{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"dnth/pets-enriched\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'filename': Value(dtype='string', id=None),\n",
       " 'caption': Value(dtype='string', id=None),\n",
       " 'image_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
       " 'objects': [{'bbox': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None),\n",
       "   'confidence': Value(dtype='float64', id=None),\n",
       "   'label': Value(dtype='string', id=None)}]}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train'].features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'filename': 'oxford-iiit-pet/images/Abyssinian_144.jpg',\n",
       " 'caption': 'a cat standing on a wooden floor next to a glass',\n",
       " 'image_labels': ['cat'],\n",
       " 'objects': [{'bbox': [91.0, 13.0, 408.0, 345.0],\n",
       "   'confidence': 0.9800000190734863,\n",
       "   'label': 'cat'}]}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'filename': 'oxford-iiit-pet/images/Abyssinian_100.jpg',\n",
       " 'caption': 'a cat is sitting in a bag',\n",
       " 'image_labels': ['cat'],\n",
       " 'objects': [{'bbox': [48.0, 72.0, 288.0, 371.0],\n",
       "   'confidence': 0.9539999961853027,\n",
       "   'label': 'cat'},\n",
       "  {'bbox': [0.0, 31.0, 148.0, 92.0],\n",
       "   'confidence': 0.4880000054836273,\n",
       "   'label': 'strap'},\n",
       "  {'bbox': [241.0, 341.0, 153.0, 160.0],\n",
       "   'confidence': 0.4309999942779541,\n",
       "   'label': 'strap'},\n",
       "  {'bbox': [193.0, 1.0, 202.0, 179.0],\n",
       "   'confidence': 0.3700000047683716,\n",
       "   'label': 'pillow'}]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train'][10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "import io\n",
    "import datasets\n",
    "\n",
    "def load_image(example):\n",
    "    image_path = example['filename']  # Assuming 'filename' contains the path\n",
    "    if os.path.exists(image_path):\n",
    "        with Image.open(image_path) as img:\n",
    "            buf = io.BytesIO()\n",
    "            img.save(buf, format='PNG')\n",
    "            example['image'] = buf.getvalue()\n",
    "    else:\n",
    "        example['image'] = None\n",
    "    return example\n",
    "\n",
    "# Assuming your dataset is called 'dataset'\n",
    "dataset = dataset.map(load_image)\n",
    "\n",
    "# Update the features of the dataset\n",
    "dataset = dataset.cast_column(\"image\", datasets.Image())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['filename', 'caption', 'image_labels', 'objects', 'image'],\n",
       "    num_rows: 7275\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f35fb0f0588e477b985cc75dfdc00569",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca299917858a4d9e8b2264da5c609be1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1455 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21e2c7755e1c4890b8710a0fc3fb2e25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "05c256e8a8b54c4a8a9c905f7fcf78ab",
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "0aa862e525f8403fa1489c11a08d9626",
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "1ae4a025e0a54f92a7932a2bfe104789",
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da530e1109d6437dbdd39fe40e091724",
       "version_major": 2,
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      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b71da5cc1cb4f12af4b04fc5597e4d1",
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3f3ec986b044612a653fc7e27faa3b0",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8761c776867044e0a39c5be67b5e4536",
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      },
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d64775cae0e46c2b96db080977f7caf",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81348e1aa35f41b9b68e178c9f7fd203",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/478 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/datasets/dnth/pets-enriched/commit/1e0711eae3c228b4bdd45096d28ae167f1ae49e7', commit_message='Upload dataset', commit_description='', oid='1e0711eae3c228b4bdd45096d28ae167f1ae49e7', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.push_to_hub('dnth/pets-enriched')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "421ffbe1dea442bc956d20ccedc539f8",
       "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": [
    "from huggingface_hub import login\n",
    "\n",
    "login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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