backend/pdf_extractor.py

"""
PDF Medical Extractor - Phase 2
Structured PDF extraction using Donut/LayoutLMv3 for medical documents.

This module provides specialized extraction for medical PDFs including
radiology reports, laboratory results, clinical notes, and ECG reports.

Author: MiniMax Agent
Date: 2025-10-29
Version: 1.0.0
"""

import os
import json
import io
import logging
from typing import Dict, List, Optional, Any, Tuple
from dataclasses import dataclass
from pathlib import Path
import numpy as np
from PIL import Image
import fitz  # PyMuPDF
import pytesseract
from transformers import DonutProcessor, VisionEncoderDecoderModel
import torch
from tqdm import tqdm

from medical_schemas import (
    MedicalDocumentMetadata, ConfidenceScore, RadiologyAnalysis, 
    LaboratoryResults, ClinicalNotesAnalysis, ValidationResult,
    validate_document_schema
)

logger = logging.getLogger(__name__)


@dataclass
class ExtractionResult:
    """Result of PDF extraction with confidence scoring"""
    raw_text: str
    structured_data: Dict[str, Any]
    confidence_scores: Dict[str, float]
    extraction_method: str  # "donut", "ocr", "hybrid"
    processing_time: float
    tables_extracted: List[Dict[str, Any]]
    images_extracted: List[str]
    metadata: Dict[str, Any]


class DonutMedicalExtractor:
    """Medical PDF extraction using Donut model for structured output"""
    
    def __init__(self, model_name: str = "naver-clova-ix/donut-base-finetuned-rvlcdip"):
        self.model_name = model_name
        self.processor = None
        self.model = None
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self._load_model()
        
    def _load_model(self):
        """Load Donut model and processor"""
        try:
            logger.info(f"Loading Donut model: {self.model_name}")
            self.processor = DonutProcessor.from_pretrained(self.model_name)
            self.model = VisionEncoderDecoderModel.from_pretrained(self.model_name)
            self.model.to(self.device)
            self.model.eval()
            logger.info("Donut model loaded successfully")
        except Exception as e:
            logger.error(f"Failed to load Donut model: {str(e)}")
            raise
    
    def extract_from_image(self, image: Image.Image, task_prompt: str = None) -> Dict[str, Any]:
        """Extract structured data from image using Donut"""
        if task_prompt is None:
            task_prompt = "<s_rvlcdip>"
        
        try:
            # Prepare image for Donut
            pixel_values = self.processor(images=image, return_tensors="pt").pixel_values
            pixel_values = pixel_values.to(self.device)
            
            # Generate structured output
            task_prompt_ids = self.processor.tokenizer(task_prompt, add_special_tokens=False, 
                                                      return_tensors="pt").input_ids
            task_prompt_ids = task_prompt_ids.to(self.device)
            
            with torch.no_grad():
                outputs = self.model.generate(
                    task_prompt_ids,
                    pixel_values,
                    max_length=512,
                    early_stopping=False,
                    pad_token_id=self.processor.tokenizer.pad_token_id,
                    eos_token_id=self.processor.tokenizer.eos_token_id,
                    use_cache=True,
                )
            
            # Decode output
            output_sequence = outputs.cpu().numpy()[0]
            decoded_output = self.processor.tokenizer.decode(output_sequence, skip_special_tokens=True)
            
            # Parse JSON from decoded output
            json_start = decoded_output.find('{')
            json_end = decoded_output.rfind('}') + 1
            
            if json_start != -1 and json_end != -1:
                json_str = decoded_output[json_start:json_end]
                structured_data = json.loads(json_str)
            else:
                structured_data = {"raw_text": decoded_output}
            
            return structured_data
            
        except Exception as e:
            logger.error(f"Donut extraction error: {str(e)}")
            return {"raw_text": "", "error": str(e)}


class MedicalPDFProcessor:
    """Medical PDF processing with multiple extraction methods"""
    
    def __init__(self):
        self.donut_extractor = None
        self.ocr_enabled = True
        
        # Initialize Donut extractor
        try:
            self.donut_extractor = DonutMedicalExtractor()
        except Exception as e:
            logger.warning(f"Donut extractor not available: {str(e)}")
            self.donut_extractor = None
        
    def process_pdf(self, pdf_path: str, document_type: str = "unknown") -> ExtractionResult:
        """
        Process medical PDF with multiple extraction methods
        
