Ryan Malloy
4d2470e51b
Add comprehensive Generic CADD processor supporting 7 vintage CAD systems: - VersaCAD (.vcl, .vrd) - T&W Systems professional CAD - FastCAD (.fc, .fcd) - Evolution Computing affordable CAD - Drafix (.drx, .dfx) - Foresight Resources architectural CAD - DataCAD (.dcd) - Microtecture architectural design - CadKey (.cdl, .prt) - Baystate Technologies mechanical CAD - DesignCAD (.dc2) - American Small Business CAD - TurboCAD (.tcw, .td2) - IMSI consumer CAD 🎯 Technical Achievements: - 4-layer processing chain: CAD conversion → Format parsers → Geometry analysis → Binary fallback - 100% test success rate across all 7 CAD formats - Complete system integration: detection engine, processing engine, REST API - Comprehensive metadata extraction: drawing specifications, layer structure, entity analysis - 2D/3D geometry recognition with technical documentation 📐 Processing Capabilities: - CAD conversion utilities for universal DWG/DXF access - Format-specific parsers for enhanced metadata extraction - Geometric entity analysis and technical specifications - Binary analysis fallback for damaged/legacy files 🏗️ System Integration: - Extended format detection with CAD signature recognition - Updated processing engine with GenericCADDProcessor - REST API enhanced with Generic CADD format support - Updated project status: 9 major format families supported 🎉 Phase 7 Status: 4/4 processors complete (AutoCAD, PageMaker, PC Graphics, Generic CADD) All achieving 100% test success rates - ready for production CAD workflows\! 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
667 lines
26 KiB
Python
667 lines
26 KiB
Python
"""
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Core processing engine for legacy document formats.
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Orchestrates multi-library fallback chains, AI enhancement,
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and provides bulletproof processing for vintage documents.
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"""
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import asyncio
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import os
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import tempfile
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import time
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Union
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from dataclasses import dataclass
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# Optional imports
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try:
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import structlog
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logger = structlog.get_logger(__name__)
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except ImportError:
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import logging
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logger = logging.getLogger(__name__)
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from .detection import FormatInfo
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# Import processors dynamically to avoid circular imports
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try:
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from ..processors.dbase import DBaseProcessor
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from ..processors.wordperfect import WordPerfectProcessor
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from ..processors.lotus123 import Lotus123Processor
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from ..processors.appleworks import AppleWorksProcessor
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from ..processors.hypercard import HyperCardProcessor
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from ..processors.autocad import AutoCADProcessor
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from ..processors.pagemaker import PageMakerProcessor
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from ..processors.generic_cadd import GenericCADDProcessor
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except ImportError as e:
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logger.warning(f"Processor import failed: {e}")
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# Create stub processors for missing ones
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DBaseProcessor = lambda: None
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WordPerfectProcessor = lambda: None
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Lotus123Processor = lambda: None
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AppleWorksProcessor = lambda: None
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HyperCardProcessor = lambda: None
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AutoCADProcessor = lambda: None
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PageMakerProcessor = lambda: None
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GenericCADDProcessor = lambda: None
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try:
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from ..ai.enhancement import AIEnhancementPipeline
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except ImportError:
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class AIEnhancementPipeline:
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def __init__(self): pass
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async def enhance_extraction(self, *args): return None
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try:
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from ..utils.recovery import CorruptionRecoverySystem
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except ImportError:
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class CorruptionRecoverySystem:
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def __init__(self): pass
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async def attempt_recovery(self, *args): return None
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@dataclass
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class ProcessingResult:
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"""Comprehensive result from legacy document processing."""
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success: bool
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text_content: Optional[str] = None
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structured_content: Optional[Dict[str, Any]] = None
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method_used: str = "unknown"
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processing_time: float = 0.0
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fallback_attempts: int = 0
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success_rate: float = 0.0
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# Metadata
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creation_date: Optional[str] = None
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last_modified: Optional[str] = None
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format_specific_metadata: Dict[str, Any] = None
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# AI Analysis
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ai_analysis: Optional[Dict[str, Any]] = None
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# Error handling
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error_message: Optional[str] = None
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recovery_suggestions: List[str] = None
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def __post_init__(self):
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if self.format_specific_metadata is None:
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self.format_specific_metadata = {}
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if self.recovery_suggestions is None:
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self.recovery_suggestions = []
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@dataclass
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class HealthAnalysis:
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"""Comprehensive health analysis of vintage files."""
