{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>path_to_image</th>\n",
       "      <th>path_to_dcm</th>\n",
       "      <th>frontal_lateral</th>\n",
       "      <th>ap_pa</th>\n",
       "      <th>deid_patient_id</th>\n",
       "      <th>patient_report_date_order</th>\n",
       "      <th>report</th>\n",
       "      <th>section_narrative</th>\n",
       "      <th>section_clinical_history</th>\n",
       "      <th>section_history</th>\n",
       "      <th>...</th>\n",
       "      <th>section_accession_number</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>race</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>interpreter_needed</th>\n",
       "      <th>insurance_type</th>\n",
       "      <th>recent_bmi</th>\n",
       "      <th>deceased</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train/patient42142/study5/view1_frontal.jpg</td>\n",
       "      <td>train/patient42142/study5/view1_frontal.dcm</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>AP</td>\n",
       "      <td>patient42142</td>\n",
       "      <td>5</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, 8-8-2005\\n \\nHISTORY...</td>\n",
       "      <td>\\nChest 1 View, 8-8-2005\\n \\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61 years Female, ICU patient\\n \\n</td>\n",
       "      <td>...</td>\n",
       "      <td>\\n 9959089\\nThis report has been anonymized. A...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>22.2</td>\n",
       "      <td>No</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train/patient42142/study8/view1_frontal.jpg</td>\n",
       "      <td>train/patient42142/study8/view1_frontal.dcm</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>AP</td>\n",
       "      <td>patient42142</td>\n",
       "      <td>8</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, 7-11-2000\\n \\nHISTOR...</td>\n",
       "      <td>\\nChest 1 View, 7-11-2000\\n \\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61 years Female, tracheostomy.\\n \\n</td>\n",
       "      <td>...</td>\n",
       "      <td>\\n64048857\\nThis report has been anonymized. A...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>22.2</td>\n",
       "      <td>No</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train/patient42142/study2/view1_frontal.jpg</td>\n",
       "      <td>train/patient42142/study2/view1_frontal.dcm</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>AP</td>\n",
       "      <td>patient42142</td>\n",
       "      <td>2</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, 11-17-2018\\n \\nHISTO...</td>\n",
       "      <td>\\nChest 1 View, 11-17-2018\\n \\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61 years Female, critical care follow-up\\n \\n</td>\n",
       "      <td>...</td>\n",
       "      <td>\\n#2452\\nThis report has been anonymized. All ...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>22.2</td>\n",
       "      <td>No</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train/patient42142/study4/view1_frontal.jpg</td>\n",
       "      <td>train/patient42142/study4/view1_frontal.dcm</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>AP</td>\n",
       "      <td>patient42142</td>\n",
       "      <td>4</td>\n",
       "      <td>NARRATIVE:\\nAP PORTABLE UPRIGHT CHEST: May 01 ...</td>\n",
       "      <td>\\nAP PORTABLE UPRIGHT CHEST: May 01 at 0518 ho...</td>\n",
       "      <td>ICU protocol.  Follow up.\\n \\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>\\n4164064\\nThis report has been anonymized. Al...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>22.2</td>\n",
       "      <td>No</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train/patient42142/study3/view1_frontal.jpg</td>\n",
       "      <td>train/patient42142/study3/view1_frontal.dcm</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>AP</td>\n",
       "      <td>patient42142</td>\n",
       "      <td>3</td>\n",
       "      <td>NARRATIVE:\\nEXAM: Chest 1 View, 10/4/2\\n \\nCLI...</td>\n",
       "      <td>\\nEXAM: Chest 1 View, 10/4/2\\n \\n</td>\n",
       "      <td>61 years Female UPRIGHT PLEASE. ICU PROTOCOL ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>\\n49286401\\nThis report has been anonymized. A...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>22.2</td>\n",
       "      <td>No</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>train/patient42142/study7/view1_frontal.jpg</td>\n",
       "      <td>train/patient42142/study7/view1_frontal.dcm</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>AP</td>\n",
       "      <td>patient42142</td>\n",
       "      <td>7</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, 1-29-2017\\n \\nHISTOR...</td>\n",
       "      <td>\\nChest 1 View, 1-29-2017\\n \\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61 years Female, shortness of breath\\n \\n</td>\n",
       "      <td>...</td>\n",
       "      <td>\\n04240392\\nThis report has been anonymized. A...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>22.2</td>\n",
       "      <td>No</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>train/patient42142/study1/view1_frontal.jpg</td>\n",
       "      <td>train/patient42142/study1/view1_frontal.dcm</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>AP</td>\n",
       "      <td>patient42142</td>\n",
       "      <td>1</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, 2004/20\\n \\nHISTORY:...</td>\n",
       "      <td>\\nChest 1 View, 2004/20\\n \\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61 years Female, Line placement\\n \\n</td>\n",
       "      <td>...</td>\n",
       "      <td>\\nljnagnwl\\nThis report has been anonymized. A...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>22.2</td>\n",
       "      <td>No</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>train/patient42142/study6/view1_frontal.jpg</td>\n",
       "      <td>train/patient42142/study6/view1_frontal.dcm</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>AP</td>\n",
       "      <td>patient42142</td>\n",
       "      <td>6</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, 1/16/2016\\n \\nHISTOR...</td>\n",
       "      <td>\\nChest 1 View, 1/16/2016\\n \\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61 years Female, ICU patient\\n \\n</td>\n",
       "      <td>...</td>\n",
       "      <td>\\n36157\\nThis report has been anonymized. All ...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>22.2</td>\n",
       "      <td>No</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>train/patient04528/study1/view2_lateral.jpg</td>\n",
       "      <td>train/patient04528/study1/view2_lateral.dcm</td>\n",
       "      <td>Lateral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>patient04528</td>\n",
       "      <td>1</td>\n",
       "      <td>NARRATIVE:\\nRADIOGRAPHIC EXAMINATION OF THE CH...</td>\n",
       "      <td>\\nRADIOGRAPHIC EXAMINATION OF THE CHEST: 6/3/1...</td>\n",
       "      <td>61 years of age, Female, R o infiltarate..AML...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>\\nRUFLZXH\\nThis report has been anonymized. Al...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>24.9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>train/patient04528/study1/view1_frontal.jpg</td>\n",
       "      <td>train/patient04528/study1/view1_frontal.dcm</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>PA</td>\n",
       "      <td>patient04528</td>\n",
       "      <td>2</td>\n",
       "      <td>NARRATIVE:\\nRADIOGRAPHIC EXAMINATION OF THE CH...</td>\n",
       "      <td>\\nRADIOGRAPHIC EXAMINATION OF THE CHEST: 11-28...</td>\n",
       "      <td>61 years of age, Female, R o infiltarate..AML...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>\\n716611072\\nThis report has been anonymized. ...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>No</td>\n",
       "      <td>Private Insurance</td>\n",
       "      <td>24.9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 path_to_image  \\\n",
