From 2ae6855f9924f19f1569836589050e73ff468d85 Mon Sep 17 00:00:00 2001
From: Neeraja Krishnakumar <n.krishkumar.a@gmail.com>
Date: Sun, 5 May 2024 18:48:47 +0530
Subject: [PATCH] Internship Application

---
 .../IndividualRound.ipynb                     | 5632 +++++++++++++++++
 .../Internship Letter.pdf                     |  Bin 0 -> 31822 bytes
 2 files changed, 5632 insertions(+)
 create mode 100644 Internship Project/Neeraja Krishnakumar/IndividualRound.ipynb
 create mode 100644 Internship Project/Neeraja Krishnakumar/Internship Letter.pdf

diff --git a/Internship Project/Neeraja Krishnakumar/IndividualRound.ipynb b/Internship Project/Neeraja Krishnakumar/IndividualRound.ipynb
new file mode 100644
index 0000000..ae559c4
--- /dev/null
+++ b/Internship Project/Neeraja Krishnakumar/IndividualRound.ipynb	
@@ -0,0 +1,5632 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DiFsNuEplzXv"
+      },
+      "source": [
+        "# **Document Title, Author(s) Extraction and Alternative Title Generation**\n",
+        "\n",
+        "\n",
+        "# Introduction\n",
+        "\n",
+        "This project aims to automatically extract document details"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZvEkxNQMmPRe"
+      },
+      "source": [
+        "# Setup\n",
+        "\n",
+        "Install pytesseract for OCR.\n",
+        "\n",
+        "Install tesseract-ocr for text recognition."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NYJeTs71FMdz",
+        "outputId": "3eebb4e1-8eb1-4eed-ec95-b978719980f3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting pytesseract\n",
+            "  Downloading pytesseract-0.3.10-py3-none-any.whl (14 kB)\n",
+            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from pytesseract) (24.0)\n",
+            "Requirement already satisfied: Pillow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from pytesseract) (9.4.0)\n",
+            "Installing collected packages: pytesseract\n",
+            "Successfully installed pytesseract-0.3.10\n",
+            "Reading package lists... Done\n",
+            "Building dependency tree... Done\n",
+            "Reading state information... Done\n",
+            "The following additional packages will be installed:\n",
+            "  tesseract-ocr-eng tesseract-ocr-osd\n",
+            "The following NEW packages will be installed:\n",
+            "  tesseract-ocr tesseract-ocr-eng tesseract-ocr-osd\n",
+            "0 upgraded, 3 newly installed, 0 to remove and 45 not upgraded.\n",
+            "Need to get 4,816 kB of archives.\n",
+            "After this operation, 15.6 MB of additional disk space will be used.\n",
+            "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-eng all 1:4.00~git30-7274cfa-1.1 [1,591 kB]\n",
+            "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-osd all 1:4.00~git30-7274cfa-1.1 [2,990 kB]\n",
+            "Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr amd64 4.1.1-2.1build1 [236 kB]\n",
+            "Fetched 4,816 kB in 1s (4,124 kB/s)\n",
+            "debconf: unable to initialize frontend: Dialog\n",
+            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 3.)\n",
+            "debconf: falling back to frontend: Readline\n",
+            "debconf: unable to initialize frontend: Readline\n",
+            "debconf: (This frontend requires a controlling tty.)\n",
+            "debconf: falling back to frontend: Teletype\n",
+            "dpkg-preconfigure: unable to re-open stdin: \n",
+            "Selecting previously unselected package tesseract-ocr-eng.\n",
+            "(Reading database ... 121920 files and directories currently installed.)\n",
+            "Preparing to unpack .../tesseract-ocr-eng_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
+            "Unpacking tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n",
+            "Selecting previously unselected package tesseract-ocr-osd.\n",
+            "Preparing to unpack .../tesseract-ocr-osd_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
+            "Unpacking tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n",
+            "Selecting previously unselected package tesseract-ocr.\n",
+            "Preparing to unpack .../tesseract-ocr_4.1.1-2.1build1_amd64.deb ...\n",
+            "Unpacking tesseract-ocr (4.1.1-2.1build1) ...\n",
+            "Setting up tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n",
+            "Setting up tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n",
+            "Setting up tesseract-ocr (4.1.1-2.1build1) ...\n",
+            "Processing triggers for man-db (2.10.2-1) ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install pytesseract\n",
+        "!sudo apt install tesseract-ocr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ym5o4WilmbeT"
+      },
+      "source": [
+        "Install unrar for extracting .rar files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0REateOyIWzd",
+        "outputId": "003ab890-f5f9-40c9-87d4-b7a53c9e0c41"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Reading package lists... Done\n",
+            "Building dependency tree... Done\n",
+            "Reading state information... Done\n",
+            "unrar is already the newest version (1:6.1.5-1).\n",
+            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n"
+          ]
+        }
+      ],
+      "source": [
+        "!sudo apt-get install unrar"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3EV5NBocmmTm"
+      },
+      "source": [
+        "#Data Upload\n",
+        "\n",
+        "Upload the dataset file (.rar format)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "EM9WD0tdKl-7",
