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install -q pandas pyarrow pillow huggingface_hub tqdm\n", + "\n", + "# ============================================================\n", + "# 1. AUTHENTICATION\n", + "# ============================================================\n", + "\n", + "from google.colab import userdata\n", + "from huggingface_hub import login, hf_hub_download, HfApi\n", + "\n", + "HF_TOKEN = userdata.get(\"HF_TOKEN\")\n", + "\n", + "if not HF_TOKEN:\n", + " raise ValueError(\"HF_TOKEN not found in Colab Secrets.\")\n", + "\n", + "login(token=HF_TOKEN)\n", + "\n", + "# ============================================================\n", + "# 2. IMPORTS\n", + "# ============================================================\n", + "\n", + "import os\n", + "import gc\n", + "import json\n", + "import base64\n", + "import random\n", + "import io\n", + "import html\n", + "import re\n", + "\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd\n", + "\n", + "from PIL import Image\n", + "from tqdm.auto import tqdm\n", + "\n", + "from IPython.display import display, HTML\n", + "\n", + "# ============================================================\n", + "# 3. CONFIGURATION\n", + "# ============================================================\n", + "\n", + "REPO_ID = \"codeShare/adcom_captioned_ai_images\"\n", + "REPO_TYPE = \"dataset\"\n", + "\n", + "PARQUET_FILE = \"captions.parquet\"\n", + "\n", + "NUM_SAMPLES = 20\n", + "\n", + "#@markdown Include only author Adcom\n", + "ADCOM = True #@param {type:'boolean'}\n", + "\n", + "#@markdown Generate a new random seed every run\n", + "RANDOMIZE_SEED = False #@param {type:'boolean'}\n", + "\n", + "#@markdown Seed value (used when randomization is disabled)\n", + "RANDOM_SEED = \"123\" #@param {type:'string'}\n", + "\n", + "# Resolve the actual seed.\n", + "if RANDOMIZE_SEED:\n", + " RANDOM_SEED = random.SystemRandom().randint(0, 2**32 - 1)\n", + "else:\n", + " try:\n", + " RANDOM_SEED = int(RANDOM_SEED.strip())\n", + " except (ValueError, AttributeError):\n", + " raise ValueError(\n", + " \"RANDOM_SEED must be a valid integer string, \"\n", + " \"for example '42' or '12345'.\"\n", + " )\n", + "\n", + "print(f\"Active random seed: {RANDOM_SEED}\")\n", + "\n", + "# Display\n", + "GRID_MIN_WIDTH = 300\n", + "IMAGE_HEIGHT = 260\n", + "\n", + "PREVIEW_MAX_SIZE = (640, 640)\n", + "PREVIEW_QUALITY = 72\n", + "\n", + "# Output\n", + "OUTPUT_DIR = Path(\"/content/sampled_images\")\n", + "IMAGE_DIR = OUTPUT_DIR / \"images\"\n", + "PREVIEW_DIR = OUTPUT_DIR / \"previews\"\n", + "\n", + "for folder in (OUTPUT_DIR, IMAGE_DIR, PREVIEW_DIR):\n", + " folder.mkdir(parents=True, exist_ok=True)\n", + "\n", + "random.seed(RANDOM_SEED)\n", + "\n", + "api = HfApi(token=HF_TOKEN)\n", + "\n", + "print(\"Configuration ready.\")\n", + "\n", + "# ============================================================\n", + "# 4. DOWNLOAD INDEXED PARQUET\n", + "# ============================================================\n", + "\n", + "print(\"Downloading metadata index...\")\n", + "\n", + "parquet_path = hf_hub_download(\n", + " repo_id=REPO_ID,\n", + " filename=PARQUET_FILE,\n", + " repo_type=REPO_TYPE,\n", + " token=HF_TOKEN\n", + ")\n", + "\n", + "# Read metadata.\n", + "metadata = pd.read_parquet(parquet_path)\n", + "\n", + "required_columns = {\n", + " \"idx\",\n", + " \"prompt\",\n", + " \"tags\",\n", + " \"author\",\n", + " \"json_file\",\n", + " \"byte_offset\",\n", + " \"byte_length\"\n", + "}\n", + "\n", + "missing_columns = required_columns - set(metadata.columns)\n", + "\n", + "if missing_columns:\n", + " raise RuntimeError(\n", + " \"The indexed Parquet columns are missing: \"\n", + " + \", \".join(sorted(missing_columns))\n", + " + \"\\n\\nThe repository migration must finish before \"\n", + " \"using this fast showcase.