{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d66436bf",
   "metadata": {},
   "source": [
    "# Extract Conversations from French Classic Books\n",
    "\n",
    "This notebook extracts character dialogues from `Volko76/french-classic-books` and formats them as conversation pairs for LLM finetuning.\n",
    "\n",
    "**Pipeline:**\n",
    "1. Re-download PDFs with preserved paragraph structure (line breaks)\n",
    "2. Extract dialogues using French dialogue markers (`«»`, `–`, `—`, `―`)\n",
    "3. Build multi-turn conversations with user/assistant roles\n",
    "4. Add contextual system prompts\n",
    "5. Push to HuggingFace Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f58dd168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "%pip install requests PyMuPDF beautifulsoup4 datasets huggingface_hub tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77dac1e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Configuration loaded!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import json\n",
    "import requests\n",
    "import fitz  # PyMuPDF\n",
    "from urllib.parse import urlparse, unquote, urljoin\n",
    "from bs4 import BeautifulSoup\n",
    "from tqdm import tqdm\n",
    "from datasets import Dataset\n",
    "from huggingface_hub import login\n",
    "\n",
    "# ============================================================================\n",
    "# CONFIGURATION\n",
    "# ============================================================================\n",
    "\n",
    "# Dialogue markers in French literature\n",
    "DIALOGUE_MARKERS = [\n",
    "    '–',    # en dash (U+2013) - most common\n",
    "    '—',    # em dash (U+2014)\n",
    "    '―',    # horizontal bar (U+2015) - quotation dash\n",
    "    '-',    # hyphen (sometimes used)\n",
    "]\n",
    "\n",
    "# Opening/closing guillemets\n",
    "GUILLEMET_OPEN = '«'\n",
    "GUILLEMET_CLOSE = '»'\n",
    "\n",
    "# Speech attribution patterns (to clean from dialogues)\n",
    "SPEECH_ATTRIBUTION_PATTERN = re.compile(\n",
    "    r',?\\s*'\n",
    "    r'(dit|répondit|demanda|s\\'écria|murmura|ajouta|reprit|continua|'\n",
    "    r'répliqua|interrompit|chuchota|souffla|gémit|hurla|cria|soupira|'\n",
    "    r'grommela|bégaya|balbutia|articula|prononça|déclara|affirma|'\n",
    "    r'interrogea|questionna|implora|supplia|ordonna|commanda|suggéra|'\n",
    "    r'proposa|observa|remarqua|nota|constata|admit|avoua|confessa|'\n",
    "    r'expliqua|précisa|annonça|fit|fis|firent)'\n",
    "    r'[-\\s]*(il|elle|je|tu|nous|vous|ils|elles|on|'\n",
    "    r'le\\s+\\w+|la\\s+\\w+|l\\'\\w+|un\\s+\\w+|une\\s+\\w+|'\n",
    "    r'\\w+)?'\n",
    "    r'[.,;:]?\\s*$',\n",
    "    re.IGNORECASE\n",
    ")\n",
    "\n",
    "# Minimum characters for a valid dialogue turn\n",
    "MIN_DIALOGUE_LENGTH = 15\n",
    "\n",
    "# Maximum turns per conversation (for multi-turn)\n",
    "MAX_TURNS_PER_CONVERSATION = 8\n",
    "\n",
    "print(\"✅ Configuration loaded!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fbb3d83",
   "metadata": {},
   "source": [
    "## Step 1: PDF Processing Functions (with preserved line breaks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "be4d17e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ PDF processing functions loaded!\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# PDF DOWNLOAD AND TEXT EXTRACTION (Preserving paragraph structure)\n",
    "# ============================================================================\n",
    "\n",
    "def sanitize_filename(name):\n",
    "    \"\"\"Remove invalid characters from filename\"\"\"\n",
    "    name = re.sub(r'[<>:\"/\\\\|?*]', '_', name)\n",
    "    return name.strip()\n",
    "\n",
    "def get_book_name_from_url(url):\n",
    "    \"\"\"Extract book name from URL\"\"\"\n",
    "    parsed = urlparse(url)\n",
    "    filename = unquote(os.path.basename(parsed.path))\n",
    "    if filename.lower().endswith('.pdf'):\n",
    "        filename = filename[:-4]\n",
    "    return sanitize_filename(filename)\n",
    "\n",
    "def download_pdf(url, temp_path):\n",
    "    \"\"\"Download PDF from URL\"\"\"\n",
    "    response = requests.get(url, stream=True, timeout=60)\n",
    "    response.raise_for_status()\n",
    "    with open(temp_path, 'wb') as f:\n",
    "        for chunk in response.iter_content(chunk_size=8192):\n",
    "            f.write(chunk)\n",
    "    return temp_path\n",
    "\n",
    "def extract_text_from_pdf(pdf_path):\n",
    "    \"\"\"Extract text from PDF using PyMuPDF\"\"\"\n",
    "    text = \"\"\n",
    "    with fitz.open(pdf_path) as doc:\n",
    "        for page in doc:\n",
    "            text += page.get_text()\n",
    "    return text\n",
    "\n",
    "def is_metadata_line(line):\n",
    "    \"\"\"Check if a line is likely metadata (not story content)\"\"\"\n",
    "    line = line.strip()\n",
    "    if not line:\n",
    "        return \"empty\"\n",
    "    if re.match(r'^\\d+$', line):\n",
    "        return \"page_number\"\n",
    "    if re.match(r'^[IVXLCDM]+$', line):\n",
    "        return \"chapter_number\"\n",
    "    if re.match(r'^(Chapitre|CHAPITRE|Chapter|CHAPTER)\\s*[IVXLCDM\\d]+', line, re.IGNORECASE):\n",
    "        return \"chapter_title\"\n",
    "    if re.match(r'^(Partie|PARTIE|Part|PART|Tome|TOME|Livre|LIVRE)\\s*[IVXLCDM\\d]+', line, re.IGNORECASE):\n",
    "        return \"part_title\"\n",
    "    return False\n",
    "\n",
    "def is_header_block(line):\n",
    "    \"\"\"Check if line is header/metadata\"\"\"\n",
    "    line = line.strip()\n",
    "    header_patterns = [\n",
    "        r'^BeQ$', r'Bibliothèque électronique', r'Collection À tous les vents',\n",
    "        r'^Volume \\d+', r'^version \\d+', r'Édition de référence',\n",
    "        r'^Traduit de', r'^Traduction', r'^Avertissement$', r'^Préface$',\n",
    "        r'^PRÉFACE$', r'^Introduction$', r'^INTRODUCTION$', r'^Table des matières',\n",
    "        r'^TABLE DES MATIÈRES', r'©|Copyright', r'ISBN', r'Imprimé en|Printed in',\n",
    "        r'^Du même auteur', r'Librairie|Éditeur|Éditions', r'^\\d{4}$',\n",
    "        r'^FIN$', r'^fin$', r'^Fin$',\n",
    "    ]\n",
    "    for pattern in header_patterns:\n",
    "        if re.search(pattern, line, re.IGNORECASE):\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def clean_book_text_preserve_structure(raw_text):\n",
    "    \"\"\"\n",
    "    Clean extracted text but PRESERVE paragraph structure for dialogue extraction.\n",
    "    Each paragraph is separated by double newline.\n",
    "    \"\"\"\n",
    "    lines = raw_text.split('\\n')\n",
    "    \n",
    "    # Find where actual content starts\n",
    "    content_start = 0\n",
    "    consecutive_long_lines = 0\n",
    "    for i, line in enumerate(lines):\n",
    "        stripped = line.strip()\n",
    "        if len(stripped) > 50 and not is_header_block(stripped):\n",
    "            consecutive_long_lines += 1\n",
    "            if consecutive_long_lines >= 3:\n",
    "                content_start = max(0, i - 3)\n",
    "                break\n",
    "        else:\n",
    "            consecutive_long_lines = 0\n",
    "    \n",
    "    # Process lines and build paragraphs (preserve structure!)\n",
    "    paragraphs = []\n",
    "    current_paragraph = []\n",
    "    \n",
    "    for i in range(content_start, len(lines)):\n",
    "        line = lines[i]\n",
    "        stripped = line.strip()\n",
    "        \n",
    "        meta_type = is_metadata_line(stripped)\n",
    "        \n",
    "        # Skip page numbers and chapter numbers\n",
    "        if meta_type in [\"page_number\", \"chapter_number\"]:\n",
    "            continue\n",
    "        \n",
    "        # Chapter/part titles cause paragraph break but are not included\n",
    "        if meta_type in [\"chapter_title\", \"part_title\"]:\n",
    "            if current_paragraph:\n",
    "                paragraphs.append(' '.join(current_paragraph))\n",
    "                current_paragraph = []\n",
    "            continue\n",
    "        \n",
    "        # Skip header blocks\n",
    "        if is_header_block(stripped):\n",
    "            if current_paragraph:\n",
    "                paragraphs.append(' '.join(current_paragraph))\n",
