Classify Manufacturing Regulations with LayoutParser and Haystack

                      
                     
"""
Production implementation for Classifying Manufacturing Regulations.
This module provides a secure, scalable way to classify regulations using LayoutParser and Haystack.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import requests
from sqlalchemy import create_engine, Column, String, Integer
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, scoped_session
from tenacity import retry, stop_after_attempt, wait_exponential

# Setting up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# SQLAlchemy setup
Base = declarative_base()
engine = create_engine(os.getenv('DATABASE_URL'))
Session = scoped_session(sessionmaker(bind=engine))

class Config:
    """
    Configuration settings for the application.
    """
    layout_parser_model: str = os.getenv('LAYOUT_PARSER_MODEL', 'default_model')

class Regulation(Base):
    """
    Model representing a manufacturing regulation.
    """
    __tablename__ = 'regulations'
    id = Column(Integer, primary_key=True)
    title = Column(String)
    content = Column(String)

    def __repr__(self) -> str:
        return f'Regulation(id={self.id}, title={self.title})'

@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=2, max=10))
def fetch_data(url: str) -> Dict[str, Any]:
    """
    Fetch data from a given URL with retry logic.

    Args:
        url: URL to fetch data from.
    Returns:
        Parsed JSON data.
    Raises:
        ValueError: If fetching fails.
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise ValueError('Failed to fetch data')

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate input data for classification.

    Args:
        data: Input data to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'title' not in data or 'content' not in data:
        raise ValueError('Missing title or content in input data')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input fields to avoid XSS or SQL injection.

    Args:
        data: Input data.
    Returns:
        Sanitized data.
    """
    return {k: v.strip() for k, v in data.items()}

def transform_records(raw_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Transform raw data into the desired format for processing.

    Args:
        raw_data: List of dictionaries containing raw data.
    Returns:
        List of transformed data.
    """
    return [{'title': record['title'], 'content': record['content']} for record in raw_data]

def process_batch(data: List[Dict[str, Any]]) -> None:
    """
    Process a batch of regulations, saving them to the database.

    Args:
        data: List of data to process.
    """
    with Session() as session:
        for record in data:
            reg = Regulation(title=record['title'], content=record['content'])
            session.add(reg)
        session.commit()  # Commit all records in one go

def aggregate_metrics() -> Dict[str, Any]:
    """
    Aggregate metrics for the processed records.

    Returns:
        Dictionary containing metrics.
    """
    with Session() as session:
        total = session.query(Regulation).count()
    return {'total_regulations': total}

class RegulationClassifier:
    """
    Main class for classifying manufacturing regulations.
    """

    def __init__(self, model: str):
        self.model = model  # Set the model for LayoutParser

    def classify(self, data: List[Dict[str, Any]]) -> List[str]:
        """
        Classify the regulations using the specified model.

        Args:
            data: List of data to classify.
        Returns:
            List of classifications.
        """
        logger.info('Classifying regulations...')
        # Placeholder for classification logic using LayoutParser
        return ['Class A' for _ in data]  # Dummy classification

def main() -> None:
    """
    Main function to execute the classification workflow.
    """
    """
    try:
        # Fetch raw data
        url = os.getenv('DATA_SOURCE_URL')
        raw_data = fetch_data(url)
        # Validate and sanitize input
        for record in raw_data:
            validate_input(record)
            sanitized_record = sanitize_fields(record)
            # Transform records for processing
            transformed_data = transform_records([sanitized_record])
            # Process the transformed data
            process_batch(transformed_data)
            # Classify data
            classifier = RegulationClassifier(Config.layout_parser_model)
            classifications = classifier.classify(transformed_data)
            logger.info(f'Classifications: {classifications}')
        # Aggregate and log metrics
        metrics = aggregate_metrics()
        logger.info(f'Metrics: {metrics}')
    except Exception as e:
        logger.error(f'An error occurred: {e}')

if __name__ == '__main__':
    main()  # Execute the main function