+        "outputId": "9a69e9b5-abdb-4982-c98b-606a3db7dac1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-a02c0fd9-d6fe-4bc2-9b2e-2a9b6247d055\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-a02c0fd9-d6fe-4bc2-9b2e-2a9b6247d055\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Saving scientific_publication.rar to scientific_publication.rar\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import files\n",
+        "\n",
+        "# Upload .rar file\n",
+        "uploaded = files.upload()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sighYoeTm8ai"
+      },
+      "source": [
+        "#Extract Images\n",
+        "\n",
+        "Extract images from the uploaded .rar file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JbDlwjnpLsMQ",
+        "outputId": "e0e6a356-58e7-4c0d-afe8-e52f025d2d4a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "UNRAR 6.11 beta 1 freeware      Copyright (c) 1993-2022 Alexander Roshal\n",
+            "\n",
+            "\n",
+            "Extracting from scientific_publication.rar\n",
+            "\n",
+            "Creating    scientific_publication                                    OK\n",
+            "Extracting  scientific_publication/doc_000016.png                        \b\b\b\b  1%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000045.png                        \b\b\b\b  4%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000050.png                        \b\b\b\b  6%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000061.png                        \b\b\b\b  7%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000089.png                        \b\b\b\b  9%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000121.png                        \b\b\b\b 11%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000128.png                        \b\b\b\b 13%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000130.png                        \b\b\b\b 15%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000171.png                        \b\b\b\b 17%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000228.png                        \b\b\b\b 18%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000235.png                        \b\b\b\b 20%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000240.png                        \b\b\b\b 22%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000250.png                        \b\b\b\b 23%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000285.png                        \b\b\b\b 25%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000298.png                        \b\b\b\b 27%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000309.png                        \b\b\b\b 29%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000311.png                        \b\b\b\b 29%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000334.png                        \b\b\b\b 32%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000345.png                        \b\b\b\b 35%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000349.png                        \b\b\b\b 36%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000392.png                        \b\b\b\b 38%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000429.png                        \b\b\b\b 39%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000437.png                        \b\b\b\b 41%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000438.png                        \b\b\b\b 43%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000445.png                        \b\b\b\b 45%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000451.png                        \b\b\b\b 47%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000453.png                        \b\b\b\b 48%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000480.png                        \b\b\b\b 50%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000498.png                        \b\b\b\b 52%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000509.png                        \b\b\b\b 54%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000513.png                        \b\b\b\b 56%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000515.png                        \b\b\b\b 58%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000522.png                        \b\b\b\b 59%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000530.png                        \b\b\b\b 61%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000584.png                        \b\b\b\b 62%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000594.png                        \b\b\b\b 63%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000615.png                        \b\b\b\b 66%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000617.png                        \b\b\b\b 68%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000697.png                        \b\b\b\b 70%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000741.png                        \b\b\b\b 72%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000756.png                        \b\b\b\b 75%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000766.png                        \b\b\b\b 77%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000767.png                        \b\b\b\b 79%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000769.png                        \b\b\b\b 81%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000779.png                        \b\b\b\b 83%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000785.png                        \b\b\b\b 84%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000821.png                        \b\b\b\b 87%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000832.png                        \b\b\b\b 89%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000845.png                        \b\b\b\b 92%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000864.png                        \b\b\b\b 93%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000891.png                        \b\b\b\b 96%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000942.png                        \b\b\b\b 97%\b\b\b\b\b  OK \n",
