{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61582349",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import gc\n",
    "import json\n",
    "import cv2\n",
    "from tqdm.auto import tqdm\n",
    "from HCFA_OCR_XML_to_DataFrame import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2dcbb30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34a48aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "HCFA_final_keys = pd.read_excel(r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_Keys_list_verification.xlsx\", sheet_name = 'Field_Names from KEY file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ead8164",
   "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>Key_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10A_PatConditionEmpN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10A_PatConditionEmpY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10B_PatAAState</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10B_PatConditionAutoN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10B_PatConditionAutoY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>9B_SecInsSexM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>9D_SecInsPlanName</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>31_RenDateSigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>11B_PriInsEmpName</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>24J_RenProvFullName</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Key_Name\n",
       "0     10A_PatConditionEmpN\n",
       "1     10A_PatConditionEmpY\n",
       "2           10B_PatAAState\n",
       "3    10B_PatConditionAutoN\n",
       "4    10B_PatConditionAutoY\n",
       "..                     ...\n",
       "152          9B_SecInsSexM\n",
       "153      9D_SecInsPlanName\n",
       "154       31_RenDateSigned\n",
       "155      11B_PriInsEmpName\n",
       "156    24J_RenProvFullName\n",
       "\n",
       "[157 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HCFA_final_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e60b549b",
   "metadata": {},
   "outputs": [],
   "source": [
    "key_names = HCFA_final_keys['Key_Name'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b3e49b64",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_charges(row):\n",
    "    ground_truth = row[\"Data\"]\n",
    "    return ground_truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f76b9e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Pat_details(row):\n",
    "    if \"?\" not in str(row[\"OCR_Optimizer\"]):\n",
    "        ground_truth = row[\"OCR_Optimizer\"]\n",
    "    else:\n",
    "        if row[\"Data\"] != '[BLANK]':\n",
    "            ground_truth = row[\"Data\"]\n",
    "        else:\n",
    "            ground_truth = row[\"OCR_Optimizer\"].replace(\"?\", \" \")\n",
    "    if ground_truth == ' ':\n",
    "        ground_truth = '[BLANK]'\n",
    "    return ground_truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e5a55f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_ground_truth(row):\n",
    "    ground_truth = '[BLANK]'\n",
    "    try:\n",
    "        if row[\"Field_Name\"] in ['28_TotalCharges','29_AmountPaid', '30_BalanceDue']:\n",
    "            ground_truth = get_charges(row)\n",
    "        elif row[\"Field_Name\"] in['5_PatAddr1', '5_PatCity', '5_PatPostCode', '5_PatState']:\n",
    "            ground_truth = get_Pat_details(row)\n",
    "        else:\n",
    "            if \"?\" not in str(row[\"OCR_Optimizer\"]):\n",
    "                if row[\"OCR_Optimizer\"] != '[BLANK]':\n",
    "                    ground_truth = row[\"OCR_Optimizer\"]\n",
    "                else:\n",
    "                    ground_truth = row[\"Data\"]\n",
    "    except:\n",
    "        ground_truth = row[\"Data\"]\n",
    "    if \"@\" in ground_truth:\n",
    "        ground_truth = '[BLANK]'\n",
    "    return ground_truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "110a36f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_annotation_dict(df):\n",
    "    result_dict = {}\n",
    "    for index, row in df.iterrows():\n",
    "        field_name = row['Field_Name']\n",
    "        gt_value = row['Data']\n",
    "        if field_name in result_dict:\n",
    "            if pd.notna(field_name):\n",
    "                result_dict[field_name] = f\"{result_dict[field_name]}; {gt_value}\"    \n",
    "        else:\n",
    "            result_dict[field_name] = gt_value\n",
    "\n",
    "    return result_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a4eff9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27913\n",
      "BSC134A9O001_001.tiff\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "img_files = os.listdir(r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\train\\img\")\n",
    "print(len(img_files))\n",
    "print(img_files[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "647049d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import xml.etree.ElementTree as ET\n",
    "# xml_file_path = r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\train\\key\\BSC134A9O001_000_HCFA.KEY\"\n",
    "# # Parse the XML file\n",
    "# tree = ET.parse(xml_file_path)\n",
    "# root = tree.getroot()\n",
    "# # Iterate through all <Fld> elements\n",
    "# for fld in root.findall('.//Fld'):\n",
    "#     typ = fld.get('Typ')  # Get the 'Typ' attribute\n",
    "#     if typ == 'SINGLE':  # If Typ is 'SINGLE', follow the previous logic\n",
    "#         nm_element = fld.find('.//Nm')\n",
    "#         if nm_element is not None:\n",
    "#             print(nm_element.text)\n",
    "#     elif typ in ['GROUP', 'TABLE']:  # If Typ is 'GROUP' or 'TABLE', follow the new logic\n",
    "#         clms = fld.findall('.//Clm')\n",
    "#         for clm in clms:\n",
    "#             nm_value = clm.get('Nm')\n",
    "#             if nm_value:\n",
    "#                 print(nm_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48efee4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "json_target_path = r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\donut_data\\train\\key\"\n",
