copilot-swe-agent[bot]
5ff7b72a80
- Rewrite app/tasks/retry_config.py with compute_countdown() function implementing per-retry delays with ±20% jitter (default: 60s, 300s, 900s) - Add BaseTaskWithRetry.retry() override to inject proper countdown - Add OcrTaskWithRetry (120s, 600s, 1800s) for OCR/AI tasks - Add UploadTaskWithRetry for cloud-storage upload tasks - Add config settings: TASK_RETRY_MAX_RETRIES, TASK_RETRY_DELAYS, TASK_RETRY_JITTER - Update process_with_ocr and process_with_azure tasks to use OcrTaskWithRetry - Update all 11 upload tasks to use UploadTaskWithRetry - Add 38 unit tests in tests/test_retry_config.py - Update docs/ConfigurationGuide.md and .env.demo Co-authored-by: christianlouis <361235+christianlouis@users.noreply.github.com>
290 lines
12 KiB
Python
290 lines
12 KiB
Python
#!/usr/bin/env python3
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"""Unified OCR processing task for DocuElevate.
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This task replaces the single-provider ``process_with_azure_document_intelligence``
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task with a multi-engine OCR pipeline that:
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1. Runs every OCR provider listed in ``OCR_PROVIDERS`` (default: ``azure``).
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2. Merges/cross-checks the results using the configured AI model when more
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than one provider is active (see ``OCR_MERGE_STRATEGY``).
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3. Writes the best searchable PDF back to the working directory.
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4. Optionally compares the OCR output against the original embedded text
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(passed as *original_text*) using a head-to-head AI review and keeps the
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higher-quality text for downstream processing.
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5. Hands off to the page-rotation and metadata-extraction pipeline exactly as
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the legacy Azure task did.
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"""
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import logging
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import os
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from typing import Optional
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from app.celery_app import celery
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from app.config import settings
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from app.database import SessionLocal
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from app.models import FileRecord
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from app.tasks.retry_config import OcrTaskWithRetry
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from app.tasks.rotate_pdf_pages import rotate_pdf_pages
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from app.utils import log_task_progress
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from app.utils.ocr_provider import OCRResult, embed_text_layer, get_ocr_providers, merge_ocr_results
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from app.utils.text_quality import TextSource, check_text_quality, compare_text_quality
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logger = logging.getLogger(__name__)
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@celery.task(base=OcrTaskWithRetry, bind=True)
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def process_with_ocr(self, filename: str, file_id: Optional[int] = None, original_text: Optional[str] = None):
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"""Run the configured OCR providers on *filename* and continue the pipeline.
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When multiple OCR providers are configured the results are merged using the
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AI model (or a simpler strategy controlled by ``OCR_MERGE_STRATEGY``).
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If *original_text* is provided (the original embedded text that failed the
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quality check), the OCR result is compared against it using a head-to-head
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AI review. The higher-quality text is passed to downstream tasks.
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Args:
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filename: Base name of the file inside ``<workdir>/tmp/``.
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file_id: Optional database record ID passed through to downstream tasks.
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original_text: Optional original embedded text for head-to-head comparison.
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"""
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task_id = self.request.id
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log_task_progress(
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task_id,
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"process_with_ocr",
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"in_progress",
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f"Starting OCR for {filename}",
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file_id=file_id,
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)
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try:
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tmp_file_path = os.path.join(settings.workdir, "tmp", filename)
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if not os.path.exists(tmp_file_path):
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raise FileNotFoundError(f"Local file not found: {tmp_file_path}")
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providers = get_ocr_providers()
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provider_names = [p.name for p in providers]
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logger.info(f"[{task_id}] Running {len(providers)} OCR provider(s): {provider_names}")
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log_task_progress(
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task_id,
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"run_ocr_providers",
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"in_progress",
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f"Running OCR providers: {', '.join(provider_names)}",
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file_id=file_id,
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)
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results = []
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errors = []
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for provider in providers:
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pname = provider.__class__.__name__
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try:
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logger.info(f"[{task_id}] Running {pname} on {filename}")
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result: OCRResult = provider.process(tmp_file_path)
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results.append(result)
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logger.info(f"[{task_id}] {pname} extracted {len(result.text)} chars")
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except Exception as exc:
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logger.error(f"[{task_id}] {pname} failed for {filename}: {exc}")
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errors.append(f"{pname}: {exc}")
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if not results:
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error_summary = "; ".join(errors)
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log_task_progress(
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task_id,
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"run_ocr_providers",
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"failure",
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"All OCR providers failed",
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file_id=file_id,
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detail=error_summary,
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)
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raise RuntimeError(f"All OCR providers failed for {filename}: {error_summary}")
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if errors:
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logger.warning(f"[{task_id}] Some OCR providers failed: {'; '.join(errors)}")
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log_task_progress(
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task_id,
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"run_ocr_providers",
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"success",
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f"{len(results)} of {len(providers)} OCR provider(s) succeeded",
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file_id=file_id,
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)
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# Merge results (no-op when only one provider succeeded)
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extracted_text, searchable_pdf_path, rotation_data = merge_ocr_results(results, filename)
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logger.info(
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f"[{task_id}] Merged OCR text: {len(extracted_text)} chars, "
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f"pdf={'yes' if searchable_pdf_path else 'no'}, "
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f"rotations={len(rotation_data)}"
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)
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# If no provider produced a searchable PDF, post-process the original
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# PDF with ocrmypdf to embed an invisible text layer so the output is
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# selectable/searchable in PDF viewers.
