Extract and Index Welding Procedure Specifications with Tesseract and LlamaIndex

"""
Production implementation for extracting and indexing welding procedure specifications using Tesseract and LlamaIndex.
Provides secure, scalable operations with comprehensive logging and error handling.
"""
from typing import Dict, Any, List
import os
import logging
import time
import cv2
import pytesseract
import llama_index as li

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    tesseract_cmd: str = os.getenv('TESSERACT_CMD', 'tesseract')
    llama_index_url: str = os.getenv('LLAMA_INDEX_URL')
    max_retries: int = int(os.getenv('MAX_RETRIES', 3))

def validate_input_data(data: Dict[str, Any]) -> bool:
    """Validate input data for extraction.
    
    Args:
        data: Input data containing image paths
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'image_paths' not in data or not isinstance(data['image_paths'], list):
        raise ValueError('Invalid input: image_paths must be a list.')
    if not data['image_paths']:
        raise ValueError('Input list is empty.')
    return True

def sanitize_fields(fields: List[str]) -> List[str]:
    """Sanitize fields extracted from images.
    
    Args:
        fields: List of fields to sanitize
    Returns:
        List[str]: Sanitized fields
    """
    return [field.strip().lower() for field in fields if field]

def fetch_data(image_paths: List[str]) -> List[str]:
    """Fetch data using Tesseract from image paths.
    
    Args:
        image_paths: List of image file paths
    Returns:
        List[str]: Extracted text from images
    """
    extracted_text = []
    for path in image_paths:
        try:
            logger.info(f'Processing image: {path}')
            image = cv2.imread(path)
            text = pytesseract.image_to_string(image, config='--psm 6')
            extracted_text.append(text)
        except Exception as e:
            logger.error(f'Error processing {path}: {e}')
    return extracted_text

def transform_records(records: List[str]) -> List[Dict[str, Any]]:
    """Transform extracted records into a structured format.
    
    Args:
        records: List of raw text records
    Returns:
        List[Dict[str, Any]]: Structured records
    """
    structured_records = []
    for record in records:
        fields = record.split('\n')  # Split lines
        sanitized_fields = sanitize_fields(fields)  # Sanitize fields
        structured_records.append({'fields': sanitized_fields})
    return structured_records

def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save structured records to the database.
    
    Args:
        records: List of structured records to save
    """
    # Here, implement the logic to save data to your database
    # This is a placeholder for actual database saving logic
    logger.info(f'Saving {len(records)} records to the database.')

def call_api(record: Dict[str, Any]) -> None:
    """Call external API with the given record.
    
    Args:
        record: Record to send to the API
    """
    # API call implementation goes here. This is a placeholder.
    logger.info(f'Calling API for record: {record}')

class WeldingProcedureExtractor:
    """Main class for extracting welding procedures."""
    def __init__(self, config: Config) -> None:
        self.config = config

    def process_batch(self, data: Dict[str, Any]) -> None:
        """Process a batch of images for welding procedures.
        
        Args:
            data: Input data containing image paths
        """
        try:
            if validate_input_data(data):
                image_paths = data['image_paths']
                extracted_text = fetch_data(image_paths)
                structured_records = transform_records(extracted_text)
                save_to_db(structured_records)  # Save the records
                for record in structured_records:
                    call_api(record)  # Call API for each record
        except ValueError as e:
            logger.error(f'Validation error: {e}')
        except Exception as e:
            logger.error(f'Error during processing batch: {e}')

if __name__ == '__main__':
    # Example usage
    config = Config()
    extractor = WeldingProcedureExtractor(config)
    sample_data = {'image_paths': ['path/to/image1.png', 'path/to/image2.png']}
    extractor.process_batch(sample_data)