\"\n", + " )\n", + "\n", + "metadata = metadata[\n", + " [\n", + " \"idx\",\n", + " \"prompt\",\n", + " \"tags\",\n", + " \"author\",\n", + " \"json_file\",\n", + " \"byte_offset\",\n", + " \"byte_length\"\n", + " ]\n", + "].copy()\n", + "\n", + "metadata[\"idx\"] = metadata[\"idx\"].astype(str)\n", + "\n", + "metadata[\"author\"] = (\n", + " metadata[\"author\"]\n", + " .fillna(\"\")\n", + " .astype(str)\n", + " .str.strip()\n", + ")\n", + "\n", + "metadata[\"json_file\"] = (\n", + " metadata[\"json_file\"]\n", + " .fillna(\"\")\n", + " .astype(str)\n", + ")\n", + "\n", + "metadata = metadata.drop_duplicates(\n", + " subset=\"idx\",\n", + " keep=\"first\"\n", + ").reset_index(drop=True)\n", + "\n", + "print(f\"Metadata records: {len(metadata):,}\")\n", + "\n", + "# ============================================================\n", + "# 5. FILTER AND SAMPLE\n", + "# ============================================================\n", + "\n", + "if ADCOM:\n", + " filtered_metadata = metadata[\n", + " metadata[\"author\"].str.casefold() == \"adcom\"\n", + " ]\n", + " selected_author = \"Adcom\"\n", + "else:\n", + " filtered_metadata = metadata\n", + " selected_author = \"All authors\"\n", + "\n", + "print(f\"\\nAuthor filter: {selected_author}\")\n", + "print(f\"Available records: {len(filtered_metadata):,}\")\n", + "\n", + "if filtered_metadata.empty:\n", + " raise RuntimeError(\"No records found for this author.\")\n", + "\n", + "sampled = filtered_metadata.sample(\n", + " n=min(NUM_SAMPLES, len(filtered_metadata)),\n", + " random_state=RANDOM_SEED\n", + ").reset_index(drop=True)\n", + "\n", + "del metadata, filtered_metadata\n", + "gc.collect()\n", + "\n", + "print(f\"Selected {len(sampled)} images.\")\n", + "\n", + "# ============================================================\n", + "# 6. IMAGE DECODING\n", + "# ============================================================\n", + "\n", + "def decode_image_data(image_value):\n", + " \"\"\"\n", + " Decode a base64 image data URI or raw base64 string.\n", + " Returns the original image bytes and a loaded RGB PIL image.\n", + " \"\"\"\n", + "\n", + " if not isinstance(image_value, str):\n", + " return None, None\n", + "\n", + " value = image_value.strip()\n", + "\n", + " if value.startswith(\"data:\") and \",\" in value:\n", + " value = value.split(\",\", 1)[1]\n", + "\n", + " value = re.sub(r\"\\s+\", \"\", value)\n", + "\n", + " try:\n", + " image_bytes = base64.b64decode(\n", + " value,\n", + " validate=False\n", + " )\n", + "\n", + " image = Image.open(io.BytesIO(image_bytes))\n", + " image.load()\n", + " image = image.convert(\"RGB\")\n", + "\n", + " return image_bytes, image\n", + "\n", + " except Exception:\n", + " return None, None\n", + "\n", + "\n", + "# ============================================================\n", + "# 7. READ ONLY THE REQUIRED JSONL SHARDS\n", + "# ============================================================\n", + "\n", + "found_images = {}\n", + "\n", + "# Only download shards containing sampled records.\n", + "shard_names = (\n", + " sampled[\"json_file\"]\n", + " .dropna()\n", + " .loc[lambda s: s.str.len() > 0]\n", + " .drop_duplicates()\n", + " .tolist()\n", + ")\n", + "\n", + "print(f\"\\nShards needed for this sample: {len(shard_names)}\")\n", + "\n", + "if not shard_names:\n", + " raise RuntimeError(\"No shard paths were found in the index.\")\n", + "\n", + "# Process one shard at a time.\n", + "for shard_name in tqdm(shard_names, desc=\"Downloading required shards\"):\n", + "\n", + " shard_rows = sampled[\n", + " sampled[\"json_file\"] == shard_name\n", + " ]\n", + "\n", + " print(f\"\\nReading: {shard_name}\")\n", + "\n", + " local_shard = hf_hub_download(\n", + " repo_id=REPO_ID,\n", + " filename=shard_name,\n", + " repo_type=REPO_TYPE,\n", + " token=HF_TOKEN\n", + " )\n", + "\n", + " with open(local_shard, \"rb\") as shard_file:\n", + "\n", + " for record in tqdm(\n", + " shard_rows.itertuples(index=False),\n", + " total=len(shard_rows),\n", + " desc=\"Extracting sampled images\",\n", + " leave=False\n", + " ):\n", + "\n", + " idx = str(record.idx)\n", + "\n", + " try:\n", + " offset = int(record.byte_offset)\n", + " length = int(record.byte_length)\n", + "\n", + " if offset < 0 or length <= 0:\n", + " raise ValueError(\"Invalid byte offset or length.\")\n", + "\n", + " # Jump directly to the selected JSONL record.\n", + " shard_file.seek(offset)\n", + " raw_record = shard_file.read(length)\n", + "\n", + " if len(raw_record) != length:\n", + " raise ValueError(\"Incomplete record read from shard.