Extract Compliance Data from Industrial Forms with Azure Document Intelligence SDK and spaCy

                      
                     
"""
Production implementation for Extract Compliance Data from Industrial Forms with Azure Document Intelligence SDK and spaCy.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import spacy
import requests
import time
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential

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

class Config:
    """
    Configuration class to hold environment variables.
    """
    endpoint: str = os.getenv('AZURE_ENDPOINT')
    api_key: str = os.getenv('AZURE_API_KEY')
    nlp_model: str = os.getenv('NLP_MODEL')

# Initialize spaCy model
nlp = spacy.load(Config.nlp_model)

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 'documents' not in data or not isinstance(data['documents'], list):
        raise ValueError('Invalid input: documents key is required and must be a list.')
    return True

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

def fetch_data(url: str) -> Dict[str, Any]:
    """
    Fetch data from a given URL.
    
    Args:
        url: URL to fetch data from
    Returns:
        JSON response
    Raises:
        RuntimeError: If unable to fetch data
    """
    try:
        response = requests.get(url)
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Failed to fetch data: {e}')
        raise RuntimeError('Data fetch error')

def process_batch(documents: List[str]) -> List[Dict[str, Any]]:
    """
    Process a batch of documents using Azure Document Intelligence.
    
    Args:
        documents: A list of document URLs
    Returns:
        List of extracted data from documents
    """
    client = DocumentAnalysisClient(endpoint=Config.endpoint, credential=AzureKeyCredential(Config.api_key))
    results = []
    for doc in documents:
        poller = client.begin_analyze_document("prebuilt-document", doc)
        result = poller.result()
        # Extract relevant fields from the result
        results.append({
            'form_type': result.form_type,
            'fields': {field_name: field.value for field_name, field in result.fields.items()}
        })
    return results

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Transform records for further processing.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed records
    """
    transformed = []
    for record in records:
        transformed.append({
            'type': record['form_type'],
            'data': record['fields']
        })
    return transformed

def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """
    Aggregate metrics from the processed records.
    
    Args:
        records: List of records to aggregate
    Returns:
        Dictionary of aggregated metrics
    """
    metrics = {'total_forms': len(records)}
    return metrics

def save_to_db(data: List[Dict[str, Any]]) -> None:
    """
    Save extracted data to the database.
    
    Args:
        data: Data to save
    Raises:
        RuntimeError: If saving fails
    """
    try:
        # Simulating database save operation
        logger.info('Saving data to database...')
        # Insert save logic here
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise RuntimeError('Database save error')

class ComplianceDataExtractor:
    """
    Orchestrator class for extracting compliance data from forms.
    """
    def __init__(self, data: Dict[str, Any]):
        self.data = data

    def run(self) -> None:
        """
        Main workflow to execute data extraction.
        """
        try:
            validate_input(self.data)
            sanitized_data = sanitize_fields(self.data)
            documents = sanitized_data['documents']
            results = process_batch(documents)
            transformed = transform_records(results)
            metrics = aggregate_metrics(transformed)
            save_to_db(transformed)
            logger.info(f'Metrics: {metrics}')
        except Exception as e:
            logger.error(f'Error in extraction process: {e}')

if __name__ == '__main__':
    # Example usage
    input_data = {'documents': ['https://example.com/form1.pdf', 'https://example.com/form2.pdf']}
    extractor = ComplianceDataExtractor(input_data)
    extractor.run()