Parse and Index Equipment Maintenance Reports with Tesseract and Docling

                      
                     
"""
Production implementation for parsing and indexing equipment maintenance reports.
Provides secure, scalable operations using Tesseract OCR and Docling.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import time
import pytesseract
from docling import Document
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, Session

# Logger setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Database setup
Base = declarative_base()

class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///reports.db')

class MaintenanceReport(Base):
    """
    Database model for maintenance reports.
    """
    __tablename__ = 'maintenance_reports'
    id = Column(Integer, primary_key=True)
    equipment_id = Column(String, nullable=False)
    report_text = Column(String, nullable=False)

# Create a database engine and session factory
engine = create_engine(Config.database_url)
Base.metadata.create_all(engine)
SessionLocal = sessionmaker(bind=engine)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'equipment_id' not in data:
        raise ValueError('Missing equipment_id')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: value.strip() for key, value in data.items()}

async def fetch_data(report_path: str) -> str:
    """Fetch report data using Tesseract OCR.
    
    Args:
        report_path: Path to the report image
    Returns:
        Extracted text from the image
    Raises:
        OSError: If OCR processing fails
    """
    try:
        text = pytesseract.image_to_string(report_path)
        logger.info('Fetched data from report.')
        return text
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise OSError('Failed to process OCR data.')

async def save_to_db(session: Session, report: MaintenanceReport) -> None:
    """Save parsed report to the database.
    
    Args:
        session: Database session
        report: MaintenanceReport object to save
    Raises:
        Exception: If saving fails
    """
    try:
        session.add(report)
        session.commit()
        logger.info('Report saved to database.')
    except Exception as e:
        session.rollback()
        logger.error(f'Error saving to database: {e}')
        raise

async def process_batch(report_paths: List[str]) -> None:
    """Process a batch of reports.
    
    Args:
        report_paths: List of paths to report images
    Raises:
        Exception: If processing fails
    """
    with SessionLocal() as session:
        for report_path in report_paths:
            try:
                text = await fetch_data(report_path)
                report = MaintenanceReport(equipment_id='123', report_text=text)
                await save_to_db(session, report)
            except Exception as e:
                logger.warning(f'Failed to process {report_path}: {e}')  # Log a warning for individual failures

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input data.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    # Example normalization logic
    data['equipment_id'] = data['equipment_id'].upper()
    return data

async def aggregate_metrics(reports: List[MaintenanceReport]) -> Dict[str, Any]:
    """Aggregate metrics from processed reports.
    
    Args:
        reports: List of MaintenanceReport objects
    Returns:
        Dictionary of aggregated metrics
    """
    return {'total_reports': len(reports)}

if __name__ == '__main__':
    # Example usage
    report_paths = ['/path/to/report1.png', '/path/to/report2.png']  # Replace with actual paths
    try:
        await process_batch(report_paths)
    except Exception as e:
        logger.error(f'Error in main processing: {e}')  # Log any errors that occur during batch processing