{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09f9237a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (4.4.2)\n",
      "Requirement already satisfied: tqdm in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (4.67.1)\n",
      "Requirement already satisfied: pillow in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (12.1.0)\n",
      "Requirement already satisfied: requests in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (2.32.5)\n",
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      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (6.7.0)\n",
      "Requirement already satisfied: propcache>=0.2.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (0.4.1)\n",
      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.22.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from pandas->datasets) (2.9.0.post0)\n",
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      "Requirement already satisfied: click>=8.0.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from typer-slim->huggingface-hub<2.0,>=0.25.0->datasets) (8.3.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets tqdm pillow requests\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f0b2e2c",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "50f55b00a27b4213b573b398e5b0d708",
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       "model_id": "8658b8414f604f0ca2fd248a214ad4aa",
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    {
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       "model_id": "d59b7dea75f84b64bb8b262b43730e51",
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       "model_id": "0c5815040f0a4a31903348a8327811a5",
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
      "        num_rows: 94481\n",
      "    })\n",
      "    val: Dataset({\n",
      "        features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
      "        num_rows: 5905\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
      "        num_rows: 17716\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "from pathlib import Path\n",
    "from PIL import Image\n",
    "import requests\n",
    "\n",
    "# куда сохраняем датасет\n",
    "DATA_DIR = Path(\"../dataset/EmoSet-118K\")\n",
    "DATA_DIR.mkdir(exist_ok=True, parents=True)\n",
    "\n",
    "# загружаем через Hugging Face\n",
    "ds = load_dataset(\"Woleek/EmoSet-118K\")\n",
    "\n",
    "print(ds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "052ab073",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "\n",
    "def save_split(split):\n",
    "    split_dir = DATA_DIR / split\n",
    "    img_dir = split_dir / \"images\"\n",
    "    img_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    labels_path = split_dir / \"labels.csv\"\n",
    "\n",
    "    # перезаписываем labels.csv\n",
    "    with open(labels_path, \"w\") as f:\n",
    "        f.write(\"filename,label\\n\")\n",
    "\n",
    "    for example in tqdm(ds[split]):\n",
    "        img = example[\"image\"]          # уже PIL.Image\n",
    "        label = example[\"emotion\"]\n",
    "        image_id = example[\"image_id\"]\n",
    "\n",
    "        fname = f\"{image_id}.jpg\"\n",
    "        img.save(img_dir / fname)\n",
    "\n",
    "        with open(labels_path, \"a\") as f:\n",
    "            f.write(f\"{fname},{label}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a74ceedf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 94481/94481 [18:43<00:00, 84.10it/s]  \n",
      "100%|██████████| 5905/5905 [01:08<00:00, 86.57it/s] \n",
      "100%|██████████| 17716/17716 [02:57<00:00, 100.01it/s]\n"
     ]
    }
   ],
   "source": [
    "save_split(\"train\")\n",
    "save_split(\"val\")\n",
    "save_split(\"test\")\n"
   ]
  }
 ],
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