{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af14ba04-bbce-47e4-8d84-3347582b50b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = \"./DATA/\"\n",
    "META_DATA = f\"{DATA}images.csv/\"\n",
    "IMAGES = f\"{DATA}images_compressed/\"\n",
    "\n",
    "hf_token = \"\"\n",
    "model_name = \"meta-llama/Llama-3.2-11b-Vision-Instruct\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "544c6687-e174-4490-b221-4b3fbed080b3",
   "metadata": {},
   "source": [
    "### Clean Corrupt Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "36c2a0b8-2953-4194-8cc0-e7338a8865fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corrupt images:\n",
      "./DATA/images_compressed/d028580f-9a98-4fb5-a6c9-5dc362ad3f09.jpg\n",
      "./DATA/images_compressed/784d67d4-b95e-4abb-baf7-8024f18dc3c8.jpg\n",
      "./DATA/images_compressed/b72ed5cd-9f5f-49a7-b12e-63a078212a17.jpg\n",
      "./DATA/images_compressed/1d0129a1-f29a-4a3f-b103-f651176183eb.jpg\n",
      "./DATA/images_compressed/c60e486d-10ed-4f64-abab-5bb698c736dd.jpg\n",
      "./DATA/images_compressed/040d73b7-21b5-4cf2-84fc-e1a80231b202.jpg\n",
      "Total corrupt images found: 6\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "from concurrent.futures import ProcessPoolExecutor\n",
    "import multiprocessing\n",
    "\n",
    "def is_image_corrupt(image_path):\n",
    "    try:\n",
    "        with Image.open(image_path) as img:\n",
    "            img.verify()\n",
    "        return False\n",
    "    except (IOError, SyntaxError, Image.UnidentifiedImageError):\n",
    "        return True\n",
    "\n",
    "def find_corrupt_images(folder_path):\n",
    "    image_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) \n",
    "                   if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
    "    \n",
    "    num_cores = multiprocessing.cpu_count()\n",
    "    with ProcessPoolExecutor(max_workers=num_cores) as executor:\n",
    "        results = executor.map(is_image_corrupt, image_files)\n",
    "    \n",
    "    corrupt_images = [img for img, is_corrupt in zip(image_files, results) if is_corrupt]\n",
    "    return corrupt_images\n",
    "\n",
    "\n",
    "folder_path = IMAGES  # Replace with your folder path\n",
    "corrupt_images = find_corrupt_images(folder_path)\n",
    "\n",
    "print(\"Corrupt images:\")\n",
    "for img in corrupt_images:\n",
    "    print(img)\n",
    "print(f\"Total corrupt images found: {len(corrupt_images)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b0ba27eb-2c5b-447e-9264-bb8db890bc12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./DATA/images_compressed/d028580f-9a98-4fb5-a6c9-5dc362ad3f09.jpg',\n",
       " './DATA/images_compressed/784d67d4-b95e-4abb-baf7-8024f18dc3c8.jpg',\n",
       " './DATA/images_compressed/b72ed5cd-9f5f-49a7-b12e-63a078212a17.jpg',\n",
       " './DATA/images_compressed/1d0129a1-f29a-4a3f-b103-f651176183eb.jpg',\n",
       " './DATA/images_compressed/c60e486d-10ed-4f64-abab-5bb698c736dd.jpg',\n",
       " './DATA/images_compressed/040d73b7-21b5-4cf2-84fc-e1a80231b202.jpg']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrupt_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "05c65335-ad2f-4735-a25b-d75adb195113",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>sender_id</th>\n",
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       "      <th>kids</th>\n",
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       "      <th>1</th>\n",
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       "      <td>148</td>\n",
       "      <td>T-Shirt</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>94</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3b86d877-2b9e-4c8b-a6a2-1d87513309d0</td>\n",
       "      <td>189</td>\n",
       "      <td>Shoes</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>5399</th>\n",
       "      <td>befa14be-8140-4faf-8061-1039947e329d</td>\n",
       "      <td>204</td>\n",
       "      <td>Body</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5400</th>\n",
       "      <td>5379356a-40ee-4890-b416-2336a7d84061</td>\n",
       "      <td>310</td>\n",
       "      <td>Shorts</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5401</th>\n",
       "      <td>65507fb8-3456-4c15-b53e-d1b03bf71a59</td>\n",
       "      <td>204</td>\n",
       "      <td>Shoes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5402</th>\n",
       "      <td>32b99302-cec7-4dec-adfa-3d4029674209</td>\n",
       "      <td>204</td>\n",
       "      <td>Skirt</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5403 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     image  sender_id     label   kids\n",
