updated_word.py

import json
from docx import Document
from docx.shared import RGBColor
import re

# Heading patterns for document structure detection
HEADING_PATTERNS = {
    "main": [
        r"NHVAS\s+Audit\s+Summary\s+Report",
        r"NATIONAL\s+HEAVY\s+VEHICLE\s+ACCREDITATION\s+AUDIT\s+SUMMARY\s+REPORT",
        r"NHVAS\s+AUDIT\s+SUMMARY\s+REPORT"
    ],
    "sub": [
        r"AUDIT\s+OBSERVATIONS\s+AND\s+COMMENTS",
        r"MAINTENANCE\s+MANAGEMENT",
        r"MASS\s+MANAGEMENT",
        r"FATIGUE\s+MANAGEMENT",
        r"Fatigue\s+Management\s+Summary\s+of\s+Audit\s+findings",
        r"MAINTENANCE\s+MANAGEMENT\s+SUMMARY\s+OF\s+AUDIT\s+FINDINGS",
        r"MASS\s+MANAGEMENT\s+SUMMARY\s+OF\s+AUDIT\s+FINDINGS",
        r"Vehicle\s+Registration\s+Numbers\s+of\s+Records\s+Examined",
        r"CORRECTIVE\s+ACTION\s+REQUEST\s+\(CAR\)",
        r"NHVAS\s+APPROVED\s+AUDITOR\s+DECLARATION",
        r"Operator\s+Declaration",
        r"Operator\s+Information",
        r"Driver\s*/\s*Scheduler\s+Records\s+Examined"
    ]
}

# ============================================================================
# UTILITY FUNCTIONS
# ============================================================================

def load_json(filepath):
    with open(filepath, 'r') as file:
        return json.load(file)

def flatten_json(y, prefix=''):
    out = {}
    for key, val in y.items():
        new_key = f"{prefix}.{key}" if prefix else key
        if isinstance(val, dict):
            out.update(flatten_json(val, new_key))
        else:
            out[new_key] = val
            out[key] = val
    return out

def is_red(run):
    color = run.font.color
    return color and (color.rgb == RGBColor(255, 0, 0) or getattr(color, "theme_color", None) == 1)

def get_value_as_string(value, field_name=""):
    if isinstance(value, list):
        if len(value) == 0:
            return ""
        elif len(value) == 1:
            return str(value[0])
        else:
            if "australian company number" in field_name.lower() or "company number" in field_name.lower():
                return value
            else:
                return " ".join(str(v) for v in value)
    else:
        return str(value)

def get_clean_text(cell):
    text = ""
    for paragraph in cell.paragraphs:
        for run in paragraph.runs:
            text += run.text
    return text.strip()

def has_red_text(cell):
    for paragraph in cell.paragraphs:
        for run in paragraph.runs:
            if is_red(run) and run.text.strip():
                return True
    return False

def has_red_text_in_paragraph(paragraph):
    for run in paragraph.runs:
        if is_red(run) and run.text.strip():
            return True
    return False

# ============================================================================
# JSON MATCHING FUNCTIONS
# ============================================================================

def find_matching_json_value(field_name, flat_json):
    """Find matching value in JSON with multiple strategies"""
    field_name = field_name.strip()
    
    # Try exact match first
    if field_name in flat_json:
        print(f"    ✅ Direct match found for key '{field_name}'")
        return flat_json[field_name]
    
    # Try case-insensitive exact match
    for key, value in flat_json.items():
        if key.lower() == field_name.lower():
            print(f"    ✅ Case-insensitive match found for key '{field_name}' with JSON key '{key}'")
            return value
    
    # Better Print Name detection for operator vs auditor
    if field_name.lower().strip() == "print name":
        operator_keys = [k for k in flat_json.keys() if "operator" in k.lower() and "print name" in k.lower()]
        auditor_keys = [k for k in flat_json.keys() if "auditor" in k.lower() and ("print name" in k.lower() or "name" in k.lower())]
        
        if operator_keys:
            print(f"    ✅ Operator Print Name match: '{field_name}' -> '{operator_keys[0]}'")
            return flat_json[operator_keys[0]]
        elif auditor_keys:
            print(f"    ✅ Auditor Name match: '{field_name}' -> '{auditor_keys[0]}'")
            return flat_json[auditor_keys[0]]
    
    # Try suffix matching (for nested keys like "section.field")
    for key, value in flat_json.items():
        if '.' in key and key.split('.')[-1].lower() == field_name.lower():
            print(f"    ✅ Suffix match found for key '{field_name}' with JSON key '{key}'")
            return value
    
    # Try partial matching - remove parentheses and special chars
    clean_field = re.sub(r'[^\w\s]', ' ', field_name.lower()).strip()
    clean_field = re.sub(r'\s+', ' ', clean_field)
    
    for key, value in flat_json.items():
        clean_key = re.sub(r'[^\w\s]', ' ', key.lower()).strip()
        clean_key = re.sub(r'\s+', ' ', clean_key)
        
        if clean_field == clean_key:
            print(f"    ✅ Clean match found for key '{field_name}' with JSON key '{key}'")
            return value
    
    # Enhanced fuzzy matching with better scoring
    field_words = set(word.lower() for word in re.findall(r'\b\w+\b', field_name) if len(word) > 2)
    if not field_words:
        return None
    
    best_match = None
    best_score = 0
    best_key = None
    
    for key, value in flat_json.items():
        key_words = set(word.lower() for word in re.findall(r'\b\w+\b', key) if len(word) > 2)
        if not key_words:
            continue
            
        # Calculate similarity score
        common_words = field_words.intersection(key_words)
        if common_words:
            # Use Jaccard similarity: intersection / union
            similarity = len(common_words) / len(field_words.union(key_words))
            
            # Bonus for high word coverage in field_name
            coverage = len(common_words) / len(field_words)
            final_score = (similarity * 0.6) + (coverage * 0.4)
            
            if final_score > best_score:
                best_score = final_score
                best_match = value
                best_key = key
    
    if best_match and best_score >= 0.25:
        print(f"    ✅ Fuzzy match found for key '{field_name}' with JSON key '{best_key}' (score: {best_score:.2f})")
        return best_match
    
    print(f"    ❌ No match found for '{field_name}'")
    return None

# ============================================================================
# RED TEXT PROCESSING FUNCTIONS
# ============================================================================

def extract_red_text_segments(cell):
    """Extract red text segments from a cell"""
    red_segments = []
    
    for para_idx, paragraph in enumerate(cell.paragraphs):
        current_segment = ""
        segment_runs = []
        
        for run_idx, run in enumerate(paragraph.runs):
            if is_red(run):
                if run.text:
                    current_segment += run.text
                segment_runs.append((para_idx, run_idx, run))
            else:
                # End of current red segment
                if segment_runs:
                    red_segments.append({
                        'text': current_segment,
                        'runs': segment_runs.copy(),
                        'paragraph_idx': para_idx
                    })
                    current_segment = ""
                    segment_runs = []
        
