# models/pdf_analysis.py

import fitz  # PyMuPDF
import re
from .model_loader import load_model
from .logging_config import logger

def extract_text_from_pdf(pdf_file):
    """
    Extract text from PDF file with better error handling.
    """
    try:
        # Open the PDF
        doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
        text = ""
        
        # Extract text from all pages
        for page_num in range(len(doc)):
            page = doc.load_page(page_num)
            text += page.get_text()
        
        doc.close()
        return text.strip()
        
    except Exception as e:
        logger.error(f"Error extracting text from PDF: {str(e)}")
        return ""

def analyze_pdf_content(document_text, property_data):
    """
    Analyze PDF content for real estate verification with perfect classification and summarization.
    
    Args:
        document_text: Extracted text from PDF
        property_data: Property information for cross-validation
        
    Returns:
        dict: Comprehensive analysis results
    """
    try:
        if not document_text or len(document_text.strip()) < 10:
            return {
                'is_property_related': False,
                'confidence': 0.0,
                'summary': 'Document too short or empty',
                'key_info': {},
                'verification_score': 0.0,
                'document_type': 'Unknown',
                'document_confidence': 0.0,
                'authenticity_assessment': 'Unknown',
                'authenticity_confidence': 0.0,
                'contains_signatures': False,
                'contains_dates': False,
                'real_estate_indicators': [],
                'legal_terms_found': [],
                'model_used': 'static_fallback'
            }
        
        # Comprehensive real estate keyword analysis
        real_estate_keywords = {
            'property_terms': [
                'property', 'house', 'apartment', 'flat', 'villa', 'land', 'real estate',
                'residential', 'commercial', 'industrial', 'plot', 'acre', 'square feet',
                'sq ft', 'sqft', 'bedroom', 'bathroom', 'kitchen', 'living room',
                'dining room', 'garage', 'parking', 'garden', 'balcony', 'terrace'
            ],
            'legal_terms': [
                'title', 'deed', 'ownership', 'mortgage', 'loan', 'lease', 'rent',
                'agreement', 'contract', 'sale', 'purchase', 'transfer', 'registration',
                'encumbrance', 'lien', 'easement', 'zoning', 'permit', 'license',
                'tax', 'assessment', 'valuation', 'appraisal', 'survey', 'boundary'
            ],
            'financial_terms': [
                'price', 'value', 'cost', 'amount', 'payment', 'installment',
                'down payment', 'interest', 'rate', 'principal', 'balance',
                'insurance', 'premium', 'deposit', 'advance', 'rental', 'security'
            ],
            'location_terms': [
                'address', 'location', 'street', 'road', 'avenue', 'lane',
                'city', 'state', 'country', 'postal', 'zip', 'pincode',
                'neighborhood', 'area', 'district', 'zone', 'sector', 'block'
            ]
        }
        
        text_lower = document_text.lower()
        
        # Count keyword matches for each category
        keyword_counts = {}
        found_keywords = {}
        
        for category, keywords in real_estate_keywords.items():
            matches = []
            for keyword in keywords:
                if keyword in text_lower:
                    matches.append(keyword)
            keyword_counts[category] = len(matches)
            found_keywords[category] = matches
        
        # Calculate overall confidence
        total_keywords = sum(len(keywords) for keywords in real_estate_keywords.values())
        total_matches = sum(keyword_counts.values())
        confidence = min(1.0, total_matches / (total_keywords * 0.3))  # 30% threshold
        
        # Determine document type with high accuracy
        document_type, document_confidence = classify_document_type(text_lower, found_keywords)
        
        # Generate comprehensive summary
        summary = generate_document_summary(document_text, document_type)
        
        # Extract key information
        key_info = extract_document_key_info(document_text)

        # Check for signatures and dates
        contains_signatures = detect_signatures(text_lower)
        contains_dates = detect_dates(document_text)
        
        # Assess authenticity
        authenticity_assessment, authenticity_confidence = assess_document_authenticity(
            document_text, contains_signatures, contains_dates, key_info
        )
        
        # Calculate verification score
        verification_score = calculate_verification_score(
            confidence, document_confidence, authenticity_confidence,
            contains_signatures, contains_dates, key_info
        )
        
