extract_red_text.py

#!/usr/bin/env python3
import re
import json
import sys
from docx import Document
from docx.oxml.ns import qn
from master_key import TABLE_SCHEMAS, HEADING_PATTERNS, PARAGRAPH_PATTERNS

def is_red_font(run):
    """Enhanced red font detection with better color checking"""
    col = run.font.color
    if col and col.rgb:
        r, g, b = col.rgb
        if r > 150 and g < 100 and b < 100 and (r-g) > 30 and (r-b) > 30:
            return True
    rPr = getattr(run._element, "rPr", None)
    if rPr is not None:
        clr = rPr.find(qn('w:color'))
        if clr is not None:
            val = clr.get(qn('w:val'))
            if val and re.fullmatch(r"[0-9A-Fa-f]{6}", val):
                rr, gg, bb = int(val[:2], 16), int(val[2:4], 16), int(val[4:], 16)
                if rr > 150 and gg < 100 and bb < 100 and (rr-gg) > 30 and (rr-bb) > 30:
                    return True
    return False

def _prev_para_text(tbl):
    """Get text from previous paragraph before table"""
    prev = tbl._tbl.getprevious()
    while prev is not None and not prev.tag.endswith("}p"):
        prev = prev.getprevious()
    if prev is None:
        return ""
    return "".join(node.text for node in prev.iter() if node.tag.endswith("}t") and node.text).strip()

def normalize_text(text):
    """Normalize text for better matching"""
    return re.sub(r'\s+', ' ', text.strip())

def fuzzy_match_heading(heading, patterns):
    """Check if heading matches any pattern with fuzzy matching"""
    heading_norm = normalize_text(heading.upper())
    for pattern in patterns:
        if re.search(pattern, heading_norm, re.IGNORECASE):
            return True
    return False

def get_table_context(tbl):
    """Get comprehensive context information for table"""
    heading = normalize_text(_prev_para_text(tbl))
    headers = [normalize_text(c.text) for c in tbl.rows[0].cells if c.text.strip()]
    col0 = [normalize_text(r.cells[0].text) for r in tbl.rows if r.cells[0].text.strip()]
    first_cell = normalize_text(tbl.rows[0].cells[0].text) if tbl.rows else ""
    all_cells = []
    for row in tbl.rows:
        for cell in row.cells:
            text = normalize_text(cell.text)
            if text:
                all_cells.append(text)
    return {
        'heading': heading,
        'headers': headers,
        'col0': col0,
        'first_cell': first_cell,
        'all_cells': all_cells,
        'num_rows': len(tbl.rows),
        'num_cols': len(tbl.rows[0].cells) if tbl.rows else 0
    }

def calculate_schema_match_score(schema_name, spec, context):
    """Calculate match score for a schema against table context"""
    score = 0
    reasons = []
    if context['first_cell'] and context['first_cell'].upper() == schema_name.upper():
        score += 100
        reasons.append(f"Direct first cell match: '{context['first_cell']}'")
    if spec.get("headings"):
        for h in spec["headings"]:
            if fuzzy_match_heading(context['heading'], [h["text"]]):
                score += 50
                reasons.append(f"Heading match: '{context['heading']}'")
                break
    if spec.get("orientation") == "left":
        labels = [normalize_text(lbl) for lbl in spec["labels"]]
        matches = 0
        for lbl in labels:
            if any(lbl.upper() in c.upper() or c.upper() in lbl.upper() for c in context['col0']):
                matches += 1
        if matches > 0:
            score += (matches / len(labels)) * 30
            reasons.append(f"Left orientation label matches: {matches}/{len(labels)}")
    elif spec.get("orientation") == "row1":
        labels = [normalize_text(lbl) for lbl in spec["labels"]]
        matches = 0
        for lbl in labels:
            if any(lbl.upper() in h.upper() or h.upper() in lbl.upper() for h in context['headers']):
                matches += 1
        if matches > 0:
            score += (matches / len(labels)) * 30
            reasons.append(f"Row1 orientation header matches: {matches}/{len(labels)}")
    if spec.get("columns"):
        cols = [normalize_text(col) for col in spec["columns"]]
        matches = 0
        for col in cols:
            if any(col.upper() in h.upper() for h in context['headers']):
                matches += 1
        if matches == len(cols):
            score += 40
            reasons.append(f"All column headers match: {cols}")
    if schema_name == "Operator Declaration" and context['first_cell'].upper() == "PRINT NAME":
        if "OPERATOR DECLARATION" in context['heading'].upper():
            score += 80
            reasons.append("Operator Declaration context match")
        elif any("MANAGER" in cell.upper() for cell in context['all_cells']):
            score += 60
            reasons.append("Manager found in cells (likely Operator Declaration)")
    if schema_name == "NHVAS Approved Auditor Declaration" and context['first_cell'].upper() == "PRINT NAME":
        if any("MANAGER" in cell.upper() for cell in context['all_cells']):
            score -= 50  # Penalty because auditors shouldn't be managers
            reasons.append("Penalty: Manager found (not auditor)")
    return score, reasons

