{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51f734f8-1015-4aa6-bcb5-2bd42799397c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import tempfile\n",
    "import xml.etree.ElementTree as ET\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from HCFA_XML_to_DF import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60af5430-15ed-406a-a928-a310d31cfb1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d23238ca-c231-4480-9be3-0c9497b957a9",
   "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>MissApp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Box19B_Provider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Box19B_NPI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Key_Name\n",
       "0          MissApp\n",
       "1  Box19B_Provider\n",
       "2       Box19B_NPI"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hcfa_key_list = pd.read_excel(\"/Data/XELP_Experiments/Notebooks/HCFA_Keys.xlsx\")\n",
    "Hcfa_key_list.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2600d374-0b7c-4669-9af9-833eba2363fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "key_list = Hcfa_key_list['Key_Name'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "baa74ee8-3b6f-4cf0-a16b-548e9afd76dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15000\n"
     ]
    }
   ],
   "source": [
    "Trans_dir = '/Data/XELP_Experiments/Training_Data/HCFA/Extracted_EV_GRPBX/TRANS/'\n",
    "trans_files_list = os.listdir(Trans_dir)\n",
    "print(len(trans_files_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7204c322-1a44-4c75-b18a-0903e482c317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "977"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7abd84bc-5151-4a61-b67e-18dec23e4407",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████| 15000/15000 [3:09:45<00:00,  1.32it/s]\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame()\n",
    "i = 0\n",
    "for each_trans_file in tqdm(trans_files_list):\n",
    "    each_trans_path = os.path.join(Trans_dir, each_trans_file)\n",
    "    #print(each_trans_path)\n",
    "    df, im_names = OCR_XML_to_DataFrame(each_trans_path, key_list)\n",
    "    #print(df.shape)\n",
    "    \n",
    "    i += 1\n",
    "    df[\"Trans_file_name\"] = each_trans_file\n",
    "    #print(im_names)\n",
    "    df[\"img_details\"] = [im_names] * len(df)\n",
    "    data  = pd.concat([data, df], ignore_index=True)\n",
    "\n",
    "    if i%200 == 0:\n",
    "        gc.collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e1007e02-7aa9-4a3a-b446-6023f445fce3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Data', 'TRANS_Value', 'Group_name', 'Image_width', 'Image_height',\n",
       "       'Field_Name', 'x1', 'x2', 'y1', 'y2', 'Page', 'OCR_OMNI', 'OCR_IGEAR',\n",
       "       'OCR_AT', 'OCR_Optimizer', 'Comments', 'Trans_file_name',\n",
       "       'img_details'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7e213b50-1c76-4cc3-93ca-3aa6d5ae8880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4371082, 18)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bc16057b-bc8e-45b6-841a-9198030b7b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "43aec1a2-2be3-4254-aa1c-c56bb322023d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3553    [1378N430MC010_002.tiff, 1378N430MC010_003.tif...\n",
       "3554    [1378N430MC010_002.tiff, 1378N430MC010_003.tif...\n",
       "3555    [1378N430MC010_002.tiff, 1378N430MC010_003.tif...\n",
       "3556    [1378N430MC010_002.tiff, 1378N430MC010_003.tif...\n",
       "Name: img_details, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[3553:3557]['img_details']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ef6766a8-0f84-4d5c-b050-0cdb6398fefd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.to_excel(\"/Data/XELP_Experiments/Training_Data/HCFA/EDA_Analysis/EV_GRPBMX.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "065d09bb-9af4-4496-8e70-cabc18068af3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "044bfc7e-c358-4071-8c36-aa0437d27700",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_parquet(\"/Data/XELP_Experiments/Training_Data/HCFA/EDA_Analysis/EV_GRPBX.parquet\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5416a40-0d73-4903-b420-a8d3170c193e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
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 "nbformat": 4,
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