+            "Extracting  scientific_publication/doc_000950.png                        \b\b\b\b 99%\b\b\b\b\b  OK \n",
+            "All OK\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Extract images from the uploaded .rar folder\n",
+        "!unrar x \"scientific_publication.rar\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pS2mNOfZnFiX"
+      },
+      "source": [
+        "#Import Libraries\n",
+        "\n",
+        "Import necessary libraries such as os, pytesseract, Image, and pandas."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "YCwiznwxVh3z"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import pytesseract\n",
+        "from PIL import Image\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ie9N8z4JnM9u"
+      },
+      "source": [
+        "#Text Extraction From Images\n",
+        "\n",
+        "**OCR Function**\n",
+        "\n",
+        "Define a function to extract text from images using OCR."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "4MwDeFEtagCf"
+      },
+      "outputs": [],
+      "source": [
+        "def extract_text_from_image(image_path):\n",
+        "    # Open the image\n",
+        "    img = Image.open(image_path)\n",
+        "\n",
+        "    # Perform OCR to extract text\n",
+        "    text = pytesseract.image_to_string(img)\n",
+        "\n",
+        "    return text\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2JPaayM_nzcM"
+      },
+      "source": [
+        "**Set Image Directory**\n",
+        "\n",
+        "Define the directory containing extracted images.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "rqA8XuqMalcV"
+      },
+      "outputs": [],
+      "source": [
+        "image_dir = '/content/scientific_publication/'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xdQdMmBzn2yu"
+      },
+      "source": [
+        "**List Image Files**\n",
+        "\n",
+        "List all image files in the specified directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "Wxu7gcycbHUp"
+      },
+      "outputs": [],
+      "source": [
+        "image_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if os.path.isfile(os.path.join(image_dir, f))]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GPs0B9u5n7zw"
+      },
+      "source": [
+        "**Extract Text from Images**\n",
+        "\n",
+        "Extract text from each image and store it in a dictionary."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "T0R5WdqoYr9b"
+      },
+      "outputs": [],
+      "source": [
+        "image_texts = {}\n",
+        "\n",
+        "for image_file in image_files:\n",
+        "    text = extract_text_from_image(image_file)\n",
+        "    image_texts[os.path.basename(image_file)] = text\n",
+        "    break\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fJXG1kPNn-ns"
+      },
+      "source": [
+        "**Display Extracted Text**\n",
+        "\n",
+        "Display the extracted text from each image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GJw_2Qp9d1YN",
+        "outputId": "707243a8-79d6-4305-d4c1-31271b99cb84"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Text extracted from doc_000891.png:\n",
+            "Reprinted from Biochemistry, 198, 27, 40\n",
+            "— copyright © 198 bythe American Choma! Soest and reprinted By peviniaon of copyright owner.\n",
+            "\n",
+            "Native Structure and Physical Properties of Bovine Brain Kinesin and\n",
+            "Identification of the ATP-Binding Subunit Polypeptide!\n",
+            "\n",
+            "George S. Bloom,\" Mark C. Wagner, K. Kevin Pfister, and Soot T- Brady\n",
+            "\n",
+            "Department of Cell Biology ond Anatomy, Unversity of Texas Souwen, “Medical Center at Dallas, $323 Harry Hines\n",
+            "‘Boulevard, Dallas, Texas 75235\n",
+            "\n",
+            "‘Received June 2, 1987; Revised Manuscript Received October 29, 1987\n",
+            "\n",
+            " \n",
+            "\n",
+            " \n",
+            "\n",
+            "anstract: Kinesin was extensively purified from bovine brain cytosol by 8 ricrotubule-binding step in\n",
+            "{fe presence of 5-adenylylimidodipbosphate (AMP-PNP),folowat by gel filtration chromatography and\n",
+            "the present on ultracentrifugation. The prodets consistently contained 12400 (124K) and 64000 (64K)\n",
+            "aaa rides, These tw popes appear to represent heat and BN chains of kinesin, respectively,\n",
+            "Galton Po aiid on suerose gradients toa constant and equimolar stoichonery ‘and bound stably\n",
+            "area ae cain the presence of AMP-PNP bat sot ATP. ‘The mobilities Of 124K and 64K in sodium\n",
+            "‘Paseo sulfate-polyacrylamide gels under reducing conditions were te S06 ‘under nonreducing conditions.\n",
+            "\n",
+            "gosGifusion coefficient of (2.24 + 0.21) X 107 ems” and 2 sedimentation coefficient of (9.56 + 0.34)\n",
+            "A ais oe ined for native Knesn by gl filtration and sucrose prion’ ultracentrifugation,\n",
+            "apectvely These values were used to eaeulate a native malerlae ight of about 379 000 an suggest\n",
+            "eee axial rato of approximately 20, Extensively purified Kine exhibited microtubule-\n",
+            "a a esti, and only the 124K subunit incorporated ATP in photoanty labeling experiments\n",
+            "sine DEPIATP. Collectively, these data favor the interpretation hat bovine brain kinesin is 2 highly\n",