        # Handle segment at end of paragraph
        if segment_runs:
            red_segments.append({
                'text': current_segment,
                'runs': segment_runs.copy(),
                'paragraph_idx': para_idx
            })
    
    return red_segments

def replace_all_red_segments(red_segments, replacement_text):
    """Replace all red segments with replacement text"""
    if not red_segments:
        return 0
    
    if '\n' in replacement_text:
        replacement_lines = replacement_text.split('\n')
    else:
        replacement_lines = [replacement_text]
    
    replacements_made = 0
    
    if red_segments and replacement_lines:
        first_segment = red_segments[0]
        if first_segment['runs']:
            first_run = first_segment['runs'][0][2]
            first_run.text = replacement_lines[0]
            first_run.font.color.rgb = RGBColor(0, 0, 0)
            replacements_made = 1
            
            for _, _, run in first_segment['runs'][1:]:
                run.text = ''
    
    for segment in red_segments[1:]:
        for _, _, run in segment['runs']:
            run.text = ''
    
    if len(replacement_lines) > 1 and red_segments:
        try:
            first_run = red_segments[0]['runs'][0][2]
            paragraph = first_run.element.getparent()
            
            for line in replacement_lines[1:]:
                if line.strip():
                    from docx.oxml import OxmlElement
                    br = OxmlElement('w:br')
                    first_run.element.append(br)
                    
                    new_run = paragraph.add_run(line.strip())
                    new_run.font.color.rgb = RGBColor(0, 0, 0)
        except:
            if red_segments and red_segments[0]['runs']:
                first_run = red_segments[0]['runs'][0][2]
                first_run.text = ' '.join(replacement_lines)
                first_run.font.color.rgb = RGBColor(0, 0, 0)
    
    return replacements_made

def replace_single_segment(segment, replacement_text):
    """Replace a single red text segment"""
    if not segment['runs']:
        return False
    
    first_run = segment['runs'][0][2]
    first_run.text = replacement_text
    first_run.font.color.rgb = RGBColor(0, 0, 0)
    
    for _, _, run in segment['runs'][1:]:
        run.text = ''
    
    return True

def replace_red_text_in_cell(cell, replacement_text):
    """Replace red text in a cell with replacement text"""
    red_segments = extract_red_text_segments(cell)
    
    if not red_segments:
        return 0
    
    return replace_all_red_segments(red_segments, replacement_text)

# ============================================================================
# SPECIALIZED TABLE HANDLERS
# ============================================================================

def handle_australian_company_number(row, company_numbers):
    """Handle Australian Company Number digit placement"""
    replacements_made = 0
    for i, digit in enumerate(company_numbers):
        cell_idx = i + 1
        if cell_idx < len(row.cells):
            cell = row.cells[cell_idx]
            if has_red_text(cell):
                cell_replacements = replace_red_text_in_cell(cell, str(digit))
                replacements_made += cell_replacements
                print(f"      -> Placed digit '{digit}' in cell {cell_idx + 1}")
    return replacements_made

def handle_vehicle_registration_table(table, flat_json):
    """Handle vehicle registration table data replacement"""
    replacements_made = 0
    
    # Try to find vehicle registration data
    vehicle_section = None
    
    for key, value in flat_json.items():
        if "vehicle registration numbers of records examined" in key.lower():
            if isinstance(value, dict):
                vehicle_section = value
                print(f"    ✅ Found vehicle data in key: '{key}'")
                break
    
    if not vehicle_section:
        potential_columns = {}
        for key, value in flat_json.items():
            if any(col_name in key.lower() for col_name in ["registration number", "sub-contractor", "weight verification", "rfs suspension"]):
                if "." in key:
                    column_name = key.split(".")[-1]
                else:
                    column_name = key
                potential_columns[column_name] = value
        
        if potential_columns:
            vehicle_section = potential_columns
            print(f"    ✅ Found vehicle data from flattened keys: {list(vehicle_section.keys())}")
        else:
            print(f"    ❌ Vehicle registration data not found in JSON")
            return 0
    
    print(f"    ✅ Found vehicle registration data with {len(vehicle_section)} columns")
    
    # Find header row
    header_row_idx = -1
    header_row = None
    
    for row_idx, row in enumerate(table.rows):
        row_text = "".join(get_clean_text(cell).lower() for cell in row.cells)
        if "registration" in row_text and "number" in row_text:
            header_row_idx = row_idx
            header_row = row
            break
    
    if header_row_idx == -1:
        print(f"    ❌ Could not find header row in vehicle table")
        return 0
    
    print(f"    ✅ Found header row at index {header_row_idx}")
    
    # Enhanced column mapping
    column_mapping = {}
    for col_idx, cell in enumerate(header_row.cells):
        header_text = get_clean_text(cell).strip()
        if not header_text or header_text.lower() == "no.":
            continue
            
        best_match = None
        best_score = 0
        
        normalized_header = header_text.lower().replace("(", " (").replace(")", ") ").strip()
        
        for json_key in vehicle_section.keys():
            normalized_json = json_key.lower().strip()
            
            if normalized_header == normalized_json:
                best_match = json_key
                best_score = 1.0
                break
            
            header_words = set(word.lower() for word in normalized_header.split() if len(word) > 2)
            json_words = set(word.lower() for word in normalized_json.split() if len(word) > 2)
            
            if header_words and json_words:
                common_words = header_words.intersection(json_words)
                score = len(common_words) / max(len(header_words), len(json_words))
                
                if score > best_score and score >= 0.3:
                    best_score = score
                    best_match = json_key
            
            header_clean = normalized_header.replace(" ", "").replace("-", "").replace("(", "").replace(")", "")
            json_clean = normalized_json.replace(" ", "").replace("-", "").replace("(", "").replace(")", "")
            
            if header_clean in json_clean or json_clean in header_clean:
                if len(header_clean) > 5 and len(json_clean) > 5:
                    substring_score = min(len(header_clean), len(json_clean)) / max(len(header_clean), len(json_clean))
                    if substring_score > best_score and substring_score >= 0.6:
                        best_score = substring_score
                        best_match = json_key
        
        if best_match:
            column_mapping[col_idx] = best_match
            print(f"      📌 Column {col_idx + 1} ('{header_text}') -> '{best_match}' (score: {best_score:.2f})")
    
    if not column_mapping:
        print(f"    ❌ No column mappings found")
        return 0
    
