documents_prep.py

import json
import zipfile
import pandas as pd
from huggingface_hub import hf_hub_download, list_repo_files
from llama_index.core import Document
from llama_index.core.text_splitter import SentenceSplitter
from logger.my_logging import log_message
from config import CHUNK_SIZE, CHUNK_OVERLAP, MAX_CHARS_TABLE, MAX_ROWS_TABLE
import re

def normalize_text(text):
    if not text:
        return text
    
    # Replace Cyrillic 'C' with Latin 'С' (U+0421)
    # This is for welding types like C-25 -> С-25
    text = text.replace('С-', 'C')
    text = re.sub(r'\bС(\d)', r'С\1', text)
    return text

def normalize_steel_designations(text):
    if not text:
        return text, 0, []

    import re
    
    changes_count = 0
    changes_list = []

    # Mapping of Cyrillic to Latin for steel designations
    replacements = {
        'Х': 'X',  
        'Н': 'H', 
        'Т': 'T', 
        'С': 'C', 
        'В': 'B',  
        'К': 'K',  
        'М': 'M', 
        'А': 'A',  
        'Р': 'P', 
    }

    # Pattern: starts with digits, then letters+digits (steel grade pattern)
    # Examples: 08Х18Н10Т, 12Х18Н9, 10Н17Н13М2Т, СВ-08Х19Н10
    pattern = r'\b\d{1,3}(?:[A-ZА-ЯЁ]\d*)+\b'
    
    # Also match welding wire patterns like СВ-08Х19Н10
    pattern_wire = r'\b[СC][ВB]-\d{1,3}(?:[A-ZА-ЯЁ]\d*)+\b'

    def replace_in_steel_grade(match):
        nonlocal changes_count, changes_list
        original = match.group(0)
        converted = ''.join(replacements.get(ch, ch) for ch in original)
        if converted != original:
            changes_count += 1
            changes_list.append(f"{original} → {converted}")
        return converted
    normalized_text = re.sub(pattern, replace_in_steel_grade, text)
    normalized_text = re.sub(pattern_wire, replace_in_steel_grade, normalized_text)

    return normalized_text, changes_count, changes_list

def extract_preamble(text):
    """
    Извлекает контекст (первое предложение или преамбулу до двоеточия)
    для вставки в продолжение чанков.
    """
    if not text:
        return ""
    
    # 1. Ищем преамбулу списка (текст до двоеточия, если оно в начале)
    colon_match = re.match(r'^.*?:', text, re.DOTALL)
    if colon_match:
        preamble = colon_match.group(0)
        if len(preamble) < 300:
            return preamble.strip()

    # 2. Если двоеточия нет, берем первое предложение
    sentence_match = re.match(r'^.*?(?:\.|\?|!)(?:\s|$)', text, re.DOTALL)
    if sentence_match:
        sentence = sentence_match.group(0)
        if len(sentence) < 300: 
            return sentence.strip()
            
    # 3. Если ничего не подошло (текст странный), берем первые 200 символов
    return text[:300] + "..."

def chunk_text_documents(documents):
    text_splitter = SentenceSplitter(
        chunk_size=CHUNK_SIZE,
        chunk_overlap=CHUNK_OVERLAP
    )
    total_normalizations = 0
    chunks_with_changes = 0
    
    chunked = []
    for doc in documents:
        parent_context = extract_preamble(doc.text)

        chunks = text_splitter.get_nodes_from_documents([doc])

        for i, chunk in enumerate(chunks):
            
            if i > 0 and parent_context:
                if not chunk.text.strip().startswith(parent_context[:20]):
                    original_len = len(chunk.text)
                    chunk.text = f"[Текст из начала п. {parent_context}] {chunk.text}"

            chunk.text, changes, change_list = normalize_steel_designations(chunk.text)  
            
            if changes > 0:
                chunks_with_changes += 1
                total_normalizations += changes
            
            chunk.metadata.update({
                'chunk_id': i,
                'total_chunks': len(chunks),
                'chunk_size': len(chunk.text)
            })
            chunked.append(chunk)
    