+            "wane ted snicrotubuleactivated ATPase comprising wo subunits cach of 124000 and 64000 daltons, that\n",
+            "nea eee pt inked to one another by dsaide Bonds, and thatthe heawy chains are the ATP-binding\n",
+            "subunits.\n",
+            "\n",
+            " \n",
+            "\n",
+            " \n",
+            " \n",
+            "\n",
+            "Beene the sor cts the mer mutiney tyne mp raed hat hae epion P\n",
+            "Bocuse tence se re Team impor Mile or ee\n",
+            "se i he faye ae NE a a\n",
+            "FE eC itomperty a) a outaly\n",
+            "Te ee re ee\n",
+            "Hk as mcctond, sal ml antesof he aon een on\n",
+            "segs at ne ae ee nace HSM ee PN\n",
+            "2S Sami Ge rt oman oR\n",
+            "Bh PO pega (60) Ee oe\n",
+            "an rim onmncii 8 and o\n",
+            "\n",
+            "   \n",
+            "\n",
+            "  \n",
+            "\n",
+            "sees) \"Together, the anterograde and retrograde or S-adensyh inidodiphosphate (AMP-PNP), was found t0 in-\n",
+            "fancile movements ate known as “fast axonal transport hibit this process (Brady et al, 1983, 1985; Lasek & Brady.\n",
+            "Bem Seganle motity has been observed inte cytoplasm 1985), EAS the presence of nearly stoichiometric levels\n",
+            "\n",
+            "Sree types as diverse as HeLa ces (Freed & Lebowitz, OEATP, AMP-PNP blocks organelle movements completely,\n",
+            "Soo), frog Keratocytes (Hayden et al, 1983: Hayden & Allen, while promeite the formation of stable complexes of orgs:\n",
+            "\n",
+            "1984), and giant amoebae (Koonce & Schliva, 1985), im- nee Microtubule, and, apparently, the motors for organelle\n",
+            "iyi that che proses of widespread biological signifieance, ans (Lasek & Brady, 1985). By contast, in other motility\n",
+            "Bone ee we of dso enhanced ight microscopy in cou» systems Sao) as the exoneme (Satir et al, 1981; Penningrath\n",
+            "\n",
+            "Jpnriion wth correlative immunoflurescence or electron 1982) and actomyosin (Greene & Eisenberg, 1980;\n",
+            "junction nas established that mirotubules serve as tracks Bicsca 6 1986), AMP-PNP promotes dissociation of the\n",
+            "rept orgenllesravel an that individual microtubules motes yaein ‘myosin) from the structure it moves (mi-\n",
+            "aon vat biectine! vse anspor (Hayden tal, 1983: crotubsies of filaments). The effects of AMP-PNP on\n",
+            "Haydon & Allen, 1983; Koonce & Sehliva, 1985;Schnapp organ ‘motility in the axon indicated, therefore, that fast\n",
+            "et al., 1983) sronal transport is driven by a new class of mechnochemica}\n",
+            "\n",
+            "aia uth leading challenges to emerge from these Findings protein tha is ‘biochemically and pharmacologically distinet\n",
+            "aan aeatife mechandchemiea proteins responsible for from dynein and yet (Lasek & Brady, 1985).\n",
+            "\n",
+            " \n",
+            "\n",
+            " \n",
+            "\n",
+            " \n",
+            "\n",
+            " \n",
+            "\n",
+            "ransiocating memorane-bounded organelles along microtu- “The evidence that AMP-PNP stabilizes binding of @ fast\n",
+            "rrpartant clues to the identity of « motor for fast axonal anspor AN to microtubules in situ suggested that\n",
+            "bales ener! were gained from studies of axopasm exr tis raleaide ibe able to serve as a selective probe for\n",
+            "\n",
+            "anon anspor, <r ant anon Videenhanced ght the biskemia denieaion ft ey ‘Accordingly,\n",
+            "\n",
+            "He ai C1985) put microtubules from cick brain extracts\n",
+            "CREST OEE pemetnw Toe plpertie\n",
+            "POSE TEE Ge oa maT ee eS amc were vty similar, but\n",
+            "{STB), Weich Foundation Grant 11077 (GSB. and STB. .)eand NIH ‘one polypeptide, with a molecular ‘weight by sodium dodecy!\n",
+            "B78 ee uonstip avard GNGIOIS (KK. aoe acrlamide ge clectopharesis (SDS-PAGE) of\n",
+            "\n",
+            "  \n",
+            "\n",
+            " \n",
+            "\n",
+            ".0006-2960/88 /0427-3409801.50/0 © 1988 American Chemical Society\n",
+            "\f\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "for image_file, text in image_texts.items():\n",
+        "    print(f\"Text extracted from {image_file}:\\n{text}\\n\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "llI6vHbHoQXn"
+      },
+      "source": [
+        "#Title, Author(s) Extraction and Alternate Title Generation\n",
+        "\n",
+        "**Install Packages**\n",
+        "\n",
+        "Install datasets, transformers, and sentencepiece for NLP tasks."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EkIuogLggRaJ",
+        "outputId": "52004253-da17-4e27-81ae-aafacf8d748e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting datasets\n",
+            "  Downloading datasets-2.19.0-py3-none-any.whl (542 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting evaluate\n",
+            "  Downloading evaluate-0.4.2-py3-none-any.whl (84 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: transformers[sentencepiece] in /usr/local/lib/python3.10/dist-packages (4.40.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.14.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
+            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (14.0.2)\n",
+            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
+            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
+            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n",
+            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
