pdf_gpt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "030347a0-0bc5-4dab-8bae-7d681a364239",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import fitz\n",
    "import requests\n",
    "import json\n",
    "import re\n",
    "import ast\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from openai import OpenAI\n",
    "from IPython.display import Image\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "client = OpenAI()\n",
    "\n",
    "import os\n",
    "import base64\n",
    "import requests\n",
    "from tqdm import tqdm_notebook\n",
    "from tqdm import tqdm\n",
    "import ast\n",
    "\n",
    "api_key = os.environ.get('OPENAI_API_KEY')\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "import cv2\n",
    "import traceback\n",
    "import easyocr\n",
    "import gc\n",
    "from glob import glob\n",
    "from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration\n",
    "from PIL import Image\n",
    "import shutil\n",
    "from sklearn.preprocessing import LabelEncoder  \n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import random\n",
    "import shutil\n",
    "import torch\n",
    "from ultralytics import YOLO\n",
    "from glob import glob\n",
    "\n",
    "from IPython.display import clear_output\n",
    "from sklearn.model_selection import train_test_split\n",
    "import yaml\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def pdf_to_png(pdf_file, output_folder = None, dpi=300):\n",
    "    # Open the PDF file\n",
    "    pdf_document = fitz.open(pdf_file)\n",
    "    image_ls = []\n",
    "    for page_number in range(pdf_document.page_count):\n",
    "        # Get the page\n",
    "        page = pdf_document[page_number]\n",
    "        \n",
    "        # Set the resolution (DPI)\n",
    "        zoom = dpi / 72.0\n",
    "        mat = fitz.Matrix(zoom, zoom)\n",
    "        image = page.get_pixmap(matrix=mat)\n",
    "        image_np = cv2.imdecode(np.frombuffer(bytearray(image.tobytes()), dtype=np.uint8), cv2.IMREAD_COLOR)\n",
    "        image_ls.append(image_np)\n",
    "        \n",
    "        # Save the image as a PNG file\n",
    "        if output_folder is not None :\n",
    "            image.save(f\"{output_folder}/page_{page_number + 1}.png\", \"png\")\n",
    "        \n",
    "\n",
    "    # Close the PDF file\n",
    "    pdf_document.close()\n",
    "    return image_ls\n",
    "\n",
    "def extract_numbers(input_str):\n",
    "    # Regular expression to find all numbers in the string\n",
    "    numbers = re.findall(r'\\d+', input_str)\n",
    "    # Join the list of numbers into a single string\n",
    "#     result = ''.join(numbers)\n",
    "    return numbers\n",
    "\n",
    "def load_imgs_from_pdf(input_pdf, output_folder = './data', dpi = 300) :\n",
    "    # Replace with your output folder\n",
    "    if output_folder is not None :\n",
    "        os.makedirs(output_folder, exist_ok = True)\n",
    "    img_ls = pdf_to_png(input_pdf, output_folder = output_folder , dpi = dpi)\n",
    "    return img_ls\n",
    "\n",
    "def read_roi_from_bbox(bboxes_i, class_no, margin = 0.001, show = True) :\n",
    "    # class_no : 0 : number, 1 : question, 2 : graph, 3: text\n",
    "    cls_dict = {0 : 'number', 1: 'question', 2:'graph', 3:'text' }\n",
    "    try :\n",
    "        bboxes_i_num = bboxes_i[bboxes_i[:,5]==class_no]\n",
    "        bboxes_i_num = bboxes_i_num[np.argmax(bboxes_i_num[:,3])][:4].astype(int)\n",
    "        roi_i_num = roi[int(bboxes_i_num[1]*(1-margin)):int(bboxes_i_num [3]*(1+margin)),\n",
    "                        int(bboxes_i_num[0]*(1-margin)):int(bboxes_i_num[2]*(1+margin))]\n",
    "        if show == True :\n",
    "            plt.imshow(roi_i_num)\n",
    "            plt.show()\n",
    "        return roi_i_num\n",
    "    except :\n",
    "        print('Error occurs in ' + cls_dict[class_no])\n",
    "        print(traceback.format_exc())\n",
    "        return None\n",
    "\n",
    "def extract_info(input_str):\n",
    "    # Extract the year and month-day numbers\n",
    "    try :\n",
    "        date_match = re.search(r'(\\d{4})-(\\d{2})-(\\d{2})', input_str)\n",
    "        if date_match:\n",
    "            year = date_match.group(1)[-2:]  # Get the last two digits of the year\n",
    "            month = date_match.group(2)  # Get the month\n",
    "            date_info = year + month  # Combine year and month\n",
    "        else:\n",
    "            date_info = ''\n",
    "\n",
    "        # Extract the grade information\n",
    "        grade_match = re.search(r'고(\\d)', input_str)\n",
    "        if grade_match:\n",
    "            grade = grade_match.group(1)\n",
    "        else:\n",
    "            grade = ''\n",
    "\n",
    "        # Combine the extracted information\n",
    "        result = f\"{grade}_{date_info}\"\n",
    "        return result\n",
    "    except :\n",
    "        return input_str\n",
    "\n",
    "def read_number(reader, roi_i_num) :\n",
    "    try :\n",
    "        result = reader.readtext(roi_i_num)\n",
    "        for r in result :\n",
    "            try :\n",
    "                number_i = int(extract_numbers(r[1])[0])\n",
    "                break\n",
    "            except :\n",
    "                number_i = None\n",
    "        return number_i  \n",
    "    except :\n",
    "        return None\n",
    "\n",
    "def read_text(reader, roi_i_text) :\n",
    "    text = str()\n",
    "    try :\n",
    "        result = reader.readtext(roi_i_text)\n",
    "        for r in result :\n",
    "            text += r[1]\n",
    "            text += ' '\n",
    "        text = text[:-1]\n",
    "    except :\n",
    "        pass\n",
    "    return text\n",
    "\n",
    "def deplot_graph(processor, model_deplot, roi_i_graph) :\n",
    "    inputs = processor(images=roi_i_graph, text=\"Generate underlying data table of the figure below:\", return_tensors=\"pt\")\n",
    "    inputs['flattened_patches']= inputs['flattened_patches'].to(device)\n",
    "    inputs['attention_mask']= inputs['attention_mask'].to(device)\n",
    "    predictions = model_deplot.generate(**inputs, max_new_tokens=512)\n",
    "    deplot_i = processor.decode(predictions[0], skip_special_tokens=True)\n",
    "    return deplot_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "c2b8c954-d379-4c6f-b77c-91f0bc9fa08e",