        Args:
            pdf_path: Path to PDF file
            document_type: Type of medical document
            
        Returns:
            ExtractionResult with structured data
        """
        import time
        start_time = time.time()
        
        try:
            # Open PDF and extract basic info
            doc = fitz.open(pdf_path)
            page_count = len(doc)
            metadata = {
                "page_count": page_count,
                "pdf_metadata": doc.metadata,
                "file_size": os.path.getsize(pdf_path)
            }
            
            # Extract text using multiple methods
            raw_text = ""
            tables = []
            images = []
            
            for page_num in range(page_count):
                page = doc.load_page(page_num)
                
                # Extract text
                page_text = page.get_text()
                raw_text += f"\n--- Page {page_num + 1} ---\n{page_text}"
                
                # Extract tables using different methods
                page_tables = self._extract_tables(page)
                tables.extend(page_tables)
                
                # Extract images
                page_images = self._extract_images(page, pdf_path, page_num)
                images.extend(page_images)
            
            doc.close()
            
            # Determine extraction method based on content
            extraction_method = self._determine_extraction_method(raw_text, document_type)
            
            # Extract structured data based on document type
            if extraction_method == "donut" and self.donut_extractor:
                structured_data = self._extract_with_donut(pdf_path, document_type)
            else:
                structured_data = self._extract_with_fallback(raw_text, document_type)
            
            # Calculate confidence scores
            confidence_scores = self._calculate_extraction_confidence(
                raw_text, structured_data, tables, images
            )
            
            processing_time = time.time() - start_time
            
            return ExtractionResult(
                raw_text=raw_text,
                structured_data=structured_data,
                confidence_scores=confidence_scores,
                extraction_method=extraction_method,
                processing_time=processing_time,
                tables_extracted=tables,
                images_extracted=images,
                metadata=metadata
            )
            
        except Exception as e:
            logger.error(f"PDF processing error: {str(e)}")
            return ExtractionResult(
                raw_text="",
                structured_data={"error": str(e)},
                confidence_scores={"overall": 0.0},
                extraction_method="error",
                processing_time=time.time() - start_time,
                tables_extracted=[],
                images_extracted=[],
                metadata={"error": str(e)}
            )
    
    def _determine_extraction_method(self, text: str, document_type: str) -> str:
        """Determine best extraction method based on content and type"""
        # High confidence cases for Donut
        if document_type in ["radiology", "ecg_report"] and len(text) > 500:
            return "donut"
        
        # Check for structured content indicators
        structured_indicators = [
            "findings:", "impression:", "technique:", "results:",
            "normal ranges:", "reference values:", "patient information:"
        ]
        
        indicator_count = sum(1 for indicator in structured_indicators if indicator.lower() in text.lower())
        
        if indicator_count >= 3 and len(text) > 1000:
            return "donut"
        
        # Fallback to text-based extraction
        return "fallback"
    
    def _extract_with_donut(self, pdf_path: str, document_type: str) -> Dict[str, Any]:
        """Extract structured data using Donut model"""
        if not self.donut_extractor:
            return self._extract_with_fallback("", document_type)
        
        try:
            # Convert PDF to images (first page for now, can be extended)
            images = self._pdf_to_images(pdf_path)
            
            if not images:
                return self._extract_with_fallback("", document_type)
            
            # Define task prompt based on document type
            task_prompts = {
                "radiology": "<s_radiology_report>",
                "laboratory": "<s_laboratory_report>",
                "clinical_notes": "<s_clinical_note>",
                "ecg_report": "<s_ecg_report>",
                "unknown": "<s_medical_document>"
            }
            
            task_prompt = task_prompts.get(document_type, "<s_medical_document>")
            