       "0     4285fab0-751a-4b74-8e9b-43af05deee22        124  Not sure  False\n",
       "1     ea7b6656-3f84-4eb3-9099-23e623fc1018        148   T-Shirt  False\n",
       "2     00627a3f-0477-401c-95eb-92642cbe078d         94  Not sure  False\n",
       "3     ea2ffd4d-9b25-4ca8-9dc2-bd27f1cc59fa         43   T-Shirt  False\n",
       "4     3b86d877-2b9e-4c8b-a6a2-1d87513309d0        189     Shoes  False\n",
       "...                                    ...        ...       ...    ...\n",
       "5398  dfd4079d-967b-4b3e-8574-fbac11b58103        204    Shorts  False\n",
       "5399  befa14be-8140-4faf-8061-1039947e329d        204      Body   True\n",
       "5400  5379356a-40ee-4890-b416-2336a7d84061        310    Shorts  False\n",
       "5401  65507fb8-3456-4c15-b53e-d1b03bf71a59        204     Shoes  False\n",
       "5402  32b99302-cec7-4dec-adfa-3d4029674209        204     Skirt  False\n",
       "\n",
       "[5403 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./DATA/images.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "761bda90-4d50-4796-b797-a4236b03adbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>sender_id</th>\n",
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       "      <th>kids</th>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5399</th>\n",
       "      <td>befa14be-8140-4faf-8061-1039947e329d</td>\n",
       "      <td>204</td>\n",
       "      <td>Body</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>5400</th>\n",
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       "      <td>Shorts</td>\n",
       "      <td>False</td>\n",
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       "      <td>32b99302-cec7-4dec-adfa-3d4029674209</td>\n",
       "      <td>204</td>\n",
       "      <td>Skirt</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5403 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     image  sender_id     label   kids\n",
       "0     4285fab0-751a-4b74-8e9b-43af05deee22        124  Not sure  False\n",
       "1     ea7b6656-3f84-4eb3-9099-23e623fc1018        148   T-Shirt  False\n",
       "2     00627a3f-0477-401c-95eb-92642cbe078d         94  Not sure  False\n",
       "3     ea2ffd4d-9b25-4ca8-9dc2-bd27f1cc59fa         43   T-Shirt  False\n",
       "4     3b86d877-2b9e-4c8b-a6a2-1d87513309d0        189     Shoes  False\n",
       "...                                    ...        ...       ...    ...\n",
       "5398  dfd4079d-967b-4b3e-8574-fbac11b58103        204    Shorts  False\n",
       "5399  befa14be-8140-4faf-8061-1039947e329d        204      Body   True\n",
       "5400  5379356a-40ee-4890-b416-2336a7d84061        310    Shorts  False\n",
       "5401  65507fb8-3456-4c15-b53e-d1b03bf71a59        204     Shoes  False\n",
       "5402  32b99302-cec7-4dec-adfa-3d4029674209        204     Skirt  False\n",
       "\n",
       "[5403 rows x 4 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dc52ddca-eaf2-4b6b-990b-f22f8c214531",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from PIL import Image as PIL_Image\n",
    "import torch\n",
    "from transformers import MllamaForConditionalGeneration, MllamaProcessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "225a1df9-6a17-4cef-a8f2-db55ca8647a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./DATA/images.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "840693d6-6df3-407e-8090-d1396cbb01c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5403 entries, 0 to 5402\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   image      5403 non-null   object\n",
      " 1   sender_id  5403 non-null   int64 \n",
      " 2   label      5403 non-null   object\n",
      " 3   kids       5403 non-null   bool  \n",
      "dtypes: bool(1), int64(1), object(2)\n",
      "memory usage: 132.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8199d67f-2b60-4a60-8fb0-58ef6b2117d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>sender_id</th>\n",
       "      <th>label</th>\n",
       "      <th>kids</th>\n",
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       "  </thead>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>148</td>\n",
       "      <td>T-Shirt</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00627a3f-0477-401c-95eb-92642cbe078d</td>\n",
       "      <td>94</td>\n",
       "      <td>Not sure</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ea2ffd4d-9b25-4ca8-9dc2-bd27f1cc59fa</td>\n",
       "      <td>43</td>\n",
       "      <td>T-Shirt</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3b86d877-2b9e-4c8b-a6a2-1d87513309d0</td>\n",
