223 lines
7.7 KiB
Python
223 lines
7.7 KiB
Python
"""CRUD operations for OCR settings and metrics."""
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from datetime import datetime, timedelta
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from typing import List, Optional
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from sqlalchemy import func, select, and_
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from sqlalchemy.ext.asyncio import AsyncSession
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from backend.modules.data_entry.db.models.ocr_settings import (
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UserOCRPreference,
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OCRJobMetrics,
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OCRMetricsSummary,
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OCREngine,
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)
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class OCRPreferenceCRUD:
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"""CRUD operations for user OCR preferences."""
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@staticmethod
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async def get_by_username(session: AsyncSession, username: str) -> Optional[UserOCRPreference]:
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"""Get user's OCR preference by username."""
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result = await session.execute(
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select(UserOCRPreference).where(UserOCRPreference.username == username)
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)
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return result.scalar_one_or_none()
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@staticmethod
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async def create_or_update(
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session: AsyncSession,
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username: str,
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preferred_engine: OCREngine
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) -> UserOCRPreference:
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"""Create or update user's OCR preference."""
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existing = await OCRPreferenceCRUD.get_by_username(session, username)
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if existing:
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existing.preferred_engine = preferred_engine
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existing.updated_at = datetime.utcnow()
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await session.commit()
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await session.refresh(existing)
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return existing
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else:
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preference = UserOCRPreference(
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username=username,
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preferred_engine=preferred_engine
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)
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session.add(preference)
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await session.commit()
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await session.refresh(preference)
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return preference
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@staticmethod
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async def delete_by_username(session: AsyncSession, username: str) -> bool:
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"""Delete user's OCR preference."""
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existing = await OCRPreferenceCRUD.get_by_username(session, username)
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if existing:
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await session.delete(existing)
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await session.commit()
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return True
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return False
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class OCRMetricsCRUD:
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"""CRUD operations for OCR job metrics."""
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@staticmethod
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async def create(
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session: AsyncSession,
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job_id: str,
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username: str,
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engine_requested: str,
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engine_used: str,
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processing_time_ms: int = 0,
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file_size_bytes: int = 0,
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file_type: str = "image/jpeg",
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original_filename: Optional[str] = None,
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success: bool = True,
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error_message: Optional[str] = None,
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overall_confidence: float = 0.0,
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fields_extracted: int = 0,
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needs_manual_review: Optional[bool] = None,
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validation_warnings_count: int = 0,
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validation_errors_count: int = 0,
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company_id: Optional[int] = None
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) -> OCRJobMetrics:
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"""Create a new OCR job metrics record."""
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metrics = OCRJobMetrics(
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job_id=job_id,
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username=username,
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company_id=company_id,
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engine_requested=engine_requested,
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engine_used=engine_used,
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processing_time_ms=processing_time_ms,
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file_size_bytes=file_size_bytes,
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file_type=file_type,
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original_filename=original_filename,
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success=success,
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error_message=error_message,
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overall_confidence=overall_confidence,
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fields_extracted=fields_extracted,
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needs_manual_review=needs_manual_review,
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validation_warnings_count=validation_warnings_count,
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validation_errors_count=validation_errors_count,
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)
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session.add(metrics)
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await session.commit()
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await session.refresh(metrics)
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return metrics
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@staticmethod
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async def get_by_job_id(session: AsyncSession, job_id: str) -> Optional[OCRJobMetrics]:
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"""Get metrics by job ID."""
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result = await session.execute(
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select(OCRJobMetrics).where(OCRJobMetrics.job_id == job_id)
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)
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return result.scalar_one_or_none()
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@staticmethod
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async def get_user_history(
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session: AsyncSession,
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username: str,
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limit: int = 50,
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offset: int = 0
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) -> List[OCRJobMetrics]:
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"""Get user's OCR job history."""
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result = await session.execute(
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select(OCRJobMetrics)
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.where(OCRJobMetrics.username == username)
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.order_by(OCRJobMetrics.created_at.desc())
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.limit(limit)
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.offset(offset)
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)
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return list(result.scalars().all())
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@staticmethod
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async def get_summary_by_engine(
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session: AsyncSession,
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days: int = 30,
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username: Optional[str] = None
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) -> List[OCRMetricsSummary]:
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"""Get summary metrics grouped by engine."""
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cutoff_date = datetime.utcnow() - timedelta(days=days)
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# Build query
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conditions = [OCRJobMetrics.created_at >= cutoff_date]
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if username:
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conditions.append(OCRJobMetrics.username == username)
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# Query for aggregated metrics
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result = await session.execute(
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select(
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OCRJobMetrics.engine_used,
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func.count(OCRJobMetrics.id).label('total_jobs'),
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func.sum(func.cast(OCRJobMetrics.success, sa.Integer)).label('successful_jobs'),
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func.avg(OCRJobMetrics.processing_time_ms).label('avg_processing_time_ms'),
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func.avg(OCRJobMetrics.overall_confidence).label('avg_confidence'),
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func.avg(OCRJobMetrics.fields_extracted).label('avg_fields_extracted'),
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)
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.where(and_(*conditions))
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.group_by(OCRJobMetrics.engine_used)
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.order_by(func.count(OCRJobMetrics.id).desc())
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)
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summaries = []
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for row in result.all():
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total = row.total_jobs or 0
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successful = row.successful_jobs or 0
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success_rate = successful / total if total > 0 else 0.0
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summaries.append(OCRMetricsSummary(
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engine=row.engine_used,
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total_jobs=total,
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successful_jobs=successful,
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failed_jobs=total - successful,
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success_rate=success_rate,
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avg_processing_time_ms=float(row.avg_processing_time_ms or 0),
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avg_confidence=float(row.avg_confidence or 0),
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avg_fields_extracted=float(row.avg_fields_extracted or 0),
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))
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return summaries
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@staticmethod
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async def get_overall_stats(
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session: AsyncSession,
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days: int = 30,
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username: Optional[str] = None
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) -> dict:
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"""Get overall OCR statistics."""
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cutoff_date = datetime.utcnow() - timedelta(days=days)
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conditions = [OCRJobMetrics.created_at >= cutoff_date]
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if username:
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conditions.append(OCRJobMetrics.username == username)
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result = await session.execute(
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select(
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func.count(OCRJobMetrics.id).label('total_jobs'),
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func.sum(func.cast(OCRJobMetrics.success, sa.Integer)).label('successful_jobs'),
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func.avg(OCRJobMetrics.processing_time_ms).label('avg_processing_time_ms'),
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func.avg(OCRJobMetrics.overall_confidence).label('avg_confidence'),
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)
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.where(and_(*conditions))
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)
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row = result.one()
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total = row.total_jobs or 0
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successful = row.successful_jobs or 0
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return {
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"total_jobs": total,
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"successful_jobs": successful,
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"failed_jobs": total - successful,
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"success_rate": (successful / total * 100) if total > 0 else 0.0,
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"avg_processing_time_ms": float(row.avg_processing_time_ms or 0),
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"avg_confidence": float(row.avg_confidence or 0),
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"period_days": days,
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}
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# Import sqlalchemy for func.cast
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import sqlalchemy as sa
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