    # Determine data rows needed
    max_data_rows = 0
    for json_key, data in vehicle_section.items():
        if isinstance(data, list):
            max_data_rows = max(max_data_rows, len(data))
    
    print(f"    📌 Need to populate {max_data_rows} data rows")
    
    # Process data rows
    for data_row_index in range(max_data_rows):
        table_row_idx = header_row_idx + 1 + data_row_index
        
        if table_row_idx >= len(table.rows):
            print(f"    ⚠️ Row {table_row_idx + 1} doesn't exist - table only has {len(table.rows)} rows")
            print(f"    ➕ Adding new row for vehicle {data_row_index + 1}")
            
            new_row = table.add_row()
            print(f"    ✅ Successfully added row {len(table.rows)} to the table")
            
        row = table.rows[table_row_idx]
        print(f"    📌 Processing data row {table_row_idx + 1} (vehicle {data_row_index + 1})")
        
        for col_idx, json_key in column_mapping.items():
            if col_idx < len(row.cells):
                cell = row.cells[col_idx]
                
                column_data = vehicle_section.get(json_key, [])
                if isinstance(column_data, list) and data_row_index < len(column_data):
                    replacement_value = str(column_data[data_row_index])
                    
                    cell_text = get_clean_text(cell)
                    if has_red_text(cell) or not cell_text.strip():
                        if not cell_text.strip():
                            cell.text = replacement_value
                            replacements_made += 1
                            print(f"      -> Added '{replacement_value}' to empty cell (column '{json_key}')")
                        else:
                            cell_replacements = replace_red_text_in_cell(cell, replacement_value)
                            replacements_made += cell_replacements
                            if cell_replacements > 0:
                                print(f"      -> Replaced red text with '{replacement_value}' (column '{json_key}')")
    
    return replacements_made

def handle_attendance_list_table_enhanced(table, flat_json):
    """Enhanced Attendance List processing with better detection"""
    replacements_made = 0
    
    # Check multiple patterns for attendance list
    attendance_patterns = [
        "attendance list",
        "names and position titles",
        "attendees"
    ]
    
    # Scan all cells in the first few rows for attendance list indicators
    found_attendance_row = None
    
    for row_idx, row in enumerate(table.rows[:3]):  # Check first 3 rows
        for cell_idx, cell in enumerate(row.cells):
            cell_text = get_clean_text(cell).lower()
            
            # Check if this cell contains attendance list header
            if any(pattern in cell_text for pattern in attendance_patterns):
                found_attendance_row = row_idx
                print(f"    🎯 ENHANCED: Found Attendance List in row {row_idx + 1}, cell {cell_idx + 1}")
                break
        
        if found_attendance_row is not None:
            break
    
    if found_attendance_row is None:
        return 0
    
    # Look for attendance data in JSON
    attendance_value = None
    attendance_search_keys = [
        "Attendance List (Names and Position Titles).Attendance List (Names and Position Titles)",
        "Attendance List (Names and Position Titles)",
        "attendance list",
        "attendees"
    ]
    
    print(f"    🔍 Searching for attendance data in JSON...")
    
    for search_key in attendance_search_keys:
        attendance_value = find_matching_json_value(search_key, flat_json)
        if attendance_value is not None:
            print(f"    ✅ Found attendance data with key: '{search_key}'")
            print(f"    📊 Raw value: {attendance_value}")
            break
    
    if attendance_value is None:
        print(f"    ❌ No attendance data found in JSON")
        return 0
    
    # Look for red text in ALL cells of the table
    target_cell = None
    
    print(f"    🔍 Scanning ALL cells in attendance table for red text...")
    
    for row_idx, row in enumerate(table.rows):
        for cell_idx, cell in enumerate(row.cells):
            if has_red_text(cell):
                print(f"        🎯 Found red text in row {row_idx + 1}, cell {cell_idx + 1}")
                
                # Get the red text to see if it looks like attendance data
                red_text = ""
                for paragraph in cell.paragraphs:
                    for run in paragraph.runs:
                        if is_red(run):
                            red_text += run.text
                
                print(f"        📋 Red text content: '{red_text[:50]}...'")
                
                # Check if this red text looks like attendance data (contains names/manager/etc)
                red_text_lower = red_text.lower()
                if any(indicator in red_text_lower for indicator in ['manager', 'herbig', 'palin', '–', '-']):
                    target_cell = cell
                    print(f"        ✅ This looks like attendance data - using this cell")
                    break
        
        if target_cell is not None:
            break
    
    # If no red text found that looks like attendance data, return
    if target_cell is None:
        print(f"    ⚠️ No red text found that looks like attendance data")
        return 0
    
    # Replace red text with properly formatted attendance list
    if has_red_text(target_cell):
        print(f"    🔧 Replacing red text with properly formatted attendance list...")
        
        # Ensure attendance_value is a list
        if isinstance(attendance_value, list):
            attendance_list = [str(item).strip() for item in attendance_value if str(item).strip()]
        else:
            attendance_list = [str(attendance_value).strip()]
        
        print(f"    📝 Attendance items to add:")
        for i, item in enumerate(attendance_list):
            print(f"        {i+1}. {item}")
        
        # Replace with line-separated attendance list
        replacement_text = "\n".join(attendance_list)
        cell_replacements = replace_red_text_in_cell(target_cell, replacement_text)
        replacements_made += cell_replacements
        
        print(f"    ✅ Added {len(attendance_list)} attendance items")
        print(f"    📊 Replacements made: {cell_replacements}")
    
    return replacements_made

def fix_management_summary_details_column(table, flat_json):
    """Fix the DETAILS column in Management Summary table"""
    replacements_made = 0
    
    print(f"    🎯 FIX: Management Summary DETAILS column processing")
    
    # Check if this is a Management Summary table
    table_text = ""
    for row in table.rows[:2]:
        for cell in row.cells:
            table_text += get_clean_text(cell).lower() + " "
    
    if not ("mass management" in table_text and "details" in table_text):
        return 0
    
    print(f"    ✅ Confirmed Mass Management Summary table")
    
