{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['HF_TOKEN'] = 'hf_………………………'\n",
    "train_file = 'source_dataset/train_captions.csv'\n",
    "validation_file = 'source_dataset/valid_captions.csv'\n",
    "train_concepts_file = 'source_dataset/train_concepts_manual.csv'\n",
    "validation_concepts_file = 'source_dataset/valid_concepts_manual.csv'\n",
    "test_file = 'source_dataset/test_captions.csv'\n",
    "test_concepts_file = 'source_dataset/test_concepts_manual.csv'\n",
    "dataset_name = 'eltorio/ROCOv2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Login to Hugging Face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hugging Face token found in environment variable\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import login\n",
    "import os\n",
    "\n",
    "HF_TOKEN = \"\"\n",
    "\n",
    "if os.environ.get('HF_TOKEN') is not None:\n",
    "  HF_TOKEN = os.environ.get('HF_TOKEN')\n",
    "  print(f\"Hugging Face token found in environment variable\")\n",
    "try:\n",
    "  import google.colab\n",
    "  from google.colab import userdata\n",
    "  if (userdata.get('HF_TOKEN') is not None) and (HF_TOKEN == \"\"):\n",
    "    HF_TOKEN = userdata.get('HF_TOKEN')\n",
    "  else:\n",
    "    raise ValueError(\"Please set your Hugging Face token in the user data panel, or pass it as an environment variable\")\n",
    "except ModuleNotFoundError:\n",
    "  if HF_TOKEN is None:\n",
    "    raise ValueError(\"Please set your Hugging Face token in the user data panel, or pass it as an environment variable\")\n",
    "\n",
    "login(\n",
    "  token=HF_TOKEN,\n",
    "  add_to_git_credential=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset, Dataset, Image as HFImage, concatenate_datasets\n",
    "import datasets\n",
    "from PIL import Image\n",
    "import pandas as pd\n",
    "import io\n",
    "\n",
    "# load image in the DataFrame\n",
    "def load_image(image_id, image_path='train'):\n",
    "    image_path = os.path.join(f\"source_dataset/{image_path}\", f\"{image_id}.jpg\")\n",
    "    image_jpg= Image.open(image_path)\n",
    "    image_bytes = io.BytesIO()\n",
    "    image_jpg.save(image_bytes, format='PNG')  # Save as PNG\n",
    "    # Replace PIL image with a new PNG image created from the bytes\n",
    "    return image_bytes.getvalue()\n",
    "\n",
    "# Function to apply load_cui with progress tracking\n",
    "def apply_with_progress(df, func, column, nb, image_path='train'):\n",
    "    result = []\n",
    "    for i, value in enumerate(df[column]):\n",
    "        result.append(func(value, image_path))\n",
    "        if (i + 1) % nb == 0:\n",
    "            print(f\"Processed {i + 1} rows\")\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C1306645'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the CUI CSV file into a pandas DataFrame\n",
    "train_concept_unique_identifier_df = pd.read_csv(train_concepts_file)\n",
    "\n",
    "# load CUI to the train_df DataFrame by looking up the CUI in the concept_unique_dentifier_df\n",
    "# concept_unique_dentifier_df is a DataFrame that contains the mapping between the image ID and the CUIs\n",
    "def load_train_cui(image_id, image_path='train'):\n",
    "    cuis = train_concept_unique_identifier_df[train_concept_unique_identifier_df['ID'] == image_id]['CUIs']\n",
    "    split = str(cuis.values[0]).split(';')\n",
    "    return split\n",
    "\n",
    "# Load a CSV file into a pandas DataFrame\n",
    "train_df = pd.read_csv(train_file)\n",
    "train_df.rename(columns={'ID': 'image_id', 'Caption': 'caption'}, inplace=True)\n",
    "train_df['image'] = apply_with_progress(train_df, load_image, 'image_id',100)\n",
    "train_df = train_df[['image', 'image_id', 'caption']]\n",
    "train_df['cui'] = apply_with_progress(train_df, load_train_cui, 'image_id',1000)\n",
    "train_dataset = Dataset.from_pandas(train_df).cast_column(\"image\", HFImage())\n",
    "train_dataset.save_to_disk('train_dataset')\n",
    "# train_dataset.push_to_hub(dataset_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the validation split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51d6b43247154559ab65ddb1cfe9fd95",
       "version_major": 2,
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Load the CUI CSV file into a pandas DataFrame\n",
    "valid_concept_unique_identifier_df = pd.read_csv(validation_concepts_file)\n",