   "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>번호</th>\n",
       "      <th>TypeString</th>\n",
       "      <th>문제</th>\n",
       "      <th>유형</th>\n",
       "      <th>정답</th>\n",
       "      <th>답변</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>글의 목적 추론</td>\n",
       "      <td>Q. 다음 글의 목적으로 가장 적절한 것은?\\nTo whom it may conce...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>이 컨텍스트의 목적은 \"농구 코트의 운영 시간 제한을 요청하려고\" 하는 것입니다. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>주인공의 심경 변화 추론</td>\n",
       "      <td>Q. 다음 글에 드러난 Chaske의 심경 변화로 가장 적절한 것은?\\n Chask...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Chaske의 심경 변화는 \"horrified → relieved\"입니다. 글에 따...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>필자의 주장 파악</td>\n",
       "      <td>Q. 다음 글에서 필자가 주장하는 바로 가장 적절한 것은?\\n Agriculture...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>필자가 주장하는 바는 \"농업이 직면한 문제 해결 및 식량과 농산물의 지속적 생산을 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   번호     TypeString                                                 문제   유형  \\\n",
       "0  18       글의 목적 추론  Q. 다음 글의 목적으로 가장 적절한 것은?\\nTo whom it may conce...  NaN   \n",
       "1  19  주인공의 심경 변화 추론  Q. 다음 글에 드러난 Chaske의 심경 변화로 가장 적절한 것은?\\n Chask...  NaN   \n",
       "2  20      필자의 주장 파악  Q. 다음 글에서 필자가 주장하는 바로 가장 적절한 것은?\\n Agriculture...  NaN   \n",
       "\n",
       "   정답                                                 답변  \n",
       "0   2  이 컨텍스트의 목적은 \"농구 코트의 운영 시간 제한을 요청하려고\" 하는 것입니다. ...  \n",
       "1   2  Chaske의 심경 변화는 \"horrified → relieved\"입니다. 글에 따...  \n",
       "2   5  필자가 주장하는 바는 \"농업이 직면한 문제 해결 및 식량과 농산물의 지속적 생산을 ...  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정답지 인식\n",
    "year_month = \"2_2312\"\n",
    "df = pd.read_excel(\"C:/Users/runia/Dropbox/★의수/영어/영어_모의고사_문제&답변.xlsx\", sheet_name=year_month)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a908c3f7-6c12-4eff-94cf-3baf3ceb8153",
   "metadata": {},
   "source": [
    "## pdf 문제 인식"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a724ffb2-ccfb-47d8-bef0-89e24413c402",
   "metadata": {},
   "source": [
    "### 그래프 문제 인식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "a413fa1e-09c7-4678-8471-e326d6d5b5f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 640x480 (no detections), 1778.6ms\n",
      "1: 640x480 (no detections), 1778.6ms\n",
      "2: 640x480 (no detections), 1778.6ms\n",
      "3: 640x480 1 00_question, 1778.6ms\n",
      "4: 640x480 (no detections), 1778.6ms\n",
      "5: 640x480 (no detections), 1778.6ms\n",
      "6: 640x480 (no detections), 1778.6ms\n",
      "7: 640x480 (no detections), 1778.6ms\n",
      "Speed: 8.8ms preprocess, 1778.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 480)\n",
      "\n",
      "0: 640x448 1 11_number, 1 12_question, 1 10_graph, 1 13_text, 3287.6ms\n",
      "Speed: 8.1ms preprocess, 3287.6ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 448)\n",
      "Results saved to \u001b[1myolo_inference\\predict\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "TITLE | View of Targeted Online Advertising Among Americans, by Age Group<0x0A>Conducted on May 1, 2019 (missing percentages to 100%: \"don't know\") | Interesting(Targeted ads are a convenient<0x0A>way to see products I'm interested in) | Intrusive(Targeted ads are an inappropriate<0x0A>use of personal data) | Total <0x0A> 18–24 | 34% | 34% <0x0A> 25–34 | 37% | 45% <0x0A> 35–44 | 27% | 48% <0x0A> 45–54 | 31% | 45% <0x0A> 55+ (Age Group) | 18% | 61%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'file': '2023-12-19 고2 3. 영어영역 문제지.pdf',\n",
       "  'testNumber': '2_2312',\n",
       "  'question': '다음 도표의 내용과 일치하지 앞늘 것은?',\n",
       "  'questionNumber': 25,\n",
       "  'article': 'The graph above shows the results of a 2019 survey on the views of American age groups On targeted online advertising OIn total; While 519 of the respondents said targeted ads were intrusive, 279 said they were interesting The percentage of respondents who believed that targeted ads were interesting was the highest in the age group of 18 to 24. @ The percentage of respondents aged 25 to 34 who said that targeted ads were intrusive was the same as that of respondents aged 45 to 54 who said the same. Among all age groups the gap between respondents who said targeted ads were interesting and those who believed them to be intrusive was the largest in the 35-to-44 age group: The age group of 55 and above was the only group where the percentage of respondents who believed targeted ads were intrusive was more than 5096.',\n",
       "  'deplot': 'TITLE | View of Targeted Online Advertising Among Americans, by Age Group<0x0A>Conducted on May 1, 2019 (missing percentages to 100%: \"don\\'t know\") | Interesting(Targeted ads are a convenient<0x0A>way to see products I\\'m interested in) | Intrusive(Targeted ads are an inappropriate<0x0A>use of personal data) | Total <0x0A> 18–24 | 34% | 34% <0x0A> 25–34 | 37% | 45% <0x0A> 35–44 | 27% | 48% <0x0A> 45–54 | 31% | 45% <0x0A> 55+ (Age Group) | 18% | 61%'}]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_path_1 = 'Utils/ultralytics/models/detection_model_1.pt'\n",
    "model_path_2 = 'Utils/ultralytics/models/detection_model_2.pt'\n",
    "b_test = 1\n",
    "img_size = 640\n",
    "device = torch.device('cuda')\n",
    "save_path = 'yolo_inference'\n",
    "model_1 = YOLO(model_path_1)\n",
    "model_2 = YOLO(model_path_2)\n",
    "\n",
    "# Load easyocr\n",
    "reader = easyocr.Reader(['en', 'ko'], model_storage_directory = 'Utils/EasyOCR/model') # this needs to run only once to load the model into memory\n",
    "\n",
    "# Load deplot\n",
    "processor = Pix2StructProcessor.from_pretrained('Utils/deplot/model')\n",
    "model_deplot = Pix2StructForConditionalGeneration.from_pretrained('Utils/deplot/model')\n",
    "model_deplot.to(device)\n",
    "\n",
    "# pdf dpi setting\n",
    "dpi = 300  # Adjust DPI as needed\n",
    "\n",
    "# Input\n",
    "q_info_ls = []\n",
    "\n",
    "# PDF_FILE_PATH = r\"F:\\#AI\\#외주\\#수능\\Data\\기출문제(고2,고3)\\고3\\3_2310_eng\\2023-10-12 고3 영어영역 문제지.pdf\"\n",
    "PDF_FILE_PATH = \"2023-12-19 고2 3. 영어영역 문제지.pdf\" # 원하는 pdf 를 넣으시오\n",
    "pdf_name = os.path.basename(PDF_FILE_PATH)\n",
    "pdf_testNumber = extract_info(pdf_name)\n",
    "output_folder = './data'  # Replace with your output folder, None if do not want to save\n",