       "0  train/patient42142/study5/view1_frontal.jpg   \n",
       "1  train/patient42142/study8/view1_frontal.jpg   \n",
       "2  train/patient42142/study2/view1_frontal.jpg   \n",
       "3  train/patient42142/study4/view1_frontal.jpg   \n",
       "4  train/patient42142/study3/view1_frontal.jpg   \n",
       "5  train/patient42142/study7/view1_frontal.jpg   \n",
       "6  train/patient42142/study1/view1_frontal.jpg   \n",
       "7  train/patient42142/study6/view1_frontal.jpg   \n",
       "8  train/patient04528/study1/view2_lateral.jpg   \n",
       "9  train/patient04528/study1/view1_frontal.jpg   \n",
       "\n",
       "                                   path_to_dcm frontal_lateral ap_pa  \\\n",
       "0  train/patient42142/study5/view1_frontal.dcm         Frontal    AP   \n",
       "1  train/patient42142/study8/view1_frontal.dcm         Frontal    AP   \n",
       "2  train/patient42142/study2/view1_frontal.dcm         Frontal    AP   \n",
       "3  train/patient42142/study4/view1_frontal.dcm         Frontal    AP   \n",
       "4  train/patient42142/study3/view1_frontal.dcm         Frontal    AP   \n",
       "5  train/patient42142/study7/view1_frontal.dcm         Frontal    AP   \n",
       "6  train/patient42142/study1/view1_frontal.dcm         Frontal    AP   \n",
       "7  train/patient42142/study6/view1_frontal.dcm         Frontal    AP   \n",
       "8  train/patient04528/study1/view2_lateral.dcm         Lateral   NaN   \n",
       "9  train/patient04528/study1/view1_frontal.dcm         Frontal    PA   \n",
       "\n",
       "  deid_patient_id  patient_report_date_order  \\\n",
       "0    patient42142                          5   \n",
       "1    patient42142                          8   \n",
       "2    patient42142                          2   \n",
       "3    patient42142                          4   \n",
       "4    patient42142                          3   \n",
       "5    patient42142                          7   \n",
       "6    patient42142                          1   \n",
       "7    patient42142                          6   \n",
       "8    patient04528                          1   \n",
       "9    patient04528                          2   \n",
       "\n",
       "                                              report  \\\n",
       "0  NARRATIVE:\\nChest 1 View, 8-8-2005\\n \\nHISTORY...   \n",
       "1  NARRATIVE:\\nChest 1 View, 7-11-2000\\n \\nHISTOR...   \n",
       "2  NARRATIVE:\\nChest 1 View, 11-17-2018\\n \\nHISTO...   \n",
       "3  NARRATIVE:\\nAP PORTABLE UPRIGHT CHEST: May 01 ...   \n",
       "4  NARRATIVE:\\nEXAM: Chest 1 View, 10/4/2\\n \\nCLI...   \n",
       "5  NARRATIVE:\\nChest 1 View, 1-29-2017\\n \\nHISTOR...   \n",
       "6  NARRATIVE:\\nChest 1 View, 2004/20\\n \\nHISTORY:...   \n",
       "7  NARRATIVE:\\nChest 1 View, 1/16/2016\\n \\nHISTOR...   \n",
       "8  NARRATIVE:\\nRADIOGRAPHIC EXAMINATION OF THE CH...   \n",
       "9  NARRATIVE:\\nRADIOGRAPHIC EXAMINATION OF THE CH...   \n",
       "\n",
       "                                   section_narrative  \\\n",
       "0                      \\nChest 1 View, 8-8-2005\\n \\n   \n",
       "1                     \\nChest 1 View, 7-11-2000\\n \\n   \n",
       "2                    \\nChest 1 View, 11-17-2018\\n \\n   \n",
       "3  \\nAP PORTABLE UPRIGHT CHEST: May 01 at 0518 ho...   \n",
       "4                  \\nEXAM: Chest 1 View, 10/4/2\\n \\n   \n",
       "5                     \\nChest 1 View, 1-29-2017\\n \\n   \n",
       "6                       \\nChest 1 View, 2004/20\\n \\n   \n",
       "7                     \\nChest 1 View, 1/16/2016\\n \\n   \n",
       "8  \\nRADIOGRAPHIC EXAMINATION OF THE CHEST: 6/3/1...   \n",
       "9  \\nRADIOGRAPHIC EXAMINATION OF THE CHEST: 11-28...   \n",
       "\n",
       "                            section_clinical_history  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                    ICU protocol.  Follow up.\\n \\n    \n",
       "4   61 years Female UPRIGHT PLEASE. ICU PROTOCOL ...   \n",
       "5                                                NaN   \n",
       "6                                                NaN   \n",
       "7                                                NaN   \n",
       "8   61 years of age, Female, R o infiltarate..AML...   \n",
       "9   61 years of age, Female, R o infiltarate..AML...   \n",
       "\n",
       "                                  section_history  ...  \\\n",
       "0               61 years Female, ICU patient\\n \\n  ...   \n",
       "1             61 years Female, tracheostomy.\\n \\n  ...   \n",
       "2   61 years Female, critical care follow-up\\n \\n  ...   \n",
       "3                                             NaN  ...   \n",
       "4                                             NaN  ...   \n",
       "5       61 years Female, shortness of breath\\n \\n  ...   \n",
       "6            61 years Female, Line placement\\n \\n  ...   \n",
       "7               61 years Female, ICU patient\\n \\n  ...   \n",
       "8                                             NaN  ...   \n",
       "9                                             NaN  ...   \n",
       "\n",
       "                            section_accession_number   age     sex   race  \\\n",
       "0  \\n 9959089\\nThis report has been anonymized. A...  62.0  Female  White   \n",
       "1  \\n64048857\\nThis report has been anonymized. A...  62.0  Female  White   \n",
       "2  \\n#2452\\nThis report has been anonymized. All ...  62.0  Female  White   \n",
       "3  \\n4164064\\nThis report has been anonymized. Al...  62.0  Female  White   \n",
       "4  \\n49286401\\nThis report has been anonymized. A...  62.0  Female  White   \n",
       "5  \\n04240392\\nThis report has been anonymized. A...  62.0  Female  White   \n",
       "6  \\nljnagnwl\\nThis report has been anonymized. A...  62.0  Female  White   \n",
       "7  \\n36157\\nThis report has been anonymized. All ...  62.0  Female  White   \n",
       "8  \\nRUFLZXH\\nThis report has been anonymized. Al...  62.0  Female  White   \n",
       "9  \\n716611072\\nThis report has been anonymized. ...  62.0  Female  White   \n",
       "\n",
       "                 ethnicity interpreter_needed     insurance_type recent_bmi  \\\n",
       "0  Non-Hispanic/Non-Latino                 No  Private Insurance       22.2   \n",
       "1  Non-Hispanic/Non-Latino                 No  Private Insurance       22.2   \n",
       "2  Non-Hispanic/Non-Latino                 No  Private Insurance       22.2   \n",
       "3  Non-Hispanic/Non-Latino                 No  Private Insurance       22.2   \n",
       "4  Non-Hispanic/Non-Latino                 No  Private Insurance       22.2   \n",
       "5  Non-Hispanic/Non-Latino                 No  Private Insurance       22.2   \n",
       "6  Non-Hispanic/Non-Latino                 No  Private Insurance       22.2   \n",
       "7  Non-Hispanic/Non-Latino                 No  Private Insurance       22.2   \n",
       "8  Non-Hispanic/Non-Latino                 No  Private Insurance       24.9   \n",
       "9  Non-Hispanic/Non-Latino                 No  Private Insurance       24.9   \n",
       "\n",
       "   deceased  split  \n",
       "0        No  train  \n",
       "1        No  train  \n",
       "2        No  train  \n",
       "3        No  train  \n",
       "4        No  train  \n",
       "5        No  train  \n",
       "6        No  train  \n",
       "7        No  train  \n",
       "8       Yes  train  \n",
       "9       Yes  train  \n",
       "\n",
       "[10 rows x 27 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "reports = pd.read_csv('/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/CheXpert-Dataset/df_chexpert_plus_240401.xls')\n",
    "reports.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NARRATIVE:\n",
      "Chest 1 View, 8-8-2005\n",
      " \n",
      "HISTORY: 61 years Female, ICU patient\n",
      " \n",
      "COMPARISON: 8/8/2005.\n",
      " \n",