def match_table_schema(tbl):
    """Improved table schema matching with scoring system"""
    context = get_table_context(tbl)
    best_match = None
    best_score = 0
    for name, spec in TABLE_SCHEMAS.items():
        score, reasons = calculate_schema_match_score(name, spec, context)
        if score > best_score:
            best_score = score
            best_match = name
    if best_score >= 20:
        return best_match
    return None

def check_multi_schema_table(tbl):
    """Check if table contains multiple schemas and split appropriately"""
    context = get_table_context(tbl)
    operator_labels = ["Operator name (Legal entity)", "NHVAS Accreditation No.", "Registered trading name/s", 
                      "Australian Company Number", "NHVAS Manual"]
    contact_labels = ["Operator business address", "Operator Postal address", "Email address", "Operator Telephone Number"]
    has_operator = any(any(op_lbl.upper() in cell.upper() for op_lbl in operator_labels) for cell in context['col0'])
    has_contact = any(any(cont_lbl.upper() in cell.upper() for cont_lbl in contact_labels) for cell in context['col0'])
    if has_operator and has_contact:
        return ["Operator Information", "Operator contact details"]
    return None

def extract_multi_schema_table(tbl, schemas):
    """Extract data from table with multiple schemas"""
    result = {}
    for schema_name in schemas:
        if schema_name not in TABLE_SCHEMAS:
            continue
        spec = TABLE_SCHEMAS[schema_name]
        schema_data = {}
        for ri, row in enumerate(tbl.rows):
            if ri == 0:
                continue
            row_label = normalize_text(row.cells[0].text)
            belongs_to_schema = False
            matched_label = None
            for spec_label in spec["labels"]:
                spec_norm = normalize_text(spec_label).upper()
                row_norm = row_label.upper()
                if spec_norm == row_norm or spec_norm in row_norm or row_norm in spec_norm:
                    belongs_to_schema = True
                    matched_label = spec_label
                    break
            if not belongs_to_schema:
                continue
            for ci, cell in enumerate(row.cells):
                red_txt = "".join(run.text for p in cell.paragraphs for run in p.runs if is_red_font(run)).strip()
                if red_txt:
                    if matched_label not in schema_data:
                        schema_data[matched_label] = []
                    if red_txt not in schema_data[matched_label]:
                        schema_data[matched_label].append(red_txt)
        if schema_data:
            result[schema_name] = schema_data
    return result

def extract_table_data(tbl, schema_name, spec):
    """Extract red text data from table based on schema"""
    labels = spec["labels"] + [schema_name]
    collected = {lbl: [] for lbl in labels}
    seen = {lbl: set() for lbl in labels}
    by_col = (spec["orientation"] == "row1")
    start_row = 1 if by_col else 0
    rows = tbl.rows[start_row:]
    for ri, row in enumerate(rows):
        for ci, cell in enumerate(row.cells):
            red_txt = "".join(run.text for p in cell.paragraphs for run in p.runs if is_red_font(run)).strip()
            if not red_txt:
                continue
            if by_col:
                if ci < len(spec["labels"]):
                    lbl = spec["labels"][ci]
                else:
                    lbl = schema_name
            else:
                raw_label = normalize_text(row.cells[0].text)
                lbl = None
                for spec_label in spec["labels"]:
                    if normalize_text(spec_label).upper() == raw_label.upper():
                        lbl = spec_label
                        break
                if not lbl:
                    for spec_label in spec["labels"]:
                        spec_norm = normalize_text(spec_label).upper()
                        raw_norm = raw_label.upper()
                        if spec_norm in raw_norm or raw_norm in spec_norm:
                            lbl = spec_label
                            break
                if not lbl:
                    lbl = schema_name
            if red_txt not in seen[lbl]:
                seen[lbl].add(red_txt)
                collected[lbl].append(red_txt)
    return {k: v for k, v in collected.items() if v}

def extract_red_text(input_doc):
    # input_doc: docx.Document object or file path
    if isinstance(input_doc, str):
        doc = Document(input_doc)
    else:
        doc = input_doc
    out = {}
    table_count = 0
    for tbl in doc.tables:
        table_count += 1
        multi_schemas = check_multi_schema_table(tbl)
        if multi_schemas:
            multi_data = extract_multi_schema_table(tbl, multi_schemas)
            for schema_name, schema_data in multi_data.items():
                if schema_data:
                    if schema_name in out:
                        for k, v in schema_data.items():
                            if k in out[schema_name]:
                                out[schema_name][k].extend(v)
                            else:
                                out[schema_name][k] = v
                    else:
                        out[schema_name] = schema_data
            continue
        schema = match_table_schema(tbl)
        if not schema:
            continue
        spec = TABLE_SCHEMAS[schema]
        data = extract_table_data(tbl, schema, spec)
        if data:
            if schema in out:
                for k, v in data.items():
                    if k in out[schema]:
                        out[schema][k].extend(v)
                    else:
                        out[schema][k] = v
            else:
                out[schema] = data
    paras = {}
    for idx, para in enumerate(doc.paragraphs):
        red_txt = "".join(r.text for r in para.runs if is_red_font(r)).strip()
        if not red_txt:
            continue
        context = None
        for j in range(idx-1, -1, -1):
            txt = normalize_text(doc.paragraphs[j].text)
            if txt:
                all_patterns = HEADING_PATTERNS["main"] + HEADING_PATTERNS["sub"]
                if any(re.search(p, txt, re.IGNORECASE) for p in all_patterns):
                    context = txt
                    break
        if not context and re.fullmatch(PARAGRAPH_PATTERNS["date_line"], red_txt):
            context = "Date"
        if not context:
            context = "(para)"
        paras.setdefault(context, []).append(red_txt)
    if paras:
        out["paragraphs"] = paras
    return out