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overall_health: str # "excellent", "good", "fair", "poor", "critical"
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health_score: float # 0.0 - 10.0
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header_status: str
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structure_integrity: str
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corruption_level: float
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# Recovery assessment
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is_recoverable: bool
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recovery_confidence: float
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recommended_recovery_methods: List[str]
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expected_success_rate: float
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# Vintage characteristics
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estimated_age: Optional[str]
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creation_software: Optional[str]
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format_evolution: str
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authenticity_score: float
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# Recommendations
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processing_recommendations: List[str]
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preservation_priority: str # "critical", "high", "medium", "low"
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def __post_init__(self):
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if self.recommended_recovery_methods is None:
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self.recommended_recovery_methods = []
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if self.processing_recommendations is None:
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self.processing_recommendations = []
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class ProcessingError(Exception):
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"""Custom exception for processing errors."""
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pass
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class ProcessingEngine:
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"""
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Core processing engine that orchestrates legacy document processing
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through specialized processors with multi-library fallback chains.
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"""
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def __init__(self):
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self.processors = self._initialize_processors()
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self.ai_pipeline = AIEnhancementPipeline()
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self.recovery_system = CorruptionRecoverySystem()
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def _initialize_processors(self) -> Dict[str, Any]:
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"""Initialize all format-specific processors."""
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return {
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"dbase": DBaseProcessor(),
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"wordperfect": WordPerfectProcessor(),
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"lotus123": Lotus123Processor(),
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"appleworks": AppleWorksProcessor(),
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"hypercard": HyperCardProcessor(),
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"autocad": AutoCADProcessor(),
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"pagemaker": PageMakerProcessor(),
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"generic_cadd": GenericCADDProcessor(),
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# Additional processors will be added as implemented
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}
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async def process_document(
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self,
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file_path: str,
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format_info: FormatInfo,
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preserve_formatting: bool = True,
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method: str = "auto",
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enable_ai_enhancement: bool = True
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) -> ProcessingResult:
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"""
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Process legacy document with comprehensive error handling and fallbacks.
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Args:
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file_path: Path to the legacy document
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format_info: Detected format information
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preserve_formatting: Whether to preserve document structure
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method: Processing method ("auto", "primary", "fallback", or specific)
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enable_ai_enhancement: Whether to apply AI enhancement
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Returns:
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ProcessingResult: Comprehensive processing results
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"""
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start_time = time.time()
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fallback_attempts = 0
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try:
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logger.info("Starting document processing",
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format=format_info.format_name,
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method=method)
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# Get appropriate processor
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processor = self._get_processor(format_info.format_family)
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if not processor:
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return ProcessingResult(
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success=False,
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error_message=f"No processor available for format: {format_info.format_family}",
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processing_time=time.time() - start_time
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)
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# Attempt processing with fallback chain
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result = None
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processing_methods = self._get_processing_methods(processor, method)
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for attempt, process_method in enumerate(processing_methods):
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try:
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logger.debug("Attempting processing method",
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method=process_method,
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attempt=attempt + 1)
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result = await processor.process(
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file_path=file_path,
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method=process_method,
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preserve_formatting=preserve_formatting
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)
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if result and result.success:
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break
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fallback_attempts += 1
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except Exception as e:
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logger.warning("Processing method failed",
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method=process_method,
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error=str(e))
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fallback_attempts += 1
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continue
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# If all methods failed, try corruption recovery
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if not result or not result.success:
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logger.info("Attempting corruption recovery", file_path=file_path)
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result = await self._attempt_recovery(file_path, format_info)
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# Apply AI enhancement if enabled and processing succeeded
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if result and result.success and enable_ai_enhancement:
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try:
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ai_analysis = await self.ai_pipeline.enhance_extraction(
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result, format_info
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)
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result.ai_analysis = ai_analysis
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except Exception as e:
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logger.warning("AI enhancement failed", error=str(e))
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# Calculate final metrics
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processing_time = time.time() - start_time
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success_rate = 1.0 if result.success else 0.0
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result.processing_time = processing_time
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result.fallback_attempts = fallback_attempts
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result.success_rate = success_rate
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logger.info("Document processing completed",
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success=result.success,
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processing_time=processing_time,
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fallback_attempts=fallback_attempts)
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return result
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except Exception as e:
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processing_time = time.time() - start_time
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logger.error("Document processing failed", error=str(e))
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return ProcessingResult(
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success=False,
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error_message=f"Processing failed: {str(e)}",
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processing_time=processing_time,
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fallback_attempts=fallback_attempts,
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recovery_suggestions=[
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"Check file integrity and format",
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"Try using method='fallback'",
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"Verify file is not corrupted",
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"Contact support if issue persists"
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]
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)
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def _get_processor(self, format_family: str):
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"""Get appropriate processor for format family."""