    "                current_paragraph = []\n",
    "            continue\n",
    "        \n",
    "        # Skip ALL CAPS lines (titles)\n",
    "        if stripped.isupper() and len(stripped) > 3:\n",
    "            if current_paragraph:\n",
    "                paragraphs.append(' '.join(current_paragraph))\n",
    "                current_paragraph = []\n",
    "            continue\n",
    "        \n",
    "        # Empty line = paragraph break\n",
    "        if meta_type == \"empty\":\n",
    "            if current_paragraph:\n",
    "                paragraphs.append(' '.join(current_paragraph))\n",
    "                current_paragraph = []\n",
    "            continue\n",
    "        \n",
    "        # Regular text line - handle hyphenation\n",
    "        if current_paragraph and current_paragraph[-1].endswith('-'):\n",
    "            current_paragraph[-1] = current_paragraph[-1][:-1] + stripped\n",
    "        else:\n",
    "            current_paragraph.append(stripped)\n",
    "    \n",
    "    if current_paragraph:\n",
    "        paragraphs.append(' '.join(current_paragraph))\n",
    "    \n",
    "    # Join paragraphs with DOUBLE NEWLINE to preserve structure\n",
    "    result = '\\n\\n'.join(paragraphs)\n",
    "    \n",
    "    # Clean up\n",
    "    result = re.sub(r' +', ' ', result)\n",
    "    result = re.sub(r' ([.,;:!?])', r'\\1', result)\n",
    "    result = re.sub(r'([.,;:!?])([A-Za-zÀ-ÿ])', r'\\1 \\2', result)\n",
    "    \n",
    "    return result.strip()\n",
    "\n",
    "def get_pdf_urls_from_beq(page_url=\"https://beq.ebooksgratuits.com/vents/\"):\n",
    "    \"\"\"Scrape all PDF links from the BEQ website\"\"\"\n",
    "    response = requests.get(page_url, timeout=60)\n",
    "    response.raise_for_status()\n",
    "    soup = BeautifulSoup(response.content, 'html.parser')\n",
    "    \n",
    "    pdf_urls = set()\n",
    "    for link in soup.find_all('a', href=True):\n",
    "        href = link['href']\n",
    "        if href.endswith('.pdf'):\n",
    "            if href.startswith('http'):\n",
    "                pdf_urls.add(href)\n",
    "            else:\n",
    "                pdf_urls.add(urljoin(page_url, href))\n",
    "    \n",
    "    final_urls = []\n",
    "    for url in sorted(pdf_urls):\n",
    "        if '/vents/' in url and '/vents-xpdf/' not in url:\n",
    "            final_urls.append(url)\n",
    "    \n",
    "    return final_urls\n",
    "\n",
    "print(\"✅ PDF processing functions loaded!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b720adfc",
   "metadata": {},
   "source": [
    "## Step 2: Dialogue Extraction Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b039e374",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Dialogue extraction functions loaded!\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# DIALOGUE EXTRACTION\n",
    "# ============================================================================\n",
    "\n",
    "def extract_dialogues_from_text(text):\n",
    "    \"\"\"\n",
    "    Extract dialogue turns from text using French dialogue markers.\n",
    "    \n",
    "    French dialogue conventions:\n",
    "    - Guillemets « » open/close dialogue sections\n",
    "    - Dashes (–, —, ―) indicate speaker changes\n",
    "    - Sometimes dialogue is inline with narrative\n",
    "    \n",
    "    Returns a list of dialogue strings.\n",
    "    \"\"\"\n",
    "    dialogues = []\n",
    "    \n",
    "    # Split into paragraphs\n",
    "    paragraphs = text.split('\\n\\n')\n",
    "    \n",
    "    for para in paragraphs:\n",
    "        para = para.strip()\n",
    "        if not para:\n",
    "            continue\n",
    "        \n",
    "        # Method 1: Extract content between guillemets\n",
    "        # Pattern: « dialogue content »\n",
    "        guillemet_pattern = re.compile(r'«\\s*([^»]+)\\s*»')\n",
    "        guillemet_matches = guillemet_pattern.findall(para)\n",
    "        \n",
    "        for match in guillemet_matches:\n",
    "            # Split by dash markers within guillemets (speaker changes)\n",
    "            parts = re.split(r'\\s*[–—―]\\s*', match)\n",
    "            for part in parts:\n",
    "                cleaned = clean_dialogue_text(part)\n",
    "                if cleaned and len(cleaned) >= MIN_DIALOGUE_LENGTH:\n",
    "                    dialogues.append(cleaned)\n",
    "        \n",
    "        # Method 2: Lines starting with dash (outside guillemets)\n",
    "        # This catches dialogue that uses dashes without guillemets\n",
    "        dash_pattern = re.compile(r'^[–—―-]\\s*(.+?)(?=[–—―]|$)', re.MULTILINE)\n",
    "        \n",
    "        # Also try splitting by dash at start of sentences\n",
    "        if not guillemet_matches:\n",
    "            # Check if paragraph starts with or contains dash-style dialogue\n",
    "            if any(para.strip().startswith(marker) for marker in DIALOGUE_MARKERS):\n",
    "                # Split by dialogue markers\n",
    "                parts = re.split(r'(?:^|\\s)[–—―-]\\s+', para)\n",
    "                for part in parts:\n",
    "                    cleaned = clean_dialogue_text(part)\n",
    "                    if cleaned and len(cleaned) >= MIN_DIALOGUE_LENGTH:\n",
    "                        dialogues.append(cleaned)\n",
    "    \n",
    "    return dialogues\n",
    "\n",
    "def clean_dialogue_text(text):\n",
    "    \"\"\"\n",
    "    Clean a dialogue turn by removing speech attribution.\n",
    "    \n",
    "    Example:\n",
    "    \"Bonjour, dit-il en souriant.\" -> \"Bonjour\"\n",
    "    \"Comment allez-vous ? demanda Marie.\" -> \"Comment allez-vous ?\"\n",
    "    \"\"\"\n",
    "    if not text:\n",
    "        return \"\"\n",
    "    \n",
    "    text = text.strip()\n",
    "    \n",
    "    # Remove speech attribution at the end\n",
    "    text = SPEECH_ATTRIBUTION_PATTERN.sub('', text)\n",
    "    \n",
    "    # Also handle mid-sentence attributions like: \"Oui, dit-il, je comprends.\"\n",
    "    mid_attribution = re.compile(\n",
    "        r',\\s*(dit|répondit|demanda|s\\'écria|murmura|ajouta|reprit|continua|'\n",
    "        r'répliqua|interrompit|chuchota|souffla|fit)'\n",
    "        r'[-\\s]*(il|elle|je|tu|nous|vous|ils|elles|on|'\n",
    "        r'le\\s+\\w+|la\\s+\\w+|l\\'\\w+|\\w+)?'\n",
    "        r',\\s*',\n",
    "        re.IGNORECASE\n",
    "    )\n",
    "    text = mid_attribution.sub(' ', text)\n",
    "    \n",
    "    # Clean up whitespace\n",
    "    text = re.sub(r'\\s+', ' ', text)\n",
    "    text = text.strip()\n",
    "    \n",
    "    # Remove leading/trailing punctuation artifacts\n",
    "    text = re.sub(r'^[,;:\\s]+', '', text)\n",
    "    text = re.sub(r'[,;:\\s]+$', '', text)\n",
    "    \n",
    "    return text\n",
    "\n",
    "def extract_dialogues_advanced(text):\n",
    "    \"\"\"\n",
    "    Advanced dialogue extraction with better handling of French text.\n",
    "    Combines multiple detection strategies.\n",
    "    \"\"\"\n",
    "    all_dialogues = []\n",
    "    \n",
    "    # Strategy 1: Guillemet-based extraction (most reliable)\n",
    "    # Pattern for dialogue within guillemets, handling nested structure\n",
    "    guillemet_sections = re.findall(r'«([^»]+)»', text)\n",
    "    \n",
    "    for section in guillemet_sections:\n",
    "        # Within a guillemet section, dashes indicate speaker changes\n",
    "        # Split by dashes that appear after sentence-ending punctuation\n",
    "        turns = re.split(r'(?<=[.!?])\\s*[–—―]\\s*', section)\n",
    "        \n",
    "        for turn in turns:\n",
    "            cleaned = clean_dialogue_text(turn)\n",
    "            if cleaned and len(cleaned) >= MIN_DIALOGUE_LENGTH:\n",
    "                all_dialogues.append(cleaned)\n",
    "    \n",
    "    # Strategy 2: Paragraph-level dash detection\n",
    "    paragraphs = text.split('\\n\\n')\n",
    "    \n",
    "    for para in paragraphs:\n",
    "        para = para.strip()\n",
    "        \n",
    "        # Skip if we already extracted from guillemets in this paragraph\n",
    "        if '«' in para and '»' in para:\n",
    "            continue\n",
    "        \n",
    "        # Check for dash-style dialogue (line starts with dash)\n",