    # Process each row looking for Std 5. and Std 6. with red text
    for row_idx, row in enumerate(table.rows):
        if len(row.cells) >= 2:
            standard_cell = row.cells[0]
            details_cell = row.cells[1]
            
            standard_text = get_clean_text(standard_cell).strip()
            
            # Look for Std 5. Verification and Std 6. Internal Review specifically
            if "Std 5." in standard_text and "Verification" in standard_text:
                if has_red_text(details_cell):
                    print(f"      🔍 Found Std 5. Verification with red text")
                    
                    json_value = find_matching_json_value("Std 5. Verification", flat_json)
                    if json_value is not None:
                        replacement_text = get_value_as_string(json_value, "Std 5. Verification")
                        cell_replacements = replace_red_text_in_cell(details_cell, replacement_text)
                        replacements_made += cell_replacements
                        print(f"      ✅ Replaced Std 5. Verification details")
            
            elif "Std 6." in standard_text and "Internal Review" in standard_text:
                if has_red_text(details_cell):
                    print(f"      🔍 Found Std 6. Internal Review with red text")
                    
                    json_value = find_matching_json_value("Std 6. Internal Review", flat_json)
                    if json_value is not None:
                        replacement_text = get_value_as_string(json_value, "Std 6. Internal Review")
                        cell_replacements = replace_red_text_in_cell(details_cell, replacement_text)
                        replacements_made += cell_replacements
                        print(f"      ✅ Replaced Std 6. Internal Review details")
    
    return replacements_made

def fix_operator_declaration_empty_values(table, flat_json):
    """Fix Operator Declaration table when values are empty or need updating"""
    replacements_made = 0
    
    print(f"    🎯 FIX: Operator Declaration empty values processing")
    
    # Check if this is an Operator Declaration table
    table_context = ""
    for row in table.rows:
        for cell in row.cells:
            table_context += get_clean_text(cell).lower() + " "
    
    if not ("print name" in table_context and "position title" in table_context):
        return 0
    
    print(f"    ✅ Confirmed Operator Declaration table")
    
    # Find the data row with Print Name and Position Title
    for row_idx, row in enumerate(table.rows):
        if len(row.cells) >= 2:
            cell1_text = get_clean_text(row.cells[0]).strip().lower()
            cell2_text = get_clean_text(row.cells[1]).strip().lower()
            
            # Check if this is the header row
            if "print name" in cell1_text and "position" in cell2_text:
                print(f"      📌 Found header row at {row_idx + 1}")
                
                # Look for the data row (next row)
                if row_idx + 1 < len(table.rows):
                    data_row = table.rows[row_idx + 1]
                    if len(data_row.cells) >= 2:
                        name_cell = data_row.cells[0]
                        position_cell = data_row.cells[1]
                        
                        # Check if cells are empty or have red text
                        name_text = get_clean_text(name_cell).strip()
                        position_text = get_clean_text(position_cell).strip()
                        
                        print(f"      📋 Current values: Name='{name_text}', Position='{position_text}'")
                        
                        # Get the Operator Declaration section data
                        operator_declaration = find_matching_json_value("Operator Declaration", flat_json)
                        
                        if operator_declaration and isinstance(operator_declaration, dict):
                            print(f"      🔍 Found Operator Declaration data: {operator_declaration}")
                            
                            # Update Print Name
                            if "Print Name" in operator_declaration:
                                print_name_value = operator_declaration["Print Name"]
                                if isinstance(print_name_value, list) and print_name_value:
                                    new_name = str(print_name_value[0]).strip()
                                    if new_name and "Pty Ltd" not in new_name and "Company" not in new_name:
                                        name_cell.text = new_name
                                        replacements_made += 1
                                        print(f"      ✅ Updated Print Name: '{name_text}' -> '{new_name}'")
                            
                            # Update Position Title
                            if "Position Title" in operator_declaration:
                                position_value = operator_declaration["Position Title"]
                                if isinstance(position_value, list) and position_value:
                                    new_position = str(position_value[0]).strip()
                                    if new_position:
                                        position_cell.text = new_position
                                        replacements_made += 1
                                        print(f"      ✅ Updated Position Title: '{position_text}' -> '{new_position}'")
                        
                        else:
                            print(f"      ❌ No Operator Declaration section found in JSON")
                            
                            # Fallback: try individual fields
                            name_value = find_matching_json_value("Operator Declaration.Print Name", flat_json)
                            if name_value:
                                new_name = get_value_as_string(name_value).strip()
                                if new_name and "Pty Ltd" not in new_name:
                                    name_cell.text = new_name
                                    replacements_made += 1
                                    print(f"      ✅ Updated Print Name (fallback): '{new_name}'")
                            
                            position_value = find_matching_json_value("Operator Declaration.Position Title", flat_json)
                            if position_value:
                                new_position = get_value_as_string(position_value).strip()
                                if new_position:
                                    position_cell.text = new_position
                                    replacements_made += 1
                                    print(f"      ✅ Updated Position Title (fallback): '{new_position}'")
                break
    
    return replacements_made

def handle_multiple_red_segments_in_cell(cell, flat_json):
    """Handle multiple red text segments within a single cell"""
    replacements_made = 0
    
    red_segments = extract_red_text_segments(cell)
    if not red_segments:
        return 0
    
    # Try to match each segment individually
    for i, segment in enumerate(red_segments):
        segment_text = segment['text'].strip()
        if segment_text:
            json_value = find_matching_json_value(segment_text, flat_json)
            if json_value is not None:
                replacement_text = get_value_as_string(json_value, segment_text)
                if replace_single_segment(segment, replacement_text):
                    replacements_made += 1
                    print(f"      ✅ Replaced segment {i+1}: '{segment_text}' -> '{replacement_text}'")
    
    return replacements_made

def handle_nature_business_multiline_fix(cell, flat_json):
    """Handle Nature of Business multiline red text"""
    replacements_made = 0
    
    # Extract red text to check if it looks like nature of business
    red_text = ""
    for paragraph in cell.paragraphs:
        for run in paragraph.runs:
            if is_red(run):
                red_text += run.text
    
    red_text = red_text.strip()
    if not red_text:
        return 0
    
    # Check if this looks like nature of business content
    nature_indicators = ["transport", "logistics", "freight", "delivery", "trucking", "haulage"]
    if any(indicator in red_text.lower() for indicator in nature_indicators):
        # Try to find nature of business in JSON
        nature_value = find_matching_json_value("Nature of Business", flat_json)
        if nature_value is not None:
            replacement_text = get_value_as_string(nature_value, "Nature of Business")
            cell_replacements = replace_red_text_in_cell(cell, replacement_text)
            replacements_made += cell_replacements
            print(f"      ✅ Fixed Nature of Business multiline content")
    
    return replacements_made

def handle_management_summary_fix(cell, flat_json):
    """Handle Management Summary content fixes"""
    replacements_made = 0
    
