Bruno Charest
cc99fea20a
- Implement tests for database generator to ensure proper session handling. - Create tests for EXIF extraction and conversion functions. - Add tests for image-related endpoints, ensuring proper data retrieval and isolation between clients. - Develop tests for OCR functionality, including language detection and text extraction. - Introduce tests for the image processing pipeline, covering success and failure scenarios. - Validate rate limiting functionality and ensure independent counters for different clients. - Implement scraper tests to verify HTML content fetching and error handling. - Add unit tests for various services, including storage and filename generation. - Establish worker entry point for ARQ to handle background image processing tasks.
98 lines
4.3 KiB
Python
98 lines
4.3 KiB
Python
"""
|
||
Modèle SQLAlchemy — Image et métadonnées associées
|
||
"""
|
||
import enum
|
||
from datetime import datetime, timezone
|
||
from sqlalchemy import (
|
||
Column, Integer, String, Text, DateTime,
|
||
JSON, Float, Enum as SAEnum, BigInteger, Boolean, ForeignKey
|
||
)
|
||
from sqlalchemy.orm import relationship
|
||
from app.database import Base
|
||
|
||
|
||
class ProcessingStatus(str, enum.Enum):
|
||
PENDING = "pending"
|
||
PROCESSING = "processing"
|
||
DONE = "done"
|
||
ERROR = "error"
|
||
|
||
|
||
class Image(Base):
|
||
__tablename__ = "images"
|
||
|
||
# ── Identité ──────────────────────────────────────────────
|
||
id = Column(Integer, primary_key=True, index=True)
|
||
uuid = Column(String(36), unique=True, index=True, nullable=False)
|
||
|
||
# ── Client (multi-tenant) ─────────────────────────────────
|
||
client_id = Column(String(36), ForeignKey("api_clients.id"), nullable=False, index=True)
|
||
client = relationship("APIClient", back_populates="images")
|
||
|
||
# ── Fichier ───────────────────────────────────────────────
|
||
original_name = Column(String(512), nullable=False)
|
||
filename = Column(String(512), nullable=False) # nom sur disque (uuid-based)
|
||
file_path = Column(String(1024), nullable=False)
|
||
thumbnail_path = Column(String(1024))
|
||
mime_type = Column(String(128))
|
||
file_size = Column(BigInteger) # bytes
|
||
width = Column(Integer)
|
||
height = Column(Integer)
|
||
uploaded_at = Column(DateTime, default=lambda: datetime.now(timezone.utc))
|
||
|
||
# ── Statut du pipeline AI ─────────────────────────────────
|
||
processing_status = Column(
|
||
SAEnum(ProcessingStatus),
|
||
default=ProcessingStatus.PENDING,
|
||
nullable=False,
|
||
index=True
|
||
)
|
||
processing_error = Column(Text)
|
||
processing_started_at = Column(DateTime)
|
||
processing_done_at = Column(DateTime)
|
||
|
||
# ── Métadonnées EXIF ──────────────────────────────────────
|
||
exif_raw = Column(JSON) # dict complet brut
|
||
exif_make = Column(String(256)) # Appareil — fabricant
|
||
exif_model = Column(String(256)) # Appareil — modèle
|
||
exif_lens = Column(String(256))
|
||
exif_taken_at = Column(DateTime) # DateTimeOriginal EXIF
|
||
exif_gps_lat = Column(Float)
|
||
exif_gps_lon = Column(Float)
|
||
exif_altitude = Column(Float)
|
||
exif_iso = Column(Integer)
|
||
exif_aperture = Column(String(32)) # ex: "f/2.8"
|
||
exif_shutter = Column(String(32)) # ex: "1/250"
|
||
exif_focal = Column(String(32)) # ex: "50mm"
|
||
exif_flash = Column(Boolean)
|
||
exif_orientation = Column(Integer)
|
||
exif_software = Column(String(256))
|
||
|
||
# ── OCR ───────────────────────────────────────────────────
|
||
ocr_text = Column(Text)
|
||
ocr_language = Column(String(64))
|
||
ocr_confidence = Column(Float) # 0.0 – 1.0
|
||
ocr_has_text = Column(Boolean, default=False)
|
||
|
||
# ── AI Vision ─────────────────────────────────────────────
|
||
ai_description = Column(Text)
|
||
ai_tags = Column(JSON) # ["nature", "paysage", ...]
|
||
ai_confidence = Column(Float) # score de confiance global
|
||
ai_model_used = Column(String(128))
|
||
ai_processed_at = Column(DateTime)
|
||
ai_prompt_tokens = Column(Integer)
|
||
ai_output_tokens = Column(Integer)
|
||
|
||
def __repr__(self):
|
||
return f"<Image id={self.id} name={self.original_name} status={self.processing_status}>"
|
||
|
||
@property
|
||
def has_gps(self) -> bool:
|
||
return self.exif_gps_lat is not None and self.exif_gps_lon is not None
|
||
|
||
@property
|
||
def dimensions(self) -> str | None:
|
||
if self.width and self.height:
|
||
return f"{self.width}x{self.height}"
|
||
return None
|