    "        if para and para[0] in DIALOGUE_MARKERS + ['-']:\n",
    "            # This paragraph is likely a dialogue turn\n",
    "            cleaned = clean_dialogue_text(para.lstrip('-–—― '))\n",
    "            if cleaned and len(cleaned) >= MIN_DIALOGUE_LENGTH:\n",
    "                all_dialogues.append(cleaned)\n",
    "    \n",
    "    return all_dialogues\n",
    "\n",
    "print(\"✅ Dialogue extraction functions loaded!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef4cb286",
   "metadata": {},
   "source": [
    "## Step 3: Conversation Building Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "68cec15e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Conversation building functions loaded!\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CONVERSATION BUILDING\n",
    "# ============================================================================\n",
    "\n",
    "def build_conversations(dialogues, max_turns=MAX_TURNS_PER_CONVERSATION):\n",
    "    \"\"\"\n",
    "    Build multi-turn conversations from a list of dialogues.\n",
    "    \n",
    "    - Pairs consecutive dialogues as user/assistant\n",
    "    - Creates multi-turn conversations (up to max_turns)\n",
    "    - Ensures each conversation ends with 'assistant'\n",
    "    \n",
    "    Returns list of conversations, each being a list of {role, content} dicts.\n",
    "    \"\"\"\n",
    "    conversations = []\n",
    "    \n",
    "    if len(dialogues) < 2:\n",
    "        return conversations\n",
    "    \n",
    "    i = 0\n",
    "    while i < len(dialogues) - 1:\n",
    "        conversation = []\n",
    "        turns_added = 0\n",
    "        \n",
    "        while i < len(dialogues) and turns_added < max_turns:\n",
    "            # Add user turn\n",
    "            conversation.append({\n",
    "                \"role\": \"user\",\n",
    "                \"content\": dialogues[i]\n",
    "            })\n",
    "            turns_added += 1\n",
    "            i += 1\n",
    "            \n",
    "            # Add assistant turn if available\n",
    "            if i < len(dialogues) and turns_added < max_turns:\n",
    "                conversation.append({\n",
    "                    \"role\": \"assistant\",\n",
    "                    \"content\": dialogues[i]\n",
    "                })\n",
    "                turns_added += 1\n",
    "                i += 1\n",
    "        \n",
    "        # Ensure conversation ends with assistant\n",
    "        if conversation and conversation[-1][\"role\"] == \"user\":\n",
    "            # Remove the last user message if there's no assistant response\n",
    "            conversation.pop()\n",
    "        \n",
    "        # Only add if we have at least one exchange (user + assistant)\n",
    "        if len(conversation) >= 2:\n",
    "            conversations.append(conversation)\n",
    "    \n",
    "    return conversations\n",
    "\n",
    "def build_conversations_pairs_only(dialogues):\n",
    "    \"\"\"\n",
    "    Build simple 2-turn conversations (user/assistant pairs).\n",
    "    Each conversation is exactly one exchange.\n",
    "    \"\"\"\n",
    "    conversations = []\n",
    "    \n",
    "    for i in range(0, len(dialogues) - 1, 2):\n",
    "        conversation = [\n",
    "            {\"role\": \"user\", \"content\": dialogues[i]},\n",
    "            {\"role\": \"assistant\", \"content\": dialogues[i + 1]}\n",
    "        ]\n",
    "        conversations.append(conversation)\n",
    "    \n",
    "    return conversations\n",
    "\n",
    "def build_conversations_sliding_window(dialogues, window_size=4, step=2):\n",
    "    \"\"\"\n",
    "    Build conversations using a sliding window approach.\n",
    "    Creates overlapping multi-turn conversations for more training data.\n",
    "    \n",
    "    Args:\n",
    "        dialogues: List of dialogue strings\n",
    "        window_size: Number of turns per conversation (must be even)\n",
    "        step: How many dialogues to skip between windows\n",
    "    \"\"\"\n",
    "    conversations = []\n",
    "    \n",
    "    # Ensure window_size is even (for user/assistant pairs)\n",
    "    window_size = window_size if window_size % 2 == 0 else window_size - 1\n",
    "    \n",
    "    for i in range(0, len(dialogues) - window_size + 1, step):\n",
    "        window = dialogues[i:i + window_size]\n",
    "        conversation = []\n",
    "        \n",
    "        for j, dialogue in enumerate(window):\n",
    "            role = \"user\" if j % 2 == 0 else \"assistant\"\n",
    "            conversation.append({\"role\": role, \"content\": dialogue})\n",
    "        \n",
    "        # Only add if ends with assistant\n",
    "        if conversation and conversation[-1][\"role\"] == \"assistant\":\n",
    "            conversations.append(conversation)\n",
    "    \n",
    "    return conversations\n",
    "\n",
    "\n",
    "\n",
    "print(\"✅ Conversation building functions loaded!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f565d8d",
   "metadata": {},
   "source": [
    "## Step 4: Main Processing Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e6c17422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Processing pipeline loaded!\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# MAIN PROCESSING PIPELINE\n",
    "# ============================================================================\n",
    "\n",
    "def process_single_book(url, temp_dir=\"temp_pdfs\"):\n",
    "    \"\"\"\n",
    "    Process a single book: download PDF, extract text, find dialogues.\n",
    "    \n",
    "    Returns:\n",
    "        tuple: (book_name, text_with_structure, dialogues)\n",
    "    \"\"\"\n",
    "    os.makedirs(temp_dir, exist_ok=True)\n",
    "    \n",
    "    book_name = get_book_name_from_url(url)\n",
    "    temp_pdf = os.path.join(temp_dir, f\"{book_name}.pdf\")\n",
    "    \n",
    "    try:\n",
    "        # Download PDF\n",
    "        download_pdf(url, temp_pdf)\n",
    "        \n",
    "        # Extract text\n",
    "        raw_text = extract_text_from_pdf(temp_pdf)\n",
    "        \n",
    "        # Clean text but preserve structure\n",
    "        cleaned_text = clean_book_text_preserve_structure(raw_text)\n",
    "        \n",
    "        # Extract dialogues\n",
    "        dialogues = extract_dialogues_advanced(cleaned_text)\n",
    "        \n",
    "        return book_name, cleaned_text, dialogues\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"Error processing {url}: {e}\")\n",
    "        return book_name, \"\", []\n",
    "    \n",
    "    finally:\n",
    "        # Cleanup\n",
    "        if os.path.exists(temp_pdf):\n",
    "            os.remove(temp_pdf)\n",
    "\n",
    "def process_all_books(urls, conversation_mode=\"sliding\"):\n",
    "    \"\"\"\n",
    "    Process all books and build conversation dataset.\n",
    "    \n",
    "    Args:\n",
    "        urls: List of PDF URLs\n",
    "        conversation_mode: \"pairs\" (2-turn), \"multi\" (variable), \"sliding\" (overlapping)\n",
    "    \n",
    "    Returns:\n",
    "        List of conversation dicts ready for dataset\n",
    "    \"\"\"\n",
    "    all_conversations = []\n",
    "    \n",
    "    for url in tqdm(urls, desc=\"Processing books\"):\n",
    "        book_name, text, dialogues = process_single_book(url)\n",
    "        \n",
    "        if len(dialogues) < 2:\n",
    "            continue\n",
    "        \n",
    "        # Build conversations based on mode\n",
    "        if conversation_mode == \"pairs\":\n",
    "            conversations = build_conversations_pairs_only(dialogues)\n",
    "        elif conversation_mode == \"multi\":\n",
    "            conversations = build_conversations(dialogues)\n",
    "        elif conversation_mode == \"sliding\":\n",
    "            conversations = build_conversations_sliding_window(dialogues, window_size=6, step=2)\n",
    "        else:\n",
    "            conversations = build_conversations_pairs_only(dialogues)\n",
    "        \n",
    "        all_conversations.extend(conversations)\n",
    "        \n",
    "        print(f\"  {book_name}: {len(dialogues)} dialogues → {len(conversations)} conversations\")\n",
    "    \n",
    "    return all_conversations\n",