      "IMPRESSION:  \n",
      " \n",
      "1.TRACHEOSTOMY TUBE REMAINS IN PLACE.  RIGHT PICC IS IN STABLE AND \n",
      "STANDARD POSITION.  THE ENTERIC TUBE HAS BEEN REMOVED.\n",
      " \n",
      "2.NO EVIDENCE OF PNEUMOTHORAX.  SLIGHTLY IMPROVED AERATION OF THE \n",
      "BILATERAL LUNGS WITH NO EVIDENCE OF FOCAL AIR SPACE OPACITIES OR \n",
      "PLEURAL EFFUSIONS.\n",
      " \n",
      " SUMMARY: 2-ABNORMAL, PREVIOUSLY REPORTED  \n",
      "I have personally reviewed the images for this examination and agreed\n",
      "with the report transcribed above.\n",
      " \n",
      "ACCESSION NUMBER:\n",
      " 9959089\n",
      "This report has been anonymized. All dates are offset from the actual dates by a fixed interval associated with the patient.\n"
     ]
    }
   ],
   "source": [
    "print(reports['report'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rename the paths and produce a new CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Define paths\n",
    "base_dir = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/CheXpert-Dataset/Data/PNG/train\"  # Base directory containing patient folders\n",
    "csv_file = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/CheXpert-Dataset/df_chexpert_plus_240401.xls\"  # Update with the actual CSV filename\n",
    "output_dir = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/Data_new\"  # Folder where renamed images will be copied\n",
    "os.makedirs(output_dir, exist_ok=True)  # Ensure output directory exists\n",
    "\n",
    "# Load CSV file\n",
    "df = pd.read_csv(csv_file)\n",
    "\n",
    "# Ensure the CSV contains necessary columns\n",
    "required_columns = {\"deid_patient_id\", \"report\", \"frontal_lateral\", \"ap_pa\", \"age\", \"sex\", \"race\", \"path_to_image\"}\n",
    "if not required_columns.issubset(df.columns):\n",
    "    raise ValueError(f\"CSV is missing required columns: {required_columns - set(df.columns)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5868/5868 [52:39<00:00,  1.86it/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Process completed! Renamed images are in /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/Data_new\n",
      "✅ Metadata CSV saved at /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/Data_new/renamed_images.csv\n"
     ]
    }
   ],
   "source": [
    "# Create a new dataframe to store the renamed file info\n",
    "new_records = []\n",
    "\n",
    "# Traverse through patient folders\n",
    "for patient_folder in tqdm(os.listdir(base_dir)):\n",
    "    patient_path = os.path.join(base_dir, patient_folder)\n",
    "    if not os.path.isdir(patient_path) or not patient_folder.startswith(\"patient\"):\n",
    "        continue  # Skip non-directory files or unrelated folders\n",
    "\n",
    "    patient_number = patient_folder.replace(\"patient\", \"\")\n",
    "\n",
    "    for study_folder in os.listdir(patient_path):\n",
    "        study_path = os.path.join(patient_path, study_folder)\n",
    "        if not os.path.isdir(study_path) or not study_folder.startswith(\"study\"):\n",
    "            continue  # Skip non-study folders\n",
    "\n",
    "        study_number = study_folder.replace(\"study\", \"\")\n",
    "\n",
    "        for image_file in os.listdir(study_path):\n",
    "            if not image_file.endswith(\".png\"):\n",
    "                continue  # Skip non-PNG files\n",
    "\n",
    "            image_path = os.path.join(study_path, image_file)\n",
    "            actual_path = image_path[image_path.find(\"train\"):]\n",
    "            actual_path = actual_path.replace('.png', '.jpg')\n",
    "            # print(\"actual image path\", actual_path)\n",
    "            # Match file path in CSV\n",
    "            csv_row = df[df[\"path_to_image\"] == actual_path]\n",
    "\n",
    "            if csv_row.empty:\n",
    "                print(f\"Warning: No metadata found for {actual_path}\")\n",
    "                continue\n",
    "\n",
    "            # Extract renaming metadata\n",
    "            view = csv_row.iloc[0][\"frontal_lateral\"]  # Frontal or Lateral\n",
    "            ap_pa = csv_row.iloc[0][\"ap_pa\"]  # Ap or PA\n",
    "            age = csv_row.iloc[0][\"age\"]\n",
    "            sex = csv_row.iloc[0][\"sex\"]\n",
    "            race = csv_row.iloc[0][\"race\"]\n",
    "            report = csv_row.iloc[0][\"report\"]\n",
    "            section_history = csv_row.iloc[0][\"section_history\"]\n",
    "            ethnicity = csv_row.iloc[0][\"ethnicity\"]\n",
    "            recent_bmi = csv_row.iloc[0][\"recent_bmi\"]\n",
    "\n",
    "            # Generate new filename\n",
    "            new_filename = f\"{patient_number}-{study_number}-{view}-{ap_pa}-{age}-{sex}-{race}.png\"\n",
    "            new_file_path = os.path.join(output_dir, new_filename)\n",
    "\n",
    "            # Copy and rename the image\n",
    "            shutil.copy(image_path, new_file_path)\n",
    "\n",
    "            # Store in CSV record\n",
    "            new_records.append([new_file_path, patient_number, study_number, view, report, section_history, age, sex, race, ethnicity, recent_bmi])\n",
    "            \n",
    "# Create a DataFrame for the new CSV\n",
    "new_df = pd.DataFrame(new_records, columns=[\"new-path\", \"patient-number\", \"study-number\", \"view\", \"reports\", \"section_history\", \"age\", \"sex\", \"race\", \"ethnicity\", \"recent_bmi\"])\n",
    "\n",
    "# Save the new CSV file\n",
    "new_csv_path = os.path.join(output_dir, \"renamed_images.csv\")\n",
    "new_df.to_csv(new_csv_path, index=False)\n",
    "\n",
    "print(f\"✅ Process completed! Renamed images are in {output_dir}\")\n",
    "print(f\"✅ Metadata CSV saved at {new_csv_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>new-path</th>\n",
       "      <th>patient-number</th>\n",
       "      <th>study-number</th>\n",
       "      <th>view</th>\n",
       "      <th>reports</th>\n",
       "      <th>section_history</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>race</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>recent_bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>2</td>\n",
       "      <td>Lateral</td>\n",
       "      <td>NARRATIVE:\\nChest 2 Views, 2006, October 26th\\...</td>\n",
       "      <td>50 years Male, BMT\\n \\n</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>2</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>NARRATIVE:\\nChest 2 Views, 5/10/2004\\n \\nHISTO...</td>\n",
       "      <td>50 years Male, BMT\\n \\n</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>3</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, December 17\\n \\nHIST...</td>\n",
       "      <td>50 years Male, Critical  care follow-up(ICU)\\...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>4</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View 2-5-2021\\n \\nCLINICAL...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>1</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, july 2006\\n \\nHISTOR...</td>\n",
       "      <td>50 years Male, SOB\\n \\n</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            new-path patient-number  \\\n",
       "0  /cluster/home/ammaa/Downloads/Projects/CheXper...          23148   \n",
       "1  /cluster/home/ammaa/Downloads/Projects/CheXper...          23148   \n",
       "2  /cluster/home/ammaa/Downloads/Projects/CheXper...          23148   \n",
       "3  /cluster/home/ammaa/Downloads/Projects/CheXper...          23148   \n",
       "4  /cluster/home/ammaa/Downloads/Projects/CheXper...          23148   \n",
       "\n",
       "  study-number     view                                            reports  \\\n",
       "0            2  Lateral  NARRATIVE:\\nChest 2 Views, 2006, October 26th\\...   \n",
       "1            2  Frontal  NARRATIVE:\\nChest 2 Views, 5/10/2004\\n \\nHISTO...   \n",
       "2            3  Frontal  NARRATIVE:\\nChest 1 View, December 17\\n \\nHIST...   \n",
       "3            4  Frontal  NARRATIVE:\\nChest 1 View 2-5-2021\\n \\nCLINICAL...   \n",