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if searchable_pdf_path is None:
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lang = getattr(settings, "tesseract_language", None) or "eng"
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log_task_progress(
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task_id,
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"embed_text_layer",
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"in_progress",
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"Embedding searchable text layer into PDF",
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file_id=file_id,
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)
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embedded = embed_text_layer(tmp_file_path, tmp_file_path, language=lang)
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if embedded:
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searchable_pdf_path = tmp_file_path
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log_task_progress(
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task_id,
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"embed_text_layer",
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"success",
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"Searchable text layer embedded via ocrmypdf",
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file_id=file_id,
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)
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else:
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log_task_progress(
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task_id,
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"embed_text_layer",
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"skipped",
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"ocrmypdf unavailable – PDF will not have a searchable text layer",
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file_id=file_id,
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)
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# ----------------------------------------------------------------
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# Head-to-head comparison with original embedded text (if provided)
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# ----------------------------------------------------------------
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final_text = extracted_text
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if original_text and original_text.strip() and extracted_text.strip():
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logger.info(f"[{task_id}] Original embedded text provided; running head-to-head quality comparison")
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"in_progress",
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"Comparing OCR result against original embedded text",
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file_id=file_id,
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)
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try:
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comparison = compare_text_quality(original_text, extracted_text)
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comparison_detail = (
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f"Original score: {comparison.original_score}/100, "
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f"OCR score: {comparison.ocr_score}/100, "
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f"Preferred: {comparison.preferred}\n"
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f"AI explanation: {comparison.explanation}"
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)
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logger.info(f"[{task_id}] OCR comparison – {comparison_detail}")
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if comparison.preferred == "original":
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# Original text is actually better – use it instead of OCR.
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final_text = original_text
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logger.info(
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f"[{task_id}] Original embedded text selected "
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f"(original={comparison.original_score} > ocr={comparison.ocr_score})"
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)
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"success",
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f"Original text preferred (original={comparison.original_score}/100 vs "
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f"ocr={comparison.ocr_score}/100)",
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file_id=file_id,
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detail=comparison_detail,
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)
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else:
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logger.info(
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f"[{task_id}] OCR text selected "
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f"(preferred={comparison.preferred!r}, "
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f"ocr={comparison.ocr_score}, original={comparison.original_score})"
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)
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"success",
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f"OCR text preferred (ocr={comparison.ocr_score}/100 vs "
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f"original={comparison.original_score}/100)",
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file_id=file_id,
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detail=comparison_detail,
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)
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except Exception as cmp_exc:
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logger.warning(f"[{task_id}] Head-to-head comparison failed ({cmp_exc}); keeping OCR text")
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"skipped",
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f"Comparison failed ({cmp_exc}); keeping OCR output",
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file_id=file_id,
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)
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elif original_text is not None:
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# original_text was provided but one side is empty – pick whichever has content.
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if not extracted_text.strip() and original_text.strip():
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final_text = original_text
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logger.info(f"[{task_id}] OCR returned empty text; falling back to original embedded text")
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"success",
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"OCR empty – using original embedded text",
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file_id=file_id,
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)
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else:
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"skipped",
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"No original text to compare; using OCR output",
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file_id=file_id,
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)
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log_task_progress(
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task_id,
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"process_with_ocr",
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"success",
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f"OCR complete for {filename}",
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file_id=file_id,
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detail=f"Extracted {len(extracted_text)} chars using {len(results)} provider(s); "
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f"final text length: {len(final_text)} chars",
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)
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# Score the final embedded text — the text that will land in ocr_text.
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# We always call check_text_quality() on final_text because:
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# - merge_ocr_results() may have AI-merged output from several engines
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# - compare_text_quality() scores are relative (not the same scale)
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# - The original may have been preferred, reversing the OCR output
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# OCR-produced (or AI-merged) text is treated as TextSource.OCR_PREVIOUS
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# so the quality AI call is always made.
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if file_id is not None:
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try:
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quality_result = check_text_quality(final_text, TextSource.OCR_PREVIOUS)
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logger.info(
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f"[{task_id}] Final text quality: score={quality_result.quality_score}/100, "
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f"good={quality_result.is_good_quality}, feedback={quality_result.feedback!r}"
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)
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with SessionLocal() as _db:
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_rec = _db.query(FileRecord).filter_by(id=file_id).first()
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if _rec:
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_rec.ocr_quality_score = quality_result.quality_score
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_db.commit()
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logger.info(f"[{task_id}] Saved ocr_quality_score={quality_result.quality_score} for file_id={file_id}")
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except Exception as _score_exc:
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logger.warning(f"[{task_id}] Could not persist ocr_quality_score: {_score_exc}")
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# Continue pipeline: rotate pages (if needed), then extract metadata
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rotate_pdf_pages.delay(filename, final_text, rotation_data, file_id)
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return {
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"file": filename,
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"searchable_pdf": searchable_pdf_path or tmp_file_path,
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"cleaned_text": final_text,
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"providers_used": [r.provider for r in results],
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}
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except Exception as exc:
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logger.error(f"[{task_id}] OCR failed for {filename}: {exc}")
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log_task_progress(
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task_id,
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"process_with_ocr",
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"failure",
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f"OCR failed for {filename}",
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file_id=file_id,
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detail=str(exc),
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)
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raise
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