    "\n",
    "print(\"✅ Processing pipeline loaded!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bc48c3",
   "metadata": {},
   "source": [
    "## Step 5: Get PDF URLs and Test on Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7acc29cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching PDF links from https://beq.ebooksgratuits.com/vents/ ...\n",
      "Found 725 PDF files!\n",
      "\n",
      "First 10 PDFs:\n",
      "  1. About-Germaine.pdf\n",
      "  2. About-montagnes.pdf\n",
      "  3. About_Le_nez_dun_notaire.pdf\n",
      "  4. About_Lhomme_a_loreille_cassee.pdf\n",
      "  5. Abrantes-Marina.pdf\n",
      "  6. Abrantes-Mathilde.pdf\n",
      "  7. Achard-Madame.pdf\n",
      "  8. Achard-dort.pdf\n",
      "  9. Achard-soldat.pdf\n",
      "  10. Adam-mesaventure.pdf\n",
      "Found 725 PDF files!\n",
      "\n",
      "First 10 PDFs:\n",
      "  1. About-Germaine.pdf\n",
      "  2. About-montagnes.pdf\n",
      "  3. About_Le_nez_dun_notaire.pdf\n",
      "  4. About_Lhomme_a_loreille_cassee.pdf\n",
      "  5. Abrantes-Marina.pdf\n",
      "  6. Abrantes-Mathilde.pdf\n",
      "  7. Achard-Madame.pdf\n",
      "  8. Achard-dort.pdf\n",
      "  9. Achard-soldat.pdf\n",
      "  10. Adam-mesaventure.pdf\n"
     ]
    }
   ],
   "source": [
    "# Fetch all PDF URLs from BEQ website\n",
    "print(\"Fetching PDF links from https://beq.ebooksgratuits.com/vents/ ...\")\n",
    "pdf_urls = get_pdf_urls_from_beq()\n",
    "print(f\"Found {len(pdf_urls)} PDF files!\\n\")\n",
    "\n",
    "# Show first 10 as preview\n",
    "print(\"First 10 PDFs:\")\n",
    "for i, url in enumerate(pdf_urls[:10], 1):\n",
    "    print(f\"  {i}. {url.split('/')[-1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ed8a2e99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing with: About-Germaine.pdf\n",
      "\n",
      "Book: About-Germaine\n",
      "Text length: 390,389 characters\n",
      "Dialogues found: 674\n",
      "\n",
      "============================================================\n",
      "Sample dialogues extracted:\n",
      "============================================================\n",
      "\n",
      "[1] Avec ça et ce que j’ai de côté, disait le maître d’hôtel, j’arrondirai ma rente viagère. On a du pain sur la planche, Dieu merci! et l’on ne se laissera manquer de rien sur ses vieux jours.\n",
      "\n",
      "[2] Parbleu! reprit le valet de chambre, vous êtes garçon; vous n’avez que vous à penser. Mais, moi, j’ai de la famille. Aussi, je donnerai mon argent à ce petit jeune homme qui va à la Bourse. Il me trip...\n",
      "\n",
      "[3] C’est une idée, ça, monsieur Ferdinand, repartit le marmiton. Portez-lui donc mes quarante francs, quand vous irez.\n",
      "\n",
      "[4] Est-il jeune! Qu’est-ce qu’on peut faire à la Bourse avec quarante francs?\n",
      "\n",
      "[5] Allons, dit le jeune homme en étouffant un soupir, je les mettrai à la caisse d’épargne!\n",
      "\n",
      "[6] Ma caisse d’épargne, à moi, la voici. C’est là que j’ai toujours placé mes fonds, et je m’en suis bien trouvé. Pas vrai, père Altroff?\n",
      "\n",
      "[7] Allais, marchais! ce qu’on a bu, on ne l’a plus. Il n’est tel placement qu’une bonne cachette dans un vieux mur ou dans un arbre creux. Argent bien enfouie, les notaires ne la mangent point!\n",
      "\n",
      "[8] Sapristi! cria-t-il, j’ai cru voir passer la misère en personne. Voilà mon jour de l’an gâté dès le matin. Vous verrez que rien ne me réussira jusqu’à la Saint-Sylvestre. Brrr! j’ai froid dans le dos.\n",
      "\n",
      "[9] Pauvre femme! dit le maître d’hôtel. Ça a eu des mille et des cents, et puis voilà! Qui est-ce qui croirait que c’est une duchesse?\n",
      "\n",
      "[10] C’est son gueux de mari qui lui a tout mangé.\n",
      "Book: About-Germaine\n",
      "Text length: 390,389 characters\n",
      "Dialogues found: 674\n",
      "\n",
      "============================================================\n",
      "Sample dialogues extracted:\n",
      "============================================================\n",
      "\n",
      "[1] Avec ça et ce que j’ai de côté, disait le maître d’hôtel, j’arrondirai ma rente viagère. On a du pain sur la planche, Dieu merci! et l’on ne se laissera manquer de rien sur ses vieux jours.\n",
      "\n",
      "[2] Parbleu! reprit le valet de chambre, vous êtes garçon; vous n’avez que vous à penser. Mais, moi, j’ai de la famille. Aussi, je donnerai mon argent à ce petit jeune homme qui va à la Bourse. Il me trip...\n",
      "\n",
      "[3] C’est une idée, ça, monsieur Ferdinand, repartit le marmiton. Portez-lui donc mes quarante francs, quand vous irez.\n",
      "\n",
      "[4] Est-il jeune! Qu’est-ce qu’on peut faire à la Bourse avec quarante francs?\n",
      "\n",
      "[5] Allons, dit le jeune homme en étouffant un soupir, je les mettrai à la caisse d’épargne!\n",
      "\n",
      "[6] Ma caisse d’épargne, à moi, la voici. C’est là que j’ai toujours placé mes fonds, et je m’en suis bien trouvé. Pas vrai, père Altroff?\n",
      "\n",
      "[7] Allais, marchais! ce qu’on a bu, on ne l’a plus. Il n’est tel placement qu’une bonne cachette dans un vieux mur ou dans un arbre creux. Argent bien enfouie, les notaires ne la mangent point!\n",
      "\n",
      "[8] Sapristi! cria-t-il, j’ai cru voir passer la misère en personne. Voilà mon jour de l’an gâté dès le matin. Vous verrez que rien ne me réussira jusqu’à la Saint-Sylvestre. Brrr! j’ai froid dans le dos.\n",
      "\n",
      "[9] Pauvre femme! dit le maître d’hôtel. Ça a eu des mille et des cents, et puis voilà! Qui est-ce qui croirait que c’est une duchesse?\n",
      "\n",
      "[10] C’est son gueux de mari qui lui a tout mangé.\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# TEST: Process a single book to verify dialogue extraction works\n",
    "# ============================================================================\n",
    "\n",
    "# Pick a book likely to have good dialogues (Balzac, Dumas, etc.)\n",
    "test_url = pdf_urls[0]  # First available book\n",
    "print(f\"Testing with: {test_url.split('/')[-1]}\\n\")\n",
    "\n",
    "book_name, text, dialogues = process_single_book(test_url)\n",
    "\n",
    "print(f\"Book: {book_name}\")\n",
    "print(f\"Text length: {len(text):,} characters\")\n",
    "print(f\"Dialogues found: {len(dialogues)}\")\n",
    "\n",
    "if dialogues:\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(\"Sample dialogues extracted:\")\n",
    "    print('='*60)\n",
    "    for i, d in enumerate(dialogues[:10], 1):\n",
    "        print(f\"\\n[{i}] {d[:200]}{'...' if len(d) > 200 else ''}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ba500e4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conversation building modes:\n",
      "\n",
      "Pairs mode: 337 conversations\n",
      "Multi-turn mode: 113 conversations\n",
      "Sliding window mode: 336 conversations\n",
      "\n",
      "============================================================\n",
      "Sample conversation:\n",
      "============================================================\n",
      "[\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"Avec ça et ce que j’ai de côté, disait le maître d’hôtel, j’arrondirai ma rente viagère. On a du pain sur la planche, Dieu merci! et l’on ne se laissera manquer de rien sur ses vieux jours.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"assistant\",\n",
      "    \"content\": \"Parbleu! reprit le valet de chambre, vous êtes garçon; vous n’avez que vous à penser. Mais, moi, j’ai de la famille. Aussi, je donnerai mon argent à ce petit jeune homme qui va à la Bourse. Il me tripotera quelque chose.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"C’est une idée, ça, monsieur Ferdinand, repartit le marmiton. Portez-lui donc mes quarante francs, quand vous irez.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"assistant\",\n",
      "    \"content\": \"Est-il jeune! Qu’est-ce qu’on peut faire à la Bourse avec quarante francs?\"\n",
      "  }\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# TEST: Build sample conversations\n",
    "# ============================================================================\n",
    "\n",
    "if len(dialogues) >= 2:\n",
    "    # Test different conversation modes\n",