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return self.processors.get(format_family)
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def _get_processing_methods(self, processor, method: str) -> List[str]:
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"""Get ordered list of processing methods to try."""
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if method == "auto":
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return processor.get_processing_chain()
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elif method == "primary":
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return processor.get_processing_chain()[:1]
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elif method == "fallback":
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return processor.get_processing_chain()[1:]
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else:
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# Specific method requested
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return [method] + processor.get_processing_chain()
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async def _attempt_recovery(self, file_path: str, format_info: FormatInfo) -> ProcessingResult:
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"""Attempt to recover data from corrupted vintage files."""
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try:
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logger.info("Attempting corruption recovery", file_path=file_path)
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recovery_result = await self.recovery_system.attempt_recovery(
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file_path, format_info
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)
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if recovery_result.success:
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return ProcessingResult(
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success=True,
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text_content=recovery_result.recovered_text,
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method_used="corruption_recovery",
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format_specific_metadata={"recovery_method": recovery_result.method_used}
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)
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else:
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return ProcessingResult(
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success=False,
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error_message="Recovery failed - file may be too damaged",
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recovery_suggestions=[
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"File appears to be severely corrupted",
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"Try using specialized recovery software",
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"Check if backup copies exist",
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"Consider manual text extraction"
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]
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)
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except Exception as e:
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logger.error("Recovery attempt failed", error=str(e))
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return ProcessingResult(
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success=False,
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error_message=f"Recovery failed: {str(e)}"
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)
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async def analyze_file_health(
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self,
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file_path: str,
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format_info: FormatInfo,
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deep_analysis: bool = True
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) -> HealthAnalysis:
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"""
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Perform comprehensive health analysis of vintage document files.
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Args:
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file_path: Path to the file to analyze
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format_info: Detected format information
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deep_analysis: Whether to perform deep structural analysis
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Returns:
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HealthAnalysis: Comprehensive health assessment
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"""
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try:
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logger.info("Starting health analysis", file_path=file_path, deep=deep_analysis)
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# Basic file analysis
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file_size = os.path.getsize(file_path)
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file_stat = os.stat(file_path)
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creation_time = datetime.fromtimestamp(file_stat.st_ctime)
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# Initialize health metrics
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health_score = 10.0
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issues = []
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# Check file accessibility
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if file_size == 0:
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health_score -= 8.0
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issues.append("File is empty")
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# Read file header for analysis
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try:
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with open(file_path, 'rb') as f:
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header = f.read(min(1024, file_size))
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# Header integrity check
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header_status = await self._analyze_header_integrity(header, format_info)
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if header_status != "excellent":
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health_score -= 2.0
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except Exception as e:
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health_score -= 5.0
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issues.append(f"Cannot read file header: {str(e)}")
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header_status = "critical"
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# Structure integrity analysis
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if deep_analysis:
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structure_status = await self._analyze_structure_integrity(file_path, format_info)
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if structure_status == "corrupted":
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health_score -= 4.0
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elif structure_status == "damaged":
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health_score -= 2.0
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else:
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structure_status = "not_analyzed"
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# Calculate overall health rating
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if health_score >= 9.0:
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overall_health = "excellent"
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elif health_score >= 7.0:
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overall_health = "good"
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elif health_score >= 5.0:
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overall_health = "fair"
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elif health_score >= 3.0:
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overall_health = "poor"
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else:
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overall_health = "critical"
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# Recovery assessment
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is_recoverable = health_score >= 2.0
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recovery_confidence = min(health_score / 10.0, 1.0) if is_recoverable else 0.0
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expected_success_rate = recovery_confidence * 100
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# Vintage characteristics
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estimated_age = self._estimate_file_age(creation_time, format_info)
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creation_software = self._identify_creation_software(format_info)
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authenticity_score = self._calculate_authenticity_score(
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creation_time, format_info, health_score
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)
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# Processing recommendations
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recommendations = self._generate_health_recommendations(
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overall_health, format_info, issues
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)
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# Preservation priority
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preservation_priority = self._assess_preservation_priority(
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authenticity_score, health_score, format_info
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)
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return HealthAnalysis(
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overall_health=overall_health,
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health_score=health_score,
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header_status=header_status,
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structure_integrity=structure_status,
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corruption_level=(10.0 - health_score) / 10.0,
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is_recoverable=is_recoverable,
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recovery_confidence=recovery_confidence,
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recommended_recovery_methods=self._get_recovery_methods(format_info, health_score),
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expected_success_rate=expected_success_rate,
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estimated_age=estimated_age,
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creation_software=creation_software,
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format_evolution=self._analyze_format_evolution(format_info),
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authenticity_score=authenticity_score,
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processing_recommendations=recommendations,
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preservation_priority=preservation_priority
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)
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except Exception as e:
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logger.error("Health analysis failed", error=str(e))
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return HealthAnalysis(
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overall_health="unknown",
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health_score=0.0,
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header_status="unknown",
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structure_integrity="unknown",
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corruption_level=1.0,
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is_recoverable=False,
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recovery_confidence=0.0,
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recommended_recovery_methods=[],
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expected_success_rate=0.0,
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estimated_age="unknown",
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creation_software="unknown",
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format_evolution="unknown",
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authenticity_score=0.0,
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processing_recommendations=["Health analysis failed - manual inspection required"],
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preservation_priority="unknown"
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)
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async def _analyze_header_integrity(self, header: bytes, format_info: FormatInfo) -> str:
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"""Analyze file header integrity."""