    "img_ls = load_imgs_from_pdf(input_pdf = PDF_FILE_PATH, output_folder = None, dpi = dpi)\n",
    "\n",
    "#  roi 추출\n",
    "preds= model_1.predict(img_ls, imgsz=(img_size, img_size), iou=0.2, conf=0.5, save_conf=False, save=False, save_txt=False, project='yolo_inference', name=\"predict\", workers = 16,\n",
    "              exist_ok=True, device=device, augment=False, verbose=True)\n",
    "# 문제에서 roi 추출\n",
    "for i, pred in enumerate(preds) :\n",
    "    bboxes = pred.boxes.data.cpu().numpy().astype(int)\n",
    "    if len(bboxes) > 0 :\n",
    "        for k, bbox in enumerate(bboxes) :\n",
    "            q_info = dict()\n",
    "            \n",
    "            \n",
    "            roi = img_ls[i][int(bbox[1]*0.98):int(bbox[3]*1.02), int(bbox[0]*0.98):int(bbox[2]*1.02)]\n",
    "#             han_imwrite(os.path.join(save_path, file_name+'_roi_{}'.format(k)+'.png'), roi)\n",
    "\n",
    "            preds_i= model_2.predict(roi, imgsz=(img_size, img_size), iou=0.2, conf=0.7, save_conf=False, save=True, save_txt=False, project='yolo_inference', name=\"predict\", workers = 16,\n",
    "                          exist_ok=True, device=device, augment=False, verbose=True)\n",
    "\n",
    "            bboxes_i = preds_i[0].boxes.data.cpu().numpy()#.astype(int)\n",
    "\n",
    "\n",
    "            roi_i_graph = read_roi_from_bbox(bboxes_i, class_no = 2, margin = 0.001)\n",
    "            if roi_i_graph is not None :\n",
    "                roi_i_num = read_roi_from_bbox(bboxes_i, class_no = 0, margin = 0.001)\n",
    "                roi_i_text = read_roi_from_bbox(bboxes_i, class_no = 3, margin = 0.001)\n",
    "                roi_i_question = read_roi_from_bbox(bboxes_i, class_no = 1, margin = 0.001)\n",
    "                plt.imshow(roi)\n",
    "\n",
    "                number_i = read_number(reader, roi_i_num)\n",
    "                if number_i is None :\n",
    "                    number_i = read_number(reader, roi)\n",
    "                    \n",
    "                question_i = read_text(reader, roi_i_question)\n",
    "                text_i = read_text(reader, roi_i_text)\n",
    "                \n",
    "                deplot_i = deplot_graph(processor, model_deplot, roi_i_graph)\n",
    "\n",
    "\n",
    "                q_info['file'] = pdf_name\n",
    "                q_info['testNumber'] = pdf_testNumber\n",
    "                q_info['question'] = question_i # 빼도 됨\n",
    "                q_info['questionNumber'] = number_i\n",
    "                q_info['article'] = text_i # 빼도 됨\n",
    "                q_info['deplot'] = deplot_i\n",
    "                q_info_ls.append(q_info)\n",
    "\n",
    "                print(number_i)\n",
    "                print(deplot_i)\n",
    "\n",
    "# 그래프 문제 인식\n",
    "q_info_ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "2eaf3b89-5548-46e2-9f16-a50ef7cb2a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TITLE | View of Targeted Online Advertising Among Americans, by Age Group<0x0A>Conducted on May 1, 2019 (missing percentages to 100%: \"don\\'t know\") | Interesting(Targeted ads are a convenient<0x0A>way to see products I\\'m interested in) | Intrusive(Targeted ads are an inappropriate<0x0A>use of personal data) | Total <0x0A> 18–24 | 34% | 34% <0x0A> 25–34 | 37% | 45% <0x0A> 35–44 | 27% | 48% <0x0A> 45–54 | 31% | 45% <0x0A> 55+ (Age Group) | 18% | 61%'"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_info_ls[0]['deplot']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a65af79-4741-4c5f-8756-ac345d4857e9",
   "metadata": {},
   "source": [
    "## 텍스트 문제 인식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "60484a5b-cad0-4e10-8e99-73e97e31dac0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "8\n",
      "  1번부터 17번까지는 듣고 답하는 문제입니다. 1번부터 15번\n",
      "까지는 한 번만 들려주고, 16번부터 17번까지는 두 번 들려줍니다. \n",
      "방송을 잘 듣고 답을 하시기 바랍니다.\n",
      "1. 다음을 듣고, 남자가 하는 말의 목적으로 가장 적절한 것을 고르시오.\n",
      "①모터쇼 임시 주차장 운영을 안내하려고\n",
      "②방송국 방문 등록 방법을 설명하려고\n",
      "③모터쇼 입장 시간 변경을 알려주려고\n",
      "④공영 주차장 요금 인상을 공지하려고\n",
      "⑤라디오 행사 지원 팀을 모집하려고\n",
      "2. 대화를 듣고, 여자의 의견으로 가장 적절한 것을 고르시오.\n",
      "①충분한 영양 섭취를 위해 다양한 과일을 먹어야 한다.\n",
      "②식사 전에 과일을 먹는 것이 건강에 도움이 된다.\n",
      "③과일을 먹기 전에 껍질을 깨끗이 씻어야 한다.\n",
      "④규칙적인 생활 습관이 체중 관리에 중요하다.\n",
      "⑤늦은 밤중에 먹는 음식은 숙면을 방해한다.\n",
      "3. 다음을 듣고, 남자가 하는 말의 요지로 가\n"
     ]
    }
   ],
   "source": [
    "path = '2023-12-19 고2 3. 영어영역 문제지.pdf'    # 2023-09-06 고2 3. 영어영역 문제지.pdf\n",
    "\n",
    "doc = fitz.open(path)\n",
    "text_ls = []\n",
    "for page in doc:\n",
    "    text = page.get_text()\n",
    "    # print(text)\n",
    "    text_ls.append(text)\n",
    "\n",
    "# 페이지별 텍스트 합치기\n",
    "text_all = \"\"\n",
    "for text in text_ls:\n",
    "    text_all = text_all + text \n",
    "\n",
    "print(text_all[:450])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "cf610bd0-e085-4f9b-a428-57bf64c4cd1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "확인 사항: \n",
      "◦답안지의 해당란에 필요한 내용을 정확히 기입(표기)했는지 확인\n",
      "하시오.\n",
      "\n",
      "28문제\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['\\n18. 다음 글의 목적으로 가장 적절한 것은?\\nTo whom it may concern,\\nI am writing to inform you of an ongoing noise issue that \\nI am experiencing. My apartment faces the basketball \\ncourts of the community center. While I fully support the \\ncommunity center’s services, I am constantly being disrupted \\nby individuals playing basketball late at night. Many \\nnights, I struggle to fall asleep because I can hear people \\nbouncing balls and shouting on the basketball courts well \\nafter 11 p.m.. Could you restrict the time the basketball \\ncourt is open to before 9 p.m.? I’m sure I’m not the only \\nperson in the neighborhood that is affected by this noise \\nissue. I appreciate your assistance.\\nSincerely, \\nIan Baldwin\\n①체육관의 바닥 교체 공사를 요구하려고\\n②농구 코트의 운영 시간 제한을 요청하려고\\n③문화 센터 시설의 대관 날짜를 변경하려고\\n④건강 증진 프로그램 신청 방법을 문의하려고\\n⑤지역 내 체육 시설의 증설 가능 여부를 확인하려고',\n",