    # Extract red text
    red_text = ""
    for paragraph in cell.paragraphs:
        for run in paragraph.runs:
            if is_red(run):
                red_text += run.text
    
    red_text = red_text.strip()
    if not red_text:
        return 0
    
    # Look for management summary data in new schema format
    management_types = ["Mass Management Summary", "Maintenance Management Summary", "Fatigue Management Summary"]
    
    for mgmt_type in management_types:
        if mgmt_type in flat_json:
            mgmt_data = flat_json[mgmt_type]
            if isinstance(mgmt_data, dict):
                # Try to match red text with any standard in this management type
                for std_key, std_value in mgmt_data.items():
                    if isinstance(std_value, list) and std_value:
                        # Check if red text matches this standard
                        if len(red_text) > 10:
                            for item in std_value:
                                if red_text.lower() in str(item).lower() or str(item).lower() in red_text.lower():
                                    replacement_text = "\n".join(str(i) for i in std_value)
                                    cell_replacements = replace_red_text_in_cell(cell, replacement_text)
                                    replacements_made += cell_replacements
                                    print(f"      ✅ Fixed {mgmt_type} - {std_key}")
                                    return replacements_made
    
    return replacements_made

def handle_operator_declaration_fix(table, flat_json):
    """Handle small Operator/Auditor Declaration tables"""
    replacements_made = 0
    
    if len(table.rows) > 4:  # Only process small tables
        return 0
    
    # Get table context
    table_text = ""
    for row in table.rows:
        for cell in row.cells:
            table_text += get_clean_text(cell).lower() + " "
    
    # Check if this is a declaration table
    if not ("print name" in table_text or "signature" in table_text or "date" in table_text):
        return 0
    
    print(f"    🎯 Processing declaration table")
    
    # Process each cell with red text
    for row_idx, row in enumerate(table.rows):
        for cell_idx, cell in enumerate(row.cells):
            if has_red_text(cell):
                # Try common declaration fields
                declaration_fields = [
                    "Print Name", "Position Title", "Signature", "Date",
                    "Operator Declaration.Print Name", "Operator Declaration.Position Title",
                    "NHVAS Approved Auditor Declaration.Print Name"
                ]
                
                replaced = False
                for field in declaration_fields:
                    field_value = find_matching_json_value(field, flat_json)
                    if field_value is not None:
                        replacement_text = get_value_as_string(field_value, field)
                        if replacement_text.strip():
                            cell_replacements = replace_red_text_in_cell(cell, replacement_text)
                            if cell_replacements > 0:
                                replacements_made += cell_replacements
                                print(f"      ✅ Fixed declaration field: {field}")
                                replaced = True
                                break
                
                # If no specific field match, try generic signature/date
                if not replaced:
                    red_text = ""
                    for paragraph in cell.paragraphs:
                        for run in paragraph.runs:
                            if is_red(run):
                                red_text += run.text
                    
                    if "signature" in red_text.lower():
                        cell_replacements = replace_red_text_in_cell(cell, "[Signature]")
                        replacements_made += cell_replacements
                    elif "date" in red_text.lower():
                        cell_replacements = replace_red_text_in_cell(cell, "[Date]")
                        replacements_made += cell_replacements
    
    return replacements_made

def handle_print_accreditation_section(table, flat_json):
    """Handle Print Accreditation section"""
    replacements_made = 0
    
    print(f"    📋 Processing Print Accreditation section")
    
    for row_idx, row in enumerate(table.rows):
        for cell_idx, cell in enumerate(row.cells):
            if has_red_text(cell):
                # Try print accreditation fields
                accreditation_fields = [
                    "(print accreditation name)",
                    "Print Name",
                    "Operator name (Legal entity)"
                ]
                
                for field in accreditation_fields:
                    field_value = find_matching_json_value(field, flat_json)
                    if field_value is not None:
                        replacement_text = get_value_as_string(field_value, field)
                        if replacement_text.strip():
                            cell_replacements = replace_red_text_in_cell(cell, replacement_text)
                            replacements_made += cell_replacements
                            if cell_replacements > 0:
                                print(f"      ✅ Fixed accreditation: {field}")
                            break
    
    return replacements_made

def process_single_column_sections(cell, key_text, flat_json):
    """Process single column sections with red text"""
    replacements_made = 0
    
    if has_red_text(cell):
        red_text = ""
        for paragraph in cell.paragraphs:
            for run in paragraph.runs:
                if is_red(run):
                    red_text += run.text
        
        if red_text.strip():
            # Try direct matching first
            section_value = find_matching_json_value(red_text.strip(), flat_json)
            if section_value is None:
                # Try key-based matching
                section_value = find_matching_json_value(key_text, flat_json)
            
            if section_value is not None:
                section_replacement = get_value_as_string(section_value, red_text.strip())
                cell_replacements = replace_red_text_in_cell(cell, section_replacement)
                replacements_made += cell_replacements
                if cell_replacements > 0:
                    print(f"      ✅ Fixed single column section: '{key_text}'")
    
    return replacements_made

def process_tables(document, flat_json):
    """Process all tables in the document with comprehensive fixes"""
    replacements_made = 0
    
    for table_idx, table in enumerate(document.tables):
        print(f"\n🔍 Processing table {table_idx + 1}:")
        
        # Get table context
        table_text = ""
        for row in table.rows[:3]:
            for cell in row.cells:
                table_text += get_clean_text(cell).lower() + " "

        # Detect Management Summary tables
        management_summary_indicators = ["mass management", "maintenance management", "fatigue management"]
        has_management = any(indicator in table_text for indicator in management_summary_indicators)
        has_details = "details" in table_text
        
        if has_management and has_details:
            print(f"    📋 Detected Management Summary table")
            summary_fixes = fix_management_summary_details_column(table, flat_json)
            replacements_made += summary_fixes
            