       "      <td>189</td>\n",
       "      <td>Shoes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  image  sender_id     label   kids\n",
       "0  4285fab0-751a-4b74-8e9b-43af05deee22        124  Not sure  False\n",
       "1  ea7b6656-3f84-4eb3-9099-23e623fc1018        148   T-Shirt  False\n",
       "2  00627a3f-0477-401c-95eb-92642cbe078d         94  Not sure  False\n",
       "3  ea2ffd4d-9b25-4ca8-9dc2-bd27f1cc59fa         43   T-Shirt  False\n",
       "4  3b86d877-2b9e-4c8b-a6a2-1d87513309d0        189     Shoes  False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3363ce8c-3226-4cb2-8e7f-e0b1ec66c13f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Missing values:\n",
      "image        0\n",
      "sender_id    0\n",
      "label        0\n",
      "kids         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Step 4: Check for missing values\n",
    "print(\"\\nMissing values:\")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fea1f2d8-48c4-4b0e-9790-3427c2517e4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique labels:\n",
      "20\n",
      "\n",
      " Label Distribution:\n",
      "label\n",
      "T-Shirt       1011\n",
      "Longsleeve     699\n",
      "Pants          692\n",
      "Shoes          431\n",
      "Shirt          378\n",
      "Dress          357\n",
      "Outwear        312\n",
      "Shorts         308\n",
      "Not sure       228\n",
      "Hat            171\n",
      "Skirt          155\n",
      "Polo           120\n",
      "Undershirt     118\n",
      "Blazer         109\n",
      "Hoodie         100\n",
      "Body            69\n",
      "Other           67\n",
      "Top             43\n",
      "Blouse          23\n",
      "Skip            12\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nUnique labels:\")\n",
    "print(df['label'].nunique())\n",
    "print(\"\\n Label Distribution:\")\n",
    "print(df['label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5b8bb4cc-b45c-4402-a0d4-316b9b250b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Distribution of kids vs. non-kids images:\n",
      "kids\n",
      "False    0.911901\n",
      "True     0.088099\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nDistribution of kids vs. non-kids images:\")\n",
    "print(df['kids'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "14a86ee1-d419-495b-86b0-7ef193e81b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "df['label'].value_counts().head(20).plot(kind='bar')\n",
    "plt.title('Clothing Labels')\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "48a00d85-011d-4632-af7d-d34c8dee6a2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape: (5403, 4)\n",
      "Cleaned dataset shape: (4927, 3)\n"
     ]
    }
   ],
   "source": [
    "df_no_kids = df[df['kids'] == False]\n",
    "df_cleaned = df_no_kids.drop('kids', axis=1)\n",
    "\n",
    "print(f\"Original dataset shape: {df.shape}\")\n",
    "print(f\"Cleaned dataset shape: {df_cleaned.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4b8bde55-a9a2-48af-a70c-e794408f5676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>sender_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4285fab0-751a-4b74-8e9b-43af05deee22</td>\n",
       "      <td>124</td>\n",
       "      <td>Not sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ea7b6656-3f84-4eb3-9099-23e623fc1018</td>\n",
       "      <td>148</td>\n",
       "      <td>T-Shirt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00627a3f-0477-401c-95eb-92642cbe078d</td>\n",
       "      <td>94</td>\n",
       "      <td>Not sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ea2ffd4d-9b25-4ca8-9dc2-bd27f1cc59fa</td>\n",
       "      <td>43</td>\n",
       "      <td>T-Shirt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3b86d877-2b9e-4c8b-a6a2-1d87513309d0</td>\n",
       "      <td>189</td>\n",
       "      <td>Shoes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5396</th>\n",
       "      <td>9bdac063-6c07-4bfc-a04a-e45224c503df</td>\n",
       "      <td>204</td>\n",
       "      <td>Undershirt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5398</th>\n",
       "      <td>dfd4079d-967b-4b3e-8574-fbac11b58103</td>\n",
       "      <td>204</td>\n",
       "      <td>Shorts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5400</th>\n",
       "      <td>5379356a-40ee-4890-b416-2336a7d84061</td>\n",
       "      <td>310</td>\n",
       "      <td>Shorts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5401</th>\n",
       "      <td>65507fb8-3456-4c15-b53e-d1b03bf71a59</td>\n",