       " '\\n19. 다음 글에 드러난 Chaske의 심경 변화로 가장 적절한 것은?\\nChaske, a Cherokee boy, was sitting on a tree stump. As a rite \\nof passage for youths in his tribe, Chaske had to survive one \\nnight in the forest wearing a blindfold, not knowing he was \\nobserved by his father. After the sunset, Chaske could hear all \\nkinds of noises. The wind blew the grass and shook his stump. A \\nsense of dread swept through his body. What if wild beasts are \\nlooking at me? I can’t stand this! Just as he was about to take off \\nthe blindfold to run away, a voice came in from somewhere. “I’m \\nhere around you. Don’t give up, and complete your mission.” It \\nwas his father’s voice. He has been watching me from nearby! \\nWith just the presence of his father, the boy regained stability. \\nWhat panicked him awfully a moment ago vanished into thin air.\\n①nervous → doubtful\\n②horrified → relieved\\n③disappointed → curious\\n④ashamed → frightened\\n⑤bored → delighted',\n",
       " '\\n20. 다음 글에서 필자가 주장하는 바로 가장 적절한 것은?\\nAgriculture includes a range of activities such as planting, \\nharvesting, fertilizing, pest management, raising animals, and \\ndistributing food and agricultural products. It is one of the oldest \\nand most essential human activities, dating back thousands of \\nyears, and has played a critical role in the development of human \\ncivilizations, allowing people to create stable food supplies and \\nsettle in one place. Today, agriculture remains a vital industry \\nthat feeds the world’s population, supports rural communities, \\nand provides raw materials for other industries. However, \\nagriculture faces numerous challenges such as climate change, \\nwater scarcity, soil degradation, and biodiversity loss. As the \\nworld’s population continues to grow, it is essential to find \\nsustainable solutions to address the challenges facing agriculture \\nand ensure the continued production of food and other agricultural \\nproducts.\\n①토\\n양\\n의\\n 질\\n을\\n 개\\n선\\n하\\n기\\n 위\\n해\\n 친\\n환\\n경\\n 농\\n법\\n의\\n 연\\n구\\n와\\n 개\\n발\\n이\\n 필\\n요\\n하\\n다\\n.\\n②세\\n계\\n 인\\n구\\n의\\n 증\\n가\\n에\\n 대\\n응\\n하\\n기\\n 위\\n해\\n 농\\n산\\n물\\n 품\\n종\\n의\\n 다\\n양\\n화\\n가\\n 필\\n요\\n하\\n다\\n.\\n③기\\n후\\n 변\\n화\\n에\\n 대\\n한\\n 지\\n속\\n 가\\n능\\n한\\n 대\\n책\\n은\\n 경\\n제\\n적\\n 관\\n점\\n에\\n서\\n 고\\n려\\n되\\n어\\n야\\n 한\\n다\\n.\\n④다\\n른\\n 산\\n업\\n 분\\n야\\n와\\n의\\n 공\\n동\\n 연\\n구\\n를\\n 통\\n해\\n 상\\n품\\n성\\n을\\n 가\\n진\\n 농\\n작\\n물\\n을\\n 개\\n발\\n해\\n야\\n 한\\n다\\n.\\n⑤농\\n업\\n이\\n 직\\n면\\n한\\n 문\\n제\\n 해\\n결\\n 및\\n 식\\n량\\n과\\n 농\\n산\\n물\\n의\\n 지\\n속\\n적\\n 생\\n산\\n을\\n 위\\n한\\n 방\\n안\\n이\\n 필\\n요\\n하\\n다\\n.\\n3\\n영어 영역\\n3\\n8',\n",
       " '\\n21. 밑줄 친 be more than just sugar on the tongue이 다음 글에서 \\n의미하는 바로 가장 적절한 것은? [3점]\\nThe arts and aesthetics offer emotional connection to the full \\nrange of human experience. “The arts can be more than just \\nsugar on the tongue,” Anjan Chatterjee, a professor at the \\nUniversity of Pennsylvania, says. “In art, when there’s something \\nchallenging, which can also be uncomfortable, this discomfort, if \\nwe’re willing to engage with it, offers the possibility of some \\nchange, some transformation. That can also be a powerful \\naesthetic experience.” The arts, in this way, become vehicles to \\ncontend with ideas and concepts that are difficult and \\nuncomfortable otherwise. When Picasso painted his masterpiece \\nGuernica in 1937, he captured the heartbreaking and cruel nature \\nof war, and offered the world a way to consider the universal \\nsuffering caused by the Spanish Civil War. When Lorraine \\nHansberry wrote her play A Raisin in the Sun, she gave us a \\npowerful story of people struggling with racism, discrimination, \\nand the pursuit of the American dream while also offering a \\ntouching portrait of family life.\\n①play a role in relieving psychological anxiety\\n②enlighten us about the absoluteness of beauty\\n③conceal the artist’s cultural and ethnic traditions\\n④embrace a variety of experiences beyond pleasure\\n⑤distort the viewers’ accurate understanding of history',\n",
       " '\\n22. 다음 글의 요지로 가장 적절한 것은?\\nMany historians have pointed to the significance of accurate \\ntime measurement to Western economic progress. The French \\nhistorian Jacques Le Goff called the birth of the public mechanical \\nclock a turning point in Western society. Until the late Middle \\nAges, people had sun or water clocks, which did not play any \\nmeaningful role in business activities. Market openings and \\nactivities started with the sunrise and typically ended at noon \\nwhen the sun was at its peak. But when the first public \\nmechanical clocks were introduced and spread across European \\ncities, market times were set by the stroke of the hour. Public \\nclocks thus greatly contributed to public life and work by \\nproviding a new concept of time that was easy for everyone to \\nunderstand. This, in turn, helped facilitate trade and commerce. \\nInteractions and transactions between consumers, retailers, and \\nwholesalers became less irregular. Important town meetings \\nbegan to follow the pace of the clock, allowing people to better \\nplan their time and allocate resources in a more efficient manner.\\n①공공 시계는 서양 사회의 경제적 진보에 영향을 미쳤다.\\n②서양에서 생산된 시계는 세계적으로 정교함을 인정받았다.\\n③서양의 시계는 교역을 통해 전파되어 세계적으로 대중화되었다.\\n④기계 시계의 발명은 다른 측량 장비들의 개발에 도움을 주었다.\\n⑤중세 시대의 시계 발명은 자연법칙을 이해하는 데 큰 전환점이 되었다.',\n",
       " '\\n23. 다음 글의 주제로 가장 적절한 것은?\\nSylvan Goldman invented the shopping cart and introduced it in \\nhis stores in 1937. It was an excellent device that would make it \\neasy for shoppers to buy as much as they wanted without getting \\ntired or seeking others’ help. But Goldman discovered that in \\nspite of his repeated advertisements and explanations, he could \\nnot persuade his shoppers to use the wheeled carts. Men were \\nreluctant because they thought they would appear weak if they \\npushed such carts instead of carrying their shopping. Women \\nwouldn’t touch them because the carts reminded them of baby \\ncarriages. It was only a few elderly shoppers who used them. \\nThat made the carts even less attractive to the majority of the \\nshoppers. Then Goldman hit upon an idea. He hired several \\nmodels, men and women, of different ages and asked them to \\nwheel the carts in the store and shop. A young woman employee \\nstanding near the entrance told the regular shoppers, ‘Look, \\neveryone is using the carts. Why don’t you?’ That was the \\nturning point. A few shills disguised as regular shoppers easily \\naccomplished what logic, explanations, and advertisements failed \\nto do. Within a few weeks shoppers readily accepted those carts.\\n* shill: 바람잡이\\n①persuasive power of peer behavior\\n②methods to help consumers shop less\\n③innovative ways to reduce waste in retail\\n④hidden nature of human beings to support materialism\\n⑤importance of a store layout based on customer needs',\n",