            # Process remaining red text in management summary
            summary_replacements = 0
            for row_idx, row in enumerate(table.rows):
                for cell_idx, cell in enumerate(row.cells):
                    if has_red_text(cell):
                        # Try direct matching with the new schema names first
                        for mgmt_type in ["Mass Management Summary", "Maintenance Management Summary", "Fatigue Management Summary"]:
                            if mgmt_type.lower().replace(" summary", "") in table_text:
                                # Look for this standard in the JSON
                                if mgmt_type in flat_json:
                                    mgmt_data = flat_json[mgmt_type]
                                    if isinstance(mgmt_data, dict):
                                        # Find matching standard
                                        for std_key, std_value in mgmt_data.items():
                                            if isinstance(std_value, list) and len(std_value) > 0:
                                                # Check if red text matches this standard data
                                                red_text = "".join(run.text for p in cell.paragraphs for run in p.runs if is_red(run)).strip()
                                                for item in std_value:
                                                    if len(red_text) > 15 and red_text.lower() in str(item).lower():
                                                        replacement_text = "\n".join(str(i) for i in std_value)
                                                        cell_replacements = replace_red_text_in_cell(cell, replacement_text)
                                                        summary_replacements += cell_replacements
                                                        print(f"        ✅ Updated {std_key} with summary data")
                                                        break
                                break
                        
                        # Fallback to existing method
                        if summary_replacements == 0:
                            cell_replacements = handle_management_summary_fix(cell, flat_json)
                            summary_replacements += cell_replacements
            
            replacements_made += summary_replacements
            continue
        
        # Detect Vehicle Registration tables
        vehicle_indicators = ["registration number", "sub-contractor", "weight verification", "rfs suspension"]
        indicator_count = sum(1 for indicator in vehicle_indicators if indicator in table_text)
        if indicator_count >= 2:
            print(f"    🚗 Detected Vehicle Registration table")
            vehicle_replacements = handle_vehicle_registration_table(table, flat_json)
            replacements_made += vehicle_replacements
            continue
        
        # Detect Attendance List tables
        if "attendance list" in table_text and "names and position titles" in table_text:
            print(f"    👥 Detected Attendance List table")
            attendance_replacements = handle_attendance_list_table_enhanced(table, flat_json)
            replacements_made += attendance_replacements
            continue
        
        # Detect Print Accreditation tables
        print_accreditation_indicators = ["print name", "position title"]
        indicator_count = sum(1 for indicator in print_accreditation_indicators if indicator in table_text)
        if indicator_count >= 1:
            print(f"    📋 Detected Print Accreditation table")
            
            # Check for declaration tables that need fixing
            if "print name" in table_text and "position" in table_text:
                declaration_fixes = fix_operator_declaration_empty_values(table, flat_json)
                replacements_made += declaration_fixes
            
            print_accreditation_replacements = handle_print_accreditation_section(table, flat_json)
            replacements_made += print_accreditation_replacements
            continue
        
        # Process regular table rows
        for row_idx, row in enumerate(table.rows):
            if len(row.cells) < 1:
                continue
                
            key_cell = row.cells[0]
            key_text = get_clean_text(key_cell)
            
            if not key_text:
                continue
            
            print(f"  📌 Row {row_idx + 1}: Key = '{key_text}'")
            
            json_value = find_matching_json_value(key_text, flat_json)
            
            if json_value is not None:
                replacement_text = get_value_as_string(json_value, key_text)
                
                # Handle Australian Company Number
                if ("australian company number" in key_text.lower() or "company number" in key_text.lower()) and isinstance(json_value, list):
                    cell_replacements = handle_australian_company_number(row, json_value)
                    replacements_made += cell_replacements
                    
                # Handle section headers
                elif ("attendance list" in key_text.lower() or "nature of" in key_text.lower()) and row_idx + 1 < len(table.rows):
                    print(f"    ✅ Section header detected, checking next row...")
                    next_row = table.rows[row_idx + 1]
                    
                    for cell_idx, cell in enumerate(next_row.cells):
                        if has_red_text(cell):
                            print(f"    ✅ Found red text in next row, cell {cell_idx + 1}")
                            if isinstance(json_value, list):
                                replacement_text = "\n".join(str(item) for item in json_value)
                            cell_replacements = replace_red_text_in_cell(cell, replacement_text)
                            replacements_made += cell_replacements
                            if cell_replacements > 0:
                                print(f"    -> Replaced section content")
                                
                # Handle single column sections
                elif len(row.cells) == 1 or (len(row.cells) > 1 and not any(has_red_text(row.cells[i]) for i in range(1, len(row.cells)))):
                    if has_red_text(key_cell):
                        cell_replacements = process_single_column_sections(key_cell, key_text, flat_json)
                        replacements_made += cell_replacements
                
                # Handle regular key-value pairs
                else:
                    for cell_idx in range(1, len(row.cells)):
                        value_cell = row.cells[cell_idx]
                        if has_red_text(value_cell):
                            print(f"    ✅ Found red text in column {cell_idx + 1}")
                            cell_replacements = replace_red_text_in_cell(value_cell, replacement_text)
                            replacements_made += cell_replacements
            
            else:
                # Fallback processing for unmatched keys
                if len(row.cells) == 1 and has_red_text(key_cell):
                    red_text = ""
                    for paragraph in key_cell.paragraphs:
                        for run in paragraph.runs:
                            if is_red(run):
                                red_text += run.text
                    if red_text.strip():
                        section_value = find_matching_json_value(red_text.strip(), flat_json)
                        if section_value is not None:
                            section_replacement = get_value_as_string(section_value, red_text.strip())
                            cell_replacements = replace_red_text_in_cell(key_cell, section_replacement)
                            replacements_made += cell_replacements
                
                # Process red text in all cells
                for cell_idx in range(len(row.cells)):
                    cell = row.cells[cell_idx]
                    if has_red_text(cell):
                        cell_replacements = handle_multiple_red_segments_in_cell(cell, flat_json)
                        replacements_made += cell_replacements
                        
                        # Apply fixes if no replacements made
                        if cell_replacements == 0:
                            surgical_fix = handle_nature_business_multiline_fix(cell, flat_json)
                            replacements_made += surgical_fix
                        
                        if cell_replacements == 0:
                            management_summary_fix = handle_management_summary_fix(cell, flat_json)
                            replacements_made += management_summary_fix
    
