Upload sort_pronvid_titles_to_parquets.ipynb

sort_pronvid_titles_to_parquets.ipynb

@@ -0,0 +1,466 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "pYlpvGTn8Fq1"
+      },
+      "outputs": [],
+      "source": [
+        "# Install required packages if needed\n",
+        "!pip install -q huggingface_hub pandas pyarrow\n",
+        "\n",
+        "from google.colab import userdata, drive\n",
+        "from huggingface_hub import hf_hub_download, list_repo_files\n",
+        "import tarfile\n",
+        "import json\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "from pathlib import Path\n",
+        "from tqdm.auto import tqdm\n",
+        "\n",
+        "# Mount Google Drive\n",
+        "drive.mount('/content/drive')\n",
+        "\n",
+        "# Get HF token from Colab secrets\n",
+        "HF_TOKEN = userdata.get('HF_TOKEN')\n",
+        "\n",
+        "# Dataset info\n",
+        "REPO_ID = \"thisnick/nsfw-video-still-caption\"\n",
+        "REPO_TYPE = \"dataset\"\n",
+        "\n",
+        "# List all tar files in the train/ folder\n",
+        "print(\"Listing files in the repository...\")\n",
+        "all_files = list_repo_files(REPO_ID, repo_type=REPO_TYPE, token=HF_TOKEN)\n",
+        "tar_files = sorted([f for f in all_files if f.startswith(\"train/\") and f.endswith(\".tar\")])\n",
+        "\n",
+        "print(f\"Found {len(tar_files)} tar files:\")\n",
+        "for f in tar_files:\n",
+        "    print(f\"  - {f}\")\n",
+        "\n",
+        "# Helper to build the description string\n",
+        "def build_description(data: dict) -> str:\n",
+        "    \"\"\"\n",
+        "    Combine relevant fields into:\n",
+        "    \"title, categories by cast, tags\"\n",
+        "    Missing fields are skipped gracefully.\n",
+        "    \"\"\"\n",
+        "    title = str(data.get(\"title\", \"\")).strip()\n",
+        "    categories = data.get(\"categories\", [])\n",
+        "    if isinstance(categories, list):\n",
+        "        categories_str = \", \".join(str(c).strip() for c in categories if c)\n",
+        "    else:\n",
+        "        categories_str = str(categories).strip() if categories else \"\"\n",
+        "\n",
+        "    # \"by cast\" – try common keys\n",
+        "    cast = data.get(\"cast\") or data.get(\"actors\") or data.get(\"performers\") or []\n",
+        "    if isinstance(cast, list):\n",
+        "        cast_str = \", \".join(str(c).strip() for c in cast if c)\n",
+        "    else:\n",
+        "        cast_str = str(cast).strip() if cast else \"\"\n",
+        "\n",
+        "    tags = data.get(\"tags\", [])\n",
+        "    if isinstance(tags, list):\n",
+        "        tags_str = \", \".join(str(t).strip() for t in tags if t)\n",
+        "    else:\n",
+        "        tags_str = str(tags).strip() if tags else \"\"\n",
+        "\n",
+        "    parts = []\n",
+        "    if title:\n",
+        "        parts.append(title)\n",
+        "    if categories_str:\n",
+        "        parts.append(categories_str)\n",
+        "    if cast_str:\n",
+        "        parts.append(f\"by {cast_str}\")\n",
+        "    if tags_str:\n",
+        "        parts.append(tags_str)\n",
+        "\n",
+        "    return \", \".join(parts)\n",
+        "\n",
+        "# Process every tar file\n",
+        "all_rows = []\n",
+        "\n",
+        "for tar_filename in tqdm(tar_files, desc=\"Processing tar files\"):\n",
+        "    print(f\"\\nDownloading {tar_filename} ...\")\n",
+        "    tar_path = hf_hub_download(\n",
+        "        repo_id=REPO_ID,\n",
+        "        filename=tar_filename,\n",
+        "        repo_type=REPO_TYPE,\n",
+        "        token=HF_TOKEN,\n",
+        "        local_dir=\"/content\",\n",
+        "        local_dir_use_symlinks=False\n",
+        "    )\n",
+        "\n",
+        "    with tarfile.open(tar_path, \"r\") as tar:\n",
+        "        for member in tar.getmembers():\n",
+        "            if member.name.endswith(\".json\"):\n",
+        "                f = tar.extractfile(member)\n",