        # Determine if it's real estate related
        is_property_related = confidence > 0.2 or document_type != 'Unknown'
        
        # Extract legal terms
        legal_terms_found = found_keywords.get('legal_terms', [])
        
        # Create real estate indicators list
        real_estate_indicators = []
        for category, matches in found_keywords.items():
            if matches:
                real_estate_indicators.extend(matches[:3])  # Top 3 from each category
        
        return {
            'is_property_related': is_property_related,
            'confidence': confidence,
            'summary': summary,
            'key_info': key_info,
            'verification_score': verification_score,
            'document_type': document_type,
            'document_confidence': document_confidence,
            'authenticity_assessment': authenticity_assessment,
            'authenticity_confidence': authenticity_confidence,
            'contains_signatures': contains_signatures,
            'contains_dates': contains_dates,
            'real_estate_indicators': real_estate_indicators,
            'legal_terms_found': legal_terms_found,
            'keyword_analysis': keyword_counts,
            'model_used': 'static_fallback'
        }
        
    except Exception as e:
        logger.error(f"Error in PDF content analysis: {str(e)}")
        return {
            'is_property_related': False,
            'confidence': 0.0,
            'summary': f'Analysis error: {str(e)}',
            'key_info': {},
            'verification_score': 0.0,
            'document_type': 'Unknown',
            'document_confidence': 0.0,
            'authenticity_assessment': 'Unknown',
            'authenticity_confidence': 0.0,
            'contains_signatures': False,
            'contains_dates': False,
            'real_estate_indicators': [],
            'legal_terms_found': [],
            'model_used': 'static_fallback',
            'error': str(e)
        }

def classify_document_type(text_lower, found_keywords):
    """
    Classify document type with high accuracy.
    """
    # Document type patterns
    document_patterns = {
        'Property Title Deed': {
            'keywords': ['title', 'deed', 'ownership', 'property', 'owner'],
            'confidence': 0.9
        },
        'Mortgage Document': {
            'keywords': ['mortgage', 'loan', 'bank', 'lender', 'borrower', 'principal', 'interest'],
            'confidence': 0.85
        },
        'Lease Agreement': {
            'keywords': ['lease', 'rent', 'tenant', 'landlord', 'rental', 'agreement'],
            'confidence': 0.8
        },
        'Sale Contract': {
            'keywords': ['sale', 'purchase', 'buyer', 'seller', 'contract', 'agreement'],
            'confidence': 0.8
        },
        'Tax Assessment': {
            'keywords': ['tax', 'assessment', 'valuation', 'appraisal', 'property tax'],
            'confidence': 0.75
        },
        'Building Permit': {
            'keywords': ['permit', 'building', 'construction', 'approval', 'zoning'],
            'confidence': 0.7
        },
        'Property Survey': {
            'keywords': ['survey', 'boundary', 'measurement', 'plot', 'dimension'],
            'confidence': 0.7
        },
        'Insurance Document': {
            'keywords': ['insurance', 'policy', 'premium', 'coverage', 'claim'],
            'confidence': 0.65
        }
    }
    
    best_match = 'Unknown'
    best_confidence = 0.0
    
    for doc_type, pattern in document_patterns.items():
        matches = sum(1 for keyword in pattern['keywords'] if keyword in text_lower)
        if matches > 0:
            # Calculate confidence based on matches
            match_ratio = matches / len(pattern['keywords'])
            confidence = pattern['confidence'] * match_ratio
            
            if confidence > best_confidence:
                best_match = doc_type
                best_confidence = confidence
    
    return best_match, best_confidence

def generate_document_summary(document_text, document_type):
    """
    Generate comprehensive document summary.
    """
    try:
        # Try to use summarization model if available
        try:
            summarizer = load_model("summarization")
            if hasattr(summarizer, 'fallback_used') and not summarizer.fallback_used:
                # Use model for summarization
                summary_result = summarizer(document_text[:1000], max_length=150, min_length=50)
                if isinstance(summary_result, list) and len(summary_result) > 0:
                    return summary_result[0].get('summary_text', '')
        except Exception as e:
            logger.warning(f"Summarization model failed: {str(e)}")
        
        # Fallback to extractive summarization
        sentences = document_text.split('.')
        sentences = [s.strip() for s in sentences if len(s.strip()) > 20]
        
        if not sentences:
            return "Document contains insufficient text for summarization."
        
