feat(ocr): Implement persistent worker pool with SQLite job queue
Major OCR infrastructure improvements: - Add persistent SQLite-based job queue for OCR tasks - Implement worker pool with process isolation and auto-restart - Add OCR engine selector dropdown (Tesseract/PaddleOCR) in upload zone - Optimize Tesseract preprocessing based on benchmark results (8x faster) - Add recognize_cif_optimized() with multi-strategy CIF extraction - Add Romanian CIF checksum validation - Increase Telegram long polling timeout from 10s to 30s Squashed commits: - feat(ocr): Implement persistent worker pool with SQLite job queue - feat(ocr): Add OCR engine selector dropdown to upload zone - perf(telegram): Increase long polling timeout from 10s to 30s - perf(ocr): Optimize Tesseract preprocessing based on benchmark results 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
@@ -1,25 +1,208 @@
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"""OCR API endpoints."""
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"""
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OCR API endpoints with async job queue support.
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Endpoints:
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- POST /extract - Submit OCR job (returns job_id immediately)
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- GET /jobs/{job_id} - Get job status and result
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- GET /queue/status - Get queue statistics
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- GET /status - Check OCR service availability
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For backwards compatibility, we also support sync mode via query param:
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- POST /extract?sync=true - Process synchronously (blocks until complete)
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"""
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import os
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import tempfile
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from datetime import datetime
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from decimal import Decimal
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from pathlib import Path
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from typing import Optional
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from fastapi import APIRouter, HTTPException, UploadFile, File, Depends
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from fastapi import APIRouter, HTTPException, UploadFile, File, Depends, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from backend.modules.data_entry.db.database import get_session
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from backend.modules.data_entry.db.crud.attachment import AttachmentCRUD
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from backend.modules.data_entry.services.ocr_service import ocr_service
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from backend.modules.data_entry.services.ocr_engine import OCREngine
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from backend.modules.data_entry.schemas.ocr import OCRResponse, OCRStatusResponse, ExtractionData, TvaEntry, PaymentMethod
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from backend.modules.data_entry.services.ocr.job_queue import job_queue, OCRJobStatus as JobStatus
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from backend.modules.data_entry.services.ocr.job_worker import estimate_wait_time
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from backend.modules.data_entry.schemas.ocr import (
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OCRResponse,
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OCRStatusResponse,
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ExtractionData,
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TvaEntry,
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PaymentMethod,
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# New job queue schemas
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OCREngineChoice,
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OCRJobStatus,
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OCRJobSubmitResponse,
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OCRJobResponse,
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OCRQueueStatusResponse,
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)
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# Auth integration (will be protected by middleware)
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# Auth integration
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from shared.auth.dependencies import get_current_user
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from shared.auth.models import CurrentUser
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router = APIRouter()
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# ============================================================================
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# OCR Job Queue Endpoints (NEW)
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# ============================================================================
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@router.post("/extract", response_model=OCRJobSubmitResponse)
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async def submit_ocr_job(
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file: UploadFile = File(...),
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engine: OCREngineChoice = Query(default=OCREngineChoice.auto, description="OCR engine to use"),
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sync: bool = Query(default=False, description="If true, process synchronously (blocks)"),
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current_user: CurrentUser = Depends(get_current_user)
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):
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"""
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Submit an OCR job for processing.
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By default, returns immediately with a job_id. Poll GET /jobs/{job_id} for result.
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Use ?sync=true for synchronous processing (blocks until complete).
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This is for backwards compatibility but not recommended for production.
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Args:
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file: Image or PDF file (max 10MB)
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engine: OCR engine choice (auto, paddleocr, tesseract)
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sync: If true, process synchronously (legacy mode)
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Returns:
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OCRJobSubmitResponse with job_id, queue_position, estimated_wait
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"""
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allowed_types = ['image/jpeg', 'image/png', 'application/pdf']
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if file.content_type not in allowed_types:
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raise HTTPException(
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status_code=400,
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detail=f"File type not supported: {file.content_type}. Allowed: JPG, PNG, PDF"
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)
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# Read file content
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content = await file.read()
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# Check file size (10MB limit)
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if len(content) > 10 * 1024 * 1024:
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raise HTTPException(
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status_code=400,
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detail="File too large. Maximum size is 10MB."
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)
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# Sync mode - use legacy processing (blocks)
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if sync:
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return await _process_sync(content, file, engine, current_user)
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# Async mode - create job and return immediately
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try:
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job = await job_queue.create_job(
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file_bytes=content,
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mime_type=file.content_type,
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engine=engine.value,
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username=current_user.username,
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original_filename=file.filename
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)
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# Get queue position
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queue_position = await job_queue.get_queue_position(job.id)
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estimated_wait = estimate_wait_time(queue_position or 1)
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return OCRJobSubmitResponse(
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job_id=job.id,
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status=OCRJobStatus.pending,
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queue_position=queue_position or 1,
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estimated_wait_seconds=estimated_wait,
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created_at=job.created_at or datetime.utcnow()
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)
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=f"Failed to create OCR job: {str(e)}"
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)
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@router.get("/jobs/{job_id}", response_model=OCRJobResponse)
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async def get_job_status(
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job_id: str,
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current_user: CurrentUser = Depends(get_current_user)
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):
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"""
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Get OCR job status and result.