+            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.2)\n",
+            "Collecting xxhash (from datasets)\n",
+            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting multiprocess (from datasets)\n",
+            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.5)\n",
+            "Collecting huggingface-hub>=0.21.2 (from datasets)\n",
+            "  Downloading huggingface_hub-0.23.0-py3-none-any.whl (401 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m35.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.0)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (2023.12.25)\n",
+            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (0.19.1)\n",
+            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (0.4.3)\n",
+            "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (0.1.99)\n",
+            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.20.3)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
+            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets) (4.11.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2024.2.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
+            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
+            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets, evaluate\n",
+            "  Attempting uninstall: huggingface-hub\n",
+            "    Found existing installation: huggingface-hub 0.20.3\n",
+            "    Uninstalling huggingface-hub-0.20.3:\n",
+            "      Successfully uninstalled huggingface-hub-0.20.3\n",
+            "Successfully installed datasets-2.19.0 dill-0.3.8 evaluate-0.4.2 huggingface-hub-0.23.0 multiprocess-0.70.16 xxhash-3.4.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install datasets evaluate transformers[sentencepiece]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mwo0TKafpCuO"
+      },
+      "source": [
+        "**Import Pipeline**\n",
+        "\n",
+        "Import pipeline from transformers for question-answering tasks.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "iUWpILF15Ngt"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import pipeline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gg4-AnIOpGZd"
+      },
+      "source": [
+        "**Title and Author Extraction**\n",
+        "\n",
+        "Define a function to extract title and author using question answering.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "gafnMn3ix9Ct"
+      },
+      "outputs": [],
+      "source": [
+        "# Function to extract title and author using question answering\n",
+        "def extract_title_and_author(text):\n",
+        "    # Initialize QA pipeline\n",
+        "    qa_pipeline = pipeline(\"question-answering\", model=\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
+        "\n",
+        "    # Define questions\n",
+        "    title_question = \"What is the title of this document?\"\n",
+        "    author_question = \"Who are the authors of this document?\"\n",
+        "\n",
+        "    # Extract title\n",
+        "    title_result = qa_pipeline(question=title_question, context=text)\n",
+        "    title = title_result['answer']\n",
+        "\n",
+        "    # Extract author\n",
+        "    author_result = qa_pipeline(question=author_question, context=text)\n",
+        "    author = author_result['answer']\n",
+        "\n",
+        "    return title, author\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4VD0MKI_pMFQ"
+      },
+      "source": [
+        "**Alternative Title Generation**\n",
+        "\n",
+        "Define a function to generate alternative titles using BART models.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 375,
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+          ]
+        },
+        "id": "Z6IJ1aOCPhTg",
+        "outputId": "1dfa9211-bc77-4e09-906c-650e3d23ad55"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "96324af20bd342cb84492e302c609bc5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0b526006050a4e7e866d6078f1b89eca",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4ca10c1ba2004b32a6a69dc03eda6d1b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d049c29f4cea4d1a9c916137d43fd190",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/1.58k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5672f4d5b43b46fab59130f7e3b89c1b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "33db67d20046442f9133a38add0bfe74",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from transformers import BartTokenizer, BartForConditionalGeneration\n",
+        "\n",
+        "# Load BART tokenizer and model\n",
+        "tokenizer = BartTokenizer.from_pretrained(\"facebook/bart-large-cnn\")\n",
+        "model = BartForConditionalGeneration.from_pretrained(\"facebook/bart-large-cnn\")\n",
+        "\n",
+        "def generate_alternative_title(text):\n",
+        "    # Tokenize input text\n",
+        "    inputs = tokenizer(text, max_length=1024, return_tensors=\"pt\", truncation=True)\n",
+        "\n",
+        "    # Generate alternative title using BART\n",
+        "    outputs = model.generate(\n",
+        "        **inputs,\n",
+        "        max_length=50,  # Set a maximum length for the output title\n",
+        "        num_beams=4,    # Use beam search for better alternatives\n",
+        "        length_penalty=0.6,  # Lower length penalty for shorter output\n",
+        "        early_stopping=True  # Stop generation when the full stop is encountered\n",
+        "    )\n",
+        "\n",
+        "    # Decode the generated title\n",
+        "    generated_title = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)\n",
+        "\n",
+        "    # Find the index of the first full stop\n",
+        "    first_fullstop_index = generated_title.find('.')\n",