       "4            1  Frontal  NARRATIVE:\\nChest 1 View, july 2006\\n \\nHISTOR...   \n",
       "\n",
       "                                     section_history   age   sex   race  \\\n",
       "0                            50 years Male, BMT\\n \\n  50.0  Male  Asian   \n",
       "1                            50 years Male, BMT\\n \\n  50.0  Male  Asian   \n",
       "2   50 years Male, Critical  care follow-up(ICU)\\...  50.0  Male  Asian   \n",
       "3                                                NaN  50.0  Male  Asian   \n",
       "4                            50 years Male, SOB\\n \\n  50.0  Male  Asian   \n",
       "\n",
       "                 ethnicity  recent_bmi  \n",
       "0  Non-Hispanic/Non-Latino        26.8  \n",
       "1  Non-Hispanic/Non-Latino        26.8  \n",
       "2  Non-Hispanic/Non-Latino        26.8  \n",
       "3  Non-Hispanic/Non-Latino        26.8  \n",
       "4  Non-Hispanic/Non-Latino        26.8  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NARRATIVE:\n",
      "SINGLE PORTABLE VIEW OF THE CHEST: 7-11-2010\n",
      "COMPARISON:\n",
      "JULY 2010\n",
      "IMPRESSION:\n",
      "1. INCREASED OPACITY AT BOTH APICES, GREATER ON THE LEFT THAN ON\n",
      "THE RIGHT PERSISTS. HEMORRHAGE CANNOT BE EXCLUDED.\n",
      "2. INTERVAL DEVELOPMENT OF A LEFT RETROCARDIAC OPACITY CONSISTENT\n",
      "WITH ATELECTASIS OR CONSOLIDATION.\n",
      "3. FRACTURE OF THE RIGHT SCAPULA AND LEFT SECOND AND PROBABLY\n",
      "LEFT FIRST RIBS ARE AGAIN NOTED.\n",
      "4. THE ENDOTRACHEAL TUBE UNCHANGED IN POSITION. THE NASOGASTRIC\n",
      "TUBE IS SEEN GOING BELOW THE LEFT HEMIDIAPHRAGM AND PROBABLY COILED\n",
      "WITHIN THE STOMACH.\n",
      "END OF IMPRESSION:\n",
      "POSSIBLE SIGNIFICANT ABNORMALITY/CHANGE, MAY NEED ACTION.\n",
      " \n",
      "ACCESSION NUMBER:\n",
      "006249\n",
      "This report has been anonymized. All dates are offset from the actual dates by a fixed interval associated with the patient.\n"
     ]
    }
   ],
   "source": [
    "print(new_df['reports'][400])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>new-path</th>\n",
       "      <th>patient-number</th>\n",
       "      <th>study-number</th>\n",
       "      <th>view</th>\n",
       "      <th>reports</th>\n",
       "      <th>section_history</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>race</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>recent_bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>2</td>\n",
       "      <td>Lateral</td>\n",
       "      <td>NARRATIVE:\\nChest 2 Views, 2006, October 26th\\...</td>\n",
       "      <td>50 years Male, BMT\\n \\n</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>2</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>NARRATIVE:\\nChest 2 Views, 5/10/2004\\n \\nHISTO...</td>\n",
       "      <td>50 years Male, BMT\\n \\n</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>3</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, December 17\\n \\nHIST...</td>\n",
       "      <td>50 years Male, Critical  care follow-up(ICU)\\...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>4</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View 2-5-2021\\n \\nCLINICAL...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/cluster/home/ammaa/Downloads/Projects/CheXper...</td>\n",
       "      <td>23148</td>\n",
       "      <td>1</td>\n",
       "      <td>Frontal</td>\n",
       "      <td>NARRATIVE:\\nChest 1 View, july 2006\\n \\nHISTOR...</td>\n",
       "      <td>50 years Male, SOB\\n \\n</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Non-Hispanic/Non-Latino</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            new-path  patient-number  \\\n",
       "0  /cluster/home/ammaa/Downloads/Projects/CheXper...           23148   \n",
       "1  /cluster/home/ammaa/Downloads/Projects/CheXper...           23148   \n",
       "2  /cluster/home/ammaa/Downloads/Projects/CheXper...           23148   \n",
       "3  /cluster/home/ammaa/Downloads/Projects/CheXper...           23148   \n",
       "4  /cluster/home/ammaa/Downloads/Projects/CheXper...           23148   \n",
       "\n",
       "   study-number     view                                            reports  \\\n",
       "0             2  Lateral  NARRATIVE:\\nChest 2 Views, 2006, October 26th\\...   \n",
       "1             2  Frontal  NARRATIVE:\\nChest 2 Views, 5/10/2004\\n \\nHISTO...   \n",
       "2             3  Frontal  NARRATIVE:\\nChest 1 View, December 17\\n \\nHIST...   \n",
       "3             4  Frontal  NARRATIVE:\\nChest 1 View 2-5-2021\\n \\nCLINICAL...   \n",
       "4             1  Frontal  NARRATIVE:\\nChest 1 View, july 2006\\n \\nHISTOR...   \n",
       "\n",
       "                                     section_history   age   sex   race  \\\n",
       "0                            50 years Male, BMT\\n \\n  50.0  Male  Asian   \n",
       "1                            50 years Male, BMT\\n \\n  50.0  Male  Asian   \n",
       "2   50 years Male, Critical  care follow-up(ICU)\\...  50.0  Male  Asian   \n",
       "3                                                NaN  50.0  Male  Asian   \n",
       "4                            50 years Male, SOB\\n \\n  50.0  Male  Asian   \n",
       "\n",
       "                 ethnicity  recent_bmi  \n",
       "0  Non-Hispanic/Non-Latino        26.8  \n",
       "1  Non-Hispanic/Non-Latino        26.8  \n",
       "2  Non-Hispanic/Non-Latino        26.8  \n",
       "3  Non-Hispanic/Non-Latino        26.8  \n",
       "4  Non-Hispanic/Non-Latino        26.8  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "csv_path = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/Raw_New/renamed_images.csv\"\n",
    "df = pd.read_csv(csv_path)\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NARRATIVE:\n",
      "PORTABLE CHEST SINGLE VIEW:  6/15/2016 AT 0409 HOURS.\n",
      " \n",
      " COMPARISON:  6-15-2016.\n",
      " \n",
      " CLINICAL HISTORY:  ICU follow up.\n",
      " \n",
      " IMPRESSION: \n",
      " \n",
      " 1.  INTERVAL REMOVAL OF THE RIGHT IJ SHEATH, REMAINING LINES AND \n",
      "TUBES ARE STABLE.\n",
      " \n",
      " 2.  NO CHANGE IN CARDIOPULMONARY STATUS WITH PERSISTENT PULMONARY \n",
      "EDEMA, BILATERAL PLEURAL EFFUSIONS, AND BIBASILAR PULMONARY OPACITY.\n",
      " \n",
      " 3.  THE CARDIOMEDIASTINAL SILHOUETTE IS STABLE.\n",
      " \n",
      " SUMMARY:4-POSSIBLE SIGNIFICANT FINDINGS, MAY NEED ACTION \n",
      " \n",
      "ACCESSION NUMBER:\n",
      "58900532\n",
      "This report has been anonymized. All dates are offset from the actual dates by a fixed interval associated with the patient.\n"
     ]
    }
   ],
   "source": [
    "import random \n",
    "num_of_records = len(df)\n",
    "index = random.randint(0, num_of_records - 1)\n",
    "random_report = df['reports'][index]\n",
    "\n",
    "print(random_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create JSONL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed 47463 reports. JSONL file saved as /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/output.jsonl.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import json\n",
    "\n",
    "def extract_findings_impressions(report):\n",
    "    \"\"\"\n",
    "    Extracts 'Findings' and 'Impressions' sections from a given radiology report.\n",
    "    Removes the section headers and keeps only the textual content.\n",
    "    \"\"\"\n",
    "    findings_pattern = r\"FINDINGS:\\s*(.*?)(?=(IMPRESSION:|\\n[A-Z ]+:|$))\"\n",
    "    impressions_pattern = r\"IMPRESSION:\\s*(.*?)(?=(\\n[A-Z ]+:|$))\"\n",
    "\n",
    "    findings_match = re.search(findings_pattern, report, re.DOTALL | re.IGNORECASE)\n",
    "    impressions_match = re.search(impressions_pattern, report, re.DOTALL | re.IGNORECASE)\n",
    "\n",
    "    findings_text = findings_match.group(1).strip() if findings_match else \"\"\n",
    "    impressions_text = impressions_match.group(1).strip() if impressions_match else \"\"\n",