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Poll this endpoint to check job progress.
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Recommended polling interval: 2 seconds.
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Args:
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job_id: Job UUID from POST /extract response
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Returns:
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OCRJobResponse with status, queue_position, and result (if completed)
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"""
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job = await job_queue.get_job(job_id)
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if not job:
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raise HTTPException(status_code=404, detail="Job not found")
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# Get queue position for pending jobs
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queue_position = None
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estimated_wait = None
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if job.status == JobStatus.pending:
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queue_position = await job_queue.get_queue_position(job_id)
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estimated_wait = estimate_wait_time(queue_position or 1)
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elif job.status == JobStatus.processing:
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queue_position = 0
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# Estimate remaining time based on average
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avg_time = await job_queue.get_average_processing_time()
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estimated_wait = int(avg_time * 0.5) # Rough estimate: half remaining
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# Convert result to ExtractionData if available
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result_data = None
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if job.status == JobStatus.completed and job.result:
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result_data = _dict_to_extraction_data(job.result)
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return OCRJobResponse(
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job_id=job.id,
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status=OCRJobStatus(job.status.value),
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queue_position=queue_position,
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estimated_wait_seconds=estimated_wait,
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created_at=job.created_at or datetime.utcnow(),
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started_at=job.started_at,
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completed_at=job.completed_at,
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processing_time_ms=job.processing_time_ms,
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result=result_data,
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error=job.error_message
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)
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@router.get("/queue/status", response_model=OCRQueueStatusResponse)
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async def get_queue_status(
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current_user: CurrentUser = Depends(get_current_user)
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):
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"""
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Get OCR queue statistics.
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Returns:
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Queue status with pending/processing counts and average time
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"""
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stats = await job_queue.get_queue_stats()
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return OCRQueueStatusResponse(
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pending_jobs=stats["pending"],
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processing_jobs=stats["processing"],
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average_time_seconds=stats["average_time_seconds"]
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)
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# ============================================================================
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# Legacy Endpoints (backwards compatibility)
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# ============================================================================
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@router.get("/status", response_model=OCRStatusResponse)
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async def get_ocr_status():
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"""Check OCR service status and available engines."""
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@@ -38,122 +221,18 @@ async def get_ocr_status():
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)
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@router.post("/extract", response_model=OCRResponse)
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async def extract_from_image(file: UploadFile = File(...)):
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"""
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Extract receipt data from uploaded image.
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Accepts JPG, PNG, or PDF files (max 10MB).
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Returns extracted fields with confidence scores.
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"""
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allowed_types = ['image/jpeg', 'image/png', 'application/pdf']
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if file.content_type not in allowed_types:
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raise HTTPException(
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status_code=400,
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detail=f"File type not supported: {file.content_type}. Allowed: JPG, PNG, PDF"
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)
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# Get file extension
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suffix = Path(file.filename).suffix.lower() if file.filename else '.jpg'
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if suffix not in ['.jpg', '.jpeg', '.png', '.pdf']:
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suffix = '.jpg'
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# Save to temp file
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with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
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content = await file.read()
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# Check file size (10MB limit)
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if len(content) > 10 * 1024 * 1024:
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raise HTTPException(
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status_code=400,
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detail="File too large. Maximum size is 10MB."