+        "    if first_fullstop_index != -1:\n",
+        "        generated_title = generated_title[:first_fullstop_index+1]  # Include the full stop\n",
+        "\n",
+        "    return generated_title"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6Pu0I-4ppQ5F"
+      },
+      "source": [
+        "**Initialize Lists**\n",
+        "\n",
+        "Initialize lists to store extracted data.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "eJRIK9CtQRC3"
+      },
+      "outputs": [],
+      "source": [
+        "# Initialize lists to store extracted data\n",
+        "titles = []\n",
+        "authors = []\n",
+        "alternate_titles = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5DrzOh8ipUd3"
+      },
+      "source": [
+        "**Extraction and Generation**\n",
+        "\n",
+        "Extract titles and authors and generate alternative titles for each document.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 325,
+          "referenced_widgets": [
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+            "b995004447224d479bd9ff05d1486c79"
+          ]
+        },
+        "id": "rjAFDtKr43Sr",
+        "outputId": "064fbecd-49b4-452d-c236-938d9e09a3c5"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1165: UserWarning: Unfeasible length constraints: `min_length` (56) is larger than the maximum possible length (50). Generation will stop at the defined maximum length. You should decrease the minimum length and/or increase the maximum length.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4a0693ab7d4b4cb7b01c4140f0815139",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/443 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a627749f8bbc4034b65f87c43a51dfb7",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Some weights of the model checkpoint at bert-large-uncased-whole-word-masking-finetuned-squad were not used when initializing BertForQuestionAnswering: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
+            "- This IS expected if you are initializing BertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ed86e893f70a48bebaaa0d6e1712c074",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "13591e902da4423da87cafaae6c1d4c2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "75135ba14fb44d338291c6d1d5e85f64",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Generate alternative titles and extract titles and authors for each text\n",
+        "for image_file, text in image_texts.items():\n",
+        "    # Generate alternative title\n",
+        "    alternative_title = generate_alternative_title(text)\n",
+        "    # Extract titles and authors\n",
+        "    title, author = extract_title_and_author(text)\n",
+        "    # Append data to lists\n",
+        "    titles.append(title)\n",
+        "    authors.append(author)\n",
+        "    alternate_titles.append(alternative_title)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KVJeBOoepYZX"
+      },
+      "source": [
+        "**Create DataFrame**\n",
+        "\n",
+        "Create a DataFrame to store extracted and generated data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "KIWd6ymI5lI6"
+      },
+      "outputs": [],
+      "source": [
+        "# Create DataFrame\n",
+        "df = pd.DataFrame({\n",
+        "    'Document Title': titles,\n",
+        "    'Author(s)': authors,\n",
+        "    'Generated Title': alternate_titles\n",
+        "}, index=image_texts.keys())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "u55Mjxi2r2IH"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0jQQ_OIWpeD6"
+      },
+      "source": [
+        "**Display DataFrame**\n",
+        "\n",
+        "Display the DataFrame containing extracted and generated data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z2XYZZM7duQh",
+        "outputId": "b63017d3-77fa-4375-ad2d-21b752dc7448"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "                Document Title                  Author(s)  \\\n",
+            "doc_000891.png  clectopharesis  American Chemical Society   \n",
+            "\n",
+            "                                                  Generated Title  \n",
+            "doc_000891.png  Kinesin was extensively purified from bovine b...  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# Display DataFrame\n",
+        "print(df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81
+        },
+        "id": "Awr8E_lDd-tH",
+        "outputId": "ae1998a1-72bf-444e-d21f-6a6f21abd727"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Document Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"clectopharesis\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Author(s)\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"American Chemical Society\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generated Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Kinesin was extensively purified from bovine brain cytosol by 8 ricrotubule-binding step in the presence of 5-adenylylimidodipbosphate (AMP-PNP),folow\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fadf1b6e-3317-42a1-8906-1dc84b9c5f43\" class=\"colab-df-container\">\n",
+              "    <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>Document Title</th>\n",
+              "      <th>Author(s)</th>\n",
+              "      <th>Generated Title</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>doc_000891.png</th>\n",
+              "      <td>clectopharesis</td>\n",
+              "      <td>American Chemical Society</td>\n",
+              "      <td>Kinesin was extensively purified from bovine b...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fadf1b6e-3317-42a1-8906-1dc84b9c5f43')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fadf1b6e-3317-42a1-8906-1dc84b9c5f43 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fadf1b6e-3317-42a1-8906-1dc84b9c5f43');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                Document Title                  Author(s)  \\\n",