       "      <td>204</td>\n",
       "      <td>Shoes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5402</th>\n",
       "      <td>32b99302-cec7-4dec-adfa-3d4029674209</td>\n",
       "      <td>204</td>\n",
       "      <td>Skirt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4927 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     image  sender_id       label\n",
       "0     4285fab0-751a-4b74-8e9b-43af05deee22        124    Not sure\n",
       "1     ea7b6656-3f84-4eb3-9099-23e623fc1018        148     T-Shirt\n",
       "2     00627a3f-0477-401c-95eb-92642cbe078d         94    Not sure\n",
       "3     ea2ffd4d-9b25-4ca8-9dc2-bd27f1cc59fa         43     T-Shirt\n",
       "4     3b86d877-2b9e-4c8b-a6a2-1d87513309d0        189       Shoes\n",
       "...                                    ...        ...         ...\n",
       "5396  9bdac063-6c07-4bfc-a04a-e45224c503df        204  Undershirt\n",
       "5398  dfd4079d-967b-4b3e-8574-fbac11b58103        204      Shorts\n",
       "5400  5379356a-40ee-4890-b416-2336a7d84061        310      Shorts\n",
       "5401  65507fb8-3456-4c15-b53e-d1b03bf71a59        204       Shoes\n",
       "5402  32b99302-cec7-4dec-adfa-3d4029674209        204       Skirt\n",
       "\n",
       "[4927 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_cleaned\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "99115476-9862-4b92-83f4-dd0145e1ee86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique categories after merging:\n",
      "['Other' 'T-Shirt' 'Shoes' 'Shorts' 'Tops' 'Pants' 'Skirts']\n"
     ]
    }
   ],
   "source": [
    "category_mapping = {\n",
    "    'T-Shirt': 'T-Shirt',\n",
    "    'Shoes': 'Shoes',\n",
    "    'Top': 'Tops',\n",
    "    'Blouse': 'Tops',\n",
    "    'Shirt': 'Tops',\n",
    "    'Polo': 'Tops',\n",
    "    'Longsleeve': 'Tops',\n",
    "    'Pants': 'Pants',\n",
    "    'Jeans': 'Jeans',\n",
    "    'Shorts': 'Shorts',\n",
    "    'Skirt': 'Skirts',\n",
    "    'Dress': 'Skirts',\n",
    "    'Footwear': 'Shoes',\n",
    "    'Outwear': 'Tops',\n",
    "    'Hat': 'Tops',\n",
    "    'Undershirt': 'T-Shirt',\n",
    "    'Body': 'Tops',\n",
    "    'Hoodie': 'Tops',\n",
    "    'Blazer': 'Tops'\n",
    "}\n",
    "\n",
    "df_cleaned['merged_category'] = df_cleaned['label'].map(category_mapping).fillna('Other')\n",
    "\n",
    "# Print the unique categories after merging\n",
    "print(\"Unique categories after merging:\")\n",
    "print(df_cleaned['merged_category'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0e9844bf-45a4-460b-bfc2-7800b920ba55",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "df_cleaned['merged_category'].value_counts().plot(kind='bar')\n",
    "plt.title('Distribution of Merged Clothing Categories')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "43b65158-1865-4535-bba0-610b32811c82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Category counts in the balanced dataset:\n",
      "merged_category\n",
      "Pants      500\n",
      "T-Shirt    500\n",
      "Tops       500\n",
      "Skirts     457\n",
      "Shoes      371\n",
      "Shorts     284\n",
      "Other      271\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3243603/3748658876.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  df_balanced = df_cleaned.groupby('merged_category').apply(balance_category).reset_index(drop=True)\n"
     ]
    }
   ],
   "source": [
    "def balance_category(group):\n",
    "    if len(group) > 500:\n",
    "        return group.sample(n=500, random_state=42)\n",
    "    return group\n",
    "\n",
    "\n",
    "df_balanced = df_cleaned.groupby('merged_category').apply(balance_category).reset_index(drop=True)\n",
    "\n",
    "# Print the count of each category in the balanced dataset\n",
    "print(\"\\nCategory counts in the balanced dataset:\")\n",
    "print(df_balanced['merged_category'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "27a59ae8-adf5-4ad1-9e5d-40dad023483d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced dataset shape: (2883, 4)\n",
      "merged_category\n",
      "Pants      500\n",
      "T-Shirt    500\n",
      "Tops       500\n",
      "Skirts     457\n",
      "Shoes      371\n",
      "Shorts     284\n",
      "Other      271\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Plot the distribution of the balanced dataset\n",
    "plt.figure(figsize=(12, 6))\n",
    "df_balanced['merged_category'].value_counts().plot(kind='bar')\n",
    "plt.title('Distribution of Merged Clothing Categories (Balanced)')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"Balanced dataset shape: {df_balanced.shape}\")\n",