    "\n",
    "    # Combine the extracted text without headers\n",
    "    combined_text = f\"{findings_text}\\n{impressions_text}\".strip()\n",
    "\n",
    "    # Normalize whitespace\n",
    "    return re.sub(r'\\s+', ' ', combined_text)\n",
    "\n",
    "# Load CSV\n",
    "input_csv = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/Raw_New/renamed_images.csv\"  # Change this to your actual CSV file path\n",
    "df = pd.read_csv(input_csv)\n",
    "\n",
    "# Ensure required columns exist\n",
    "if \"reports\" not in df.columns or \"new-path\" not in df.columns:\n",
    "    raise ValueError(\"CSV file must contain 'reports' and 'new_path' columns.\")\n",
    "\n",
    "# Process reports\n",
    "jsonl_data = []\n",
    "for _, row in df.iterrows():\n",
    "    report_text = row[\"reports\"]\n",
    "    image_name = row[\"new-path\"].split(\"/\")[-1]  # Extract image file name\n",
    "    extracted_text = extract_findings_impressions(report_text)\n",
    "    \n",
    "    if extracted_text:  # Ensure there is relevant content\n",
    "        jsonl_data.append({\"image\": image_name, \"suffix\": extracted_text})\n",
    "\n",
    "# Save to JSONL file\n",
    "output_jsonl = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/output.jsonl\"\n",
    "with open(output_jsonl, \"w\", encoding=\"utf-8\") as f:\n",
    "    for entry in jsonl_data:\n",
    "        f.write(json.dumps(entry) + \"\\n\")\n",
    "\n",
    "print(f\"Processed {len(jsonl_data)} reports. JSONL file saved as {output_jsonl}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Collecting nltk\n",
      "  Obtaining dependency information for nltk from https://files.pythonhosted.org/packages/4d/66/7d9e26593edda06e8cb531874633f7c2372279c3b0f46235539fe546df8b/nltk-3.9.1-py3-none-any.whl.metadata\n",
      "  Downloading nltk-3.9.1-py3-none-any.whl.metadata (2.9 kB)\n",
      "Collecting click (from nltk)\n",
      "  Obtaining dependency information for click from https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl.metadata\n",
      "  Downloading click-8.1.8-py3-none-any.whl.metadata (2.3 kB)\n",
      "Requirement already satisfied: joblib in /cluster/home/ammaa/.conda/envs/ammars/lib/python3.11/site-packages (from nltk) (1.3.2)\n",
      "Requirement already satisfied: regex>=2021.8.3 in /cluster/home/ammaa/.conda/envs/ammars/lib/python3.11/site-packages (from nltk) (2024.11.6)\n",
      "Requirement already satisfied: tqdm in /cluster/home/ammaa/.conda/envs/ammars/lib/python3.11/site-packages (from nltk) (4.67.1)\n",
      "Downloading nltk-3.9.1-py3-none-any.whl (1.5 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading click-8.1.8-py3-none-any.whl (98 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.2/98.2 kB\u001b[0m \u001b[31m339.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: click, nltk\n",
      "Successfully installed click-8.1.8 nltk-3.9.1\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create JSONL from CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ JSONL file saved: extracted_impressions.jsonl\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import json\n",
    "\n",
    "# Load the CSV file with 'new_path' and 'reports' columns\n",
    "csv_file = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/Raw_New/renamed_images.csv\"  # Update with actual file path\n",
    "df = pd.read_csv(csv_file)\n",
    "\n",
    "# Function to extract the Impression section\n",
    "def extract_impression(text):\n",
    "    \"\"\"\n",
    "    Extracts the impression part from a report.\n",
    "    The impression starts with \"1.\" and ends when bullet points stop.\n",
    "    \"\"\"\n",
    "    if pd.isna(text):\n",
    "        return None  # Skip if the report is empty or NaN\n",
    "    \n",
    "    # Use regex to find all bullet points (e.g., \"1. xyz\", \"2. xyz\", etc.)\n",
    "    bullet_points = re.findall(r\"\\d+\\.\\s[^\\n]+\", text)\n",
    "    \n",
    "    if not bullet_points:\n",
    "        return None  # No impression found\n",
    "\n",
    "    # Join the extracted bullet points into a single string\n",
    "    return \" \".join(bullet_points)\n",
    "\n",
    "# Apply function to extract impressions\n",
    "df[\"impression\"] = df[\"reports\"].apply(extract_impression)\n",
    "\n",
    "# Drop rows where no impression was found\n",
    "df = df.dropna(subset=[\"impression\"])\n",
    "\n",
    "# Function to extract filename (excluding path and extension)\n",
    "def extract_filename(path):\n",
    "    return os.path.splitext(os.path.basename(path))[0]\n",
    "\n",
    "df[\"image\"] = df[\"new-path\"].apply(extract_filename)\n",
    "\n",
    "# Create JSONL file\n",
    "jsonl_file = \"extracted_impressions.jsonl\"\n",
    "\n",
    "with open(jsonl_file, \"w\", encoding=\"utf-8\") as f:\n",
    "    for _, row in df.iterrows():\n",
    "        json_line = {\n",
    "            \"image\": row[\"image\"],  # Extracted filename\n",
    "            \"prefix\": \"caption en\",\n",
    "            \"suffix\": row[\"impression\"]\n",
    "        }\n",
    "        f.write(json.dumps(json_line) + \"\\n\")\n",
    "\n",
    "print(f\"✅ JSONL file saved: {jsonl_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON CLEANUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Cleaned JSONL file saved as: /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/cleaned_output.jsonl\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import re\n",
    "import jsonlines\n",
    "\n",
    "# Input and output file paths\n",
    "input_file = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/output.jsonl\"  # Update with your actual file name\n",
    "output_file = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/cleaned_output.jsonl\"\n",
    "\n",
    "# Function to clean text by removing numbers and special characters\n",
    "def clean_text(text):\n",
    "    text = re.sub(r\"\\d+\", \"\", text)  # Remove numbers\n",
    "    text = re.sub(r\"[^\\w\\s]\", \"\", text)  # Remove special characters except whitespace\n",
    "    text = re.sub(r\"\\s+\", \" \", text).strip()  # Remove extra spaces\n",
    "    text = text.lower()\n",
    "    return text\n",
    "\n",
    "# Process JSONL file\n",
    "with jsonlines.open(input_file, \"r\") as reader, jsonlines.open(output_file, \"w\") as writer:\n",
    "    for obj in reader:\n",
    "        if \"suffix\" in obj:\n",
    "            obj[\"suffix\"] = clean_text(obj[\"suffix\"])  # Clean the suffix\n",
    "        writer.write(obj)  # Save cleaned entry\n",
    "\n",
    "print(f\"✅ Cleaned JSONL file saved as: {output_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove duplicated entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed duplicates. Final count: 40268 entries. JSONL saved as /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/deduplicated_output.jsonl.\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "# Input and output JSONL file paths\n",
    "input_jsonl = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/cleaned_output.jsonl\"  # Change to your actual file name\n",
    "output_jsonl = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/deduplicated_output.jsonl\"\n",
    "\n",
    "# Track unique suffixes\n",
    "unique_suffixes = set()\n",
    "deduplicated_data = []\n",
    "\n",
    "# Read JSONL file and remove duplicates\n",
    "with open(input_jsonl, \"r\", encoding=\"utf-8\") as infile:\n",
    "    for line in infile:\n",
    "        entry = json.loads(line.strip())\n",
    "        suffix_text = entry[\"suffix\"]\n",
    "\n",
    "        if suffix_text not in unique_suffixes:\n",
    "            unique_suffixes.add(suffix_text)\n",
    "            deduplicated_data.append(entry)\n",
    "\n",
    "# Save the deduplicated JSONL file\n",
    "with open(output_jsonl, \"w\", encoding=\"utf-8\") as outfile:\n",
    "    for entry in deduplicated_data:\n",
    "        outfile.write(json.dumps(entry) + \"\\n\")\n",