    # Log statistics
    if chunked:
        avg_size = sum(len(c.text) for c in chunked) / len(chunked)
        min_size = min(len(c.text) for c in chunked)
        max_size = max(len(c.text) for c in chunked)
        log_message(f"✓ Text: {len(documents)} docs → {len(chunked)} chunks")
        log_message(f"  Size stats: avg={avg_size:.0f}, min={min_size}, max={max_size} chars")
        log_message(f"  Steel designation normalization:")
        log_message(f"    - Chunks with changes: {chunks_with_changes}/{len(chunked)}")
        log_message(f"    - Total steel grades normalized: {total_normalizations}")
        log_message(f"    - Avg per affected chunk: {total_normalizations/chunks_with_changes:.1f}" if chunks_with_changes > 0 else "    - No normalizations needed")
    
    log_message("="*60)
    
    return chunked

def chunk_table_by_content(table_data, doc_id, max_chars=MAX_CHARS_TABLE, max_rows=MAX_ROWS_TABLE):
    headers = table_data.get('headers', [])
    rows = table_data.get('data', [])
    table_num = table_data.get('table_number', 'unknown')
    table_title = table_data.get('table_title', '')
    section = table_data.get('section', '')
    sheet_name = table_data.get('sheet_name', '')
   
    # Нормализация
    table_title, _, _ = normalize_steel_designations(str(table_title))
    section, _, _ = normalize_steel_designations(section)
    table_num_clean = str(table_num).strip()
    
    # Логика определения идентификатора
    import re
    if table_num_clean in ['-', '', 'unknown', 'nan']:
        if 'приложени' in sheet_name.lower() or 'приложени' in section.lower():
            appendix_match = re.search(r'приложени[еия]\s*[№]?\s*(\d+)', (sheet_name + ' ' + section).lower())
            table_identifier = f"Приложение {appendix_match.group(1)}" if appendix_match else "Приложение"
        else:
            if table_title:
                table_identifier = ' '.join(table_title.split()[:5])
            else:
                table_identifier = section.split(',')[0] if section else "БезНомера"
    else:
        if 'приложени' in section.lower():
            appendix_match = re.search(r'приложени[еия]\s*[№]?\s*(\d+)', section.lower())
            table_identifier = f"{table_num_clean} Приложение {appendix_match.group(1)}" if appendix_match else table_num_clean
        else:
            table_identifier = table_num_clean
    
    if not rows:
        return []
    
    # Нормализация строк
    normalized_rows = []
    for row in rows:
        if isinstance(row, dict):
            normalized_row = {}
            for k, v in row.items():
                normalized_val, _, _ = normalize_steel_designations(str(v))
                normalized_row[k] = normalized_val
            normalized_rows.append(normalized_row)
        else:
            normalized_rows.append(row)
    
    # 1. Формируем ВСТУПЛЕНИЕ 
    intro_content = format_table_header(table_title)
    
    # 2. Формируем КОНТЕКСТ 
    context_content = format_table_footer(section, doc_id, table_identifier)
    
    # Считаем место (учитываем и начало, и конец)
    static_size = len(intro_content) + len(context_content)
    available_space = max_chars - static_size - 50 
    
    # --- ВАРИАНТ 1: ВСЯ ТАБЛИЦА ВЛЕЗАЕТ ---
    full_rows_content = format_table_rows([{**row, '_idx': i+1} for i, row in enumerate(normalized_rows)])
    
    if static_size + len(full_rows_content) <= max_chars and len(normalized_rows) <= max_rows:
        # СБОРКА: Вступление -> Данные -> Контекст
        content = intro_content + full_rows_content + "\n" + context_content
        
        metadata = {
            'type': 'table',
            'document_id': doc_id,
            'table_number': table_num_clean if table_num_clean not in ['-', 'unknown'] else table_identifier,
            'table_identifier': table_identifier,
            'table_title': table_title,
            'section': section,
            'sheet_name': sheet_name,
            'total_rows': len(normalized_rows),
            'chunk_size': len(content),
            'is_complete_table': True
        }
        
        return [Document(text=content, metadata=metadata)]

    # --- ВАРИАНТ 2: РАЗБИВКА НА ЧАСТИ ---
    chunks = []
    current_rows = []
    current_size = 0
    chunk_num = 0
    
    for i, row in enumerate(normalized_rows):
        row_text = format_single_row(row, i + 1)
        row_size = len(row_text)
        
        should_split = (current_size + row_size > available_space or 
                       len(current_rows) >= max_rows) and current_rows
        
        if should_split:
            rows_content = format_table_rows(current_rows)
            # СБОРКА: Вступление -> Данные -> Строки X-Y -> Контекст
            content = f"{intro_content}{rows_content}{'='*5}\nСтроки: {current_rows[0]['_idx']}-{current_rows[-1]['_idx']}\n{context_content}"
            