            # Extract using Donut
            structured_data = self.donut_extractor.extract_from_image(images[0], task_prompt)
            
            # Post-process based on document type
            if document_type == "radiology":
                structured_data = self._postprocess_radiology(structured_data)
            elif document_type == "laboratory":
                structured_data = self._postprocess_laboratory(structured_data)
            elif document_type == "clinical_notes":
                structured_data = self._postprocess_clinical_notes(structured_data)
            elif document_type == "ecg_report":
                structured_data = self._postprocess_ecg(structured_data)
            
            return structured_data
            
        except Exception as e:
            logger.error(f"Donut extraction error: {str(e)}")
            return self._extract_with_fallback("", document_type)
    
    def _extract_with_fallback(self, text: str, document_type: str) -> Dict[str, Any]:
        """Fallback extraction using text processing and OCR if needed"""
        try:
            # Basic text cleaning
            cleaned_text = text.strip()
            
            # Document-type specific extraction
            if document_type == "radiology":
                return self._extract_radiology_from_text(cleaned_text)
            elif document_type == "laboratory":
                return self._extract_laboratory_from_text(cleaned_text)
            elif document_type == "clinical_notes":
                return self._extract_clinical_notes_from_text(cleaned_text)
            elif document_type == "ecg_report":
                return self._extract_ecg_from_text(cleaned_text)
            else:
                return {
                    "raw_text": cleaned_text,
                    "document_type": document_type,
                    "extraction_method": "fallback_text"
                }
                
        except Exception as e:
            logger.error(f"Fallback extraction error: {str(e)}")
            return {"raw_text": text, "error": str(e), "extraction_method": "fallback"}
    
    def _extract_radiology_from_text(self, text: str) -> Dict[str, Any]:
        """Extract radiology information from text"""
        lines = text.split('\n')
        findings = []
        impression = []
        technique = []
        
        current_section = None
        
        for line in lines:
            line = line.strip()
            if not line:
                continue
                
            line_lower = line.lower()
            
            if any(keyword in line_lower for keyword in ["findings:", "findings"]):
                current_section = "findings"
                continue
            elif any(keyword in line_lower for keyword in ["impression:", "impression", "conclusion:"]):
                current_section = "impression"
                continue
            elif any(keyword in line_lower for keyword in ["technique:", "protocol:"]):
                current_section = "technique"
                continue
            
            if current_section == "findings":
                findings.append(line)
            elif current_section == "impression":
                impression.append(line)
            elif current_section == "technique":
                technique.append(line)
        
        return {
            "findings": " ".join(findings),
            "impression": " ".join(impression),
            "technique": " ".join(technique),
            "document_type": "radiology",
            "extraction_method": "text_pattern_matching"
        }
    
    def _extract_laboratory_from_text(self, text: str) -> Dict[str, Any]:
        """Extract laboratory results from text"""
        lines = text.split('\n')
        tests = []
        
        for line in lines:
            line = line.strip()
            if not line:
                continue
            
            # Look for test patterns
            # Pattern: Test Name    Value    Units    Reference Range    Flag
            parts = line.split()
            if len(parts) >= 3:
                # Try to identify test components
                test_data = {
                    "raw_line": line,
                    "potential_test": parts[0] if len(parts) > 0 else "",
                    "potential_value": parts[1] if len(parts) > 1 else "",
                    "potential_unit": parts[2] if len(parts) > 2 else "",
                }
                tests.append(test_data)
        
        return {
            "tests": tests,
            "document_type": "laboratory",
            "extraction_method": "text_pattern_matching"
        }
    
    def _extract_clinical_notes_from_text(self, text: str) -> Dict[str, Any]:
        """Extract clinical notes sections from text"""
        lines = text.split('\n')
        sections = {}
        current_section = "general"
        
        for line in lines:
            line = line.strip()
            if not line:
                continue
            
            line_lower = line.lower()
            
            # Identify section headers
            if any(keyword in line_lower for keyword in ["chief complaint:", "chief complaint", "cc:"]):
                current_section = "chief_complaint"
                continue
            elif any(keyword in line_lower for keyword in ["history of present illness:", "hpi:", "history:"]):
                current_section = "history_present_illness"
                continue
            elif any(keyword in line_lower for keyword in ["assessment:", "diagnosis:", "impression:"]):
                current_section = "assessment"
                continue
            elif any(keyword in line_lower for keyword in ["plan:", "treatment:", "recommendations:"]):
                current_section = "plan"
                continue
            
            # Add line to current section
            if current_section not in sections:
                sections[current_section] = []
            sections[current_section].append(line)
        