    "print(df_balanced['merged_category'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4883769-292b-40e1-9237-09f354c17225",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the balanced dataset\n",
    "df_balanced.to_csv('balanced_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef86acd6-9cee-4044-ae08-e4d1d46be806",
   "metadata": {},
   "source": [
    "### Removing Corrupt Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b489ed6a-09f3-4628-9f29-29e7e300d9a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5798ee82-e237-4dd4-8a07-7777694a8981",
   "metadata": {},
   "source": [
    "### Labelling these images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "180f9da6-fb85-4aa9-8ff7-b532f9fd6a6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bc738efed2c4fa3a3a5c94430af6300",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = MllamaForConditionalGeneration.from_pretrained(model_name, device_map=\"auto\", torch_dtype=torch.bfloat16, token=hf_token)\n",
    "processor = MllamaProcessor.from_pretrained(model_name, token=hf_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "id": "2d08ea82-d3ed-4fb2-8691-0e1b1e2afa8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!ls {IMAGES}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "id": "8112f7bb-377c-4556-90a6-3e576321c152",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path = f\"{IMAGES}/fac591c6-d884-44bf-9f73-3f9dd8e39c0a.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "id": "c9e95d8f-9bb2-4aba-8abd-c114fe2a3e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image(image_path):\n",
    "    with open(image_path, \"rb\") as f:\n",
    "        return PIL_Image.open(f).convert(\"RGB\")\n",
    "\n",
    "image = get_image(image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "id": "1ba044ea-e45c-43d3-a5d7-fcf0f998026f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x534>"
      ]
     },
     "execution_count": 709,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "id": "1de59227-6042-441b-a1f8-b19ce83f7c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "USER_TEXT = \"\"\"\n",
    "You are an expert fashion captioner, we are writing descriptions of clothes, look at the image closely and write a caption for it.\n",
    "\n",
    "Write the following Title, Size, Category, Gender, Type, Description in JSON FORMAT, PLEASE DO NOT FORGET JSON, I WILL BE VERY SAD AND CRY\n",
    "\n",
    "ALSO START WITH THE JSON AND NOT ANY THING ELSE, FIRST CHAR IN YOUR RESPONSE IS ITS OPENING BRACE, I WILL DRINK CHAI IF YOU FOLLOW THIS\n",
    "\n",
    "FOLLOW THESE STEPS CLOSELY WHEN WRITING THE CAPTION: \n",
    "1. Only start your response with a dictionary like the example below, nothing else, I NEED TO PARSE IT LATER, SO DONT ADD ANYTHING ELSE-IT WILL BREAK MY CODE AND I WILL BE VERY SAD \n",
    "Remember-DO NOT SAY ANYTHING ELSE ABOUT WHAT IS GOING ON, just the opening brace is the first thing in your response nothing else ok?\n",
    "2. REMEMBER TO CLOSE THE DICTIONARY WITH '}'BRACE, IT GOES AFTER THE END OF DESCRIPTION-YOU ALWAYS FORGET IT, THIS WILL CAUSE A FIRE ON A PRODUCTION SERVER BEING USE BY MILLIONS\n",
    "3. If you cant tell the size from image, guess it! its okay but dont literally write that you guessed it\n",
    "4. Do not make the caption very literal, all of these are product photos, DO NOT CAPTION HOW OR WHERE THEY ARE PLACED, FOCUS ON WRITING ABOUT THE PIECE OF CLOTHING\n",
    "5. BE CREATIVE WITH THE DESCRIPTION BUT FOLLOW EVERYTHING CLOSELY FOR STRUCTURE\n",
    "6. Return your answer in dictionary format, see the example below\n",
    "7. Please do NOT add new lines or tabs in the JSON\n",
    "8. I REPEAT DO NOT GIVE ME YOUR EXPLAINATION START WITH THE JSON\n",
    "\n",
    "{\"Title\": \"Title of item of clothing\", \"Size\": {'S', 'M', 'L', 'XL'}, #select one randomly if you cant tell from the image. DO NOT TELL ME YOU ESTIMATE OR GUESSED IT ONLY THE LETTER IS ENOUGH\", Category\":  {T-Shirt, Shoes, Tops, Pants, Jeans, Shorts, Skirts, Shoes, Footwear}, \"Gender\": {M, F, U}, \"Type\": {Casual, Formal, Work Casual, Lounge}, \"Description\": \"Write it here\"}\n",
    "\n",
    "Example: ALWAYS RETURN ANSWERS IN THE DICTIONARY FORMAT BELOW OK?\n",
    "\n",
    "{\"Title\": \"Casual White pant with logo on it\", \"size\": \"L\", \"Category\": \"Jeans\", \"Gender\": \"U\", \"Type\": \"Work Casual\", \"Description\": \"Write it here, this is where your stuff goes\"} \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "id": "ab307328-ad5e-436e-a3d5-30bfb8e24a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "USER_TEXT_OPTION_2 = \"\"\"\n",