+        "                if f is not None:\n",
+        "                    try:\n",
+        "                        data = json.load(f)\n",
+        "                        description = build_description(data)\n",
+        "                        if description:  # skip completely empty descriptions\n",
+        "                            all_rows.append({\n",
+        "                                \"__key__\": Path(member.name).stem,\n",
+        "                                \"source_tar\": tar_filename,\n",
+        "                                \"description\": description\n",
+        "                            })\n",
+        "                    except Exception as e:\n",
+        "                        print(f\"  Skipping {member.name}: {e}\")\n",
+        "\n",
+        "    # Free disk space after each tar (optional but recommended)\n",
+        "    try:\n",
+        "        os.remove(tar_path)\n",
+        "    except OSError:\n",
+        "        pass\n",
+        "\n",
+        "print(f\"\\nTotal records extracted: {len(all_rows)}\")\n",
+        "\n",
+        "# Create DataFrame\n",
+        "df = pd.DataFrame(all_rows)\n",
+        "\n",
+        "# Remove duplicate descriptions (keep first occurrence)\n",
+        "before = len(df)\n",
+        "df = df.drop_duplicates(subset=[\"description\"], keep=\"first\").reset_index(drop=True)\n",
+        "after = len(df)\n",
+        "print(f\"Removed {before - after} duplicate descriptions. Remaining: {after}\")\n",
+        "\n",
+        "# Save to Drive\n",
+        "output_dir = \"/content/drive/MyDrive/nsfw_video_still_caption\"\n",
+        "os.makedirs(output_dir, exist_ok=True)\n",
+        "\n",
+        "parquet_path = os.path.join(output_dir, \"all_train_descriptions.parquet\")\n",
+        "df.to_parquet(parquet_path, index=False)\n",
+        "\n",
+        "print(f\"\\nSaved parquet with {len(df)} unique descriptions to:\")\n",
+        "print(parquet_path)\n",
+        "print(\"\\nColumns:\", list(df.columns))\n",
+        "print(df.head(10))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Adapted Pipeline – Your parquet → clean → T5 → KMeans → JS TXT → ZIP\n",
+        "\n",
+        "!pip install -q transformers scikit-learn tqdm\n",
+        "\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "\n",
+        "import os\n",
+        "import re\n",
+        "import zipfile\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import torch\n",
+        "from pathlib import Path\n",
+        "from transformers import T5Tokenizer, T5Model\n",
+        "from sklearn.cluster import KMeans\n",
+        "from tqdm.auto import tqdm\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "# ============================================================\n",
+        "# Configuration\n",
+        "# ============================================================\n",
+        "parquet_path = \"/content/drive/MyDrive/nsfw_video_still_caption/all_train_descriptions.parquet\"\n",
+        "t5_model_name = \"t5-base\"                                   #@param [\"t5-small\", \"t5-base\"]\n",
+        "max_tokens = 500                                            #@param {type:\"slider\", min:128, max:512, step:32}\n",
+        "batch_size = 1                                              # keep 1 for safety on Colab\n",
+        "max_clusters = 100                                          # Goal 9 – maximum folders\n",
+        "max_texts_per_file = 1000\n",
+        "min_texts_per_file = 300                                    # merge small clusters\n",
+        "\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "print(f\"Device: {device}\")\n",
+        "\n",
+        "output_base = Path(\"/content/output\")\n",
+        "js_root = output_base / \"js_files\"\n",
+        "os.makedirs(js_root, exist_ok=True)\n",
+        "\n",
+        "# ============================================================\n",
+        "# 0. Load your parquet\n",
+        "# ============================================================\n",
+        "print(f\"Loading: {parquet_path}\")\n",