    "    print(\"Testing conversation building modes:\\n\")\n",
    "    \n",
    "    # Pairs only\n",
    "    conv_pairs = build_conversations_pairs_only(dialogues)\n",
    "    print(f\"Pairs mode: {len(conv_pairs)} conversations\")\n",
    "    \n",
    "    # Multi-turn\n",
    "    conv_multi = build_conversations(dialogues, max_turns=6)\n",
    "    print(f\"Multi-turn mode: {len(conv_multi)} conversations\")\n",
    "    \n",
    "    # Sliding window\n",
    "    conv_sliding = build_conversations_sliding_window(dialogues, window_size=4, step=2)\n",
    "    print(f\"Sliding window mode: {len(conv_sliding)} conversations\")\n",
    "    \n",
    "    # Show a sample conversation\n",
    "    if conv_sliding:\n",
    "        print(f\"\\n{'='*60}\")\n",
    "        print(\"Sample conversation:\")\n",
    "        print('='*60)\n",
    "        print(json.dumps(conv_sliding[0], indent=2, ensure_ascii=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a08796f",
   "metadata": {},
   "source": [
    "## Step 6: Process All Books (Full Run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "64b89c58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 700 books...\n",
      "Mode: sliding\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   0%|          | 1/700 [00:01<12:05,  1.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  About-Germaine: 674 dialogues → 335 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   0%|          | 2/700 [00:02<11:33,  1.01it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  About-montagnes: 126 dialogues → 61 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   0%|          | 3/700 [00:02<08:29,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  About_Le_nez_dun_notaire: 20 dialogues → 8 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   1%|          | 4/700 [00:03<08:26,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  About_Lhomme_a_loreille_cassee: 428 dialogues → 212 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   1%|          | 5/700 [00:03<06:56,  1.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Abrantes-Marina: 3 dialogues → 0 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   1%|          | 6/700 [00:03<05:30,  2.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Abrantes-Mathilde: 2 dialogues → 0 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   1%|          | 7/700 [00:04<06:28,  1.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Achard-Madame: 692 dialogues → 344 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   1%|          | 8/700 [00:04<06:10,  1.87it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Achard-dort: 7 dialogues → 1 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   1%|▏         | 10/700 [00:05<04:57,  2.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Achard-soldat: 9 dialogues → 2 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   2%|▏         | 12/700 [00:06<03:32,  3.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Adam_La_glebe: 11 dialogues → 3 conversations\n",
      "  Adam_Le_conte_futur: 14 dialogues → 5 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   2%|▏         | 13/700 [00:06<05:22,  2.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Ages-destinee: 5 dialogues → 0 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   2%|▏         | 14/700 [00:10<15:15,  1.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-Aucas: 15 dialogues → 5 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   2%|▏         | 15/700 [00:11<15:06,  1.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-Delgres: 122 dialogues → 59 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   2%|▏         | 16/700 [00:12<13:55,  1.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-Forestier: 23 dialogues → 9 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   2%|▏         | 17/700 [00:13<14:05,  1.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-Guaranis: 1349 dialogues → 672 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   3%|▎         | 18/700 [00:15<15:02,  1.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-Lynch: 10 dialogues → 3 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   3%|▎         | 19/700 [00:15<12:28,  1.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-Michigan: 18 dialogues → 7 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   3%|▎         | 20/700 [00:17<13:04,  1.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-Olonnais: 112 dialogues → 54 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   3%|▎         | 21/700 [00:18<12:15,  1.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-Ourson: 8 dialogues → 2 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   3%|▎         | 22/700 [00:19<13:18,  1.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-Vent-en-panne: 18 dialogues → 7 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   3%|▎         | 23/700 [00:20<12:14,  1.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-bohemes: 5 dialogues → 0 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   3%|▎         | 24/700 [00:22<15:23,  1.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-chercheur: 7 dialogues → 1 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   4%|▎         | 25/700 [00:24<17:49,  1.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-eclaireur: 6 dialogues → 1 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   4%|▎         | 26/700 [00:25<14:05,  1.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-mangeur: 23 dialogues → 9 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   4%|▍         | 27/700 [00:25<11:53,  1.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-panthere: 7 dialogues → 1 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   4%|▍         | 28/700 [00:26<12:23,  1.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-pirates: 9 dialogues → 2 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   4%|▍         | 29/700 [00:27<12:12,  1.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-rats: 8 dialogues → 2 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   4%|▍         | 30/700 [00:29<15:31,  1.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-riviere: 1240 dialogues → 618 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   4%|▍         | 31/700 [00:31<14:44,  1.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-rodeurs: 5 dialogues → 0 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   5%|▍         | 32/700 [00:31<12:31,  1.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard-terres: 21 dialogues → 8 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   5%|▍         | 33/700 [00:32<10:07,  1.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard_Jim_lIndien: 6 dialogues → 1 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   5%|▍         | 34/700 [00:32<08:46,  1.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard_LAigle_Noir_des_Dacotahs: 38 dialogues → 17 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   5%|▌         | 35/700 [00:33<09:10,  1.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard_Les_bandits_de_lArizona: 151 dialogues → 73 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   5%|▌         | 36/700 [00:34<09:33,  1.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard_Les_pieds_fourchus: 51 dialogues → 23 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   5%|▌         | 37/700 [00:36<12:26,  1.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard_Les_trappeurs_de_lArkansas: 71 dialogues → 33 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   5%|▌         | 38/700 [00:37<11:19,  1.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Aimard_maitrise: 373 dialogues → 184 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   6%|▌         | 39/700 [00:38<12:39,  1.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Alcott-Marsch: 1301 dialogues → 648 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   6%|▌         | 40/700 [00:39<12:00,  1.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Alcott-Rose: 1087 dialogues → 541 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   6%|▌         | 42/700 [00:40<08:01,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-Captaincap: 60 dialogues → 28 conversations\n",