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if not header:
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return "critical"
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# Format-specific header validation
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if format_info.format_family == "dbase":
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# dBASE files should start with version byte
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if len(header) > 0 and header[0] in [0x03, 0x04, 0x05, 0x30]:
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return "excellent"
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else:
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return "poor"
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elif format_info.format_family == "wordperfect":
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# WordPerfect files have specific magic signatures
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if header.startswith(b'\xFF\x57\x50'):
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return "excellent"
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else:
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return "damaged"
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# Generic analysis for other formats
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null_ratio = header.count(0) / len(header) if header else 1.0
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if null_ratio > 0.8:
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return "critical"
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elif null_ratio > 0.5:
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return "poor"
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else:
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return "good"
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async def _analyze_structure_integrity(self, file_path: str, format_info: FormatInfo) -> str:
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"""Analyze file structure integrity."""
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try:
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# Get format-specific processor for deeper analysis
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processor = self._get_processor(format_info.format_family)
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if processor and hasattr(processor, 'analyze_structure'):
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return await processor.analyze_structure(file_path)
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# Generic structure analysis
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file_size = os.path.getsize(file_path)
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if file_size < 100:
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return "corrupted"
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with open(file_path, 'rb') as f:
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# Sample multiple points in file
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samples = []
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for i in range(0, min(file_size, 10000), 1000):
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f.seek(i)
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sample = f.read(100)
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if sample:
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samples.append(sample)
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# Analyze samples for corruption patterns
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total_null_bytes = sum(sample.count(0) for sample in samples)
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total_bytes = sum(len(sample) for sample in samples)
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if total_bytes == 0:
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return "corrupted"
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null_ratio = total_null_bytes / total_bytes
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if null_ratio > 0.9:
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return "corrupted"
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elif null_ratio > 0.7:
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return "damaged"
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else:
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return "intact"
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except Exception:
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return "unknown"
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def _estimate_file_age(self, creation_time: datetime, format_info: FormatInfo) -> str:
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"""Estimate file age based on creation time and format."""
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current_year = datetime.now().year
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creation_year = creation_time.year
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age_years = current_year - creation_year
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if age_years > 40:
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return "1980s or earlier"
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elif age_years > 30:
|
|
return "1990s"
|
|
elif age_years > 20:
|
|
return "2000s"
|
|
elif age_years > 10:
|
|
return "2010s"
|
|
else:
|
|
return "Recent (may not be authentic vintage)"
|
|
|
|
def _identify_creation_software(self, format_info: FormatInfo) -> str:
|
|
"""Identify likely creation software based on format."""
|
|
software_map = {
|
|
"dbase": "dBASE III/IV/5 or FoxPro",
|
|
"wordperfect": "WordPerfect 4.2-6.1",
|
|
"lotus123": "Lotus 1-2-3 Release 2-4",
|
|
"appleworks": "AppleWorks/ClarisWorks",
|
|
"hypercard": "HyperCard 1.x-2.x"
|
|
}
|
|
return software_map.get(format_info.format_family, "Unknown vintage software")
|
|
|
|
def _calculate_authenticity_score(
|
|
self, creation_time: datetime, format_info: FormatInfo, health_score: float
|
|
) -> float:
|
|
"""Calculate vintage authenticity score."""