    "You are an expert fashion captioner, we are writing descriptions of clothes, look at the image closely and write a caption for it.\n",
    "\n",
    "Write the following Title, Size, Category, Gender, Type, Description in JSON FORMAT, PLEASE DO NOT FORGET JSON,\n",
    "\n",
    "ALSO START WITH THE JSON AND NOT ANY THING ELSE, FIRST CHAR IN YOUR RESPONSE IS ITS OPENING BRACE\n",
    "\n",
    "FOLLOW THESE STEPS CLOSELY WHEN WRITING THE CAPTION: \n",
    "1. Only start your response with a dictionary like the example below, nothing else, I NEED TO PARSE IT LATER, SO DONT ADD ANYTHING ELSE-IT WILL BREAK MY CODE\n",
    "Remember-DO NOT SAY ANYTHING ELSE ABOUT WHAT IS GOING ON, just the opening brace is the first thing in your response nothing else ok?\n",
    "2. REMEMBER TO CLOSE THE DICTIONARY WITH '}'BRACE, IT GOES AFTER THE END OF DESCRIPTION-YOU ALWAYS FORGET IT, THIS WILL CAUSE A LOT OF ISSUES\n",
    "3. If you cant tell the size from image, guess it! its okay but dont literally write that you guessed it\n",
    "4. Do not make the caption very literal, all of these are product photos, DO NOT CAPTION HOW OR WHERE THEY ARE PLACED, FOCUS ON WRITING ABOUT THE PIECE OF CLOTHING\n",
    "5. BE CREATIVE WITH THE DESCRIPTION BUT FOLLOW EVERYTHING CLOSELY FOR STRUCTURE\n",
    "6. Return your answer in dictionary format, see the example below\n",
    "\n",
    "{\"Title\": \"Title of item of clothing\", \"Size\": {'S', 'M', 'L', 'XL'}, #select one randomly if you cant tell from the image. DO NOT TELL ME YOU ESTIMATE OR GUESSED IT ONLY THE LETTER IS ENOUGH\", Category\":  {T-Shirt, Shoes, Tops, Pants, Jeans, Shorts, Skirts, Shoes, Footwear}, \"Gender\": {M, F, U}, \"Type\": {Casual, Formal, Work Casual, Lounge}, \"Description\": \"Write it here\"}\n",
    "\n",
    "Example: ALWAYS RETURN ANSWERS IN THE DICTIONARY FORMAT BELOW OK?\n",
    "\n",
    "{\"Title\": \"Casual White pant with logo on it\", \"size\": \"L\", \"Category\": \"Jeans\", \"Gender\": \"U\", \"Type\": \"Work Casual\", \"Description\": \"Write it here, this is where your stuff goes\"} \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "id": "e6041537-ada0-4b06-92db-725e4da999e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'end_header_id|>\\n\\n{\\n    \"Title\": \"Grey Jeans\",\\n    \"Size\": \"M\",\\n    \"Category\": \"Jeans\",\\n    \"Gender\": \"M\",\\n    \"Type\": \"Casual\",\\n    \"Description\": \"These grey jeans are a stylish and versatile choice for everyday wear. They feature a classic five-pocket design and a straight-leg silhouette, making them perfect for both casual and dressy occasions. The grey color is neutral and timeless, allowing you to pair them with a variety of tops and shoes. The jeans are made from a comfortable and durable fabric, ensuring they can withstand daily wear and tear. Overall, these grey jeans are a great addition to any wardrobe, offering both style and practicality.\"\\n}<|eot_id|>'"
      ]
     },
     "execution_count": 712,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversation = [\n",
    "        {\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": USER_TEXT}]}\n",
    "    ]\n",
    "prompt = processor.apply_chat_template(conversation, add_special_tokens=False, add_generation_prompt=True, tokenize=False)\n",
    "inputs = processor(prompt, image, return_tensors=\"pt\").to(model.device)\n",
    "output = model.generate(**inputs, temperature=1, top_p=0.9, max_new_tokens=512)\n",
    "processor.decode(output[0])[len(prompt):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "id": "b4e2add9-7c5c-435c-8a11-505819fc72b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end_header_id|>\n",
      "\n",
      "{\n",
      "    \"Title\": \"Grey Jeans\",\n",
      "    \"Size\": \"M\",\n",
      "    \"Category\": \"Jeans\",\n",
      "    \"Gender\": \"M\",\n",
      "    \"Type\": \"Casual\",\n",
      "    \"Description\": \"These grey jeans are a stylish and versatile choice for everyday wear. They feature a classic five-pocket design and a straight-leg silhouette, making them perfect for both casual and dressy occasions. The grey color is neutral and timeless, allowing you to pair them with a variety of tops and shoes. The jeans are made from a comfortable and durable fabric, ensuring they can withstand daily wear and tear. Overall, these grey jeans are a great addition to any wardrobe, offering both style and practicality.\"\n",
      "}<|eot_id|>\n"
     ]
    }
   ],
   "source": [
