src/models/layoutlm.py

import torch
from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
from PIL import Image
import numpy as np
from typing import Dict, List, Tuple, Optional
import logging

logger = logging.getLogger(__name__)

class FormIQModel:
    def __init__(
        self,
        model_name: str = "microsoft/layoutlmv3-base",
        device: str = "cuda" if torch.cuda.is_available() else "cpu"
    ):
        """Initialize the FormIQ model with LayoutLMv3.
        
        Args:
            model_name: Name of the pre-trained model to use
            device: Device to run the model on ('cuda' or 'cpu')
        """
        self.device = device
        self.processor = LayoutLMv3Processor.from_pretrained(model_name)
        self.model = LayoutLMv3ForTokenClassification.from_pretrained(model_name)
        self.model.to(device)
        logger.info(f"Model initialized on {device}")

    def preprocess_image(self, image: Image.Image) -> Dict[str, torch.Tensor]:
        """Preprocess the input image for the model.
        
        Args:
            image: PIL Image to process
            
        Returns:
            Dictionary of processed inputs
        """
        try:
            # Process image and text
            encoding = self.processor(
                image,
                return_tensors="pt",
                truncation=True,
                max_length=512
            )
            
            # Move tensors to device
            encoding = {k: v.to(self.device) for k, v in encoding.items()}
            return encoding
            
        except Exception as e:
            logger.error(f"Error preprocessing image: {str(e)}")
            raise

    def predict(
        self,
        image: Image.Image,
        confidence_threshold: float = 0.5
    ) -> Dict[str, List[Dict[str, any]]]:
        """Extract information from the document image.
        
        Args:
            image: PIL Image of the document
            confidence_threshold: Minimum confidence score for predictions
            
        Returns:
            Dictionary containing extracted fields and their metadata
        """
        try:
            # Preprocess image
            inputs = self.preprocess_image(image)
            
            # Get model predictions
            with torch.no_grad():
                outputs = self.model(**inputs)
                predictions = outputs.logits.argmax(-1).squeeze().cpu().numpy()
                scores = torch.softmax(outputs.logits, dim=-1).max(-1)[0].squeeze().cpu().numpy()
            
            # Process predictions
            extracted_fields = self._process_predictions(predictions, scores, confidence_threshold)
            
            return {
                "fields": extracted_fields,
                "metadata": {
                    "confidence_scores": scores.tolist(),
                    "model_version": self.model.config.model_type
                }
            }
            
        except Exception as e:
            logger.error(f"Error during prediction: {str(e)}")
            raise

    def _process_predictions(
        self,
        predictions: np.ndarray,
        scores: np.ndarray,
        confidence_threshold: float
    ) -> List[Dict[str, any]]:
        """Process raw model predictions into structured output.
        
        Args:
            predictions: Array of predicted class indices
            scores: Array of confidence scores
            confidence_threshold: Minimum confidence score
            
        Returns:
            List of dictionaries containing field information
        """
        # TODO: Implement field-specific post-processing
        # This is a placeholder implementation
        processed_fields = []
        
        for pred, score in zip(predictions, scores):
            if score >= confidence_threshold:
                field_info = {
                    "label": self.model.config.id2label[pred],
                    "confidence": float(score),
                    "bbox": None  # TODO: Add bounding box information
                }
                processed_fields.append(field_info)
        
        return processed_fields

    def validate_extraction(
        self,
        extracted_fields: Dict[str, List[Dict[str, any]]],
        document_type: str
    ) -> Dict[str, any]:
        """Validate extracted fields based on document type rules.
        
        Args:
            extracted_fields: Dictionary of extracted fields
            document_type: Type of document (e.g., 'invoice', 'receipt')
            
        Returns:
            Dictionary containing validation results
        """
        # TODO: Implement field validation logic
        # This is a placeholder implementation
        return {
            "is_valid": True,
            "validation_errors": [],
            "confidence_score": 1.0
        }