            metadata = {
                'type': 'table',
                'document_id': doc_id,
                'table_identifier': table_identifier,
                'table_title': table_title,
                'section': section,
                'chunk_id': chunk_num,
                'row_start': current_rows[0]['_idx'] - 1,
                'row_end': current_rows[-1]['_idx'],
                'total_rows': len(normalized_rows),
                'chunk_size': len(content),
                'is_complete_table': False
            }
            
            chunks.append(Document(text=content, metadata=metadata))
            
            chunk_num += 1
            current_rows = []
            current_size = 0
        
        row_copy = row.copy() if isinstance(row, dict) else {'data': row}
        row_copy['_idx'] = i + 1
        current_rows.append(row_copy)
        current_size += row_size

    if current_rows:
        rows_content = format_table_rows(current_rows)
        content = f"{intro_content}{rows_content}{'='*5}\nСтроки: {current_rows[0]['_idx']}-{current_rows[-1]['_idx']}\n{context_content}"
        
        metadata = {
            'type': 'table',
            'document_id': doc_id,
            'table_identifier': table_identifier,
            'table_title': table_title,
            'section': section,
            'chunk_id': chunk_num,
            'row_start': current_rows[0]['_idx'] - 1,
            'row_end': current_rows[-1]['_idx'],
            'total_rows': len(normalized_rows),
            'chunk_size': len(content),
            'is_complete_table': False
        }
        
        chunks.append(Document(text=content, metadata=metadata))
    
    return chunks

def format_table_header(table_title):
    content = ""

    if table_title:
        content += f"ТАБЛИЦА {normalize_text(table_title)}\n"
    
    content += "ДАННЫЕ:\n"

    return content

def format_single_row(row, idx):
    if isinstance(row, dict):
        parts = [f"{k}: {v}" for k, v in row.items() 
                if v and str(v).strip() and str(v).lower() not in ['nan', 'none', '']]
        if parts:
            return f"{idx}. {' | '.join(parts)}\n"
    elif isinstance(row, list):
        parts = [str(v) for v in row if v and str(v).strip() and str(v).lower() not in ['nan', 'none', '']]
        if parts:
            return f"{idx}. {' | '.join(parts)}\n"
    return ""

def format_table_rows(rows):
    content = ""
    for row in rows:
        idx = row.get('_idx', 0)
        content += format_single_row(row, idx)
    return content

def format_table_footer(table_identifier, doc_id, section):
    content = ""

    if table_identifier:
        content += f"НОМЕР ТАБЛИЦЫ: {normalize_text(table_identifier)}\n"

    if section:
        content += f"РАЗДЕЛ: {normalize_text(section)}\n"

    if doc_id:
        content += f"ДОКУМЕНТ: {doc_id}\n"

    return content

def load_json_documents(repo_id, hf_token, json_dir):
    import zipfile
    import tempfile
    import os
    
    log_message("Loading JSON documents...")
    
    files = list_repo_files(repo_id=repo_id, repo_type="dataset", token=hf_token)
    json_files = [f for f in files if f.startswith(json_dir) and f.endswith('.json')]
    zip_files = [f for f in files if f.startswith(json_dir) and f.endswith('.zip')]
    
    log_message(f"Found {len(json_files)} JSON files and {len(zip_files)} ZIP files")
    
    documents = []
    stats = {'success': 0, 'failed': 0, 'empty': 0}
    
    for file_path in json_files:
        try:
            log_message(f"  Loading: {file_path}")
            local_path = hf_hub_download(
                repo_id=repo_id,
                filename=file_path,
                repo_type="dataset",
                token=hf_token
            )
            
            docs = extract_sections_from_json(local_path)
            if docs:
                documents.extend(docs)
                stats['success'] += 1
                log_message(f"    ✓ Extracted {len(docs)} sections")
            else:
                stats['empty'] += 1
                log_message(f"    ⚠ No sections found")
            
        except Exception as e:
            stats['failed'] += 1
            log_message(f"    ✗ Error: {e}")
    
    for zip_path in zip_files:
        try:
            log_message(f"  Processing ZIP: {zip_path}")
            local_zip = hf_hub_download(
                repo_id=repo_id,
                filename=zip_path,
                repo_type="dataset",
                token=hf_token
            )
            
            with zipfile.ZipFile(local_zip, 'r') as zf:
                json_files_in_zip = [f for f in zf.namelist() 
                                    if f.endswith('.json') 
                                    and not f.startswith('__MACOSX')
                                    and not f.startswith('.')
                                    and not '._' in f]
                
                log_message(f"    Found {len(json_files_in_zip)} JSON files in ZIP")
                
                for json_file in json_files_in_zip:
                    try:
                        file_content = zf.read(json_file)
                        