+              "doc_000891.png  clectopharesis  American Chemical Society   \n",
+              "\n",
+              "                                                  Generated Title  \n",
+              "doc_000891.png  Kinesin was extensively purified from bovine b...  "
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "50nMC34w2Yu5"
+      },
+      "outputs": [],
+      "source": [
+        "Expand to include more fields"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KhSjxR2z2Xzc"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOR4UtX5tdSQ",
+        "outputId": "54395f2b-fc68-4b33-f925-98cb33fad954"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Some weights of the model checkpoint at bert-large-uncased-whole-word-masking-finetuned-squad were not used when initializing BertForQuestionAnswering: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
+            "- This IS expected if you are initializing BertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "            Title  Year                     Author      Type  \\\n",
+            "0  clectopharesis  1988  American Chemical Society  SDS-PAGE   \n",
+            "\n",
+            "  Short description       Focal constructs  \\\n",
+            "0   one polypeptide  ATP-binding\\nsubunits   \n",
+            "\n",
+            "              Theoretical perspective  \\\n",
+            "0  selective probe for\\n\\nanon anspor   \n",
+            "\n",
+            "                                   Context     Study design        Level(s)  \\\n",
+            "0  as a selective probe for\\n\\nanon anspor  selective probe  stoichiometric   \n",
+            "\n",
+            "               Methods notes  \\\n",
+            "0  American Chemical Society   \n",
+            "\n",
+            "                                 Main study findings  \\\n",
+            "0  Native Structure and Physical Properties of Bo...   \n",
+            "\n",
+            "  LLM Generated Executive Summary Insights related to my research question  \\\n",
+            "0                        SDS-PAGE    microtubules from cick brain extracts   \n",
+            "\n",
+            "     Quotable points                                  Potential use  \\\n",
+            "0  0427-3409801.50/0  serve as a selective probe for\\n\\nanon anspor   \n",
+            "\n",
+            "            Additional Notes  \n",
+            "0  American Chemical Society  \n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# Define a function to extract additional details using question answering\n",
+        "qa_pipeline = pipeline(\"question-answering\", model=\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
+        "\n",
+        "def extract_additional_details(image_texts):\n",
+        "    questions = {\n",
+        "        \"Title\":\"What is the title of the paper?\",\n",
+        "        \"Year\": \"What is the publication year?\",\n",
+        "        \"Author\":\"Which persons are the authors of the paper?\",\n",
+        "        \"Type\": \"What type of document is it?\",\n",
+        "        \"Short description\": \"Can you provide a short description?\",\n",
+        "        \"Focal constructs\": \"What are the focal constructs?\",\n",
+        "        \"Theoretical perspective\": \"What is the theoretical perspective?\",\n",
+        "        \"Context\": \"Can you describe the context?\",\n",
+        "        \"Study design\": \"What study design was used?\",\n",
+        "        \"Level(s)\": \"What are the levels?\",\n",
+        "        \"Methods notes\": \"Do you have any methodological notes?\",\n",
+        "        \"Main study findings\": \"What are the main findings of the study?\",\n",
+        "        \"LLM Generated Executive Summary\": \"Can you provide a generated executive summary?\",\n",
+        "        \"Insights related to my research question\": \"What insights are related to your research question?\",\n",
+        "        \"Quotable points\": \"Can you provide any quotable points?\",\n",
+        "        \"Potential use\": \"How can this document be used?\",\n",
+        "        \"Additional Notes\": \"Do you have any additional notes?\"\n",
+        "    }\n",
+        "\n",
+        "    data = []\n",
+        "    for _, text in image_texts.items():\n",
+        "        details = []\n",
+        "        for question in questions.values():\n",
+        "            answer = qa_pipeline(question=question, context=text)\n",
+        "            details.append(answer[\"answer\"])\n",
+        "        data.append(details)\n",
+        "\n",
+        "    return pd.DataFrame(data, columns=questions.keys())\n",
+        "\n",
+        "# Example usage:\n",
+        "additional_details_df = extract_additional_details(image_texts)\n",
+        "print(additional_details_df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 219
+        },
+        "id": "DSlXR2VAtzO7",