        # Convert lists to text
        for section in sections:
            sections[section] = " ".join(sections[section])
        
        return {
            "sections": sections,
            "document_type": "clinical_notes",
            "extraction_method": "text_pattern_matching"
        }
    
    def _extract_ecg_from_text(self, text: str) -> Dict[str, Any]:
        """Extract ECG information from text"""
        lines = text.split('\n')
        ecg_data = {}
        
        for line in lines:
            line = line.strip().lower()
            
            # Extract ECG measurements
            if "heart rate" in line or "hr" in line:
                import re
                hr_match = re.search(r'(\d+)', line)
                if hr_match:
                    ecg_data["heart_rate"] = int(hr_match.group(1))
            
            if "rhythm" in line:
                ecg_data["rhythm"] = line
            
            if any(interval in line for interval in ["pr interval", "qrs", "qt"]):
                ecg_data[line.split(':')[0]] = line
        
        return {
            "ecg_data": ecg_data,
            "document_type": "ecg_report",
            "extraction_method": "text_pattern_matching"
        }
    
    def _postprocess_radiology(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Post-process radiology extraction results"""
        # Ensure required fields exist
        if "findings" not in data:
            data["findings"] = ""
        if "impression" not in data:
            data["impression"] = ""
        
        data["document_type"] = "radiology"
        return data
    
    def _postprocess_laboratory(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Post-process laboratory extraction results"""
        # Ensure tests array exists
        if "tests" not in data:
            data["tests"] = []
        
        data["document_type"] = "laboratory"
        return data
    
    def _postprocess_clinical_notes(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Post-process clinical notes extraction results"""
        # Ensure sections exist
        if "sections" not in data:
            data["sections"] = {}
        
        data["document_type"] = "clinical_notes"
        return data
    
    def _postprocess_ecg(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Post-process ECG extraction results"""
        # Ensure ecg_data exists
        if "ecg_data" not in data:
            data["ecg_data"] = {}
        
        data["document_type"] = "ecg_report"
        return data
    
    def _pdf_to_images(self, pdf_path: str) -> List[Image.Image]:
        """Convert PDF pages to images for Donut processing"""
        images = []
        try:
            doc = fitz.open(pdf_path)
            for page_num in range(min(3, len(doc))):  # Process first 3 pages
                page = doc.load_page(page_num)
                mat = fitz.Matrix(2.0, 2.0)  # 2x zoom for better OCR
                pix = page.get_pixmap(matrix=mat)
                img_data = pix.tobytes("png")
                image = Image.open(io.BytesIO(img_data))
                images.append(image)
            doc.close()
        except Exception as e:
            logger.error(f"PDF to image conversion error: {str(e)}")
        
        return images
    
    def _extract_tables(self, page) -> List[Dict[str, Any]]:
        """Extract tables from PDF page"""
        tables = []
        try:
            # Use PyMuPDF table extraction if available
            tables_data = page.find_tables()
            for table in tables_data:
                table_dict = table.extract()
                tables.append({
                    "rows": len(table_dict),
                    "columns": len(table_dict[0]) if table_dict else 0,
                    "data": table_dict
                })
        except Exception as e:
            logger.debug(f"Table extraction failed: {str(e)}")
        
        return tables
    
    def _extract_images(self, page, pdf_path: str, page_num: int) -> List[str]:
        """Extract images from PDF page"""
        images = []
        try:
            image_list = page.get_images()
            for img_index, img in enumerate(image_list):
                xref = img[0]
                pix = fitz.Pixmap(page.parent, xref)
                if pix.n - pix.alpha < 4:  # GRAY or RGB
                    img_path = f"{Path(pdf_path).stem}_page{page_num+1}_img{img_index+1}.png"
                    pix.save(img_path)
                    images.append(img_path)
                pix = None
        except Exception as e:
            logger.debug(f"Image extraction failed: {str(e)}")
        
        return images
    
    def _calculate_extraction_confidence(self, raw_text: str, structured_data: Dict[str, Any], 
                                       tables: List[Dict], images: List[str]) -> Dict[str, float]:
        """Calculate confidence scores for extraction quality"""
        confidence_scores = {}
        