       " '\\n24. 다음 글의 제목으로 가장 적절한 것은?\\nIn response to human-like care robots, critics might charge \\nthat human-robot interactions create moral hazards for dementia \\npatients. Even if deception is sometimes allowed when it serves \\nworthy goals, should it be allowed for vulnerable users? Just as \\nchildren on the autism spectrum with robot companions might be \\neasily fooled into thinking of robots as friends, older adults with \\ncognitive deficits might be. According to Alexis Elder, a professor \\nat UMD, robots are false friends, inferior to true friendship. \\nReasoning along similar lines, John Sullins, a professor at Sonoma \\nState University, holds that robots should “remain iconic or \\ncartoonish so that they are easily distinguished as synthetic even \\nby unsophisticated users.” At least then no one is fooled. Making \\nrobots clearly fake also avoids the so-called “uncanny valley,” \\nwhere robots are perceived as scary because they so closely \\nresemble us, but not quite. Other critics of robot deception argue \\nthat when care recipients are deceived into thinking that robots \\ncare, this crosses a line and violates human dignity.\\n* dementia: 치매  ** autism: 자폐성\\n①The Importance of Protecting Human Dignity\\n②Robots Can’t Surpass Human Beings in Nursing Jobs\\n③Why Robots for Vulnerable People Should Look Like Robots\\n④Can Robots Learn Ethical Behavior Through Human Interaction?\\n⑤Healthcare Robots: Opening the Era of Online Medical Checkups\\n4\\n영어 영역\\n4\\n8',\n",
       " '\\n25. 다음 도표의 내용과 일치하지 않는 것은?\\nThe graph above shows the results of a 2019 survey on the views \\nof American age groups on targeted online advertising. ①In total, \\nwhile 51% of the respondents said targeted ads were intrusive, 27% \\nsaid they were interesting. ②The percentage of respondents who \\nbelieved that targeted ads were interesting was the highest in the \\nage group of 18 to 24. ③The percentage of respondents aged 25 to \\n34 who said that targeted ads were intrusive was the same as that \\nof respondents aged 45 to 54 who said the same. ④Among all age \\ngroups, the gap between respondents who said targeted ads were \\ninteresting and those who believed them to be intrusive was the \\nlargest in the 35-to-44 age group. ⑤The age group of 55 and \\nabove was the only group where the percentage of respondents who \\nbelieved targeted ads were intrusive was more than 50%.',\n",
       " '\\n26. Maggie L. Walker에 관한 다음 글의 내용과 일치하지 않는 것은?\\nMaggie L. Walker \\nachieved \\nnational \\nprominence \\nas \\na \\nbusinesswoman and community leader. She was among the \\nearliest Black students to attend newly-established public \\nschools for African Americans. After graduating, she worked as a \\nteacher for three years at the Valley School, where she had \\nstudied. In the early 1900s, Virginia banks owned by white \\nbankers were unwilling to do business with African American \\norganizations or individuals. The racial discrimination by white \\nbankers drove her to study banking and financial laws. She \\nestablished a newspaper to promote closer communication \\nbetween the charitable organization she belonged to and the \\npublic. Soon after, she founded the St. Luke Penny Savings Bank, \\nwhich survived the Great Depression and merged with two other \\nbanks. \\nIt \\nthrived \\nas \\nthe \\noldest \\ncontinually \\nAfrican \\nAmerican-operated bank until 2009. Walker achieved successes \\nwith the vision to make improvements in the way of life for \\nAfrican Americans.\\n①아프리카계 미국인을 위해 설립된 학교에 다녔다.\\n②졸업 후 자신이 공부했던 학교에서 교사로 일했다.\\n③인종 차별로 인해 은행 금융법 공부를 시작할 수 없었다.\\n④자선 단체와 대중 간의 소통을 장려하고자 신문사를 설립했다.\\n⑤그녀가 설립한 은행은 대공황에서 살아남아 다른 은행들과 합병했다.',\n",
       " '\\n27. 2024 Youth Tennis Camp에 관한 다음 안내문의 내용과 일치하지 \\n않는 것은?\\n2024 Youth Tennis Camp\\n   2024 Youth Tennis Camp is where your child can get \\ninstruction from qualified tennis players at indoor tennis courts. \\nIt will provide fundamental tennis skills to your children!\\nWho: Ages 13 to 18\\nWhen: January 15 - 18, 2024\\n          Monday to Thursday, 9:00 a.m. - 12:00 p.m.\\nRegistration Fee: $100(lunch included)\\nCancellation Policy\\n• 5 days before the class: 100% refund\\n• 1-4 days before the class: 50% refund\\n• On the day of the class and afterwards: No refund\\nNotes\\n• No outside food is allowed.\\n• Participants must bring their own tennis equipment.\\nRegistration is ONLY available online and will start on December \\n16. Visit our website at www.ytc2024.com to register.\\n①자격을 가진 테니스 선수가 지도한다.\\n②금요일에는 강습이 없다.\\n③등록비에는 점심 식사가 포함된다.\\n④강습 당일 취소 시 환불받을 수 있다.\\n⑤참가자들은 테니스 장비를 가져와야 한다.',\n",
       " '\\n28. Cherrywood High School’s T-shirt Design Contest에 관한 다음 \\n안내문의 내용과 일치하는 것은?\\nCherrywood High School’s T-shirt Design Contest\\nHelp us to design our new school shirts! A panel of \\nstudent council members will select the winning design. \\nTake this chance at being the designer for the new school \\nT-shirt. This contest is open to all students!\\nSubmission Deadline: 16:00 on December 22, 2023\\nWinner Announcement Date: December 29, 2023\\nLocation for Submissions: Art Teacher’s Office\\nContest Rules\\n• Sketch your design on a piece of plain paper.\\n• Write your student number and name on your paper.\\n• Include the school name and logo in your design.\\n• Max of 4 colors can be used.\\nGood luck and thanks for your participation!\\n①교사들이 수상 디자인을 선정할 예정이다.\\n②수상자 발표일은 제출 마감일 다음 날이다.\\n③출품작은 학생회실에 제출해야 한다.\\n④종이에 자신의 학번과 이름을 써야 한다.\\n⑤사용 가능한 색상 수에 제한이 없다.\\n5\\n영어 영역\\n5\\n8',\n",
       " '\\n29. 다음 글의 밑줄 친 부분 중, 어법상 틀린 것은?\\nLectins are large proteins that serve as a crucial weapon that \\nplants use to defend ①themselves. The lectins in most plants \\nbind to carbohydrates as we consume the plant. They also bind to \\nsugar molecules ②found in the gut, in the brain, between nerve \\nendings, in joints and in all bodily fluids. According to Dr. Steven \\nGundry, these sticky proteins can interrupt messaging between \\ncells and ③cause toxic and inflammatory reactions. Brain fog is \\njust one result of lectins interrupting communication between \\nnerves. An upset stomach is another common symptom of lectin \\noverload. Dr. Gundry lists a wide range of other health problems \\nincluding aching joints, dementia, headaches and infertility ④that \\nhave been resolved in his patients once they eliminated lectins \\nfrom their diets. Dr. Paul Saladino writes that the hypothesis that \\nlectins are involved in Parkinson’s disease is also gaining support, \\nwith animal studies ⑤showed that ‘lectins, once eaten, may be \\ndamaging the gut and travelling to the brain, where they appear \\nto be toxic to dopaminergic neurons’.\\n* inflammatory: 염증성의',\n",