+        "df = pd.read_parquet(parquet_path)\n",
+        "print(f\"Shape: {df.shape}\")\n",
+        "print(f\"Columns: {df.columns.tolist()}\")\n",
+        "print(df.head(3))\n",
+        "\n",
+        "assert \"description\" in df.columns, \"Column 'description' not found!\"\n",
+        "\n",
+        "# ============================================================\n",
+        "# Helper: Text cleaning (Goals 3 → 6)\n",
+        "# ============================================================\n",
+        "def clean_text(text: str) -> str:\n",
+        "    if not isinstance(text, str) or not text.strip():\n",
+        "        return \"\"\n",
+        "\n",
+        "    text = text.strip()\n",
+        "\n",
+        "    # 4) Replace double quotes with single quotes\n",
+        "    text = text.replace('\"', \"'\")\n",
+        "\n",
+        "    # 5) Ensure commas have one space before and after\n",
+        "    text = re.sub(r'\\s*,\\s*', ' , ', text)\n",
+        "\n",
+        "    # 5) Remove forbidden symbols: { } [ ] | ^ / \\\n",
+        "    text = re.sub(r'[{}\\[\\]\\|\\^/\\\\]', '', text)\n",
+        "\n",
+        "    # Normalize whitespace\n",
+        "    text = re.sub(r'\\s+', ' ', text).strip()\n",
+        "\n",
+        "    # 6) Lowercase everything except start of sentence / start of quote\n",
+        "    def smart_case(s):\n",
+        "        parts = re.split(r'([.!?]\\s+)', s)\n",
+        "        result = []\n",
+        "        capitalize_next = True\n",
+        "\n",
+        "        for part in parts:\n",
+        "            if not part:\n",
+        "                continue\n",
+        "\n",
+        "            if capitalize_next and part and part[0].isalpha():\n",
+        "                part = part[0].upper() + part[1:].lower()\n",
+        "            else:\n",
+        "                part = part.lower()\n",
+        "\n",
+        "            # Capitalize letter after a single quote (e.g. 'Tree)\n",
+        "            part = re.sub(\n",
+        "                r\"(['‘’])([a-z])\",\n",
+        "                lambda m: m.group(1) + m.group(2).upper(),\n",
+        "                part\n",
+        "            )\n",
+        "\n",
+        "            result.append(part)\n",
+        "            capitalize_next = part.endswith(('.', '!', '?')) or bool(re.search(r'[.!?]\\s*$', part))\n",
+        "\n",
+        "        return ''.join(result)\n",
+        "\n",
+        "    return smart_case(text)\n",
+        "\n",
+        "\n",
+        "def prepare_text(row) -> str:\n",
+        "    # Only one text column in your parquet\n",
+        "    return clean_text(str(row.get(\"description\", \"\")))\n",
+        "\n",
+        "# ============================================================\n",
+        "# Prepare cleaned texts\n",
+        "# ============================================================\n",
+        "print(\"\\nCleaning texts...\")\n",
+        "df = df.copy()\n",
+        "df[\"cleaned_text\"] = df.apply(prepare_text, axis=1)\n",
+        "df = df[df[\"cleaned_text\"].str.len() > 10].reset_index(drop=True)\n",
+        "print(f\"Usable rows after cleaning: {len(df)}\")\n",
+        "\n",
+        "if len(df) < 2:\n",
+        "    raise ValueError(\"Not enough usable texts to continue.\")\n",
+        "\n",
+        "texts = df[\"cleaned_text\"].tolist()\n",
+        "\n",
+        "# ============================================================\n",
+        "# Load T5 encoder\n",
+        "# ============================================================\n",
+        "print(f\"\\nLoading {t5_model_name}...\")\n",
+        "tokenizer = T5Tokenizer.from_pretrained(t5_model_name)\n",
+        "model = T5Model.from_pretrained(t5_model_name).to(device)\n",
+        "model.eval()\n",
+        "\n",
+        "def get_t5_embeddings(texts):\n",
+        "    all_embs = []\n",
+        "    for i in tqdm(range(0, len(texts), batch_size), desc=\"Encoding with T5\"):\n",
+        "        batch = texts[i : i + batch_size]\n",
+        "        inputs = tokenizer(\n",