    "\n",
    "print(f\"Removed duplicates. Final count: {len(deduplicated_data)} entries. JSONL saved as {output_jsonl}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete images with no associated entries in JSONL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Removed: 00695-3-Frontal-PA-32.0-Female-White.png\n",
      "Removed: 04083-30-Frontal-PA-40.0-Male-White.png\n",
      "Removed: 29161-2-Frontal-PA-58.0-Female-White.png\n",
      "Removed: 13143-1-Frontal-PA-55.0-Male-Asian.png\n",
      "Removed: 34503-3-Frontal-PA-70.0-Female-Other.png\n",
      "Removed: 01725-1-Frontal-AP-89.0-Female-White.png\n",
      "Removed: 24428-21-Frontal-AP-61.0-Male-White.png\n",
      "Removed: 07061-26-Frontal-PA-18.0-Female-White.png\n",
      "Removed: 16418-29-Frontal-AP-46.0-Female-Unknown.png\n",
      "Removed: 35969-1-Frontal-AP-72.0-Female-White.png\n",
      "Removed: 28966-34-Frontal-PA-22.0-Male-White.png\n",
      "Removed: 29592-5-Frontal-AP-69.0-Female-White.png\n",
      "Removed: 16215-1-Frontal-PA-57.0-Male-White.png\n",
      "Removed: 22848-5-Frontal-PA-67.0-Male-White.png\n",
      "Removed: 31376-1-Frontal-PA-51.0-Female-Unknown.png\n",
      "Removed: 24428-39-Frontal-PA-61.0-Male-White.png\n",
      "Removed: 35260-5-Frontal-AP-67.0-Male-White.png\n",
      "Removed: 02875-8-Frontal-PA-65.0-Male-Asian.png\n",
      "Removed: 32756-4-Frontal-PA-55.0-Male-White.png\n",
      "Removed: 23212-16-Frontal-PA-50.0-Male-White.png\n",
      "Removed: 27715-6-Frontal-PA-28.0-Male-White.png\n",
      "Removed: 33307-3-Frontal-PA-70.0-Male-White.png\n",
      "Removed: 10139-33-Frontal-AP-81.0-Female-White.png\n",
      "Removed: 29732-32-Frontal-PA-42.0-Female-Other.png\n",
      "Removed: 26840-1-Frontal-AP-65.0-Male-White.png\n",
      "Removed: 29269-1-Frontal-PA-22.0-Male-Other.png\n",
      "Removed: 34521-7-Frontal-PA-66.0-Male-White.png\n",
      "Removed: 07001-3-Frontal-PA-43.0-Male-Other.png\n",
      "Removed: 04050-14-Frontal-AP-25.0-Male-Asian.png\n",
      "Removed: 34070-39-Frontal-PA-51.0-Female-Black.png\n",
      "Removed: 24549-3-Frontal-PA-67.0-Male-White.png\n",
      "Removed: 47472-3-Frontal-AP-25.0-Male-Unknown.png\n",
      "Removed: 30667-5-Frontal-PA-62.0-Male-White.png\n",
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      "Removed: 05706-1-Frontal-PA-49.0-Male-Other.png\n",
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      "Removed: 21495-8-Frontal-AP-82.0-Female-Black.png\n",
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      "Removed: 17426-5-Frontal-PA-60.0-Female-Unknown.png\n",
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      "Removed: 08318-7-Frontal-PA-53.0-Male-Unknown.png\n",
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      "Removed: 11162-5-Frontal-PA-62.0-Male-Asian.png\n",
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      "Removed: 01457-58-Frontal-AP-26.0-Male-Other.png\n",
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      "Removed: 09398-10-Frontal-PA-24.0-Female-Asian.png\n",
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      "Removed: 40985-3-Frontal-AP-31.0-Male-White.png\n",
      "Removed: 27610-1-Frontal-PA-60.0-Male-White.png\n",
      "Removed: 25296-1-Frontal-PA-60.0-Male-White.png\n",
      "Removed: 54381-1-Frontal-AP-60.0-Male-Black.png\n",
      "Removed: 13162-21-Frontal-PA-30.0-Male-Asian.png\n",
      "Removed: 24031-4-Frontal-PA-50.0-Female-White.png\n",
      "Removed: 21074-1-Frontal-AP-53.0-Male-White.png\n",
      "Removed: 13849-30-Frontal-AP-37.0-Male-White.png\n",
      "Removed: 21172-15-Frontal-AP-49.0-Male-White.png\n",
      "Removed: 16719-5-Frontal-PA-58.0-Male-White.png\n",
      "Removed: 05959-3-Frontal-PA-70.0-Male-White.png\n",
      "Removed: 08708-1-Frontal-PA-78.0-Female-White.png\n",
      "Removed: 34381-2-Frontal-PA-58.0-Male-Unknown.png\n",
      "Removed: 05233-3-Frontal-AP-51.0-Male-White.png\n",
      "Removed: 03763-10-Frontal-AP-75.0-Female-White.png\n",
      "Removed: 15103-4-Frontal-PA-41.0-Male-Asian.png\n",
      "Removed: 16209-8-Frontal-PA-61.0-Female-Asian.png\n",
      "Removed: 11439-1-Frontal-PA-89.0-Female-White.png\n",
      "Removed: 12978-1-Frontal-AP-85.0-Male-White.png\n",
      "Removed: 28966-28-Frontal-AP-22.0-Male-White.png\n",
      "Removed: 15373-1-Frontal-PA-61.0-Male-White.png\n",
      "Removed: 41165-6-Frontal-AP-74.0-Male-White.png\n",
      "Removed: 22665-6-Frontal-AP-50.0-Male-Asian.png\n",
      "Removed: 10182-10-Frontal-PA-61.0-Male-White.png\n",
      "Removed: 18897-1-Frontal-PA-24.0-Female-Asian.png\n",
      "Removed: 13663-1-Frontal-PA-41.0-Male-White.png\n",
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      "Removed: 42969-3-Frontal-AP-84.0-Male-Asian.png\n",
      "Removed: 12978-3-Frontal-AP-85.0-Male-White.png\n",
      "Removed: 26704-22-Frontal-PA-29.0-Male-Pacific Islander.png\n",
      "Removed: 15819-7-Frontal-PA-50.0-Male-White.png\n",
      "Removed: 11381-2-Frontal-PA-54.0-Male-Other.png\n",
      "Removed: 31969-7-Frontal-PA-72.0-Male-White.png\n",
      "Removed: 09711-3-Frontal-PA-81.0-Male-White.png\n",
      "Removed: 26199-1-Frontal-PA-56.0-Male-White.png\n",
      "Removed: 02660-1-Frontal-PA-41.0-Male-Unknown.png\n",
      "Removed: 36583-15-Frontal-AP-59.0-Male-Asian.png\n",
      "Removed: 23299-3-Frontal-PA-39.0-Female-White.png\n",
      "Removed: 30224-5-Frontal-PA-70.0-Male-White.png\n",
      "Removed: 02324-2-Frontal-PA-46.0-Male-White.png\n",
      "Removed: 27254-6-Frontal-PA-70.0-Female-White.png\n",
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      "Removed: 10140-18-Frontal-AP-71.0-Female-Unknown.png\n",
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      "Removed: 04586-3-Frontal-PA-31.0-Male-White.png\n",
      "Removed: 19008-2-Frontal-PA-44.0-Female-White.png\n",
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      "Removed: 28590-6-Frontal-AP-82.0-Male-Asian.png\n",
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      "Removed: 10316-8-Frontal-PA-50.0-Female-Asian.png\n",
      "Removed: 13162-44-Frontal-PA-30.0-Male-Asian.png\n",
      "Removed: 15583-5-Frontal-PA-78.0-Female-Pacific Islander.png\n",
      "Removed: 34026-9-Frontal-AP-67.0-Male-Unknown.png\n",
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      "Removed: 46741-5-Frontal-AP-66.0-Female-Asian.png\n",
      "Removed: 30975-1-Frontal-AP-62.0-Female-White.png\n",
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      "Removed: 30063-1-Frontal-PA-22.0-Male-Unknown.png\n",
      "Removed: 10734-37-Frontal-AP-83.0-Male-White.png\n",
      "Removed: 16464-4-Frontal-PA-49.0-Male-White.png\n",
      "Removed: 31871-1-Frontal-AP-68.0-Male-White.png\n",
      "Removed: 31969-5-Frontal-AP-72.0-Male-White.png\n",
      "Removed: 34503-5-Frontal-PA-70.0-Female-Other.png\n",
      "Removed: 36518-8-Frontal-AP-60.0-Female-Unknown.png\n",
      "Removed: 24508-1-Frontal-PA-60.0-Male-Other.png\n",
      "Removed: 23353-2-Frontal-PA-75.0-Female-Other.png\n",
      "Removed: 35480-23-Frontal-AP-48.0-Male-Unknown.png\n",
      "Removed: 24537-2-Frontal-PA-87.0-Male-White.png\n",
      "Removed: 14889-3-Frontal-AP-47.0-Male-Unknown.png\n",
      "Removed: 22798-1-Frontal-PA-73.0-Male-Pacific Islander.png\n",
      "Removed: 01071-25-Frontal-AP-82.0-Male-White.png\n",
      "Removed: 28069-6-Frontal-PA-20.0-Female-White.png\n",
      "Removed: 08673-1-Frontal-PA-20.0-Male-White.png\n",
      "Removed: 17396-1-Frontal-PA-89.0-Female-White.png\n",
      "Removed: 01335-2-Frontal-PA-66.0-Male-White.png\n",
      "Removed: 25649-1-Frontal-PA-65.0-Male-White.png\n",
      "Removed: 15639-1-Frontal-PA-18.0-Male-White.png\n",
      "Removed: 09151-1-Frontal-PA-73.0-Female-White.png\n",
      "Removed: 03098-1-Frontal-PA-66.0-Male-White.png\n",
      "Removed: 33518-1-Frontal-PA-89.0-Female-White.png\n",
      "Removed: 32592-3-Frontal-PA-60.0-Male-White.png\n",
      "Removed: 01708-2-Frontal-AP-73.0-Male-White.png\n",
      "Removed: 25683-1-Frontal-PA-71.0-Female-White.png\n",
