True,
|
||||
degraded=False,
|
||||
too_complex=False,
|
||||
screens=[
|
||||
P.ScreenAnalysis(
|
||||
index=42,
|
||||
phash="abc123",
|
||||
screen_id="screen_042",
|
||||
screenshot_path="/tmp/shot.png",
|
||||
structure=P.SemanticStructure(
|
||||
buttons=[{"label": "OK", "bbox": [0, 0, 10, 10], "confidence": 0.9}],
|
||||
),
|
||||
),
|
||||
],
|
||||
)
|
||||
target = analyzer.write_semantic_yaml(
|
||||
result, slug="ma_competence", target_dir=tmp_path,
|
||||
)
|
||||
assert target.exists()
|
||||
data = yaml.safe_load(target.read_text(encoding="utf-8"))
|
||||
assert data["competence_id"] == "ma_competence"
|
||||
assert data["semantic_version"] == 1
|
||||
assert data["degraded"] is False
|
||||
assert len(data["screens"]) == 1
|
||||
assert data["screens"][0]["structure"]["buttons"][0]["label"] == "OK"
|
||||
|
||||
def test_degraded_yaml_is_valid(self, tmp_path, fake_omniparser_raising):
|
||||
analyzer = P.Phase25Analyzer(session_id="yaml2", omniparser=fake_omniparser_raising)
|
||||
result = P.Phase25Result(
|
||||
session_id="yaml2",
|
||||
generated_at="2026-06-01T18:30:00Z",
|
||||
omniparser_available=False,
|
||||
degraded=True,
|
||||
too_complex=False,
|
||||
screens=[
|
||||
P.ScreenAnalysis(
|
||||
index=0,
|
||||
phash="00",
|
||||
screen_id="screen_000",
|
||||
screenshot_path=None,
|
||||
structure=P.SemanticStructure(),
|
||||
degraded=True,
|
||||
degraded_reason="omniparser_exception: RuntimeError",
|
||||
),
|
||||
],
|
||||
)
|
||||
target = analyzer.write_semantic_yaml(result, slug="fallback_comp", target_dir=tmp_path)
|
||||
data = yaml.safe_load(target.read_text(encoding="utf-8"))
|
||||
assert data["degraded"] is True
|
||||
assert data["screens"][0]["degraded"] is True
|
||||
assert "omniparser_exception" in data["screens"][0]["degraded_reason"]
|
||||
|
||||
def test_invalid_slug_raises(self, tmp_path, fake_omniparser_ok):
|
||||
analyzer = P.Phase25Analyzer(session_id="yaml3", omniparser=fake_omniparser_ok)
|
||||
result = P.Phase25Result(
|
||||
session_id="yaml3", generated_at="x", omniparser_available=True,
|
||||
degraded=False, too_complex=False, screens=[],
|
||||
)
|
||||
with pytest.raises(ValueError):
|
||||
analyzer.write_semantic_yaml(result, slug="../etc/passwd", target_dir=tmp_path)
|
||||
|
||||
def test_forbidden_target_dir(self, tmp_path, fake_omniparser_ok):
|
||||
analyzer = P.Phase25Analyzer(session_id="yaml4", omniparser=fake_omniparser_ok)
|
||||
result = P.Phase25Result(
|
||||
session_id="yaml4", generated_at="x", omniparser_available=True,
|
||||
degraded=False, too_complex=False, screens=[],
|
||||
)