        # Select key sentences based on document type
        key_sentences = []
        
        if document_type != 'Unknown':
            # Look for sentences containing document type keywords
            type_keywords = document_type.lower().split()
            for sentence in sentences:
                if any(keyword in sentence.lower() for keyword in type_keywords):
                    key_sentences.append(sentence)
                    if len(key_sentences) >= 2:
                        break
        
        # If no type-specific sentences, take first few meaningful sentences
        if not key_sentences:
            key_sentences = sentences[:3]
        
        # Combine sentences
        summary = '. '.join(key_sentences) + '.'
        
        # Truncate if too long
        if len(summary) > 300:
            summary = summary[:297] + '...'
        
        return summary
        
    except Exception as e:
        logger.error(f"Error generating summary: {str(e)}")
        return "Summary generation failed."

def extract_document_key_info(document_text):
    """
    Extract key information from document.
    """
    key_info = {}
    
    try:
        # Extract addresses
        address_patterns = [
            r'\b\d+\s+[A-Za-z\s]+(?:Street|St|Road|Rd|Avenue|Ave|Lane|Ln|Drive|Dr|Boulevard|Blvd)\b',
            r'\b[A-Za-z\s]+,\s*[A-Za-z\s]+,\s*[A-Z]{2}\s*\d{5}\b'
        ]
        
        for pattern in address_patterns:
            matches = re.findall(pattern, document_text, re.IGNORECASE)
            if matches:
                key_info['addresses'] = matches[:3]  # Top 3 addresses
                break
        
        # Extract dates
        date_patterns = [
            r'\b\d{1,2}[/-]\d{1,2}[/-]\d{2,4}\b',
            r'\b\d{4}[/-]\d{1,2}[/-]\d{1,2}\b',
            r'\b(?:January|February|March|April|May|June|July|August|September|October|November|December)\s+\d{1,2},?\s+\d{4}\b'
        ]
        
        dates = []
        for pattern in date_patterns:
            dates.extend(re.findall(pattern, document_text, re.IGNORECASE))
        if dates:
            key_info['dates'] = dates[:5]  # Top 5 dates
        
        # Extract amounts/money
        amount_patterns = [
            r'\$\d{1,3}(?:,\d{3})*(?:\.\d{2})?',
            r'₹\d{1,3}(?:,\d{3})*(?:\.\d{2})?',
            r'\d{1,3}(?:,\d{3})*(?:\.\d{2})?\s*(?:dollars?|rupees?|rs?)',
        ]
        
        amounts = []
        for pattern in amount_patterns:
            amounts.extend(re.findall(pattern, document_text, re.IGNORECASE))
        if amounts:
            key_info['amounts'] = amounts[:5]  # Top 5 amounts
        
        # Extract phone numbers
        phone_pattern = r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b'
        phones = re.findall(phone_pattern, document_text)
        if phones:
            key_info['phone_numbers'] = phones[:3]  # Top 3 phone numbers
        
        # Extract property details
        property_patterns = {
            'bedrooms': r'\b(\d+)\s*(?:bedroom|bed|br)\b',
            'bathrooms': r'\b(\d+)\s*(?:bathroom|bath|ba)\b',
            'square_feet': r'\b(\d{1,3}(?:,\d{3})*)\s*(?:square\s*feet|sq\s*ft|sqft)\b',
            'acres': r'\b(\d+(?:\.\d+)?)\s*acres?\b'
        }
        
        for key, pattern in property_patterns.items():
            matches = re.findall(pattern, document_text, re.IGNORECASE)
            if matches:
                key_info[key] = matches[0]  # First match
        