    "images_source_path = r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\train\\img\"\n",
    "images_target_path = r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\donut_data\\train\\img\"\n",
    "Key_dir = r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\train\\key\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e30a7c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(json_target_path, exist_ok=True)\n",
    "os.makedirs(images_target_path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "986f8a36",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 27919/27919 [1:48:46<00:00,  4.28it/s]\n"
     ]
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()\n",
    "import os\n",
    "import json\n",
    "import shutil\n",
    "from tqdm import tqdm\n",
    "\n",
    "file_counter = 0\n",
    "for file in tqdm(os.listdir(Key_dir)):\n",
    "    try:\n",
    "        Annotation_dict = {}\n",
    "        xml_file_name = os.path.join(Key_dir, file)\n",
    "        grnd_df, single_fields, grp_field_names, table_field_names, image_name = OCR_XML_to_DataFrame(xml_file_name, key_names)\n",
    "        \n",
    "        if image_name[0][0] in img_files:\n",
    "            # Generate ground truth and create annotation dictionary\n",
    "            grnd_df.fillna(\"[BLANK]\", inplace=True)\n",
    "            grnd_df[\"Ground_truth\"] = grnd_df.apply(generate_ground_truth, axis=1)\n",
    "            Annotation_dict = create_annotation_dict(grnd_df)\n",
    "\n",
    "            # Generate the base filename (without extension)\n",
    "            base_filename = os.path.splitext(file)[0]\n",
    "\n",
    "            # Save the annotation dictionary as JSON with the base filename\n",
    "            json_file_path = os.path.join(json_target_path, base_filename + \".json\")\n",
    "            with open(json_file_path, 'w') as json_file:\n",
    "                json.dump(Annotation_dict, json_file)\n",
    "\n",
    "            source_image_path = os.path.join(images_source_path, image_name[0][0])\n",
    "            target_image_path = os.path.join(images_target_path, base_filename + os.path.splitext(image_name[0][0])[1])\n",
    "            if os.path.exists(source_image_path):\n",
    "                shutil.copy(source_image_path, target_image_path)\n",
    "\n",
    "#             print(f\"{file} : {len(Annotation_dict)}\")\n",
    "#             print(Annotation_dict)\n",
    "    \n",
    "    except Exception as e:\n",
    "        raise e\n",
    "\n",
    "    file_counter += 1\n",
    "    if file_counter%200 == 0:\n",
    "        gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0713f7d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0570dbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from tqdm import tqdm\n",
    "key_file_dir = r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\donut_data\\valid\\key\"\n",
    "train_key_files = r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\donut_data\\train\\key\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e785b6da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 27919/27919 [00:14<00:00, 1925.41it/s]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "files_with_151 = []\n",
    "files_with_157 = []\n",
    "others = []\n",
    "key_files = os.listdir(train_key_files)\n",
    "for each_key_file in tqdm(key_files):\n",
    "    each_path = os.path.join(train_key_files, each_key_file)   \n",
    "    # Reading the JSON file\n",
    "    with open(each_path, 'r') as file:\n",
    "        data = json.load(file)\n",
    "        if len(data) == 151:\n",
    "            files_with_151.append(each_key_file)\n",
    "        elif len(data) == 157:\n",
    "            files_with_157.append(each_key_file)\n",
    "        else:\n",
    "            others.append(each_key_file)\n",
    "print(len(files_with_151), len(files_with_157), len(others))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "db89b159",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 27919 0\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e27a2c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bf2f2e9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 27919/27919 [01:19<00:00, 353.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files with 151 keys: 27320\n",
      "Files with 157 keys: 599\n",
      "Other files: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "from tqdm import tqdm\n",
    "\n",
    "extra_keys = {\n",
    "    \"24_AnesStartTime\": \"[BLANK]\",\n",
    "    \"24_AnesEndTime\": \"[BLANK]\",\n",
    "    \"24_AnesTotalTime\": \"[BLANK]\",\n",
    "    \"24_AnesTotalMin\": \"[BLANK]\",\n",
    "    \"24_AnesMod\": \"[BLANK]\",\n",
    "    \"24_AnesUnits\": \"[BLANK]\"\n",
    "}\n",
    "\n",
    "files_with_151 = []\n",
    "files_with_157 = []\n",
    "others = []\n",
    "train_key_files = r\"D:\\Xelp_work\\FSL Project\\Sprint_2\\HCFA_data\\donut_data\\train\\key\"\n",
    "\n",
    "key_files = os.listdir(train_key_files)\n",
    "for each_key_file in tqdm(key_files):\n",
    "    each_path = os.path.join(train_key_files, each_key_file)   \n",
    "    # Reading the JSON file\n",
    "    with open(each_path, 'r') as file:\n",
    "        data = json.load(file)\n",
    "        \n",
    "        if len(data) == 151:\n",
    "            files_with_151.append(each_key_file)\n",
    "            # Add the extra keys and values\n",
    "            data.update(extra_keys)\n",
    "            \n",
    "            # Save the updated JSON data back to the file\n",
    "            with open(each_path, 'w') as file:\n",
    "                json.dump(data, file, indent=4)\n",
    "                \n",
    "        elif len(data) == 157:\n",
    "            files_with_157.append(each_key_file)\n",
    "        else:\n",
    "            others.append(each_key_file)\n",
    "\n",
    "print(f\"Files with 151 keys: {len(files_with_151)}\")\n",
    "print(f\"Files with 157 keys: {len(files_with_157)}\")\n",
    "print(f\"Other files: {len(others)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39aa5696",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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