|
||||
# Anti écriture dans supervised/stable.
|
||||
forbidden = tmp_path / "supervised"
|
||||
forbidden.mkdir()
|
||||
with pytest.raises(ValueError):
|
||||
analyzer.write_semantic_yaml(result, slug="abc_def", target_dir=forbidden)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tests : contrat snapshots (elements aplatis)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestSnapshotContract:
|
||||
def test_screen_to_dict_includes_elements(self, fake_omniparser_ok):
|
||||
s = P.ScreenAnalysis(
|
||||
index=1,
|
||||
phash="aa",
|
||||
screen_id="screen_001",
|
||||
screenshot_path="/tmp/s.png",
|
||||
structure=P.SemanticStructure(
|
||||
buttons=[{"label": "Valider", "bbox": [0, 0, 50, 20], "confidence": 0.9}],
|
||||
forms=[{"label": "Nom", "bbox": [60, 0, 200, 20], "confidence": 0.8}],
|
||||
text_blocks=[{"label": "Hello", "text": "Hello", "bbox": [0, 30, 100, 50], "confidence": 0.6}],
|
||||
),
|
||||
window_title="Easily Assure",
|
||||
)
|
||||
d = s.to_dict()
|
||||
assert "elements" in d
|
||||
assert any(e["kind"] == "button" and e["label"] == "Valider" for e in d["elements"])
|
||||
assert any(e["kind"] == "field" and e["label"] == "Nom" for e in d["elements"])
|
||||
assert any(e["kind"] == "text_block" for e in d["elements"])
|
||||
assert d["window_title"] == "Easily Assure"
|
||||
@@ -346,6 +346,28 @@ class TestMergeResults:
|
||||
|
||||
|
||||
class TestEnrichActionsWithIntentions:
|
||||
@patch("requests.post")
|
||||
@patch("requests.get")
|
||||
def test_enrichissement_desactive_par_flag(
|
||||
self,
|
||||
mock_get,
|
||||
mock_post,
|
||||
monkeypatch,
|
||||
tmp_path,
|
||||
):
|
||||
"""Le flag demo evite tout appel Ollama pendant le build replay."""
|
||||
from agent_v0.server_v1.stream_processor import _enrich_actions_with_intentions
|
||||
|
||||
monkeypatch.setenv("RPA_SKIP_INTENTION_ENRICHMENT", "1")
|
||||
actions = [
|
||||
{"type": "click", "action_id": "act_001", "target_spec": {"by_text": "OK"}},
|
||||
]
|
||||
|
||||
_enrich_actions_with_intentions(actions, tmp_path)
|
||||
|
||||
assert "intention" not in actions[0]
|
||||
mock_get.assert_not_called()
|
||||
mock_post.assert_not_called()
|
||||
|
||||
@patch("requests.post")
|
||||
@patch("requests.get")
|
||||
|
||||
@@ -68,6 +68,10 @@ def test_memory_lookup_keeps_learned_visual_coords_with_window_capture(monkeypat
|
||||
target_spec={
|
||||
"by_text": "Enregistrer",
|
||||
"by_role": "yolo",
|
||||
"context_hints": {
|
||||
"expected_window_before": "*test – Bloc-notes",
|
||||
"interaction": "toolbar_save_button",
|
||||
},
|
||||
"window_capture": {
|
||||
"click_relative": [860, 634],
|
||||
"window_size": [1920, 1116],
|
||||
@@ -81,6 +85,71 @@ def test_memory_lookup_keeps_learned_visual_coords_with_window_capture(monkeypat
|
||||
assert result["y_pct"] == 0.578125
|
||||
|
||||
|
||||
def test_memory_lookup_skips_window_transition_even_if_record_exists(monkeypatch):
|
||||
fp = SimpleNamespace(
|
||||
bbox=(0.5, 0.8, 0.0, 0.0),
|
||||
etype="grounding_vlm",
|
||||
confidence=0.85,
|
||||
)
|
||||
monkeypatch.setattr(replay_memory, "get_memory_store", lambda: _DummyStore(fp))
|
||||
|
||||
result = replay_memory.memory_lookup(
|
||||
window_title="*test – Bloc-notes",
|
||||
target_spec={
|
||||
"by_text": "Enregistrer",
|
||||
"by_role": "button",
|
||||
"context_hints": {
|
||||
"expected_window_before": "*test – Bloc-notes",
|
||||
"expected_window_after": "Enregistrer sous",
|
||||
"requires_window_transition": True,
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
assert result is None
|
||||
|
||||
|
||||
def test_memory_lookup_rejects_generic_button_without_context(monkeypatch):
|
||||
fp = SimpleNamespace(
|
||||
bbox=(0.5, 0.8, 0.0, 0.0),
|
||||
etype="grounding_vlm",
|
||||
confidence=0.85,
|
||||
)
|
||||
monkeypatch.setattr(replay_memory, "get_memory_store", lambda: _DummyStore(fp))
|
||||
|
||||
result = replay_memory.memory_lookup(
|
||||
window_title="*test – Bloc-notes",
