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   "source": [
    "Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install pillow datasets pandas pypng uuid\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preproccessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import uuid\n",
    "import shutil\n",
    "\n",
    "def rename_and_move_images(source_dir, target_dir):\n",
    "    # Create the target directory if it doesn't exist\n",
    "    os.makedirs(target_dir, exist_ok=True)\n",
    "\n",
    "    # List of common image file extensions\n",
    "    image_extensions = ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff')\n",
    "\n",
    "    # Walk through the source directory and its subdirectories\n",
    "    for root, dirs, files in os.walk(source_dir):\n",
    "        for file in files:\n",
    "            # Check if the file has an image extension\n",
    "            if file.lower().endswith(image_extensions):\n",
    "                # Generate a new filename with UUID\n",
    "                new_filename = str(uuid.uuid4()) + os.path.splitext(file)[1]\n",
    "                \n",
    "                # Construct full file paths\n",
    "                old_path = os.path.join(root, file)\n",
    "                new_path = os.path.join(target_dir, new_filename)\n",
    "                \n",
    "                # Move and rename the file\n",
    "                shutil.move(old_path, new_path)\n",
    "                print(f\"Moved and renamed: {old_path} -> {new_path}\")\n",
    "\n",
    "# Usage\n",
    "source_directory = \"images\"\n",
    "target_directory = \"train\"\n",
    "\n",
    "rename_and_move_images(source_directory, target_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import png\n",
    "import pandas as pd\n",
    "\n",
    "# Directory containing images\n",
    "image_dir = 'train'\n",
    "metadata_list = []\n",
    "\n",
    "# Function to extract the JSON data from the tEXt chunk in PNG images\n",
    "def extract_metadata_from_png(image_path):\n",
    "    with open(image_path, 'rb') as f:\n",
    "        reader = png.Reader(file=f)\n",
    "        chunks = reader.chunks()\n",
    "        for chunk_type, chunk_data in chunks:\n",
    "            if chunk_type == b'tEXt':\n",
    "                # Convert bytes to string\n",
    "                chunk_text = chunk_data.decode('latin1')\n",
    "                if 'prompt' in chunk_text:\n",
    "                    try:\n",
    "                        # Extract JSON string after \"prompt\\0\"\n",
    "                        json_str = chunk_text.split('prompt\\0', 1)[1]\n",
    "                        json_data = json.loads(json_str)\n",
    "                        inputs = json_data.get('3', {}).get('inputs', {})\n",
    "                        seed = inputs.get('seed', 'N/A')\n",
    "                        positive_prompt = json_data.get('6', {}).get('inputs', {}).get('text', 'N/A')\n",
    "                        negative_prompt = json_data.get('7', {}).get('inputs', {}).get('text', 'N/A')\n",
    "                        model = json_data.get('4', {}).get('inputs', {}).get('ckpt_name', 'N/A')\n",
    "                        steps = inputs.get('steps', 'N/A')\n",
    "                        cfg = inputs.get('cfg', 'N/A')\n",
    "                        sampler_name = inputs.get('sampler_name', 'N/A')\n",
    "                        scheduler = inputs.get('scheduler', 'N/A')\n",
    "                        denoise = inputs.get('denoise', 'N/A')\n",
    "                        return {\n",
    "                            'seed': seed,\n",
    "                            'positive_prompt': positive_prompt,\n",
    "                            'negative_prompt': negative_prompt,\n",
    "                            'model': model,\n",
    "                            'steps': steps,\n",
    "                            'cfg': cfg,\n",
    "                            'sampler_name': sampler_name,\n",
    "                            'scheduler': scheduler,\n",
    "                            'denoise': denoise\n",
    "                        }\n",
    "                    except json.JSONDecodeError:\n",
    "                        pass\n",
    "    return {}\n",
    "\n",
    "# Loop through all images in the directory\n",
    "for file_name in os.listdir(image_dir):\n",
    "    if file_name.endswith('.png'):\n",
    "        image_path = os.path.join(image_dir, file_name)\n",
    "        metadata = extract_metadata_from_png(image_path)\n",
    "        metadata['file_name'] = file_name\n",
    "        metadata_list.append(metadata)\n",
    "\n",
    "# Convert metadata to DataFrame\n",
    "metadata_df = pd.DataFrame(metadata_list)\n",
    "\n",
    "# Ensure 'file_name' is the first column\n",
    "columns_order = ['file_name', 'seed', 'positive_prompt', 'negative_prompt', 'model', 'steps', 'cfg', 'sampler_name', 'scheduler', 'denoise']\n",
    "metadata_df = metadata_df[columns_order]\n",
    "\n",
    "# Save metadata to a CSV file\n",
    "metadata_csv_path = 'train/metadata.csv'\n",
    "metadata_df.to_csv(metadata_csv_path, index=False)\n",
    "\n",
    "print(\"Metadata extraction complete. Metadata saved to:\", metadata_csv_path)\n",
    "\n",
    "\n"
   ]
  }
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