    "print(processor.decode(output[0])[len(prompt):])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "id": "0fad7afe-5018-495f-b064-8494d3baf1b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = processor.decode(output[0])[len(prompt):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "id": "1f2480a3-4856-4ec7-a8fd-45b2f25c5b75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'end_header_id|>\\n\\n{\\n    \"Title\": \"Grey Jeans\",\\n    \"Size\": \"M\",\\n    \"Category\": \"Jeans\",\\n    \"Gender\": \"M\",\\n    \"Type\": \"Casual\",\\n    \"Description\": \"These grey jeans are a stylish and versatile choice for everyday wear. They feature a classic five-pocket design and a straight-leg silhouette, making them perfect for both casual and dressy occasions. The grey color is neutral and timeless, allowing you to pair them with a variety of tops and shoes. The jeans are made from a comfortable and durable fabric, ensuring they can withstand daily wear and tear. Overall, these grey jeans are a great addition to any wardrobe, offering both style and practicality.\"\\n}<|eot_id|>'"
      ]
     },
     "execution_count": 715,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "id": "c02a4c4f-8ad9-4c7d-b2d5-4809ad1e085e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./DATA/images_compressed/'"
      ]
     },
     "execution_count": 716,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "id": "562b119c-ceeb-4487-9169-0de89e5b781d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26f2327cf8db4d6ba2a94a4eae61b217",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [04:54<00:00,  5.89s/it]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'csv' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[717], line 55\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[38;5;66;03m# Write the results to a CSV file\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(output_csv_file_path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m, newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m csvfile:\n\u001b[0;32m---> 55\u001b[0m     writer \u001b[38;5;241m=\u001b[39m csv\u001b[38;5;241m.\u001b[39mwriter(csvfile)\n\u001b[1;32m     56\u001b[0m     writer\u001b[38;5;241m.\u001b[39mwriterow([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilename\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdescription\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n",
      "\u001b[0;31mNameError\u001b[0m: name 'csv' is not defined"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from PIL import Image as PIL_Image\n",
    "import torch\n",
    "from transformers import MllamaForConditionalGeneration, MllamaProcessor\n",
    "from tqdm import tqdm\n",
    "\n",
    "hf_token = \"\"\n",
    "model_name = \"meta-llama/Llama-3.2-11b-Vision-Instruct\"\n",
    "\n",
    "model = MllamaForConditionalGeneration.from_pretrained(model_name, device_map=\"auto\", torch_dtype=torch.bfloat16, token=hf_token)\n",
    "processor = MllamaProcessor.from_pretrained(model_name, token=hf_token)\n",
    "\n",
    "# Define the input folder path\n",
    "input_folder_path = IMAGES\n",
    "\n",
    "# Define the output CSV file path\n",
    "output_csv_file_path = \"./captions.csv\"\n",
    "\n",
    "# Create an empty list to store the results\n",
    "results = []\n",
    "\n",
    "# Loop through the first 50 files in the input folder\n",
    "for filename in tqdm(os.listdir(input_folder_path)[:50], desc=\"Processing files\"):\n",
    "    # Check if the file is an image\n",
    "    if filename.endswith(\".jpg\") or filename.endswith(\".jpeg\") or filename.endswith(\".png\"):\n",
    "        # Get the image path\n",
    "        image_path = os.path.join(input_folder_path, filename)\n",
    "\n",
    "        # Load the image\n",
    "        image = get_image(image_path)\n",
    "\n",
    "        # Create a conversation\n",
    "        conversation = [\n",
    "            {\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": USER_TEXT}]}\n",
    "        ]\n",
    "\n",
    "        # Apply chat template and tokenize\n",
    "        prompt = processor.apply_chat_template(conversation, add_special_tokens=False, add_generation_prompt=True, tokenize=False)\n",
    "        inputs = processor(prompt, image, return_tensors=\"pt\").to(model.device)\n",
    "\n",
    "        # Generate the output\n",
    "        output = model.generate(**inputs, temperature=1, top_p=0.9, max_new_tokens=512)\n",
    "\n",
    "        # Decode the output\n",
    "        decoded_output = processor.decode(output[0])[len(prompt):]\n",
    "\n",
    "        # Append the result to the list\n",