      "Cleanup complete. 6382 images were deleted.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "# Define file paths\n",
    "jsonl_file = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/deduplicated_output.jsonl\"  # Change to match your JSONL file\n",
    "image_folder = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/Raw_New/images\"  # Change this to your image folder path\n",
    "\n",
    "# Step 1: Extract image filenames from JSONL\n",
    "valid_images = set()\n",
    "with open(jsonl_file, \"r\", encoding=\"utf-8\") as infile:\n",
    "    for line in infile:\n",
    "        entry = json.loads(line.strip())\n",
    "        valid_images.add(entry[\"image\"])  # Store valid image names\n",
    "\n",
    "# Step 2: Traverse the image folder and remove unmatched images\n",
    "removed_count = 0\n",
    "for image_file in os.listdir(image_folder):\n",
    "    image_path = os.path.join(image_folder, image_file)\n",
    "\n",
    "    # Check if the file is an image and if it's not in the valid image set\n",
    "    if os.path.isfile(image_path) and image_file not in valid_images:\n",
    "        os.remove(image_path)\n",
    "        removed_count += 1\n",
    "        print(f\"Removed: {image_file}\")\n",
    "\n",
    "print(f\"Cleanup complete. {removed_count} images were deleted.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete JSONL entries with no associated images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixed JSONL file created: /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/fixed_output.jsonl\n",
      "Original JSONL entries: 40268\n",
      "Valid JSONL entries (matching images): 40268\n",
      "Removed 0 extra JSON entries.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "# Define file paths\n",
    "jsonl_file = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/deduplicated_output.jsonl\"  # Change this to match your JSONL file\n",
    "image_folder = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/Raw_New/images\"  # Change this to your actual image folder\n",
    "output_jsonl = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/fixed_output.jsonl\"  # New JSONL file with only valid entries\n",
    "\n",
    "# Step 1: Get the list of actual images in the folder\n",
    "existing_images = set(os.listdir(image_folder))  # List all image files in the directory\n",
    "\n",
    "# Step 2: Filter the JSONL file to keep only valid entries\n",
    "valid_entries = []\n",
    "with open(jsonl_file, \"r\", encoding=\"utf-8\") as infile:\n",
    "    for line in infile:\n",
    "        entry = json.loads(line.strip())\n",
    "        image_name = entry[\"image\"]\n",
    "        \n",
    "        # Check if the image exists in the folder\n",
    "        if image_name in existing_images:\n",
    "            valid_entries.append(entry)\n",
    "\n",
    "# Step 3: Write the cleaned JSONL file\n",
    "with open(output_jsonl, \"w\", encoding=\"utf-8\") as outfile:\n",
    "    for entry in valid_entries:\n",
    "        outfile.write(json.dumps(entry) + \"\\n\")\n",
    "\n",
    "print(f\"Fixed JSONL file created: {output_jsonl}\")\n",
    "print(f\"Original JSONL entries: {len(open(jsonl_file).readlines())}\")\n",
    "print(f\"Valid JSONL entries (matching images): {len(valid_entries)}\")\n",
    "print(f\"Removed {len(open(jsonl_file).readlines()) - len(valid_entries)} extra JSON entries.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SPLIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Splitting completed! Data saved in /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/train, test, val\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import jsonlines\n",
    "import shutil\n",
    "import random\n",
    "\n",
    "# Define file paths\n",
    "jsonl_file = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/deduplicated_output.jsonl\"  #  cleaned JSONL file\n",
    "image_folder = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/Raw_New/images\"  # Folder containing images\n",
    "output_folder = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits\"  # Where splits will be stored\n",
    "\n",
    "# Ensure output directories exist\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "os.makedirs(os.path.join(output_folder, \"train\"), exist_ok=True)\n",
    "os.makedirs(os.path.join(output_folder, \"test\"), exist_ok=True)\n",
    "os.makedirs(os.path.join(output_folder, \"val\"), exist_ok=True)\n",
    "\n",
    "# Read JSONL data\n",
    "with jsonlines.open(jsonl_file, \"r\") as reader:\n",
    "    data = list(reader)\n",
    "\n",
    "# Shuffle the data for randomness\n",
    "random.shuffle(data)\n",
    "\n",
    "# Split the dataset\n",
    "total_samples = len(data)\n",
    "train_split = int(0.7 * total_samples)\n",
    "test_split = int(0.1 * total_samples)\n",
    "\n",
    "train_data = data[:train_split]\n",
    "test_data = data[train_split:train_split + test_split]\n",
    "val_data = data[train_split + test_split:]\n",
    "\n",
    "# Function to save JSONL files and copy images\n",
    "def save_split(data, split_name):\n",
    "    split_jsonl_path = os.path.join(output_folder, f\"{split_name}.jsonl\")\n",
    "    split_image_folder = os.path.join(output_folder, split_name)\n",
    "\n",
    "    with jsonlines.open(split_jsonl_path, \"w\") as writer:\n",
    "        for obj in data:\n",
    "            writer.write(obj)\n",
    "            # Copy the corresponding image\n",
    "            image_name = obj[\"image\"]\n",
    "            src_image_path = os.path.join(image_folder, image_name)\n",
    "            dst_image_path = os.path.join(split_image_folder, image_name)\n",
    "            if os.path.exists(src_image_path):  # Ensure the image exists\n",
    "                shutil.copy(src_image_path, dst_image_path)\n",
    "\n",
    "# Save each split\n",
    "save_split(train_data, \"train\")\n",
    "save_split(test_data, \"test\")\n",
    "save_split(val_data, \"val\")\n",
    "\n",
    "print(f\"✅ Splitting completed! Data saved in {output_folder}/train, test, val\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Cleanup to remove empty entries and remove corresponding images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import jsonlines\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Define dataset paths\n",
    "datasets = {\n",
    "    \"train\": {\"jsonl\": \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/train.jsonl\", \"folder\": \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/train\"},\n",
    "    \"val\": {\"jsonl\": \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/val.jsonl\", \"folder\": \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/val\"},\n",
    "    \"test\": {\"jsonl\": \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/test.jsonl\", \"folder\": \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/test\"},\n",
    "}\n",
    "\n",
    "def clean_dataset(jsonl_file, image_folder):\n",
    "    \"\"\"\n",
    "    1. Removes entries with empty captions in JSONL.\n",
    "    2. Deletes corresponding images of removed entries.\n",
    "    3. Deletes any extra images in the folder that have no JSONL entry.\n",
    "    \"\"\"\n",
    "\n",
    "    cleaned_data = []\n",
    "    removed_entries = 0\n",
    "    json_image_set = set()\n",
    "    i = 0\n",
    "\n",
    "    # Step 1: Read JSONL file & filter out empty captions\n",
    "    with jsonlines.open(jsonl_file, \"r\") as reader:\n",
    "        data = list(reader)\n",
    "\n",
    "    for entry in tqdm(data, desc=f\"Processing {jsonl_file}\"):\n",
    "        if entry[\"suffix\"].strip():  # Keep only non-empty captions\n",
    "            cleaned_data.append(entry)\n",
    "            json_image_set.add(entry[\"image\"] + \".png\")  # Store valid image names\n",
    "        else:\n",
    "            # Remove associated image\n",
    "            print(removed_entries)\n",
    "            image_path = os.path.join(image_folder, entry[\"image\"] + \".png\")\n",