def handle_management_summary_table(table, flat_json):
    """Enhanced function to handle Management Summary tables specifically"""
    replacements_made = 0
    
    # Check if this is a Management Summary table
    table_text = ""
    for row in table.rows[:3]:
        for cell in row.cells:
            table_text += get_clean_text(cell).lower() + " "
    
    # Detect which type of management summary
    management_type = None
    if "mass management" in table_text and "details" in table_text:
        management_type = "Mass Management"
    elif "maintenance management" in table_text and "details" in table_text:
        management_type = "Maintenance Management"  
    elif "fatigue management" in table_text and "details" in table_text:
        management_type = "Fatigue Management"
    
    if not management_type:
        return 0
    
    print(f"    📋 Detected {management_type} Summary table with DETAILS column")
    
    # Process each row to find standards and update DETAILS column
    for row_idx, row in enumerate(table.rows):
        if len(row.cells) < 2:
            continue
            
        # Skip header row
        if row_idx == 0:
            continue
            
        standard_cell = row.cells[0]
        details_cell = row.cells[1]
        
        standard_text = get_clean_text(standard_cell).strip()
        
        # Check if this row contains a standard (Std 1., Std 2., etc.)
        if not re.match(r'Std \d+\.', standard_text):
            continue
            
        print(f"    📌 Processing {standard_text}")
        
        # Only process if DETAILS cell has red text
        if not has_red_text(details_cell):
            continue
            
        # Try multiple approaches to find matching data
        json_value = None
        
        # Approach 1: Try direct standard match in the base management section
        base_management_data = flat_json.get(management_type, {})
        if isinstance(base_management_data, dict):
            for key, value in base_management_data.items():
                if standard_text in key and isinstance(value, list) and len(value) > 0:
                    json_value = value
                    print(f"        ✅ Found match in {management_type}: '{key}'")
                    break
        
        # Approach 2: Try the summary section
        if json_value is None:
            summary_section = flat_json.get(f"{management_type} Summary", {})
            if isinstance(summary_section, dict):
                for key, value in summary_section.items():
                    if standard_text in key and isinstance(value, list) and len(value) > 0:
                        json_value = value
                        print(f"        ✅ Found match in {management_type} Summary: '{key}'")
                        break
        
        # Approach 3: Try fuzzy matching with all keys
        if json_value is None:
            json_value = find_matching_json_value(standard_text, flat_json)
        
        # Replace red text if we found data
        if json_value is not None:
            replacement_text = get_value_as_string(json_value, standard_text)
            if isinstance(json_value, list):
                replacement_text = "\n".join(str(item) for item in json_value if str(item).strip())
            
            cell_replacements = replace_red_text_in_cell(details_cell, replacement_text)
            replacements_made += cell_replacements
            
            if cell_replacements > 0:
                print(f"        ✅ Updated DETAILS for {standard_text}")
        else:
            print(f"        ❌ No data found for {standard_text}")
    
    return replacements_made

def extract_red_text_filelike(input_file, output_file):
    """
    Accepts:
      input_file: file-like object (BytesIO/File) or path
      output_file: file-like object (opened for writing text) or path
    """
    if hasattr(input_file, "seek"):
        input_file.seek(0)
    doc = Document(input_file)
    result = extract_red_text(doc)
    if hasattr(output_file, "write"):
        json.dump(result, output_file, indent=2, ensure_ascii=False)
        output_file.flush()
    else:
        with open(output_file, "w", encoding="utf-8") as f:
            json.dump(result, f, indent=2, ensure_ascii=False)
    return result

if __name__ == "__main__":
    # Support both script and app/file-like usage
    if len(sys.argv) == 3:
        input_docx = sys.argv[1]
        output_json = sys.argv[2]
        doc = Document(input_docx)
        word_data = extract_red_text(doc)
        with open(output_json, 'w', encoding='utf-8') as f:
            json.dump(word_data, f, indent=2, ensure_ascii=False)
        print(json.dumps(word_data, indent=2, ensure_ascii=False))
    else:
        print("To use as a module: extract_red_text_filelike(input_file, output_file)")