       " '\\n30. 다음 글의 밑줄 친 부분 중, 문맥상 낱말의 쓰임이 적절하지 않은 \\n것은? [3점]\\nTechnology changes how individuals and societies understand \\nthe concept of privacy. The fact that someone has a new ability \\nto access information or watch the actions of another does not \\n①justify doing so. Rather, advances in technology require \\ncitizens and policy makers to consider how privacy protections \\nshould be expanded. For example, when cameras first became \\navailable for commercial and private use, nations and citizens \\nstruggled over whether new laws should be enacted to ②protect \\nindividuals from being photographed without their permission. \\nThe ③reconsideration of privacy brought about by this new \\ntechnology re-affirmed a distinction between private and public \\nspaces. It was determined by most cultures that people \\nautomatically gave ④consent to being seen ― and thus recorded\\n― once they voluntarily stepped into a public space. Although \\nsome people might be uncomfortable with the spread of \\nsurveillance cameras, citizens in most cultures have adjusted to \\nthe fact that giving up the right not to be observed in these \\ncircumstances causes ⑤more harm to the community than failing \\nto have surveillance.\\n* surveillance: 감시\\n',\n",
       " '[31 ~ 34] 다음 빈칸에 들어갈 말로 가장 적절한 것을 고르시오.\\n31. Coincidence that is statistically impossible seems to us like an \\nirrational event, and some define it as a miracle. But, as \\nMontaigne has said, “the origin of a miracle is in our                    \\n                      , at the level of our knowledge of nature, and not \\nin nature itself.” Glorious miracles have been later on discovered \\nto be obedience to the laws of nature or a technological \\ndevelopment that was not widely known at the time. As the \\nGerman poet, Goethe, phrased it: “Things that are mysterious are \\nnot yet miracles.” The miracle assumes the intervention of a \\n“higher power” in its occurrence that is beyond human capability \\nto grasp. Yet there are methodical and simple ways to “cause a \\nmiracle” without divine revelation and inspiration. Instead of \\nchecking it out, investigating and finding the source of the event, \\nwe define it as a miracle. The miracle, then, is the excuse of \\nthose who are too lazy to think. [3점]\\n* revelation: 계시\\n①ignorance\\n②flexibility\\n③excellence\\n④satisfaction\\n⑤exaggeration',\n",
       " '\\n32. Information encountered after an event can influence subsequent \\nremembering. External information can easily integrate into a \\nwitness’s memory, especially if the event was poorly encoded or \\nthe memory is from a distant event, in which case time and \\nforgetting have degraded the original memory. With reduced \\ninformation available in memory with which to confirm the \\nvalidity of post-event misinformation, it is less likely that           \\n                                                                                           . \\nInstead, especially when it fits the witness’s current thinking and \\ncan be used to create a story that makes sense to him or her, it \\nmay be integrated as part of the original experience. This \\nprocess can be explicit (i.e., the witness knows it is happening), \\nbut it is often unconscious. That is, the witness might find \\nhimself or herself thinking about the event differently without \\nawareness. Over time, the witness may not even know the source \\nof information that led to the (new) memory. Sources of \\nmisinformation in forensic contexts can be encountered \\nanywhere, from discussions with other witnesses to social media \\nsearches to multiple interviews with investigators or other legal \\nprofessionals, and even in court. [3점]\\n* forensic: 법정의\\n①this new information will be rejected\\n②people will deny the experience of forgetting\\n③interference between conflicting data will occur\\n④the unconscious will be involved in the recall process\\n⑤a recent event will last longer in memory than a distant one\\n6\\n영어 영역\\n6\\n8',\n",
       " '\\n33. Correlations are powerful because the insights they offer are \\nrelatively clear. These insights are often covered up when we \\nbring causality back into the picture. For instance, a used-car \\ndealer supplied data to statisticians to predict which of the \\nvehicles available for purchase at an auction were likely to have \\nproblems. A correlation analysis showed that orange-colored cars \\nwere far less likely to have defects. Even as we read this, we \\nalready think about why it might be so: Are orange-colored car \\nowners likely to be car enthusiasts and take better care of their \\nvehicles? Or, is it because orange-colored cars are more \\nnoticeable on the road and therefore less likely to be in accidents, \\nso they’re in better condition when resold? Quickly we are caught \\nin a web of competing causal hypotheses. But our attempts to \\nilluminate things this way only make them cloudier. \\nCorrelations exist; we can show them mathematically. We can’t \\neasily do the same for causal links. So we would do well to        \\n                                                                                         . \\n[3점]\\n①stay away from simply accepting the data as they are\\n②point out every phenomenon in light of cause and effect\\n③apply a psychological approach to color preferences\\n④admit that correlations are within the framework of causality\\n⑤hold off from trying to explain the reason behind the correlations',\n",