    "        results.append((filename, decoded_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "id": "bdc72764-12cf-4e0f-b6b5-0743f277be71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "# Write the results to a CSV file\n",
    "with open(output_csv_file_path, \"w\", newline=\"\") as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerow([\"filename\", \"description\"])\n",
    "    for result in results:\n",
    "        writer.writerow(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "id": "7dae4854-e7e0-4c8a-92ef-61d35f045cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>d7ed1d64-2c65-427f-9ae4-eb4aaa3e2389.jpg</td>\n",
       "      <td>end_header_id|&gt;\\n\\nThe image features a white,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        filename  \\\n",
       "count                                         50   \n",
       "unique                                        50   \n",
       "top     d7ed1d64-2c65-427f-9ae4-eb4aaa3e2389.jpg   \n",
       "freq                                           1   \n",
       "\n",
       "                                              description  \n",
       "count                                                  50  \n",
       "unique                                                 50  \n",
       "top     end_header_id|>\\n\\nThe image features a white,...  \n",
       "freq                                                    1  "
      ]
     },
     "execution_count": 720,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./captions.csv\")\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "id": "9b3b129e-0a32-4ef6-acd4-297e1419c4c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image(image_path):\n",
    "    try:\n",
    "        with open(image_path, \"rb\") as f:\n",
    "            return PIL_Image.open(f).convert(\"RGB\")\n",
    "    except UnidentifiedImageError:\n",
    "        print(f\"Skipping corrupt image {image_path}\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "id": "2f9e25d8-4164-43f1-9daa-3d3a09d1e7cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c11e92bc68df4eb6880b6bc8452927d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 139/139 [06:58<00:00,  3.01s/it]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from PIL import Image as PIL_Image\n",
    "import torch\n",
    "from transformers import MllamaForConditionalGeneration, MllamaProcessor\n",
    "from tqdm import tqdm\n",
    "\n",
    "hf_token = \"\"\n",
    "model_name = \"meta-llama/Llama-3.2-11b-Vision-Instruct\"\n",
    "\n",
    "model = MllamaForConditionalGeneration.from_pretrained(model_name, device_map=\"cuda:7\", torch_dtype=torch.bfloat16, token=hf_token)\n",
    "processor = MllamaProcessor.from_pretrained(model_name, token=hf_token)\n",
    "\n",
    "# Define the input folder path\n",
    "input_folder_path = IMAGES\n",
    "\n",
    "# Define the output CSV file path\n",
    "output_csv_file_path = \"./second_p2_360_to_500.csv\"\n",
    "# Create an empty list to store the results\n",
    "results = []\n",
    "\n",
    "# Loop through the first 50 files in the input folder\n",
    "for filename in tqdm(os.listdir(input_folder_path)[361:500], desc=\"Processing files\"):\n",
    "    if filename.endswith(\".jpg\") or filename.endswith(\".jpeg\") or filename.endswith(\".png\"):\n",
    "        image_path = os.path.join(input_folder_path, filename)\n",
    "        image = get_image(image_path)\n",
    "        if image is not None:\n",
    "            conversation = [\n",
    "                {\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": USER_TEXT}]}\n",
    "            ]\n",
    "            prompt = processor.apply_chat_template(conversation, add_special_tokens=False, add_generation_prompt=True, tokenize=False)\n",
    "            inputs = processor(prompt, image, return_tensors=\"pt\").to(model.device)\n",
    "            output = model.generate(**inputs, temperature=1, top_p=0.9, max_new_tokens=512)\n",
    "            decoded_output = processor.decode(output[0])[len(prompt):]\n",
    "            results.append((filename, decoded_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "id": "81b88c42-c8c8-41ff-8d1b-9528362651e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "# Write the results to a CSV file\n",
    "with open(output_csv_file_path, \"w\", newline=\"\") as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerow([\"filename\", \"description\"])\n",
    "    for result in results:\n",
    "        writer.writerow(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29f88023-61b1-4861-93e7-bde0f3c90cff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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