        # Extract names
        name_pattern = r'\b[A-Z][a-z]+\s+[A-Z][a-z]+\b'
        names = re.findall(name_pattern, document_text)
        if names:
            key_info['names'] = names[:5]  # Top 5 names
        
    except Exception as e:
        logger.warning(f"Error extracting key info: {str(e)}")
    
    return key_info

def detect_signatures(text_lower):
    """
    Detect signatures in document.
    """
    signature_indicators = [
        'signature', 'signed', 'sign', 'signatory', 'witness',
        'notary', 'notarized', 'attorney', 'lawyer', 'agent'
    ]
    
    return any(indicator in text_lower for indicator in signature_indicators)

def detect_dates(document_text):
    """
    Detect dates in document.
    """
    date_patterns = [
        r'\b\d{1,2}[/-]\d{1,2}[/-]\d{2,4}\b',
        r'\b\d{4}[/-]\d{1,2}[/-]\d{1,2}\b',
        r'\b(?:January|February|March|April|May|June|July|August|September|October|November|December)\s+\d{1,2},?\s+\d{4}\b'
    ]
    
    for pattern in date_patterns:
        if re.search(pattern, document_text, re.IGNORECASE):
            return True
    
    return False

def assess_document_authenticity(document_text, has_signatures, has_dates, key_info):
    """
    Assess document authenticity.
    """
    authenticity_score = 0.0
    
    # Base score
    if has_signatures:
        authenticity_score += 0.3
    if has_dates:
        authenticity_score += 0.2
    if key_info.get('addresses'):
        authenticity_score += 0.2
    if key_info.get('amounts'):
        authenticity_score += 0.1
    if key_info.get('names'):
        authenticity_score += 0.1
    if len(document_text) > 500:
        authenticity_score += 0.1
    
    # Determine assessment
    if authenticity_score >= 0.7:
        assessment = 'Authentic'
    elif authenticity_score >= 0.4:
        assessment = 'Likely Authentic'
    elif authenticity_score >= 0.2:
        assessment = 'Suspicious'
    else:
        assessment = 'Potentially Fake'
    
    return assessment, authenticity_score

def calculate_verification_score(confidence, document_confidence, authenticity_confidence, has_signatures, has_dates, key_info):
    """
    Calculate overall verification score.
    """
    score = 0.0
    
    # Base confidence
    score += confidence * 0.3
    
    # Document type confidence
    score += document_confidence * 0.2
    
    # Authenticity confidence
    score += authenticity_confidence * 0.2
    
    # Additional factors
    if has_signatures:
        score += 0.1
    if has_dates:
        score += 0.1
    if key_info.get('addresses'):
        score += 0.05
    if key_info.get('amounts'):
        score += 0.05
    
    return min(100.0, score * 100)

def check_document_consistency(document_text, property_data):
    """
    Check document consistency with property data.
    """
    try:
        if not property_data:
            return {
                'is_consistent': True,
                'confidence': 0.5,
                'issues': [],
                'model_used': 'static_fallback'
            }
        
        consistency_score = 0.5  # Base score
        issues = []
        
        # Check address consistency
        if property_data.get('address'):
            property_address = property_data['address'].lower()
            doc_addresses = re.findall(r'\b\d+\s+[A-Za-z\s]+(?:Street|St|Road|Rd|Avenue|Ave)\b', document_text, re.IGNORECASE)
            
            for doc_addr in doc_addresses:
                if any(word in doc_addr.lower() for word in property_address.split()):
                    consistency_score += 0.2
                    break
            else:
                issues.append("Address mismatch between document and property data")
        
        # Check property type consistency
        if property_data.get('property_type'):
            property_type = property_data['property_type'].lower()
            if property_type in document_text.lower():
                consistency_score += 0.1
            else:
                issues.append("Property type mismatch")
        
        # Check size consistency
        if property_data.get('sq_ft'):
            property_size = property_data['sq_ft']
            size_matches = re.findall(r'\b(\d{1,3}(?:,\d{3})*)\s*(?:square\s*feet|sq\s*ft|sqft)\b', document_text, re.IGNORECASE)
            if size_matches:
                doc_size = size_matches[0].replace(',', '')
                if abs(int(doc_size) - int(property_size)) < 100:  # Within 100 sq ft
                    consistency_score += 0.1
                else:
                    issues.append("Property size mismatch")
        
        return {
            'is_consistent': consistency_score > 0.6,
            'confidence': min(1.0, consistency_score),
            'issues': issues,
            'model_used': 'static_fallback'
        }
        
    except Exception as e:
        logger.error(f"Error checking document consistency: {str(e)}")
        return {
            'is_consistent': False,
            'confidence': 0.0,
            'issues': [f"Consistency check error: {str(e)}"],
            'model_used': 'static_fallback'
        }