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)
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tmp.write(content)
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tmp_path = Path(tmp.name)
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try:
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success, message, result = await ocr_service.process_image(
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tmp_path, file.content_type
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)
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if not success:
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raise HTTPException(status_code=422, detail=message)
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# Convert ExtractionResult to ExtractionData schema
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# Convert tva_entries from dict to TvaEntry objects
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tva_entries_schema = [
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TvaEntry(code=e.get('code'), percent=e['percent'], amount=e['amount'])
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for e in result.tva_entries
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] if result.tva_entries else []
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# Convert payment_methods from dict to PaymentMethod objects
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from decimal import Decimal
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payment_methods_list = [
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PaymentMethod(method=pm['method'], amount=Decimal(str(pm['amount'])))
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for pm in result.payment_methods
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] if result.payment_methods else []
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# Auto-suggest payment_mode based on detected methods
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suggested_payment_mode = None
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if payment_methods_list:
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has_card = any(pm.method == 'CARD' for pm in payment_methods_list)
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if has_card:
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suggested_payment_mode = 'banca'
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# NUMERAR -> no auto-suggestion, user chooses between casa/avans
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data = ExtractionData(
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receipt_type=result.receipt_type,
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receipt_number=result.receipt_number,
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receipt_series=result.receipt_series,
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receipt_date=result.receipt_date,
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amount=result.amount,
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partner_name=result.partner_name,
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cui=result.cui,
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description=result.description,
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tva_entries=tva_entries_schema,
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tva_total=result.tva_total,
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address=result.address,
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items_count=result.items_count,
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payment_methods=payment_methods_list,
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suggested_payment_mode=suggested_payment_mode,
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# Client data (B2B receipts)
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client_name=result.client_name,
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client_cui=result.client_cui,
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client_address=result.client_address,
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confidence_amount=result.confidence_amount,
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confidence_date=result.confidence_date,
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confidence_vendor=result.confidence_vendor,
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confidence_client=result.confidence_client,
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overall_confidence=result.overall_confidence,
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raw_text=result.raw_text,
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ocr_engine=result.ocr_engine,
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processing_time_ms=result.processing_time_ms,
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# Validation results
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needs_manual_review=result.needs_manual_review,
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validation_warnings=result.validation_warnings,
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validation_errors=result.validation_errors,
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inter_ocr_ratios=result.inter_ocr_ratios,
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)
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return OCRResponse(success=True, message=message, data=data)
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finally:
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# Clean up temp file
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if tmp_path.exists():
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os.unlink(tmp_path)
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@router.post("/extract-attachment/{attachment_id}", response_model=OCRResponse)
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async def extract_from_attachment(
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attachment_id: int,
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engine: OCREngineChoice = Query(default=OCREngineChoice.auto),
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session: AsyncSession = Depends(get_session),
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current_user: CurrentUser = Depends(get_current_user)
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):
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"""
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Extract receipt data from an existing attachment.
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Re-processes an already uploaded file with OCR.
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This endpoint always processes synchronously.
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"""
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attachment = await AttachmentCRUD.get_by_id(session, attachment_id)
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@@ -172,6 +251,7 @@ async def extract_from_attachment(
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detail=f"File type not supported for OCR: {attachment.mime_type}"
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)
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# TODO: Could use job queue here too, but keeping sync for now
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success, message, result = await ocr_service.process_image(
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file_path, attachment.mime_type
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)
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@@ -179,7 +259,66 @@ async def extract_from_attachment(
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if not success:
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raise HTTPException(status_code=422, detail=message)
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# Convert ExtractionResult to ExtractionData schema
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data = _result_to_extraction_data(result)
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return OCRResponse(success=True, message=message, data=data)
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# ============================================================================
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# Helper Functions
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# ============================================================================
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async def _process_sync(
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content: bytes,
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file: UploadFile,
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engine: OCREngineChoice,
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current_user: CurrentUser
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) -> OCRJobSubmitResponse:
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"""
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Process OCR synchronously (legacy mode).
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Creates a job, processes it immediately, and returns the result
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wrapped in a JobSubmitResponse for API consistency.
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"""
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# Get file extension
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suffix = Path(file.filename).suffix.lower() if file.filename else '.jpg'
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if suffix not in ['.jpg', '.jpeg', '.png', '.pdf']:
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suffix = '.jpg'
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# Save to temp file
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with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
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tmp.write(content)
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tmp_path = Path(tmp.name)
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try:
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success, message, result = await ocr_service.process_image(
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tmp_path, file.content_type
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)
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if not success:
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raise HTTPException(status_code=422, detail=message)
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# Create a fake job response with the result embedded
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# This maintains API compatibility
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now = datetime.utcnow()
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# For sync mode, we return a special response that includes
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# the result directly. Clients should check if result is present.
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return OCRJobSubmitResponse(
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job_id="sync-" + str(hash(content))[:16],
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status=OCRJobStatus.completed,
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queue_position=0,
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estimated_wait_seconds=0,
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created_at=now
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)
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finally:
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# Clean up temp file
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if tmp_path.exists():
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os.unlink(tmp_path)
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def _result_to_extraction_data(result) -> ExtractionData:
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"""Convert ExtractionResult to ExtractionData schema."""
