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fix: Corregir todos los warnings de linting (ruff, black)

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- Corregir import ordering (TC002) moviendo User a TYPE_CHECKING
- Corregir type hints implícitos (RUF013) usando | None
- Corregir boolean traps (FBT001/FBT002) usando keyword-only args
- Corregir logging warnings (G201) usando logger.exception()
- Formatear código con ruff format y black

Co-authored-by: dawnsystem <42047891+dawnsystem@users.noreply.github.com>
This commit is contained in:
copilot-swe-agent[bot] 2025-11-12 13:17:08 +00:00
parent 2d7345f0bc
commit 8a5ece9e2b
3 changed files with 38 additions and 26 deletions
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4
src/documents/ai_deletion_manager.py
View file

@ -14,9 +14,11 @@ According to agents.md requirements:
from __future__ import annotations
import logging
from typing import TYPE_CHECKING
from typing import Any
from django.contrib.auth.models import User
if TYPE_CHECKING:
from django.contrib.auth.models import User
logger = logging.getLogger("paperless.ai_deletion")

38
src/documents/ai_scanner.py
View file

@ -51,7 +51,8 @@ class AIScanResult:
None # (storage_path_id, confidence)
)
self.custom_fields: dict[
int, tuple[Any, float],
int,
tuple[Any, float],
] = {} # {field_id: (value, confidence), ...}
self.workflows: list[tuple[int, float]] = [] # [(workflow_id, confidence), ...]
self.extracted_entities: dict[str, Any] = {} # NER results
@ -97,8 +98,9 @@ class AIDocumentScanner:
self,
auto_apply_threshold: float = 0.80,
suggest_threshold: float = 0.60,
enable_ml_features: bool = None,
enable_advanced_ocr: bool = None,
*,
enable_ml_features: bool | None = None,
enable_advanced_ocr: bool | None = None,
):
"""
Initialize AI scanner.
@ -188,7 +190,7 @@ class AIDocumentScanner:
self,
document: Document,
document_text: str,
original_file_path: str = None,
original_file_path: str | None = None,
) -> AIScanResult:
"""
Perform comprehensive AI scan of a document.
@ -215,7 +217,9 @@ class AIDocumentScanner:
# Analyze and suggest tags
result.tags = self._suggest_tags(
document, document_text, result.extracted_entities,
document,
document_text,
result.extracted_entities,
)
# Detect correspondent
@ -296,7 +300,7 @@ class AIDocumentScanner:
logger.debug("Extracted entities from NER")
return entities
except Exception as e:
logger.error(f"Entity extraction failed: {e}", exc_info=True)
logger.exception(f"Entity extraction failed: {e}")
return {}
def _suggest_tags(
@ -357,7 +361,7 @@ class AIDocumentScanner:
logger.debug(f"Suggested {len(suggestions)} tags")
except Exception as e:
logger.error(f"Tag suggestion failed: {e}", exc_info=True)
logger.exception(f"Tag suggestion failed: {e}")
return suggestions
@ -384,7 +388,8 @@ class AIDocumentScanner:
try:
# Use existing matching logic
matched_correspondents = match_correspondents(
document, self._get_classifier(),
document,
self._get_classifier(),
)
if matched_correspondents:
@ -413,7 +418,7 @@ class AIDocumentScanner:
return (correspondent.id, confidence)
except Exception as e:
logger.error(f"Correspondent detection failed: {e}", exc_info=True)
logger.exception(f"Correspondent detection failed: {e}")
return None
@ -452,7 +457,7 @@ class AIDocumentScanner:
pass
except Exception as e:
logger.error(f"Document type classification failed: {e}", exc_info=True)
logger.exception(f"Document type classification failed: {e}")
return None
@ -484,7 +489,7 @@ class AIDocumentScanner:
return (storage_path.id, confidence)
except Exception as e:
logger.error(f"Storage path suggestion failed: {e}", exc_info=True)
logger.exception(f"Storage path suggestion failed: {e}")
return None
@ -523,7 +528,7 @@ class AIDocumentScanner:
)
except Exception as e:
logger.error(f"Custom field extraction failed: {e}", exc_info=True)
logger.exception(f"Custom field extraction failed: {e}")
return extracted_fields
@ -628,7 +633,7 @@ class AIDocumentScanner:
)
except Exception as e:
logger.error(f"Workflow suggestion failed: {e}", exc_info=True)
logger.exception(f"Workflow suggestion failed: {e}")
return suggestions
@ -699,7 +704,7 @@ class AIDocumentScanner:
return suggested_title[:127] # Respect title length limit
except Exception as e:
logger.error(f"Title suggestion failed: {e}", exc_info=True)
logger.exception(f"Title suggestion failed: {e}")
return None
@ -719,13 +724,14 @@ class AIDocumentScanner:
logger.debug(f"Extracted {len(tables)} tables from document")
return tables
except Exception as e:
logger.error(f"Table extraction failed: {e}", exc_info=True)
logger.exception(f"Table extraction failed: {e}")
return []
def apply_scan_results(
self,
document: Document,
scan_result: AIScanResult,
*,
auto_apply: bool = True,
user_confirmed: bool = False,
) -> dict[str, Any]:
@ -842,7 +848,7 @@ class AIDocumentScanner:
document.save()
except Exception as e:
logger.error(f"Failed to apply scan results: {e}", exc_info=True)
logger.exception(f"Failed to apply scan results: {e}")
return {
"applied": applied,

16
src/documents/consumer.py
View file

@ -489,9 +489,11 @@ class ConsumerPlugin(
document=document,
logging_group=self.logging_group,
classifier=classifier,
original_file=self.unmodified_original
original_file=(
self.unmodified_original
if self.unmodified_original
else self.working_copy,
else self.working_copy
),
)
# After everything is in the database, copy the files into
@ -502,9 +504,11 @@ class ConsumerPlugin(
self._write(
document.storage_type,
(
self.unmodified_original
if self.unmodified_original is not None
else self.working_copy,
else self.working_copy
),
document.source_path,
)
@ -865,9 +869,9 @@ class ConsumerPreflightPlugin(
Confirm the input file still exists where it should
"""
if TYPE_CHECKING:
assert isinstance(self.input_doc.original_file, Path), (
self.input_doc.original_file
)
assert isinstance(
self.input_doc.original_file, Path,
), self.input_doc.original_file
if not self.input_doc.original_file.is_file():
self._fail(
ConsumerStatusShortMessage.FILE_NOT_FOUND,