\")\n", + "\n", + " image_record = json.loads(\n", + " raw_record.decode(\"utf-8\")\n", + " )\n", + "\n", + " # Confirm the index points to the expected image.\n", + " if str(image_record.get(\"idx\")) != idx:\n", + " raise ValueError(\n", + " \"Record ID does not match the Parquet index.\"\n", + " )\n", + "\n", + " image_bytes, image = decode_image_data(\n", + " image_record.get(\"image\")\n", + " )\n", + "\n", + " if image is None:\n", + " raise ValueError(\"Could not decode image.\")\n", + "\n", + " # Save a full-size JPEG copy.\n", + " image_path = IMAGE_DIR / f\"{idx}.jpg\"\n", + "\n", + " image.save(\n", + " image_path,\n", + " format=\"JPEG\",\n", + " quality=90,\n", + " optimize=False\n", + " )\n", + "\n", + " found_images[idx] = image_path\n", + "\n", + " # Create a preview from the loaded image.\n", + " image.thumbnail(\n", + " PREVIEW_MAX_SIZE,\n", + " Image.Resampling.LANCZOS\n", + " )\n", + "\n", + " preview_path = PREVIEW_DIR / f\"{idx}.jpg\"\n", + "\n", + " image.save(\n", + " preview_path,\n", + " format=\"JPEG\",\n", + " quality=PREVIEW_QUALITY,\n", + " optimize=False\n", + " )\n", + "\n", + " image.close()\n", + "\n", + " except Exception as error:\n", + " print(f\"Could not extract image {idx}: {error}\")\n", + "\n", + " # Release the shard reference before moving to the next one.\n", + " del local_shard\n", + " gc.collect()\n", + "\n", + "# ============================================================\n", + "# 8. REPORT EXTRACTION RESULTS\n", + "# ============================================================\n", + "\n", + "selected_ids = set(sampled[\"idx\"].tolist())\n", + "found_ids = set(found_images.keys())\n", + "missing_ids = selected_ids - found_ids\n", + "\n", + "print(\"\\nImage extraction completed.\")\n", + "print(f\"Requested: {len(selected_ids)}\")\n", + "print(f\"Found: {len(found_ids)}\")\n", + "print(f\"Missing: {len(missing_ids)}\")\n", + "\n", + "if missing_ids:\n", + " print(\"\\nMissing IDs:\")\n", + " for idx in sorted(missing_ids)[:20]:\n", + " print(\" \", idx)\n", + "\n", + "if not found_images:\n", + " raise RuntimeError(\"No sampled images could be extracted.\")\n", + "\n", + "# ============================================================\n", + "# 9. PREPARE HTML PREVIEWS\n", + "# ============================================================\n", + "\n", + "display_df = sampled[\n", + " sampled[\"idx\"].isin(found_ids)\n", + "].copy()\n", + "\n", + "image_data_uris = []\n", + "\n", + "for record in tqdm(\n", + " display_df.itertuples(index=False),\n", + " total=len(display_df),\n", + " desc=\"Preparing previews\"\n", + "):\n", + "\n", + " idx = str(record.idx)\n", + " preview_path = PREVIEW_DIR / f\"{idx}.jpg\"\n", + "\n", + " try:\n", + " with open(preview_path, \"rb\") as f:\n", + " encoded = base64.b64encode(\n", + " f.read()\n", + " ).decode(\"ascii\")\n", + "\n", + " tags = record.tags\n", + "\n", + " if tags is None:\n", + " tags_text = \"\"\n", + " else:\n", + " try:\n", + " tags_text = \"\" if pd.isna(tags) else str(tags)\n", + " except (TypeError, ValueError):\n", + " tags_text = str(tags)\n", + "\n", + " prompt = \"\" if record.prompt is None else str(record.prompt)\n", + " author = \"\" if record.author is None else str(record.author)\n", + "\n", + " image_data_uris.append({\n", + " \"idx\": idx,\n", + " \"uri\": f\"data:image/jpeg;base64,{encoded}\",\n", + " \"prompt\": prompt,\n", + " \"tags\": tags_text,\n", + " \"author\": author\n", + " })\n", + "\n", + " except Exception as error:\n", + " print(f\"Preview error for {idx}: {error}\")\n", + "\n", + "if not image_data_uris:\n", + " raise RuntimeError(\"No previews could be prepared.\")\n", + "\n", + "# ============================================================\n", + "# 10. BUILD HTML CARDS\n", + "# ============================================================\n", + "\n", + "cards = []\n", + "\n", + "for item in image_data_uris:\n", + "\n", + " idx = html.escape(item[\"idx\"])\n", + " author = html.escape(item[\"author\"])\n", + " prompt_html = html.escape(item[\"prompt\"])\n", + " tags_html = html.escape(item[\"tags\"])\n", + "\n", + " prompt_b64 = base64.b64encode(\n", + " item[\"prompt\"].encode(\"utf-8\")\n", + " ).decode(\"ascii\")\n", + "\n", + " card = f\"\"\"\n", + "