      "  Allais-Silverie: 2 dialogues → 0 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   6%|▌         | 43/700 [00:40<07:01,  1.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-amours: 3 dialogues → 0 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   6%|▋         | 44/700 [00:41<06:14,  1.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-aroseur: 71 dialogues → 33 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   6%|▋         | 45/700 [00:41<05:57,  1.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-bec: 25 dialogues → 10 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   7%|▋         | 46/700 [00:42<05:43,  1.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-bile: 32 dialogues → 14 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   7%|▋         | 47/700 [00:42<05:37,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-boeufs: 23 dialogues → 9 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   7%|▋         | 48/700 [00:43<05:25,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-boomerang: 108 dialogues → 52 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   7%|▋         | 49/700 [00:43<05:37,  1.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-frappons: 28 dialogues → 12 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   7%|▋         | 50/700 [00:44<05:58,  1.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-oeil: 43 dialogues → 19 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   7%|▋         | 51/700 [00:44<05:41,  1.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-parapluie: 45 dialogues → 20 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   7%|▋         | 52/700 [00:45<05:47,  1.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-pomme: 19 dialogues → 7 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   8%|▊         | 53/700 [00:45<05:38,  1.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais-ribouldinguant: 69 dialogues → 32 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   8%|▊         | 54/700 [00:46<05:52,  1.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais_A_la_une: 284 dialogues → 140 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   8%|▊         | 55/700 [00:47<06:28,  1.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais_A_se_tordre: 12 dialogues → 4 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   8%|▊         | 56/700 [00:48<08:55,  1.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais_Deux_et_deux_font_cinq: 90 dialogues → 43 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   8%|▊         | 57/700 [00:49<10:02,  1.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais_Faits_divers: 116 dialogues → 56 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   8%|▊         | 58/700 [00:50<08:53,  1.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais_Laffaire_Blaireau: 76 dialogues → 36 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   8%|▊         | 59/700 [00:50<08:09,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais_Plaisir_dhumour: 221 dialogues → 108 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:   9%|▊         | 60/700 [00:51<07:24,  1.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Allais_Pour_cause_de_fin_de_bail: 36 dialogues → 16 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  97%|█████████▋| 682/700 [09:28<00:14,  1.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  gautier-nouvelles-1: 128 dialogues → 62 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  98%|█████████▊| 683/700 [09:29<00:13,  1.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  gautier-nouvelles-2: 97 dialogues → 46 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  98%|█████████▊| 684/700 [09:29<00:12,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  gautier-nouvelles-3: 101 dialogues → 48 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  98%|█████████▊| 685/700 [09:30<00:12,  1.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  gogol-tarass: 424 dialogues → 210 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  98%|█████████▊| 686/700 [09:31<00:11,  1.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  grimm-contes: 899 dialogues → 447 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  98%|█████████▊| 687/700 [09:32<00:10,  1.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  hoffmann-1: 60 dialogues → 28 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  98%|█████████▊| 688/700 [09:33<00:09,  1.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  hoffmann-2: 24 dialogues → 10 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  98%|█████████▊| 689/700 [09:34<00:09,  1.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  hoffmann-3: 127 dialogues → 61 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  99%|█████████▊| 690/700 [09:34<00:08,  1.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  hoffmann-4: 33 dialogues → 14 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  99%|█████████▊| 691/700 [09:35<00:06,  1.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  hoffmann-5: 9 dialogues → 2 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  99%|█████████▉| 692/700 [09:36<00:06,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  hoffmann-6: 391 dialogues → 193 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  99%|█████████▉| 693/700 [09:36<00:04,  1.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  hugo-claude: 20 dialogues → 8 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  99%|█████████▉| 694/700 [09:37<00:03,  1.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  huysmans-drageoir: 57 dialogues → 26 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books:  99%|█████████▉| 695/700 [09:37<00:03,  1.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  huysmans-vatard: 67 dialogues → 31 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books: 100%|█████████▉| 697/700 [09:39<00:02,  1.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  leroy-jacquou: 139 dialogues → 67 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books: 100%|█████████▉| 698/700 [09:39<00:01,  1.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  loti-matelot: 46 dialogues → 21 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books: 100%|█████████▉| 699/700 [09:40<00:00,  1.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  merimee-1: 850 dialogues → 423 conversations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing books: 100%|██████████| 700/700 [09:41<00:00,  1.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  merimee-2: 155 dialogues → 75 conversations\n",
      "\n",
      "============================================================\n",
      "TOTAL CONVERSATIONS: 77583\n",
      "============================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# FULL PROCESSING: Process all books\n",
    "# ============================================================================\n",
    "# WARNING: This will take a long time! Consider starting with a smaller subset.\n",
    "\n",
    "# Configuration\n",
    "URLS_TO_PROCESS = pdf_urls[:700]  # Adjust this number as needed\n",
    "CONVERSATION_MODE = \"sliding\"     # \"pairs\", \"multi\", or \"sliding\"\n",
    "\n",
    "print(f\"Processing {len(URLS_TO_PROCESS)} books...\")\n",
    "print(f\"Mode: {CONVERSATION_MODE}\")\n",
    "print()\n",
    "\n",
    "all_conversations = process_all_books(\n",
    "    URLS_TO_PROCESS,\n",
    "    conversation_mode=CONVERSATION_MODE\n",
    ")\n",
    "\n",
    "print(f\"\\n{'='*60}\")\n",
    "print(f\"TOTAL CONVERSATIONS: {len(all_conversations)}\")\n",
    "print('='*60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252e8dc2",
   "metadata": {},
   "source": [
    "## Step 7: Create and Upload Dataset to HuggingFace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b8c6959b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c2170fdaf4d48d186222476bcf95127",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Login to Hugging Face\n",
    "login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc343f74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset created with 77583 conversations\n",
      "Sample row:\n",