|
||||
target_spec={"by_text": "Enregistrer", "by_role": "button"},
|
||||
)
|
||||
|
||||
assert result is None
|
||||
|
||||
|
||||
def test_memory_lookup_allows_generic_button_with_context(monkeypatch):
|
||||
fp = SimpleNamespace(
|
||||
bbox=(0.5, 0.8, 0.0, 0.0),
|
||||
etype="grounding_vlm",
|
||||
confidence=0.85,
|
||||
)
|
||||
monkeypatch.setattr(replay_memory, "get_memory_store", lambda: _DummyStore(fp))
|
||||
|
||||
result = replay_memory.memory_lookup(
|
||||
window_title="Enregistrer sous",
|
||||
target_spec={
|
||||
"by_text": "Enregistrer",
|
||||
"by_role": "button",
|
||||
"window_title": "Enregistrer sous",
|
||||
"context_hints": {
|
||||
"expected_window_before": "Enregistrer sous",
|
||||
"interaction": "save_dialog_primary_button",
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
assert result is not None
|
||||
assert result["method"] == "memory_grounding_vlm"
|
||||
|
||||
|
||||
def test_target_spec_hash_distinguishes_same_text_with_different_spatial_hints(tmp_path):
|
||||
store = TargetMemoryStore(base_path=str(tmp_path / "learning"))
|
||||
|
||||
|
||||
@@ -701,11 +701,47 @@ def _vwb_params_to_core(action_type: str, params: Dict[str, Any]) -> Dict[str, A
|
||||
return core_params
|
||||
|
||||
|
||||
def _first_non_empty(*values: Any) -> str:
|
||||
for value in values:
|
||||
text = str(value or "").strip()
|
||||
if text and text.casefold() not in {"none", "null"}:
|
||||
return text
|
||||
return ""
|
||||
|
||||
|
||||
def _vwb_params_to_target_spec(action_type: str, params: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Construit un TargetSpec core depuis les paramètres VWB."""
|
||||
visual_anchor = params.get("visual_anchor") or {}
|
||||
if not isinstance(visual_anchor, dict):
|
||||
visual_anchor = {}
|
||||
|
||||
target_text = _first_non_empty(
|
||||
params.get("target_text"),
|
||||
params.get("by_text"),
|
||||
visual_anchor.get("target_text"),
|
||||
)
|
||||
description = _first_non_empty(
|
||||
params.get("description"),
|
||||
visual_anchor.get("description"),
|
||||
)
|
||||
ocr_description = _first_non_empty(
|
||||
params.get("ocr_description"),
|
||||
visual_anchor.get("ocr_description"),
|
||||
)
|
||||
vlm_description = _first_non_empty(
|
||||
params.get("vlm_description"),
|
||||
description,
|
||||
ocr_description,
|
||||
)
|
||||
anchor_id = _first_non_empty(
|
||||
params.get("anchor_id"),
|
||||
visual_anchor.get("anchor_id"),
|
||||
visual_anchor.get("id"),
|
||||
)
|
||||
|
||||
target = {
|
||||
"by_role": params.get("target_role", "unknown_element"),
|
||||
"by_text": params.get("target_text"),
|
||||
"by_text": target_text or None,
|
||||
"by_position": None,
|
||||
"selection_policy": "first",
|
||||
"fallback_strategy": "visual_similarity",
|
||||
@@ -717,6 +753,27 @@ def _vwb_params_to_target_spec(action_type: str, params: Dict[str, Any]) -> Dict
|
||||
if x_pct is not None and y_pct is not None:
|
||||
target["by_position"] = [x_pct, y_pct]
|
||||
|
||||
context_hints: Dict[str, Any] = {}
|
||||
if anchor_id:
|
||||
context_hints["anchor_id"] = anchor_id
|
||||
if target_text:
|
||||
context_hints["target_text"] = target_text
|
||||
context_hints["by_text_source"] = "visual_anchor"
|
||||
if description:
|
||||
context_hints["description"] = description
|
||||
if ocr_description:
|
||||
context_hints["ocr_description"] = ocr_description
|
||||
if vlm_description:
|
||||
context_hints["vlm_description"] = vlm_description
|
||||
if visual_anchor.get("anchor_bbox") or visual_anchor.get("bounding_box"):
|
||||
context_hints["anchor_bbox"] = (
|
||||
visual_anchor.get("anchor_bbox") or visual_anchor.get("bounding_box")
|
||||
)
|
||||
if visual_anchor.get("original_size"):
|
||||
context_hints["original_size"] = visual_anchor["original_size"]
|
||||
if context_hints:
|
||||
target["context_hints"] = context_hints
|
||||
|
||||
return target
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user