    "            if os.path.exists(image_path):\n",
    "                os.remove(image_path)\n",
    "                removed_entries += 1\n",
    "\n",
    "    # Step 2: Save cleaned JSONL file\n",
    "    cleaned_jsonl = jsonl_file.replace(\".jsonl\", \"_cleaned.jsonl\")\n",
    "    with jsonlines.open(cleaned_jsonl, \"w\") as writer:\n",
    "        writer.write_all(cleaned_data)\n",
    "\n",
    "    print(f\"{jsonl_file}: Removed {removed_entries} entries with empty captions. Cleaned file saved as {cleaned_jsonl}\")\n",
    "\n",
    "    # Step 3: Delete images not present in JSONL\n",
    "    all_image_files = set(os.listdir(image_folder))\n",
    "    extra_images = all_image_files - json_image_set  # Images without JSONL entries\n",
    "\n",
    "    for extra_image in tqdm(extra_images, desc=f\"Deleting extra images from {image_folder}\"):\n",
    "        os.remove(os.path.join(image_folder, extra_image))\n",
    "\n",
    "    print(f\"{image_folder}: Removed {len(extra_images)} extra images that had no entry in {jsonl_file}.\")\n",
    "\n",
    "# Apply cleaning process to all datasets\n",
    "for dataset_name, paths in datasets.items():\n",
    "    clean_dataset(paths[\"jsonl\"], paths[\"folder\"])\n",
    "\n",
    "print(\"Cleaning process completed!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/train.jsonl: 100%|██████████| 23912/23912 [00:00<00:00, 952606.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/train.jsonl: Removed 0 entries where 'suffix' was exactly 'NO'. Cleaned file saved as /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/train_cleaned.jsonl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/val.jsonl: 100%|██████████| 6875/6875 [00:00<00:00, 1032210.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/val.jsonl: Removed 0 entries where 'suffix' was exactly 'NO'. Cleaned file saved as /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/val_cleaned.jsonl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/test.jsonl: 100%|██████████| 3419/3419 [00:00<00:00, 1129337.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/test.jsonl: Removed 0 entries where 'suffix' was exactly 'NO'. Cleaned file saved as /cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/test_cleaned.jsonl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import jsonlines\n",
    "import re\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Remove NO, FINDINGS, IMPRESSION, COMPARISON\n",
    "def remove_exact_no_entries(jsonl_file, image_folder):\n",
    "    \"\"\"\n",
    "    Removes entries where 'suffix' contains only the word 'NO' (case-insensitive) \n",
    "    and deletes the corresponding images.\n",
    "    \"\"\"\n",
    "    cleaned_data = []\n",
    "    removed_entries = 0\n",
    "\n",
    "    # ✅ Read JSONL file\n",
    "    with jsonlines.open(jsonl_file, \"r\") as reader:\n",
    "        data = list(reader)\n",
    "\n",
    "    for entry in tqdm(data, desc=f\"Processing {jsonl_file}\"):\n",
    "        # ✅ Remove if 'suffix' is exactly \"NO\" (ignoring case and extra spaces)\n",
    "        if not re.fullmatch(r'\\s*FINDINGS\\s*', entry[\"suffix\"], re.IGNORECASE):  \n",
    "            cleaned_data.append(entry)\n",
    "        else:\n",
    "            # ✅ Remove associated image\n",
    "            image_path = os.path.join(image_folder, entry[\"image\"] + \".png\")\n",
    "            if os.path.exists(image_path):\n",
    "                os.remove(image_path)\n",
    "                removed_entries += 1\n",
    "\n",
    "    # ✅ Save cleaned JSONL file\n",
    "    cleaned_jsonl = jsonl_file.replace(\".jsonl\", \"_cleaned.jsonl\")\n",
    "    with jsonlines.open(cleaned_jsonl, \"w\") as writer:\n",
    "        writer.write_all(cleaned_data)\n",
    "\n",
    "    print(f\"✅ {jsonl_file}: Removed {removed_entries} entries where 'suffix' was exactly 'NO'. Cleaned file saved as {cleaned_jsonl}\")\n",
    "\n",
    "remove_exact_no_entries(\"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/train.jsonl\", \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/train\")\n",
    "remove_exact_no_entries(\"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/val.jsonl\", \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/val\")\n",
    "remove_exact_no_entries(\"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/test.jsonl\", \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n",
      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
      "Loading checkpoint shards: 100%|██████████| 8/8 [00:37<00:00,  4.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 5,242,880 || all params: 3,749,922,816 || trainable%: 0.1398\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import jsonlines\n",
    "from PIL import Image\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "from transformers import BlipForConditionalGeneration, BlipProcessor, Blip2ForConditionalGeneration, AutoProcessor\n",
    "from torch.optim import AdamW\n",
    "from transformers import get_scheduler\n",
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "# Use Mixed Precision (AMP) to save memory\n",
    "scaler = torch.cuda.amp.GradScaler()\n",
    "\n",
    "# Load BLIP model\n",
    "# model_name = \"Salesforce/blip2-opt-2.7b\"\n",
    "# device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "# processor = BlipProcessor.from_pretrained(model_name)\n",
    "# model = BlipForConditionalGeneration.from_pretrained(\"ybelkada/blip2-opt-2.7b-fp16-sharded\").to(device)\n",
    "\n",
    "# Image transformations\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)), \n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
    "])\n",
    "\n",
    "processor = AutoProcessor.from_pretrained(\"Salesforce/blip2-opt-2.7b\")\n",
    "model = Blip2ForConditionalGeneration.from_pretrained(\"ybelkada/blip2-opt-2.7b-fp16-sharded\", load_in_8bit=True)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# model.to(device)\n",
    "\n",
    "# Let's define the LoraConfig\n",
    "config = LoraConfig(\n",
    "    r=16,\n",
    "    lora_alpha=32,\n",
    "    lora_dropout=0.05,\n",
    "    bias=\"none\",\n",
    "    target_modules=[\"q_proj\", \"k_proj\"]\n",
    ")\n",
    "\n",
    "# Apply LoRA\n",
    "model = get_peft_model(model, config)\n",
    "model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from PIL import Image\n",
    "\n",
    "# ✅ Function to Generate Caption and Display Image\n",
    "def generate_caption(image_path):\n",
    "    image = Image.open(image_path).convert(\"RGB\")\n",
    "    \n",
    "    # ✅ Save Original Image for Display\n",
    "    original_image = image.copy()\n",
    "    \n",
    "    # ✅ Apply Transformations for Model Input\n",
    "    image = transform(image).unsqueeze(0).to(device)\n",
    "\n",
    "    # ✅ Generate Caption\n",
    "    with torch.no_grad():\n",
    "        generated_ids = model.generate(pixel_values=image, max_new_tokens=50)\n",
    "    caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
    "\n",
    "    # ✅ Plot Image with Caption\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.imshow(original_image)\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(caption, fontsize=12, wrap=True)\n",
    "    plt.show()\n",
    "    \n",
    "    return caption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Both `max_new_tokens` (=50) and `max_length`(=51) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated Caption: a chest x - ray shows the location of the heart\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import random \n",
    "\n",
    "root_dir = \"/cluster/home/ammaa/Downloads/Projects/CheXpert-Report-Generation/dataset_splits/test\"\n",
    "\n",
    "test_dir = os.listdir(root_dir)\n",
    "num_of_images = len(test_dir)\n",
    "\n",
    "index = random.randint(0, num_of_images)\n",
    "# Test on a new image\n",
    "test_image = os.path.join(root_dir, test_dir[index])\n",
    "print(\"Generated Caption:\", generate_caption(test_image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}