        # Text extraction confidence
        text_length = len(raw_text.strip())
        confidence_scores["text_extraction"] = min(1.0, text_length / 1000) if text_length > 0 else 0.0
        
        # Structured data completeness
        required_fields = 0
        present_fields = 0
        
        if "findings" in structured_data or "impression" in structured_data:
            required_fields += 1
            if structured_data.get("findings") or structured_data.get("impression"):
                present_fields += 1
        
        if "tests" in structured_data:
            required_fields += 1
            if structured_data.get("tests"):
                present_fields += 1
        
        if "sections" in structured_data:
            required_fields += 1
            if structured_data.get("sections"):
                present_fields += 1
        
        confidence_scores["structural_completeness"] = present_fields / max(required_fields, 1)
        
        # Table extraction confidence
        confidence_scores["table_extraction"] = min(1.0, len(tables) * 0.3)
        
        # Image extraction confidence
        confidence_scores["image_extraction"] = min(1.0, len(images) * 0.2)
        
        # Overall confidence (weighted average)
        overall = (
            0.4 * confidence_scores["text_extraction"] +
            0.4 * confidence_scores["structural_completeness"] +
            0.1 * confidence_scores["table_extraction"] +
            0.1 * confidence_scores["image_extraction"]
        )
        confidence_scores["overall"] = overall
        
        return confidence_scores
    
    def convert_to_schema_format(self, extraction_result: ExtractionResult, 
                                document_type: str) -> Optional[Dict[str, Any]]:
        """Convert extraction result to canonical schema format"""
        try:
            # Create metadata
            metadata = MedicalDocumentMetadata(
                source_type=document_type,
                data_completeness=extraction_result.confidence_scores.get("overall", 0.0)
            )
            
            # Create confidence score
            confidence = ConfidenceScore(
                extraction_confidence=extraction_result.confidence_scores.get("overall", 0.0),
                model_confidence=0.8,  # Default assumption
                data_quality=extraction_result.confidence_scores.get("text_extraction", 0.0)
            )
            
            # Convert based on document type
            if document_type == "radiology":
                return self._convert_to_radiology_schema(extraction_result, metadata, confidence)
            elif document_type == "laboratory":
                return self._convert_to_laboratory_schema(extraction_result, metadata, confidence)
            elif document_type == "clinical_notes":
                return self._convert_to_clinical_notes_schema(extraction_result, metadata, confidence)
            else:
                return None
                
        except Exception as e:
            logger.error(f"Schema conversion error: {str(e)}")
            return None
    
    def _convert_to_radiology_schema(self, result: ExtractionResult, metadata: MedicalDocumentMetadata, 
                                   confidence: ConfidenceScore) -> Dict[str, Any]:
        """Convert to radiology schema format"""
        data = result.structured_data
        
        return {
            "metadata": metadata.dict(),
            "image_references": [],
            "findings": {
                "findings_text": data.get("findings", ""),
                "impression_text": data.get("impression", ""),
                "technique_description": data.get("technique", "")
            },
            "segmentations": [],
            "metrics": {},
            "confidence": confidence.dict(),
            "criticality_level": "routine",
            "follow_up_recommendations": []
        }
    
    def _convert_to_laboratory_schema(self, result: ExtractionResult, metadata: MedicalDocumentMetadata,
                                     confidence: ConfidenceScore) -> Dict[str, Any]:
        """Convert to laboratory schema format"""
        data = result.structured_data
        
        return {
            "metadata": metadata.dict(),
            "tests": data.get("tests", []),
            "confidence": confidence.dict(),
            "critical_values": [],
            "abnormal_count": 0,
            "critical_count": 0
        }
    
    def _convert_to_clinical_notes_schema(self, result: ExtractionResult, metadata: MedicalDocumentMetadata,
                                        confidence: ConfidenceScore) -> Dict[str, Any]:
        """Convert to clinical notes schema format"""
        data = result.structured_data
        sections = data.get("sections", {})
        
        return {
            "metadata": metadata.dict(),
            "sections": [{"section_type": k, "content": v, "confidence": 0.8} for k, v in sections.items()],
            "entities": [],
            "confidence": confidence.dict()
        }


# Export main classes
__all__ = [
    "MedicalPDFProcessor",
    "DonutMedicalExtractor", 
    "ExtractionResult"
]