       " '\\n34. Most mice in the wild are eaten or die before their life \\nspan of two years is over. They die from external causes, \\nsuch as disease, starvation, or predators, not due to internal \\ncauses, such as aging. That is why nature has made mice to \\nlive, on average, for no longer than two years. Now we have \\narrived at an important point: The average life span of an \\nanimal species, or the rate at which it ages, is determined by     \\n                                                                                           . \\nThat explains why a bat can live to be 30 years old. In contrast \\nto mice, bats can fly, which is why they can escape from danger \\nmuch faster. Thanks to their wings, bats can also cover longer \\ndistances and are better able to find food. Every genetic change \\nin the past that made it possible for a bat to live longer was \\nuseful, because bats are much better able than mice to flee from \\ndanger, find food, and survive.\\n①the distance that migrating species can travel for their survival\\n②the average time that this animal species can survive in the wild\\n③the amount of energy that members of the species expend in a day\\n④the extent to which this species is able to protect its source of food\\n⑤the maximum size of the habitat in which it and its neighbors coexist',\n",
       " '\\n35. 다음 글에서 전체 흐름과 관계 없는 문장은? [3점]\\nMoral excellence, according to Aristotle, is the result of habit \\nand repetition, though modern science would also suggest that it \\nmay have an innate, genetic component. ①This means that moral \\nexcellence will be broadly set early in our lives, which is why the \\nquestion of how early to teach it is so important. ②Freud \\nsuggested that we don’t change our personality much after age \\nfive or thereabouts, but as in many other things, Freud was \\nwrong. ③A person of moral excellence cannot help doing good ―\\nit is as natural as the change of seasons or the rotation of the \\nplanets. ④Recent psychological research shows that personality \\ntraits stabilize around age thirty in both men and women and \\nregardless of ethnicity as the human brain continues to develop, \\nboth neuroanatomically and in terms of cognitive skills, until the \\nmid-twenties. ⑤The advantage of this new understanding is \\nthat we can be a bit more optimistic than Aristotle and Freud \\nabout being able to teach moral excellence.\\n* neuroanatomically: 신경 해부학적으로\\n',\n",
       " '[36 ~ 37] 주어진 글 다음에 이어질 글의 순서로 가장 적절한 것을 \\n고르시오.\\n36.\\nThe size of a species is not accidental. It’s a fine-tuned \\ninteraction between a species and the world it inhabits. Over \\nlarge periods of time, size fluctuations have often signalled \\nsignificant changes in the environment.\\n(A) But we are beginning to see changes in this trend. Scientists \\nhave discovered that many animals are shrinking. Around \\nthe world, species in every category have been found to be \\ngetting smaller, and one major cause appears to be the heat.\\n(B) Generally speaking, over the last five hundred million years, \\nthe trend has been towards animals getting larger. It’s \\nparticularly notable in marine animals, whose average body \\nsize has increased 150-fold in this time.\\n(C) Animals living in the Italian Alps, for example, have seen \\ntemperatures rise by three to four degrees Celsius since the \\n1980s. To avoid overheating, chamois goats now spend more \\nof their days resting rather than searching for food, and as a \\nresult, in just a few decades, the new generations of chamois \\nare 25 percent smaller.\\n①(A) - (C) - (B)\\n②(B) - (A) - (C)\\n③(B) - (C) - (A)\\n④(C) - (A) - (B)\\n⑤(C) - (B) - (A)\\n7\\n영어 영역\\n7\\n8',\n",
       " '\\n37.\\nFor a long time, random sampling was a good shortcut. It \\nmade analysis of large data problems possible in the \\npre-digital era.\\n(A) There is no need to focus at the beginning, since collecting all \\nthe information makes it possible to do that afterwards. \\nBecause rays from the entire light field are included, it is \\ncloser to all the data. As a result, the information is more \\n“reuseable” than ordinary pictures, where the photographer has \\nto decide what to focus on before she presses the shutter.\\n(B) But much as converting a digital image or song into a smaller \\nfile results in loss of data, information is lost when sampling. \\nHaving the full (or close to the full) dataset provides a lot \\nmore freedom to explore, to look at the data from different \\nangles or to look closer at certain aspects of it.\\n(C) A fitting example may be the light-field camera, which \\ncaptures not just a single plane of light, as with conventional \\ncameras, but rays from the entire light field, some 11 million \\nof them. The photographers can decide later which element \\nof an image to focus on in the digital file. [3점]\\n①(A) - (C) - (B)\\n②(B) - (A) - (C)\\n③(B) - (C) - (A)\\n④(C) - (A) - (B)\\n⑤(C) - (B) - (A)\\n',\n",
       " '[38 ~ 39] 글의 흐름으로 보아, 주어진 문장이 들어가기에 가장 적절한 \\n곳을 고르시오.\\n38.\\nWe must reexamine this stereotype, however, as it doesn’t \\nalways hold true.\\nIntroverted leaders do have to overcome the strong \\ncultural presumption that extroverts are more effective \\nleaders. ( ①) Although the population splits into almost equal \\nparts between introverts and extroverts, more than 96 percent \\nof managers and executives are extroverted. ( ②) In a study \\ndone in 2006, 65 percent of senior corporate executives viewed \\nintroversion as a barrier to leadership. ( ③) Regent \\nUniversity found that a desire to be of service to others and to \\nempower them to grow, which is more common among \\nintroverts than extroverts, is a key factor in becoming a leader \\nand retaining leadership. ( ④) So-called servant leadership, \\ndating back to ancient philosophical literature, adheres to the \\nbelief that a company’s goals are best achieved by helping \\nworkers or customers achieve their goals. ( ⑤) Such leaders \\ndo not seek attention but rather want to shine a light on others’ \\nwins and achievements; servant leadership requires humility, but \\nthat humility ultimately pays off.\\n* humility: 겸손',\n",