    # Handle Operator/Auditor Declaration tables (check last few tables)
    print(f"\n🎯 Final check for Declaration tables...")
    for table in document.tables[-3:]:
        if len(table.rows) <= 4:
            declaration_fix = handle_operator_declaration_fix(table, flat_json)
            replacements_made += declaration_fix
    
    return replacements_made

def process_paragraphs(document, flat_json):
    """Process all paragraphs in the document"""
    replacements_made = 0
    print(f"\n🔍 Processing paragraphs:")
    
    for para_idx, paragraph in enumerate(document.paragraphs):
        red_runs = [run for run in paragraph.runs if is_red(run) and run.text.strip()]
        if red_runs:
            red_text_only = "".join(run.text for run in red_runs).strip()
            print(f"  📌 Paragraph {para_idx + 1}: Found red text: '{red_text_only}'")
            
            json_value = find_matching_json_value(red_text_only, flat_json)
            
            if json_value is None:
                # Enhanced pattern matching for signatures and dates
                if "AUDITOR SIGNATURE" in red_text_only.upper() or "DATE" in red_text_only.upper():
                    json_value = find_matching_json_value("auditor signature", flat_json)
                elif "OPERATOR SIGNATURE" in red_text_only.upper():
                    json_value = find_matching_json_value("operator signature", flat_json)
                    
            if json_value is not None:
                replacement_text = get_value_as_string(json_value)
                print(f"    ✅ Replacing red text with: '{replacement_text}'")
                red_runs[0].text = replacement_text
                red_runs[0].font.color.rgb = RGBColor(0, 0, 0)
                for run in red_runs[1:]:
                    run.text = ''
                replacements_made += 1
                
    return replacements_made

def process_headings(document, flat_json):
    """Process headings and their related content"""
    replacements_made = 0
    print(f"\n🔍 Processing headings:")
    
    paragraphs = document.paragraphs
    
    for para_idx, paragraph in enumerate(paragraphs):
        paragraph_text = paragraph.text.strip()
        
        if not paragraph_text:
            continue
            
        # Check if this is a heading
        matched_heading = None
        for category, patterns in HEADING_PATTERNS.items():
            for pattern in patterns:
                if re.search(pattern, paragraph_text, re.IGNORECASE):
                    matched_heading = pattern
                    break
            if matched_heading:
                break
        
        if matched_heading:
            print(f"  📌 Found heading at paragraph {para_idx + 1}: '{paragraph_text}'")
            
            # Check current heading paragraph
            if has_red_text_in_paragraph(paragraph):
                print(f"    🔴 Found red text in heading itself")
                heading_replacements = process_red_text_in_paragraph(paragraph, paragraph_text, flat_json)
                replacements_made += heading_replacements
            
            # Look ahead for related content
            for next_para_offset in range(1, 6):
                next_para_idx = para_idx + next_para_offset
                if next_para_idx >= len(paragraphs):
                    break
                    
                next_paragraph = paragraphs[next_para_idx]
                next_text = next_paragraph.text.strip()
                
                if not next_text:
                    continue
                
                # Stop if we hit another heading
                is_another_heading = False
                for category, patterns in HEADING_PATTERNS.items():
                    for pattern in patterns:
                        if re.search(pattern, next_text, re.IGNORECASE):
                            is_another_heading = True
                            break
                    if is_another_heading:
                        break
                
                if is_another_heading:
                    break
                
                # Process red text with context
                if has_red_text_in_paragraph(next_paragraph):
                    print(f"    🔴 Found red text in paragraph {next_para_idx + 1} after heading")
                    
                    context_replacements = process_red_text_in_paragraph(
                        next_paragraph, 
                        paragraph_text,
                        flat_json
                    )
                    replacements_made += context_replacements
    
    return replacements_made

def process_red_text_in_paragraph(paragraph, context_text, flat_json):
    """Process red text within a paragraph using context"""
    replacements_made = 0
    
    red_text_segments = []
    for run in paragraph.runs:
        if is_red(run) and run.text.strip():
            red_text_segments.append(run.text.strip())
    
    if not red_text_segments:
        return 0
    
    combined_red_text = " ".join(red_text_segments).strip()
    print(f"      🔍 Red text found: '{combined_red_text}'")
    
    json_value = None
    
    # Direct matching
    json_value = find_matching_json_value(combined_red_text, flat_json)
    
    # Context-based matching
    if json_value is None:
        if "NHVAS APPROVED AUDITOR" in context_text.upper():
            auditor_fields = ["auditor name", "auditor", "nhvas auditor", "approved auditor", "print name"]
            for field in auditor_fields:
                json_value = find_matching_json_value(field, flat_json)
                if json_value is not None:
                    print(f"      ✅ Found auditor match with field: '{field}'")
                    break
                    
        elif "OPERATOR DECLARATION" in context_text.upper():
            operator_fields = ["operator name", "operator", "company name", "organisation name", "print name"]
            for field in operator_fields:
                json_value = find_matching_json_value(field, flat_json)
                if json_value is not None:
                    print(f"      ✅ Found operator match with field: '{field}'")
                    break
    
    # Combined context queries
    if json_value is None:
        context_queries = [
            f"{context_text} {combined_red_text}",
            combined_red_text,
            context_text
        ]
        
        for query in context_queries:
            json_value = find_matching_json_value(query, flat_json)
            if json_value is not None:
                print(f"      ✅ Found match with combined query")
                break
    
    # Replace if match found
    if json_value is not None:
        replacement_text = get_value_as_string(json_value, combined_red_text)
        
        red_runs = [run for run in paragraph.runs if is_red(run) and run.text.strip()]
        if red_runs:
            red_runs[0].text = replacement_text
            red_runs[0].font.color.rgb = RGBColor(0, 0, 0)
            
            for run in red_runs[1:]:
                run.text = ''
            
            replacements_made = 1
            print(f"      ✅ Replaced with: '{replacement_text}'")
    else:
        print(f"      ❌ No match found for red text: '{combined_red_text}'")
    
    return replacements_made

def force_red_text_replacement(document, flat_json):
    """Force replacement of any remaining red text by trying ALL JSON values"""
    replacements_made = 0
    print(f"\n🎯 FORCE FIX: Scanning for any remaining red text...")
    