                        # Skip if file is too small
                        if len(file_content) < 10:
                            log_message(f"      ✗ Skipping: {json_file} (file too small)")
                            stats['failed'] += 1
                            continue
                        
                        try:
                            text_content = file_content.decode('utf-8')
                        except UnicodeDecodeError:
                            try:
                                text_content = file_content.decode('utf-8-sig')
                            except UnicodeDecodeError:
                                try:
                                    text_content = file_content.decode('utf-16')
                                except UnicodeDecodeError:
                                    try:
                                        text_content = file_content.decode('windows-1251')
                                    except UnicodeDecodeError:
                                        log_message(f"      ✗ Skipping: {json_file} (encoding failed)")
                                        stats['failed'] += 1
                                        continue
                        
                        # Validate JSON structure
                        if not text_content.strip().startswith('{') and not text_content.strip().startswith('['):
                            log_message(f"      ✗ Skipping: {json_file} (not valid JSON)")
                            stats['failed'] += 1
                            continue
                        
                        with tempfile.NamedTemporaryFile(mode='w', delete=False, 
                                                        suffix='.json', encoding='utf-8') as tmp:
                            tmp.write(text_content)
                            tmp_path = tmp.name
                        
                        docs = extract_sections_from_json(tmp_path)
                        if docs:
                            documents.extend(docs)
                            stats['success'] += 1
                            log_message(f"      ✓ {json_file}: {len(docs)} sections")
                        else:
                            stats['empty'] += 1
                            log_message(f"      ⚠ {json_file}: No sections")
                        
                        os.unlink(tmp_path)
                        
                    except json.JSONDecodeError as e:
                        stats['failed'] += 1
                        log_message(f"      ✗ {json_file}: Invalid JSON")
                    except Exception as e:
                        stats['failed'] += 1
                        log_message(f"      ✗ {json_file}: {str(e)[:100]}")
                        
        except Exception as e:
            log_message(f"    ✗ Error with ZIP: {e}")
    
    log_message(f"="*60)
    log_message(f"JSON Loading Stats:")
    log_message(f"  Success: {stats['success']}")
    log_message(f"  Empty: {stats['empty']}")
    log_message(f"  Failed: {stats['failed']}")
    log_message(f"="*60)
    
    return documents

def extract_sections_from_json(json_path):
    documents = []
    
    try:
        with open(json_path, 'r', encoding='utf-8') as f:
            data = json.load(f)
        
        doc_id = data.get('document_metadata', {}).get('document_id', 'unknown')
        
        # Extract all section levels
        for section in data.get('sections', []):
            if section.get('section_text', '').strip():
                documents.append(Document(
                    text=section['section_text'],
                    metadata={
                        'type': 'text',
                        'document_id': doc_id,
                        'section_id': section.get('section_id', '')
                    }
                ))
            
            # Subsections
            for subsection in section.get('subsections', []):
                if subsection.get('subsection_text', '').strip():
                    documents.append(Document(
                        text=subsection['subsection_text'],
                        metadata={
                            'type': 'text',
                            'document_id': doc_id,
                            'section_id': subsection.get('subsection_id', '')
                        }
                    ))
                
                # Sub-subsections
                for sub_sub in subsection.get('sub_subsections', []):
                    if sub_sub.get('sub_subsection_text', '').strip():
                        documents.append(Document(
                            text=sub_sub['sub_subsection_text'],
                            metadata={
                                'type': 'text',
                                'document_id': doc_id,
                                'section_id': sub_sub.get('sub_subsection_id', '')
                            }
                        ))
    
    except Exception as e:
        log_message(f"Error extracting from {json_path}: {e}")
    
    return documents

def load_table_documents(repo_id, hf_token, table_dir):
    log_message("Loading tables...")
    log_message("="*60)    
    files = list_repo_files(repo_id=repo_id, repo_type="dataset", token=hf_token)
    table_files = [f for f in files if f.startswith(table_dir) and (f.endswith('.json') or f.endswith('.xlsx') or f.endswith('.xls'))]
    
    all_chunks = []
    tables_processed = 0
    
    for file_path in table_files:
        try:
            local_path = hf_hub_download(
                repo_id=repo_id,
                filename=file_path,
                repo_type="dataset",
                token=hf_token
            )
            