+        "            batch,\n",
+        "            return_tensors=\"pt\",\n",
+        "            padding=True,\n",
+        "            truncation=True,\n",
+        "            max_length=max_tokens,\n",
+        "        ).to(device)\n",
+        "\n",
+        "        with torch.no_grad():\n",
+        "            outputs = model.encoder(**inputs)\n",
+        "            last_hidden = outputs.last_hidden_state\n",
+        "            mask = inputs.attention_mask.unsqueeze(-1).float()\n",
+        "            summed = (last_hidden * mask).sum(dim=1)\n",
+        "            counts = mask.sum(dim=1).clamp(min=1e-9)\n",
+        "            mean_pooled = summed / counts\n",
+        "            all_embs.append(mean_pooled.cpu().numpy())\n",
+        "    return np.vstack(all_embs)\n",
+        "\n",
+        "# ============================================================\n",
+        "# Goal 8: Encode\n",
+        "# ============================================================\n",
+        "print(\"\\nEncoding texts...\")\n",
+        "embeddings = get_t5_embeddings(texts)\n",
+        "print(f\"Embeddings shape: {embeddings.shape}\")\n",
+        "\n",
+        "# ============================================================\n",
+        "# Goal 9: K-Means (max 100 folders)\n",
+        "# ============================================================\n",
+        "k = min(max_clusters, max(2, len(texts) // 8))\n",
+        "k = min(k, len(texts))\n",
+        "print(f\"Using k = {k} clusters\")\n",
+        "\n",
+        "kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
+        "labels = kmeans.fit_predict(embeddings)\n",
+        "df[\"cluster\"] = labels\n",
+        "\n",
+        "# Collect texts per cluster\n",
+        "cluster_texts_list = []\n",
+        "for c in range(k):\n",
+        "    cluster_texts = df.loc[df[\"cluster\"] == c, \"cleaned_text\"].tolist()\n",
+        "    if cluster_texts:\n",
+        "        cluster_texts_list.append(cluster_texts)\n",
+        "\n",
+        "print(f\"Raw clusters created: {len(cluster_texts_list)}\")\n",
+        "\n",
+        "# ============================================================\n",
+        "# Merge small clusters (< 300 items)\n",
+        "# ============================================================\n",
+        "large_clusters = []\n",
+        "small_pool = []\n",
+        "\n",
+        "for texts_list in cluster_texts_list:\n",
+        "    if len(texts_list) >= min_texts_per_file:\n",
+        "        large_clusters.append(texts_list)\n",
+        "    else:\n",
+        "        small_pool.extend(texts_list)\n",
+        "\n",
+        "print(f\"Large clusters (≥{min_texts_per_file}): {len(large_clusters)}\")\n",
+        "print(f\"Texts moved to small pool: {len(small_pool)}\")\n",
+        "\n",
+        "merged_small_clusters = []\n",
+        "if small_pool:\n",
+        "    for start in range(0, len(small_pool), max_texts_per_file):\n",
+        "        chunk = small_pool[start : start + max_texts_per_file]\n",
+        "        if (len(chunk) < min_texts_per_file and merged_small_clusters\n",
+        "                and len(merged_small_clusters[-1]) + len(chunk) <= max_texts_per_file):\n",
+        "            merged_small_clusters[-1].extend(chunk)\n",
+        "        else:\n",
+        "            merged_small_clusters.append(chunk)\n",
+        "\n",
+        "final_folders = large_clusters + merged_small_clusters\n",
+        "print(f\"Final folders after merging: {len(final_folders)}\")\n",
+        "\n",
+        "# ============================================================\n",
+        "# Helper: write one JS file\n",
+        "# ============================================================\n",
+        "def write_js_file(cluster_dir, gname, folder_id, part_idx, chunk):\n",
+        "    dataset_str = \"{\" + \"|\".join(chunk) + \"}\"\n",
+        "\n",
+        "    content = f\"\"\"//start\n",
+        "group = \"{gname}\"\n",
+        "folder_id = \"{folder_id}\"\n",
+        "file_index = {part_idx + 1}\n",