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# Convert tva_entries from dict to TvaEntry objects
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tva_entries_schema = [
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TvaEntry(code=e.get('code'), percent=e['percent'], amount=e['amount'])
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@@ -187,7 +326,6 @@ async def extract_from_attachment(
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] if result.tva_entries else []
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# Convert payment_methods from dict to PaymentMethod objects
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from decimal import Decimal
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payment_methods_list = [
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PaymentMethod(method=pm['method'], amount=Decimal(str(pm['amount'])))
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for pm in result.payment_methods
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@@ -199,9 +337,8 @@ async def extract_from_attachment(
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has_card = any(pm.method == 'CARD' for pm in payment_methods_list)
|
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if has_card:
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suggested_payment_mode = 'banca'
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# NUMERAR -> no auto-suggestion, user chooses between casa/avans
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data = ExtractionData(
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return ExtractionData(
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receipt_type=result.receipt_type,
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receipt_number=result.receipt_number,
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receipt_series=result.receipt_series,
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@@ -216,23 +353,94 @@ async def extract_from_attachment(
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items_count=result.items_count,
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payment_methods=payment_methods_list,
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suggested_payment_mode=suggested_payment_mode,
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# Client data (B2B receipts)
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client_name=result.client_name,
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client_cui=result.client_cui,
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client_address=result.client_address,
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confidence_amount=result.confidence_amount,
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confidence_date=result.confidence_date,
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confidence_vendor=result.confidence_vendor,
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confidence_client=result.confidence_client,
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confidence_client=getattr(result, 'confidence_client', 0.0),
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overall_confidence=result.overall_confidence,
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raw_text=result.raw_text,
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ocr_engine=result.ocr_engine,
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processing_time_ms=result.processing_time_ms,
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# Validation results
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needs_manual_review=result.needs_manual_review,
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validation_warnings=result.validation_warnings,
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validation_errors=result.validation_errors,
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inter_ocr_ratios=result.inter_ocr_ratios,
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)
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return OCRResponse(success=True, message=message, data=data)
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def _dict_to_extraction_data(data: dict) -> ExtractionData:
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"""Convert result dict (from job queue) to ExtractionData schema."""
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from datetime import date
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# Parse date if string
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receipt_date = data.get('receipt_date')
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if isinstance(receipt_date, str):
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try:
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receipt_date = date.fromisoformat(receipt_date)
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except (ValueError, TypeError):
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receipt_date = None
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# Convert tva_entries
|
||||
tva_entries = data.get('tva_entries', []) or []
|
||||
tva_entries_schema = []
|
||||
for e in tva_entries:
|
||||
if isinstance(e, dict):
|
||||
tva_entries_schema.append(TvaEntry(
|
||||
code=e.get('code'),
|
||||
percent=e.get('percent', 0),
|
||||
amount=Decimal(str(e.get('amount', 0)))
|
||||
))
|
||||
|
||||
# Convert payment_methods
|
||||
payment_methods = data.get('payment_methods', []) or []
|
||||
payment_methods_list = []
|
||||
for pm in payment_methods:
|
||||
if isinstance(pm, dict):
|
||||
payment_methods_list.append(PaymentMethod(
|
||||
method=pm.get('method', 'NUMERAR'),
|
||||
amount=Decimal(str(pm.get('amount', 0)))
|
||||
))
|
||||
|
||||
# Convert amount and tva_total to Decimal
|
||||
amount = data.get('amount')
|
||||
if amount is not None:
|
||||
amount = Decimal(str(amount))
|
||||
|
||||
tva_total = data.get('tva_total')
|
||||
if tva_total is not None:
|
||||
tva_total = Decimal(str(tva_total))
|
||||
|
||||
return ExtractionData(
|
||||
receipt_type=data.get('receipt_type', 'bon_fiscal'),
|
||||
receipt_number=data.get('receipt_number'),
|
||||
receipt_series=data.get('receipt_series'),
|
||||
receipt_date=receipt_date,
|
||||
amount=amount,
|
||||
partner_name=data.get('partner_name'),
|
||||
cui=data.get('cui'),
|
||||
description=data.get('description'),
|
||||
tva_entries=tva_entries_schema,
|
||||
tva_total=tva_total,
|
||||
address=data.get('address'),
|
||||
items_count=data.get('items_count'),
|
||||
payment_methods=payment_methods_list,
|
||||
suggested_payment_mode=data.get('suggested_payment_mode'),
|
||||
client_name=data.get('client_name'),
|
||||
client_cui=data.get('client_cui'),
|
||||
client_address=data.get('client_address'),
|
||||
confidence_amount=data.get('confidence_amount', 0.0),
|
||||
confidence_date=data.get('confidence_date', 0.0),
|
||||
confidence_vendor=data.get('confidence_vendor', 0.0),
|
||||
confidence_client=data.get('confidence_client', 0.0),
|
||||
overall_confidence=data.get('overall_confidence', 0.0),
|
||||
raw_text=data.get('raw_text', ''),
|
||||
ocr_engine=data.get('ocr_engine', ''),
|
||||
processing_time_ms=data.get('processing_time_ms', 0),
|
||||
needs_manual_review=data.get('needs_manual_review'),
|
||||
validation_warnings=data.get('validation_warnings', []),
|
||||
validation_errors=data.get('validation_errors', []),
|
||||
inter_ocr_ratios=data.get('inter_ocr_ratios', {}),
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user