    "\n",
    "def load_valid_cui(image_id, image_path='train'):\n",
    "    cuis = valid_concept_unique_identifier_df[valid_concept_unique_identifier_df['ID'] == image_id]['CUIs']\n",
    "    split = str(cuis.values[0]).split(';')\n",
    "    return split\n",
    "\n",
    "valid_df = pd.read_csv(validation_file)\n",
    "valid_df.rename(columns={'ID': 'image_id', 'Caption': 'caption'}, inplace=True)\n",
    "valid_df['image'] = apply_with_progress(valid_df, load_image, 'image_id',100, 'valid')\n",
    "valid_df = valid_df[['image', 'image_id', 'caption']]\n",
    "valid_df['cui'] = apply_with_progress(valid_df, load_valid_cui, 'image_id',1000)\n",
    "valid_dataset = Dataset.from_pandas(valid_df).cast_column(\"image\", HFImage())\n",
    "valid_dataset.save_to_disk('valid_dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81c64f32e1e143fa8bcabe18df1571a1",
       "version_major": 2,
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Load the CUI CSV file into a pandas DataFrame\n",
    "test_concept_unique_identifier_df = pd.read_csv(test_concepts_file)\n",
    "\n",
    "def load_test_cui(image_id, image_path='train'):\n",
    "    cuis = test_concept_unique_identifier_df[test_concept_unique_identifier_df['ID'] == image_id]['CUIs']\n",
    "    split = str(cuis.values[0]).split(';')\n",
    "    return split\n",
    "\n",
    "\n",
    "test_df = pd.read_csv(test_file)\n",
    "test_df.rename(columns={'ID': 'image_id', 'Caption': 'caption'}, inplace=True)\n",
    "test_df['image'] = apply_with_progress(test_df, load_image, 'image_id',100, 'test')\n",
    "test_df = test_df[['image', 'image_id', 'caption']]\n",
    "test_df['cui'] = apply_with_progress(test_df, load_test_cui, 'image_id',1000)\n",
    "test_dataset = Dataset.from_pandas(test_df).cast_column(\"image\", HFImage())\n",
    "test_dataset.save_to_disk('test_dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Push the datasets to the Hugging Face hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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    {
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      "'(MaxRetryError(\"HTTPSConnectionPool(host='hf-hub-lfs-us-east-1.s3-accelerate.amazonaws.com', port=443): Max retries exceeded with url: /repos/0c/b0/0cb0f6820132af26830970ad86609d1f6804b03c7dd65ababd05546d4cbdab40/e6cb01b149566e971debf7d0bd7508125ae803821611511346b543bda7a3b875?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQLC2QXPN7%2F20241112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20241112T083433Z&X-Amz-Expires=86400&X-Amz-Signature=e77f5d49b7be4a0ede506ef2ba135cae5409cc5e52d3393be040290ea5b5b3cc&X-Amz-SignedHeaders=host&partNumber=6&uploadId=oO1PLiGgJUbmKmebYSE_ddfO1yGN6PM9grmjEKjG35voIKgOYLzCO0wQZfszljmzzygKHNa5cEh3RVhZk3mE26g68sCFbRP411IoI.Ci55F6HYj7MoU.dfus742eOQws&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2417)')))\"), '(Request ID: 192b94c4-25c9-40d3-b796-0ef087c5c758)')' thrown while requesting PUT https://hf-hub-lfs-us-east-1.s3-accelerate.amazonaws.com/repos/0c/b0/0cb0f6820132af26830970ad86609d1f6804b03c7dd65ababd05546d4cbdab40/e6cb01b149566e971debf7d0bd7508125ae803821611511346b543bda7a3b875?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQLC2QXPN7%2F20241112%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20241112T083433Z&X-Amz-Expires=86400&X-Amz-Signature=e77f5d49b7be4a0ede506ef2ba135cae5409cc5e52d3393be040290ea5b5b3cc&X-Amz-SignedHeaders=host&partNumber=6&uploadId=oO1PLiGgJUbmKmebYSE_ddfO1yGN6PM9grmjEKjG35voIKgOYLzCO0wQZfszljmzzygKHNa5cEh3RVhZk3mE26g68sCFbRP411IoI.Ci55F6HYj7MoU.dfus742eOQws&x-id=UploadPart\n",
      "Retrying in 1s [Retry 1/5].\n"
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    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/datasets/eltorio/ROCOv2/commit/49c74bfdc49365b9ac1792f22914211aff5ba534', commit_message='Upload dataset', commit_description='', oid='49c74bfdc49365b9ac1792f22914211aff5ba534', pr_url=None, repo_url=RepoUrl('https://huggingface.co/datasets/eltorio/ROCOv2', endpoint='https://huggingface.co', repo_type='dataset', repo_id='eltorio/ROCOv2'), pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.push_to_hub(dataset_name, split='train')\n",
    "valid_dataset.push_to_hub(dataset_name, split='validation')\n",
    "test_dataset.push_to_hub(dataset_name, split='test')"
   ]
  }
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