+        "name = \"nsfw_video_still_caption\"\n",
+        "emoji = \"🎬\"\n",
+        "showComments = true\n",
+        "showDemo = true\n",
+        "\n",
+        "pageTitle = [emoji] fusion-t2i-[name]-[group]-[folder_id]-[file_index]\n",
+        "pageDescription = Captioned works from nsfw_video_still_caption parquet – {gname} – {folder_id}\n",
+        "\n",
+        "tagBlackListRaw = [\"\"]\n",
+        "tagBlackList = [tagBlackListRaw.replaceText(' ' , ',')]\n",
+        "\n",
+        "// Generator browse destinations\n",
+        "backward = []\n",
+        "forward = []\n",
+        "backward_em = {{emoji}}\n",
+        "forward_em = {{emoji}}\n",
+        "numItemsStart = 1\n",
+        "//----//\n",
+        "\n",
+        "image_url = [\"https://www.iguides.ru/upload/iblock/ccf/ccf3e83b95a8b350d5089424687fc133.jpg\"]\n",
+        "image_description = [\"placeholder\"]\n",
+        "optional_image_caption = [\"placeholder\"]\n",
+        "\n",
+        "$meta\n",
+        "  title = [emoji] [name] [group] [folder_id] [file_index]\n",
+        "  description = Subset of {gname} – {folder_id} – part {part_idx+1}\n",
+        "  image = https://i.imgur.com/TPHIzaa.jpeg\n",
+        "$output\n",
+        "  [dataset]\n",
+        "\n",
+        "data = [(($output.selectMany(document.numItems).join(\" \")+' '))]\n",
+        "\n",
+        "dataset\n",
+        "  [\"{dataset_str}\"]\n",
+        "//----//\n",
+        "\"\"\"\n",
+        "\n",
+        "    filename = f\"fusion-t2i-nsfw_video_still_caption-{gname}-{folder_id}-part{part_idx+1:02d}.txt\"\n",
+        "    with open(cluster_dir / filename, \"w\", encoding=\"utf-8\") as f:\n",
+        "        f.write(content)\n",
+        "\n",
+        "# ============================================================\n",
+        "# Write the files (single group because we have no rating/solo columns)\n",
+        "# ============================================================\n",
+        "gname = \"all_descriptions\"\n",
+        "group_dir = js_root / gname\n",
+        "os.makedirs(group_dir, exist_ok=True)\n",
+        "\n",
+        "for folder_idx, folder_texts in enumerate(final_folders):\n",
+        "    folder_id = f\"cluster_{folder_idx:03d}\"\n",
+        "    cluster_dir = group_dir / folder_id\n",
+        "    os.makedirs(cluster_dir, exist_ok=True)\n",
+        "\n",
+        "    for part_idx, start in enumerate(range(0, len(folder_texts), max_texts_per_file)):\n",
+        "        chunk = folder_texts[start : start + max_texts_per_file]\n",
+        "        write_js_file(cluster_dir, gname, folder_id, part_idx, chunk)\n",
+        "\n",
+        "print(f\"\\n→ Saved under {group_dir}\")\n",
+        "\n",
+        "# ============================================================\n",
+        "# Goal 11: Create ZIP\n",
+        "# ============================================================\n",
+        "zip_path = \"/content/output/nsfw_video_still_caption_js_files.zip\"\n",
+        "\n",
+        "with zipfile.ZipFile(zip_path, \"w\", zipfile.ZIP_DEFLATED) as zipf:\n",
+        "    for root, dirs, files in os.walk(js_root):\n",
+        "        for file in files:\n",
+        "            if file.endswith(\".txt\"):\n",
+        "                full = os.path.join(root, file)\n",
+        "                arcname = os.path.relpath(full, js_root)\n",
+        "                zipf.write(full, arcname=arcname)\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*70)\n",
+        "print(\"✅ ALL DONE\")\n",
+        "print(f\"ZIP file: {zip_path}\")\n",
+        "print(\"Structure inside ZIP:\")\n",
+        "print(\"  all_descriptions/\")\n",
+        "print(\"    cluster_000/\")\n",
+        "print(\"      fusion-t2i-....txt\")\n",
+        "print(\"    cluster_001/\")\n",
+        "print(\"      ...\")\n",
+        "print(\"=\"*70)\n",
+        "print(\"Download the ZIP from the Colab file browser (left panel) or copy it to Drive.\")"
+      ],
+      "metadata": {
+        "id": "FGBfwwUTBcJY"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}