      "{'conversations': '[{\"role\": \"user\", \"content\": \"Avec ça et ce que j’ai de côté, disait le maître d’hôtel, j’arrondirai ma rente viagère. On a du pain sur la planche, Dieu merci! et l’on ne se laissera manquer de rien sur ses vieux jours.\"}, {\"role\": \"assistant\", \"content\": \"Parbleu! reprit le valet de chambre, vous êtes garçon; vous n’avez que vous à penser. Mais, moi, j’ai de la famille. Aussi, je donnerai mon argent à ce petit jeune homme qui va à la Bourse. Il me tripotera quelque chose.\"}, {\"role\": \"user\", \"content\": \"C’est une idée, ça, monsieur Ferdinand, repartit le marmiton. Portez-lui donc mes quarante francs, quand vous irez.\"}, {\"role\": \"assistant\", \"content\": \"Est-il jeune! Qu’est-ce qu’on peut faire à la Bourse avec quarante francs?\"}, {\"role\": \"user\", \"content\": \"Allons, dit le jeune homme en étouffant un soupir, je les mettrai à la caisse d’épargne!\"}, {\"role\": \"assistant\", \"content\": \"Ma caisse d’épargne, à moi, la voici. C’est là que j’ai toujours placé mes fonds, et je m’en suis bien trouvé. Pas vrai, père Altroff?\"}]'}\n",
      "\n",
      "Uploading to HuggingFace Hub as 'Volko76/french-classic-conversations'...\n"
     ]
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ? shards/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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    },
    {
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      "text/plain": [
       "Processing Files (0 / 0)      : |          |  0.00B /  0.00B            "
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     "metadata": {},
     "output_type": "display_data"
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      "text/plain": [
       "New Data Upload               : |          |  0.00B /  0.00B            "
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       "                              :   0%|          | 93.0kB / 33.3MB            "
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     "metadata": {},
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Dataset uploaded successfully!\n",
      "View it at: https://huggingface.co/datasets/Volko76/french-classic-conversations\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CREATE DATASET\n",
    "# ============================================================================\n",
    "\n",
    "def create_conversation_dataset(conversations, dataset_name, username):\n",
    "    \"\"\"\n",
    "    Create and upload a conversation dataset to HuggingFace Hub.\n",
    "    \n",
    "    The dataset will have a 'conversations' column compatible with:\n",
    "    - ShareGPT format\n",
    "    - ChatML format\n",
    "    - Most finetuning frameworks (axolotl, unsloth, etc.)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Convert conversations to the format expected by most finetuning tools\n",
    "    # Each row has a 'conversations' field which is a list of message dicts\n",
    "    data = {\n",
    "        \"conversations\": [json.dumps(conv, ensure_ascii=False) for conv in conversations]\n",
    "    }\n",
    "    \n",
    "    dataset = Dataset.from_dict(data)\n",
    "    \n",
    "    print(f\"Dataset created with {len(dataset)} conversations\")\n",
    "    print(f\"Sample row:\\n{dataset[0]}\\n\")\n",
    "    \n",
    "    # Push to Hub\n",
    "    repo_id = f\"{username}/{dataset_name}\"\n",
    "    print(f\"Uploading to HuggingFace Hub as '{repo_id}'...\")\n",
    "    \n",
    "    dataset.push_to_hub(repo_id, private=False)\n",
    "    \n",
    "    print(f\"\\n✅ Dataset uploaded successfully!\")\n",
    "    print(f\"View it at: https://huggingface.co/datasets/{repo_id}\")\n",
    "    \n",
    "    return dataset\n",
    "\n",
    "# Create and upload the dataset\n",
    "dataset = create_conversation_dataset(\n",
    "    all_conversations,\n",
    "    dataset_name=\"french-classic-conversations\",\n",
    "    username=\"Volko76\"  # Your HF username\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbfcb90e",
   "metadata": {},
   "source": [
    "## Step 8: Dataset Statistics and Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9e65322a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Dataset Statistics\n",
      "========================================\n",
      "Total conversations: 77,583\n",
      "Average turns per conversation: 6.0\n",
      "Min turns: 6\n",
      "Max turns: 6\n",
      "\n",
      "Total characters: 102,931,512\n",
      "User characters: 51,646,277\n",
      "Assistant characters: 51,285,235\n",
      "\n",
      "Avg chars per conversation: 1,326\n",
      "\n",
      "============================================================\n",
      "Random conversation samples:\n",
      "============================================================\n",
      "\n",
      "--- Sample 1 ---\n",
      "[\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"Ça, c’est sûr. Que le bon Dieu nous garde le colonel, car, s’il n’était plus là, Mlle Trépiez nous reviendrait peut-être.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"assistant\",\n",
      "    \"content\": \"Elle serait capable d’essayer! En attendant, elle ne fait rien de bon à Paris. On dit que ses cancans brouillent de plus en plus M. Robert avec ce camarade qu’il n’aimait pas; tu sais?\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"Ah! celui dont il parlait toujours en montrant le poing, Horace Croquart? Il paraît que c’est un mauvais diable, lui; tandis que le nôtre, c’est un bon diable.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"assistant\",\n",
      "    \"content\": \"Déjà pas si bon!\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"Je te dis qu’il a bon cœur, ma femme; et qu’un jour ou l’autre il se corrigera tout à fait.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"assistant\",\n",
      "    \"content\": \"Tant mieux!... Pourvu que notre chère dame ne s’en aille pas de ce monde avant de voir ça!\"\n",
      "  }\n",
      "]\n",
      "...\n",
      "\n",
      "--- Sample 2 ---\n",
      "[\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"Paris est maintenant gâté pour moi; je n’y serais plus, en y retournant, ce que j’étais avant d’avoir revêtu ce fatal habit vert: un jeune homme qui peut-être un jour sera quelque chose. On verrait en moi un homme incapable d’être rien, même sous-lieutenant.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"assistant\",\n",
      "    \"content\": \"Que vous importe l’opinion des autres, si, au fond, vous vous amusez.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"Hélas! j’ai une vanité que vous, mon sage ami, ne pouvez comprendre; ma position serait intolérable; je ne pourrais répondre à certaines plaisanteries. Je ne vois que la guerre pour me tirer du pot au noir où je me suis fourré sans savoir ce que je faisais.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"assistant\",\n",
      "    \"content\": \"Je ne dirai pas mon malheur, mais mon ennui serait redoublé si je devenais le sujet des plaisanteries de mon père et de ces hommes aimables dont l’absence me fait voir la vie en noir.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"cont\n",
      "...\n",
      "\n",
      "--- Sample 3 ---\n",
      "[\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"Vous me connaissez! riposte Félicien stupéfait.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"assistant\",\n",
      "    \"content\": \"Depuis votre débarquement, monsieur Félicien Navarre, vous m’avez été signalé. « Votre affaire se fera... dix mille bouteilles à cinquante sous, cela exige vingt-cinq mille francs. « Cinq mille pour les manœuvres, cinq mille pour Joë, autant pour la douane et dix mille... pour moi... C’est pour rien...\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"Touchez là! Je suis votre homme.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"assistant\",\n",
      "    \"content\": \"Lui aussi! gémit douloureusement Bob, en voyant le Français secouer, sans apparence de dégoût, la main du misérable.\"\n",
      "  },\n",
      "  {\n",
      "    \"role\": \"user\",\n",
      "    \"content\": \"Et maintenant, en route! ordonne Jonathan d’un ton qui ne souffre pas de réplique. « Bob, le premier en tête, puis Nick, puis Peter, puis Ned Moore, et après, vous, monsieur Navarre, puis enfin mon compère Joë Sullivan... « Moi, je ferme la marche. « Go ahead! e\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# DATASET STATISTICS\n",