    # Collect all possible replacement values from JSON
    all_values = {}
    for key, value in flat_json.items():
        if value:
            value_str = get_value_as_string(value, key)
            
            if value_str and isinstance(value_str, str) and value_str.strip():
                all_values[key] = value_str.strip()
                
                # Store individual items from lists for partial matching
                if isinstance(value, list):
                    for i, item in enumerate(value):
                        item_str = str(item).strip() if item else ""
                        if item_str:
                            all_values[f"{key}_item_{i}"] = item_str
    
    print(f"    Found {len(all_values)} potential replacement values")
    
    # Process all tables
    for table_idx, table in enumerate(document.tables):
        for row_idx, row in enumerate(table.rows):
            for cell_idx, cell in enumerate(row.cells):
                if has_red_text(cell):
                    print(f"    🔍 Found red text in Table {table_idx + 1}, Row {row_idx + 1}, Cell {cell_idx + 1}")
                    
                    # Extract all red text from this cell
                    red_text_parts = []
                    for paragraph in cell.paragraphs:
                        for run in paragraph.runs:
                            if is_red(run) and run.text.strip():
                                red_text_parts.append(run.text.strip())
                    
                    combined_red_text = " ".join(red_text_parts).strip()
                    print(f"        Red text: '{combined_red_text}'")
                    
                    # Find best match
                    best_match = None
                    best_key = None
                    
                    # Exact matching
                    for key, value in all_values.items():
                        if combined_red_text.lower() == value.lower():
                            best_match = value
                            best_key = key
                            break
                    
                    # Partial matching
                    if not best_match:
                        for key, value in all_values.items():
                            if (len(value) > 3 and value.lower() in combined_red_text.lower()) or \
                               (len(combined_red_text) > 3 and combined_red_text.lower() in value.lower()):
                                best_match = value
                                best_key = key
                                break
                    
                    # Word-by-word matching for names/dates
                    if not best_match:
                        red_words = set(word.lower() for word in combined_red_text.split() if len(word) > 2)
                        best_score = 0
                        
                        for key, value in all_values.items():
                            value_words = set(word.lower() for word in str(value).split() if len(word) > 2)
                            if red_words and value_words:
                                common_words = red_words.intersection(value_words)
                                if common_words:
                                    score = len(common_words) / len(red_words)
                                    if score > best_score and score >= 0.5:  # At least 50% match
                                        best_score = score
                                        best_match = value
                                        best_key = key
                    
                    # Replace if we found a match
                    if best_match:
                        print(f"        ✅ Replacing with: '{best_match}' (from key: '{best_key}')")
                        cell_replacements = replace_red_text_in_cell(cell, best_match)
                        replacements_made += cell_replacements
                        print(f"        Made {cell_replacements} replacements")
                    else:
                        print(f"        ❌ No suitable replacement found")
    
    # Process all paragraphs
    for para_idx, paragraph in enumerate(document.paragraphs):
        if has_red_text_in_paragraph(paragraph):
            red_text_parts = []
            for run in paragraph.runs:
                if is_red(run) and run.text.strip():
                    red_text_parts.append(run.text.strip())
            
            combined_red_text = " ".join(red_text_parts).strip()
            if combined_red_text:
                print(f"    🔍 Found red text in Paragraph {para_idx + 1}: '{combined_red_text}'")
                
                # Same matching logic as above
                best_match = None
                best_key = None
                
                # Exact match
                for key, value in all_values.items():
                    if combined_red_text.lower() == value.lower():
                        best_match = value
                        best_key = key
                        break
                
                # Partial match
                if not best_match:
                    for key, value in all_values.items():
                        if (len(value) > 3 and value.lower() in combined_red_text.lower()) or \
                           (len(combined_red_text) > 3 and combined_red_text.lower() in value.lower()):
                            best_match = value
                            best_key = key
                            break
                
                # Word match
                if not best_match:
                    red_words = set(word.lower() for word in combined_red_text.split() if len(word) > 2)
                    best_score = 0
                    
                    for key, value in all_values.items():
                        value_words = set(word.lower() for word in str(value).split() if len(word) > 2)
                        if red_words and value_words:
                            common_words = red_words.intersection(value_words)
                            if common_words:
                                score = len(common_words) / len(red_words)
                                if score > best_score and score >= 0.5:
                                    best_score = score
                                    best_match = value
                                    best_key = key
                
                # Replace if found
                if best_match:
                    print(f"        ✅ Replacing with: '{best_match}' (from key: '{best_key}')")
                    red_runs = [run for run in paragraph.runs if is_red(run) and run.text.strip()]
                    if red_runs:
                        red_runs[0].text = best_match
                        red_runs[0].font.color.rgb = RGBColor(0, 0, 0)
                        for run in red_runs[1:]:
                            run.text = ''
                        replacements_made += 1
                        print(f"        Made 1 paragraph replacement")
                else:
                    print(f"        ❌ No suitable replacement found")
    
    return replacements_made

def process_hf(json_file, docx_file, output_file):
    """Main processing function with comprehensive error handling"""
    try:
        # Load JSON
        if hasattr(json_file, "read"):
            json_data = json.load(json_file)
        else:
            with open(json_file, 'r', encoding='utf-8') as f:
                json_data = json.load(f)
        
        flat_json = flatten_json(json_data)
        print("📄 Available JSON keys (sample):")
        for i, (key, value) in enumerate(sorted(flat_json.items())):
            if i < 10:
                print(f"  - {key}: {value}")
        print(f"  ... and {len(flat_json) - 10} more keys\n")

        # Load DOCX
        if hasattr(docx_file, "read"):
            doc = Document(docx_file)
        else:
            doc = Document(docx_file)

        # Process document with all fixes
        print("🚀 Starting comprehensive document processing...")
        
        table_replacements = process_tables(doc, flat_json)
        paragraph_replacements = process_paragraphs(doc, flat_json)
        heading_replacements = process_headings(doc, flat_json)
        
        # Final force fix for any remaining red text
        force_replacements = force_red_text_replacement(doc, flat_json)
        
        total_replacements = table_replacements + paragraph_replacements + heading_replacements + force_replacements

        # Save output
        if hasattr(output_file, "write"):
            doc.save(output_file)
        else:
            doc.save(output_file)
            
        print(f"\n✅ Document saved as: {output_file}")
        print(f"✅ Total replacements: {total_replacements}")
        print(f"   📊 Tables: {table_replacements}")
        print(f"   📝 Paragraphs: {paragraph_replacements}")
        print(f"   📋 Headings: {heading_replacements}")
        print(f"   🎯 Force fixes: {force_replacements}")
        print(f"🎉 Processing complete!")

    except FileNotFoundError as e:
        print(f"❌ File not found: {e}")
    except Exception as e:
        print(f"❌ Error: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    import sys
    if len(sys.argv) != 4:
        print("Usage: python pipeline.py <input_docx> <updated_json> <output_docx>")
        exit(1)
    docx_path = sys.argv[1]
    json_path = sys.argv[2]
    output_path = sys.argv[3]
    process_hf(json_path, docx_path, output_path)