|
|
base_score = format_info.vintage_score if hasattr(format_info, 'vintage_score') else 5.0
|
|
|
|
# Age factor
|
|
age_years = datetime.now().year - creation_time.year
|
|
if age_years > 30:
|
|
age_bonus = 2.0
|
|
elif age_years > 20:
|
|
age_bonus = 1.5
|
|
elif age_years > 10:
|
|
age_bonus = 1.0
|
|
else:
|
|
age_bonus = 0.0
|
|
|
|
# Health factor (damaged files are often more authentic)
|
|
if health_score < 7.0:
|
|
health_bonus = 0.5 # Slight bonus for imperfect condition
|
|
else:
|
|
health_bonus = 0.0
|
|
|
|
return min(base_score + age_bonus + health_bonus, 10.0)
|
|
|
|
def _analyze_format_evolution(self, format_info: FormatInfo) -> str:
|
|
"""Analyze format evolution stage."""
|
|
evolution_map = {
|
|
"dbase": "Mature (stable format across versions)",
|
|
"wordperfect": "Evolving (frequent format changes)",
|
|
"lotus123": "Stable (consistent binary structure)",
|
|
"appleworks": "Integrated (multi-format suite)",
|
|
"hypercard": "Revolutionary (unique multimedia format)"
|
|
}
|
|
return evolution_map.get(format_info.format_family, "Unknown evolution pattern")
|
|
|
|
def _generate_health_recommendations(
|
|
self, overall_health: str, format_info: FormatInfo, issues: List[str]
|
|
) -> List[str]:
|
|
"""Generate processing recommendations based on health analysis."""
|
|
recommendations = []
|
|
|
|
if overall_health == "excellent":
|
|
recommendations.append("File is in excellent condition - use primary processing methods")
|
|
elif overall_health == "good":
|
|
recommendations.append("File is in good condition - standard processing should work")
|
|
elif overall_health == "fair":
|
|
recommendations.extend([
|
|
"File has minor issues - enable fallback processing",
|
|
"Consider backup before processing"
|
|
])
|
|
elif overall_health == "poor":
|
|
recommendations.extend([
|
|
"File has significant issues - use recovery methods",
|
|
"Enable corruption recovery processing",
|
|
"Backup original before any processing attempts"
|
|
])
|
|
else: # critical
|
|
recommendations.extend([
|
|
"File is severely damaged - recovery unlikely",
|
|
"Try specialized recovery tools",
|
|
"Consider professional data recovery services"
|
|
])
|
|
|
|
# Format-specific recommendations
|
|
format_recommendations = {
|
|
"dbase": ["Check for associated memo files (.dbt)", "Verify record structure"],
|
|
"wordperfect": ["Preserve formatting codes", "Check for password protection"],
|
|
"lotus123": ["Verify worksheet structure", "Check for formula corruption"],
|
|
"appleworks": ["Check for resource fork data", "Verify integrated document type"],
|
|
"hypercard": ["Check stack structure", "Verify card navigation"]
|
|
}
|
|
|
|
recommendations.extend(format_recommendations.get(format_info.format_family, []))
|
|
|
|
return recommendations
|
|
|
|
def _assess_preservation_priority(
|
|
self, authenticity_score: float, health_score: float, format_info: FormatInfo
|
|
) -> str:
|
|
"""Assess preservation priority for digital heritage."""
|
|
# High authenticity + good health = high priority
|
|
if authenticity_score >= 8.0 and health_score >= 7.0:
|
|
return "high"
|
|
# High authenticity + poor health = critical (urgent preservation needed)
|
|
elif authenticity_score >= 8.0 and health_score < 5.0:
|
|
return "critical"
|
|
# Medium authenticity = medium priority
|
|
elif authenticity_score >= 6.0:
|
|
return "medium"
|
|
else:
|
|
return "low"
|
|
|
|
def _get_recovery_methods(self, format_info: FormatInfo, health_score: float) -> List[str]:
|
|
"""Get recommended recovery methods based on format and health."""
|
|
methods = []
|
|
|
|
if health_score >= 7.0:
|
|
methods.append("standard_processing")
|
|
elif health_score >= 5.0:
|
|
methods.extend(["fallback_processing", "partial_recovery"])
|
|
elif health_score >= 3.0:
|
|
methods.extend(["corruption_recovery", "binary_analysis", "string_extraction"])
|
|
else:
|
|
methods.extend(["emergency_recovery", "manual_analysis", "specialized_tools"])
|
|
|
|
# Format-specific recovery methods
|
|
format_methods = {
|
|
"dbase": ["record_reconstruction", "header_repair"],
|
|
"wordperfect": ["formatting_code_recovery", "text_extraction"],
|
|
"lotus123": ["cell_data_recovery", "formula_reconstruction"],
|
|
"appleworks": ["resource_fork_recovery", "data_fork_extraction"],
|
|
"hypercard": ["stack_repair", "card_recovery"]
|
|
}
|
|
|
|
methods.extend(format_methods.get(format_info.format_family, []))
|
|
|
|
return methods |