    "# ============================================================================\n",
    "\n",
    "def analyze_dataset(conversations):\n",
    "    \"\"\"Compute statistics about the conversation dataset.\"\"\"\n",
    "    \n",
    "    total_convos = len(conversations)\n",
    "    \n",
    "    # Count turns\n",
    "    turn_counts = [len(conv) for conv in conversations]\n",
    "    avg_turns = sum(turn_counts) / total_convos if total_convos > 0 else 0\n",
    "    \n",
    "    # Count characters\n",
    "    total_chars = 0\n",
    "    user_chars = 0\n",
    "    assistant_chars = 0\n",
    "    \n",
    "    for conv in conversations:\n",
    "        for msg in conv:\n",
    "            chars = len(msg['content'])\n",
    "            total_chars += chars\n",
    "            if msg['role'] == 'user':\n",
    "                user_chars += chars\n",
    "            elif msg['role'] == 'assistant':\n",
    "                assistant_chars += chars\n",
    "    \n",
    "    print(\"📊 Dataset Statistics\")\n",
    "    print(\"=\" * 40)\n",
    "    print(f\"Total conversations: {total_convos:,}\")\n",
    "    print(f\"Average turns per conversation: {avg_turns:.1f}\")\n",
    "    print(f\"Min turns: {min(turn_counts) if turn_counts else 0}\")\n",
    "    print(f\"Max turns: {max(turn_counts) if turn_counts else 0}\")\n",
    "    print(f\"\\nTotal characters: {total_chars:,}\")\n",
    "    print(f\"User characters: {user_chars:,}\")\n",
    "    print(f\"Assistant characters: {assistant_chars:,}\")\n",
    "    print(f\"\\nAvg chars per conversation: {total_chars // total_convos if total_convos else 0:,}\")\n",
    "\n",
    "# Analyze the dataset\n",
    "analyze_dataset(all_conversations)\n",
    "\n",
    "# Show a few random samples\n",
    "import random\n",
    "print(f\"\\n{'='*60}\")\n",
    "print(\"Random conversation samples:\")\n",
    "print('='*60)\n",
    "\n",
    "for i, conv in enumerate(random.sample(all_conversations, min(3, len(all_conversations))), 1):\n",
    "    print(f\"\\n--- Sample {i} ---\")\n",
    "    print(json.dumps(conv, indent=2, ensure_ascii=False)[:1000])\n",
    "    print(\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf8f120",
   "metadata": {},
   "source": [
    "## Optional: Export to Different Formats\n",
    "\n",
    "The cell below allows you to export the dataset in different formats commonly used for finetuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4a20a30",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "Processing Files (0 / 0)      : |          |  0.00B /  0.00B            "
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      "text/plain": [
       "New Data Upload               : |          |  0.00B /  0.00B            "
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     },
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      "text/plain": [
       "  ...yp6t/sharegpt_format.json:  16%|#6        | 22.7MB /  139MB            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Uploaded ShareGPT format\n"
     ]
    },
    {
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      "text/plain": [
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     "metadata": {},
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      "text/plain": [
       "  ...zyp6t/chatml_format.jsonl:  13%|#2        | 15.9MB /  124MB            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Uploaded ChatML format\n"
     ]
    },
    {
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    {
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      "text/plain": [
       "  ...bzyp6t/alpaca_format.json:  16%|#6        | 6.50MB / 40.6MB            "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Uploaded Alpaca format\n",
      "\n",
      "🎉 All formats uploaded to https://huggingface.co/datasets/Volko76/french-classic-conversations\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# EXPORT TO DIFFERENT FORMATS AND UPLOAD TO HF REPO\n",
    "# ============================================================================\n",
    "\n",
    "from huggingface_hub import HfApi\n",
    "import tempfile\n",
    "\n",
    "def export_and_upload_formats(conversations, repo_id=\"Volko76/french-classic-conversations\"):\n",
    "    \"\"\"\n",
    "    Export conversations to different formats and upload to HuggingFace repo.\n",
    "    \"\"\"\n",
    "    api = HfApi()\n",
    "    \n",
    "    # Create temporary files and upload each format\n",
    "    with tempfile.TemporaryDirectory() as tmpdir:\n",
    "        \n",
    "        # 1. ShareGPT format\n",
    "        sharegpt_path = os.path.join(tmpdir, \"sharegpt_format.json\")\n",
    "        sharegpt_data = []\n",
    "        for conv in conversations:\n",
    "            sharegpt_conv = {\n",
    "                \"conversations\": [\n",
    "                    {\"from\": \"human\" if msg[\"role\"] == \"user\" else \"gpt\", \n",
    "                     \"value\": msg[\"content\"]}\n",
    "                    for msg in conv\n",
    "                ]\n",
    "            }\n",
    "            sharegpt_data.append(sharegpt_conv)\n",
    "        \n",
    "        with open(sharegpt_path, 'w', encoding='utf-8') as f:\n",
    "            json.dump(sharegpt_data, f, ensure_ascii=False, indent=2)\n",
    "        \n",
    "        api.upload_file(\n",
    "            path_or_fileobj=sharegpt_path,\n",
    "            path_in_repo=\"sharegpt_format.json\",\n",
    "            repo_id=repo_id,\n",
    "            repo_type=\"dataset\"\n",
    "        )\n",
    "        print(f\"✅ Uploaded ShareGPT format\")\n",
    "        \n",
    "        # 2. ChatML format (JSONL)\n",
    "        chatml_path = os.path.join(tmpdir, \"chatml_format.jsonl\")\n",
    "        with open(chatml_path, 'w', encoding='utf-8') as f:\n",
    "            for conv in conversations:\n",
    "                chatml_text = \"\"\n",
    "                for msg in conv:\n",
    "                    role = msg[\"role\"]\n",
    "                    content = msg[\"content\"]\n",
    "                    chatml_text += f\"<|im_start|>{role}\\n{content}<|im_end|>\\n\"\n",
    "                f.write(json.dumps({\"text\": chatml_text}, ensure_ascii=False) + \"\\n\")\n",
    "        \n",
    "        api.upload_file(\n",
    "            path_or_fileobj=chatml_path,\n",
    "            path_in_repo=\"chatml_format.jsonl\",\n",
    "            repo_id=repo_id,\n",
    "            repo_type=\"dataset\"\n",
    "        )\n",
    "        print(f\"✅ Uploaded ChatML format\")\n",
    "        \n",
    "        # 3. Alpaca format\n",
    "        alpaca_path = os.path.join(tmpdir, \"alpaca_format.json\")\n",
    "        alpaca_data = []\n",
    "        for conv in conversations:\n",
    "            user_msg = None\n",
    "            assistant_msg = None\n",
    "            for msg in conv:\n",
    "                if msg[\"role\"] == \"user\" and user_msg is None:\n",
    "                    user_msg = msg[\"content\"]\n",
    "                elif msg[\"role\"] == \"assistant\" and assistant_msg is None:\n",
    "                    assistant_msg = msg[\"content\"]\n",
    "            if user_msg and assistant_msg:\n",
    "                alpaca_data.append({\n",
    "                    \"instruction\": \"\",\n",
    "                    \"input\": user_msg,\n",
    "                    \"output\": assistant_msg\n",
    "                })\n",
    "        \n",
    "        with open(alpaca_path, 'w', encoding='utf-8') as f:\n",
    "            json.dump(alpaca_data, f, ensure_ascii=False, indent=2)\n",
    "        \n",
    "        api.upload_file(\n",
    "            path_or_fileobj=alpaca_path,\n",
    "            path_in_repo=\"alpaca_format.json\",\n",
    "            repo_id=repo_id,\n",
    "            repo_type=\"dataset\"\n",
    "        )\n",
    "        print(f\"✅ Uploaded Alpaca format\")\n",
    "    \n",
    "    print(f\"\\n🎉 All formats uploaded to https://huggingface.co/datasets/{repo_id}\")\n",
    "\n",
    "# Export and upload all formats to the HF repo\n",
    "export_and_upload_formats(all_conversations, repo_id=\"Volko76/french-classic-conversations\")"
   ]
  }
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