+        "outputId": "0e53939f-3a40-4c42-8791-c79b2b76c608"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"additional_details_df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"clectopharesis\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1988\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Author\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"American Chemical Society\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"SDS-PAGE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Short description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"one polypeptide\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Focal constructs\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"ATP-binding\\nsubunits\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Theoretical perspective\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"selective probe for\\n\\nanon anspor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Context\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"as a selective probe for\\n\\nanon anspor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Study design\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"selective probe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Level(s)\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"stoichiometric\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Methods notes\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"American Chemical Society\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Main study findings\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Native Structure and Physical Properties of Bovine Brain Kinesin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LLM Generated Executive Summary\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"SDS-PAGE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insights related to my research question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"microtubules from cick brain extracts\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quotable points\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"0427-3409801.50/0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Potential use\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"serve as a selective probe for\\n\\nanon anspor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Additional Notes\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"American Chemical Society\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "additional_details_df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c032574c-9800-40c2-bde1-0de3a79504d2\" class=\"colab-df-container\">\n",
+              "    <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>Title</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Author</th>\n",
+              "      <th>Type</th>\n",
+              "      <th>Short description</th>\n",
+              "      <th>Focal constructs</th>\n",
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+              "      <th>Context</th>\n",
+              "      <th>Study design</th>\n",
+              "      <th>Level(s)</th>\n",
+              "      <th>Methods notes</th>\n",
+              "      <th>Main study findings</th>\n",
+              "      <th>LLM Generated Executive Summary</th>\n",
+              "      <th>Insights related to my research question</th>\n",
+              "      <th>Quotable points</th>\n",
+              "      <th>Potential use</th>\n",
+              "      <th>Additional Notes</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>clectopharesis</td>\n",
+              "      <td>1988</td>\n",
+              "      <td>American Chemical Society</td>\n",
+              "      <td>SDS-PAGE</td>\n",
+              "      <td>one polypeptide</td>\n",
+              "      <td>ATP-binding\\nsubunits</td>\n",
+              "      <td>selective probe for\\n\\nanon anspor</td>\n",
+              "      <td>as a selective probe for\\n\\nanon anspor</td>\n",
+              "      <td>selective probe</td>\n",
+              "      <td>stoichiometric</td>\n",
+              "      <td>American Chemical Society</td>\n",
+              "      <td>Native Structure and Physical Properties of Bo...</td>\n",
+              "      <td>SDS-PAGE</td>\n",
+              "      <td>microtubules from cick brain extracts</td>\n",
+              "      <td>0427-3409801.50/0</td>\n",
+              "      <td>serve as a selective probe for\\n\\nanon anspor</td>\n",
+              "      <td>American Chemical Society</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c032574c-9800-40c2-bde1-0de3a79504d2')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "                                                    [key], {});\n",
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+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "            Title  Year                     Author      Type  \\\n",
+              "0  clectopharesis  1988  American Chemical Society  SDS-PAGE   \n",
+              "\n",
+              "  Short description       Focal constructs  \\\n",
+              "0   one polypeptide  ATP-binding\\nsubunits   \n",
+              "\n",
+              "              Theoretical perspective  \\\n",
+              "0  selective probe for\\n\\nanon anspor   \n",
+              "\n",
+              "                                   Context     Study design        Level(s)  \\\n",
+              "0  as a selective probe for\\n\\nanon anspor  selective probe  stoichiometric   \n",
+              "\n",
+              "               Methods notes  \\\n",
+              "0  American Chemical Society   \n",
+              "\n",
+              "                                 Main study findings  \\\n",
+              "0  Native Structure and Physical Properties of Bo...   \n",
+              "\n",
+              "  LLM Generated Executive Summary Insights related to my research question  \\\n",
+              "0                        SDS-PAGE    microtubules from cick brain extracts   \n",
+              "\n",
+              "     Quotable points                                  Potential use  \\\n",
+              "0  0427-3409801.50/0  serve as a selective probe for\\n\\nanon anspor   \n",
+              "\n",
+              "            Additional Notes  \n",
+              "0  American Chemical Society  "
+            ]
+          },
+          "execution_count": 44,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
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