            # Convert Excel to JSON if needed
            if file_path.endswith(('.xlsx', '.xls')):
                from converters.converter import convert_single_excel_to_json
                import tempfile
                import os
                
                with tempfile.TemporaryDirectory() as temp_dir:
                    json_path = convert_single_excel_to_json(local_path, temp_dir)
                    local_path = json_path
            
            with open(local_path, 'r', encoding='utf-8') as f:
                data = json.load(f)
            
            file_doc_id = data.get('document_id', data.get('document', 'unknown'))
            
            for sheet in data.get('sheets', []):
                sheet_doc_id = sheet.get('document_id', sheet.get('document', file_doc_id))
                tables_processed += 1
                
                chunks = chunk_table_by_content(sheet, sheet_doc_id, 
                                               max_chars=MAX_CHARS_TABLE, 
                                               max_rows=MAX_ROWS_TABLE)
                all_chunks.extend(chunks)
                
        except Exception as e:
            log_message(f"Error loading {file_path}: {e}")
    
    log_message(f"✓ Loaded {len(all_chunks)} table chunks from {tables_processed} tables")
    log_message("="*60)
    
    return all_chunks


def load_image_documents(repo_id, hf_token, image_dir):
    log_message("Loading images...")
    
    files = list_repo_files(repo_id=repo_id, repo_type="dataset", token=hf_token)
    csv_files = [f for f in files if f.startswith(image_dir) and (f.endswith('.csv') or f.endswith('.xlsx') or f.endswith('.xls'))]
    
    documents = []
    for file_path in csv_files:
        try:
            local_path = hf_hub_download(
                repo_id=repo_id,
                filename=file_path,
                repo_type="dataset",
                token=hf_token
            )
            
            # Convert Excel to CSV if needed
            if file_path.endswith(('.xlsx', '.xls')):
                from converters.converter import convert_single_excel_to_csv
                import tempfile
                import os
                
                with tempfile.TemporaryDirectory() as temp_dir:
                    csv_path = convert_single_excel_to_csv(local_path, temp_dir)
                    local_path = csv_path
            
            df = pd.read_csv(local_path)
            
            for _, row in df.iterrows():
                content = f"Документ: {row.get('Обозначение документа', 'unknown')}\n"
                content += f"Рисунок: {row.get('№ Изображения', 'unknown')}\n"
                content += f"Название: {row.get('Название изображения', '')}\n"
                content += f"Описание: {row.get('Описание изображение', '')}\n"
                content += f"Раздел: {row.get('Раздел документа', '')}\n"
                
                chunk_size = len(content)
                
                documents.append(Document(
                    text=content,
                    metadata={
                        'type': 'image',
                        'document_id': str(row.get('Обозначение документа', 'unknown')),
                        'image_number': str(row.get('№ Изображения', 'unknown')),
                        'section': str(row.get('Раздел документа', '')),
                        'chunk_size': chunk_size
                    }
                ))
        except Exception as e:
            log_message(f"Error loading {file_path}: {e}")
    
    if documents:
        avg_size = sum(d.metadata['chunk_size'] for d in documents) / len(documents)
        log_message(f"✓ Loaded {len(documents)} images (avg size: {avg_size:.0f} chars)")
    
    return documents

def load_all_documents(repo_id, hf_token, json_dir, table_dir, image_dir):
    """Main loader - combines all document types"""
    log_message("="*60)
    log_message("STARTING DOCUMENT LOADING")
    log_message("="*60)
    
    # Load text sections
    text_docs = load_json_documents(repo_id, hf_token, json_dir)
    text_chunks = chunk_text_documents(text_docs)
    
    # Load tables (already chunked)
    table_chunks = load_table_documents(repo_id, hf_token, table_dir)
    
    # Load images (no chunking needed)
    image_docs = load_image_documents(repo_id, hf_token, image_dir)
    
    all_docs = text_chunks + table_chunks + image_docs
    
    log_message("="*60)
    log_message(f"TOTAL DOCUMENTS: {len(all_docs)}")
    log_message(f"  Text chunks: {len(text_chunks)}")
    log_message(f"  Table chunks: {len(table_chunks)}")
    log_message(f"  Images: {len(image_docs)}")
    log_message("="*60)
    
    return all_docs