Parse and Validate Industrial Safety Datasheets with Marker and Haystack

"""
Production implementation for parsing and validating industrial safety datasheets.
Provides secure, scalable operations and integrates with Marker and Haystack.
"""
from typing import Dict, List, Any
import os
import logging
import requests
import json
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

# Set up logging for the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to handle environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    api_base_url: str = os.getenv('API_BASE_URL')

# Create a database engine with connection pooling
engine = create_engine(Config.database_url, pool_size=20, max_overflow=0)
Session = sessionmaker(bind=engine)

def validate_input_data(data: Dict[str, Any]) -> bool:
    """Validate the input data for presence of required fields.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    required_fields = ['id', 'name', 'hazard_class']
    for field in required_fields:
        if field not in data:
            raise ValueError(f'Missing required field: {field}')  # Raise error if a field is missing
    return True


def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized_data = {k: v.strip() for k, v in data.items() if isinstance(v, str)}  # Strip whitespace
    return sanitized_data


def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize the data for consistency.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    normalized_data = data.copy()
    normalized_data['name'] = normalized_data['name'].lower()  # Normalize name to lowercase
    return normalized_data


def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform a list of records into a desired format.
    
    Args:
        data: List of input records
    Returns:
        Transformed records
    """
    transformed = []  # Prepare list for transformed data
    for record in data:
        sanitized = sanitize_fields(record)  # Sanitize each record
        normalized = normalize_data(sanitized)  # Normalize sanitized data
        transformed.append(normalized)
    return transformed


def fetch_data(url: str) -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Args:
        url: URL of the API to fetch data from
    Returns:
        List of records fetched
    Raises:
        Exception: If request fails
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise error for bad responses
        return response.json()  # Return JSON response
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')  # Log any errors during fetch
        raise Exception('Failed to fetch data')


def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save validated data to the database.
    
    Args:
        data: List of data to save
    Raises:
        Exception: If database operation fails
    """
    session = Session()  # Create a new database session
    try:
        for record in data:
            # Here you would typically use an ORM to add the record
            session.add(record)  # Example of adding each record
        session.commit()  # Commit the transaction
    except Exception as e:
        session.rollback()  # Rollback in case of error
        logger.error(f'Error saving to database: {e}')  # Log the error
        raise Exception('Failed to save data')
    finally:
        session.close()  # Always close the session


def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from the processed data.
    
    Args:
        data: List of processed data
    Returns:
        Dictionary with aggregated metrics
    """
    total_records = len(data)  # Count total records
    return {'total_records': total_records}


def format_output(metrics: Dict[str, Any]) -> str:
    """Format metrics for output.
    
    Args:
        metrics: Metrics to format
    Returns:
        Formatted string output
    """
    return json.dumps(metrics, indent=4)  # Convert metrics to pretty JSON string


class DatasheetParser:
    """Main class to orchestrate parsing and validating datasheets.
    
    Attributes:
        api_url: API URL to fetch datasheets
    """
    def __init__(self, api_url: str):
        self.api_url = api_url  # Initialize API URL

    def process(self) -> None:
        """Main processing workflow.
        
        Raises:
            Exception: If any step fails
        """
        try:
            raw_data = fetch_data(self.api_url)  # Fetch raw data from API
            validated_data = []  # Prepare list for validated data
            for entry in raw_data:
                if validate_input_data(entry):  # Validate each entry
                    validated_data.append(entry)  # Add valid entry

            transformed_data = transform_records(validated_data)  # Transform validated records
            save_to_db(transformed_data)  # Save transformed records to DB
            metrics = aggregate_metrics(transformed_data)  # Aggregate metrics from transformed records
            output = format_output(metrics)  # Format output for display
            logger.info(f'Processed data: {output}')  # Log processed output
        except Exception as e:
            logger.error(f'Error during processing: {e}')  # Log any processing error
            raise  # Re-raise exception for further handling


if __name__ == '__main__':
    # Example usage of DatasheetParser
    parser = DatasheetParser(api_url=Config.api_base_url)
    parser.process()  # Execute the processing workflow