       " '\\n39.\\nHowever, contrary to the trend of the past several decades, in many \\nnew situations that are occurring today, allowing for imprecision―\\nfor messiness―may be a positive feature, not a shortcoming.\\nBy the nineteenth century, France had developed a system of \\nprecisely defined units of measurement to capture space, time, \\nand more, and had begun to get other nations to adopt the same \\nstandards. ( ①) Just half a century later, in the 1920s, the \\ndiscoveries of quantum mechanics forever destroyed the dream \\nof comprehensive and perfect measurement. ( ②) And yet, \\noutside a relatively small circle of physicists, the mindset of \\nhumankind’s drive to flawlessly measure continued among \\nengineers and scientists. ( ③) In the world of business it even \\nexpanded, as the precision-oriented sciences of mathematics and \\nstatistics began to influence all areas of commerce. ( ④) As a \\ntradeoff for relaxing the standards of allowable errors, one can \\nget a hold of much more data. ( ⑤) It isn’t just that “more is \\nbetter than some,” but that, in fact, sometimes “more is greater \\nthan better.”',\n",
       " '\\n40. 다음 글의 내용을 한 문장으로 요약하고자 한다. 빈칸 (A), (B)에 \\n들어갈 말로 가장 적절한 것은?\\nMultiple laboratory studies show that cooperative people \\ntend to receive social advantages from others. One way to \\ndemonstrate this is to give people the opportunity to act \\npositively or negatively toward contributors. For example, Pat \\nBarclay, a professor at the University of Guelph, had \\nparticipants play a cooperative game where people could \\ncontribute money toward a group fund which helped all group \\nmembers, and then allowed participants to give money to \\nother participants based on their reputations. People who \\ncontributed more to the group fund were given responsibility \\nfor more money than people who contributed less. Similar \\nresults have been found by other researchers. People who \\ncontribute toward their groups are also chosen more often as \\ninteraction partners, preferred as leaders, rated as more \\ndesirable partners for long-term relationships, and are \\nperceived to be trustworthy and have high social status. \\nUncooperative people tend to receive verbal criticism or even \\nmore severe punishment.\\n뾞\\nStudies suggest that individuals who act with \\n(A)\\n toward \\ntheir communities are more likely to be viewed as deserving of \\n(B)\\n  by members of that community than those who don’t.\\n    (A)\\n     (B)\\n   (A)\\n    (B)\\n①generosity …… benefit\\n②hostility …… support\\n③generosity …… humiliation\\n④hostility …… hospitality\\n⑤tolerance\\n…… dishonor\\n8\\n영어 영역\\n8\\n8\\n',\n",
       " '[41 ~ 42] 다음 글을 읽고, 물음에 답하시오.\\nIn Western society, many music performance settings make \\na clear distinction between performers and audience \\nmembers: the performers are the “doers” and those in the \\naudience take a decidedly passive role. The performance \\nspace itself may further (a) reinforce the distinction with a \\nphysical separation between the stage and audience seating. \\nPerhaps because this distinction is so common, audiences \\nseem to greatly value opportunities to have special “access” \\nto performers that affords understanding about performers’ \\nstyle of music. Some performing musicians have won great \\napproval by regularly (b) incorporating “audience participation” \\ninto their concerts. Whether by leading a sing-along activity \\nor teaching a rhythm to be clapped at certain points, including \\naudience members in the music making can (c) boost the \\nlevel of engagement and enjoyment for all involved. \\nPerformers \\nwho \\nare \\nuncomfortable \\nleading \\naudience \\nparticipation can still connect with the audience simply by \\ngiving a special glimpse of the performer (d) perspective. It is \\nquite common in classical music to provide audiences with \\nprogram notes. Typically, this text in a program gives \\nbackground information about pieces of music being performed \\nand perhaps biographical information about historically significant \\ncomposers. What may be of more interest to audience members \\nis background information about the very performers who are \\nonstage, including an explanation of why they have chosen the \\nmusic they are presenting. Such insight can make audience \\nmembers feel (e) distant to the musicians onstage, both \\nmetaphorically and emotionally. This connection will likely \\nenhance the expressive and communicative experience.\\n41. 윗글의 제목으로 가장 적절한 것은?\\n①Bridge the Divide and Get the Audience Involved\\n②Musical Composition Reflects the Musician’s Experience\\n③Why a Performer’s Style Changes with Each Performance\\n④Understanding Performers on Stage: An Audience’s Responsibility\\n⑤The Effect of Theater Facilities on the Success of a Performance',\n",
       " '\\n42. 밑줄 친 (a)~(e) 중에서 문맥상 낱말의 쓰임이 적절하지 않은 것은?\\n① (a)\\n② (b)\\n③ (c)\\n④ (d)\\n⑤ (e)\\n',\n",
       " '[43 ~ 45] 다음 글을 읽고, 물음에 답하시오.\\n(A)\\nOnce upon a time, two brothers, Robert and James, who \\nlived on neighboring farms fell into conflict. It was the first \\nserious fight in 40 years of farming side by side. It began \\nwith a small misunderstanding and it grew into a major \\nargument, and finally it exploded into an exchange of bitter \\nwords followed by weeks of silence. One morning there was a \\nknock on Robert’s door. (a) He opened it to find a carpenter \\nwith a toolbox.\\n(B)\\nThe two brothers stood awkwardly for a moment, but soon \\nmet on the bridge and shook hands. They saw the carpenter \\nleaving with his toolbox. “No, wait! Stay a few more days.” \\nRobert told him. “Thank you for (b) your invitation. But I \\nneed to go build more bridges. Don’t forget. The fence leads \\nto isolation and the bridge to openness,” said carpenter. The \\ntwo brothers nodded at the carpenter’s words.\\n(C)\\nLooking at Robert, the carpenter said, “I’m looking for a few \\ndays’ work. Do (c) you have anything to repair?” “I have \\nnothing to be repaired, but I have a job for you. Look across \\nthe creek at that farm. Last week, my younger brother James \\ntook his bulldozer and put that creek in the meadow between \\nus. Well, (d) I will do even worse. I want you to build me an \\n8-foot tall fence which will block him from seeing my place,” \\nsaid Robert. The carpenter seemed to understand the situation.\\n(D)\\nRobert prepared all the materials the carpenter needed. The \\nnext day, Robert left to work on another farm, so he couldn’t \\nwatch the carpenter for some days. When Robert returned and \\nsaw the carpenter’s work, his jaw dropped. Instead of a fence, \\nthe carpenter had built a bridge that stretched from one side of \\nthe creek to the other. His brother was walking over, waving \\n(e) his hand in the air. Robert laughed and said to the \\ncarpenter, “You really can fix anything.”\\n43. 주어진 글 (A)에 이어질 내용을 순서에 맞게 배열한 것으로 가장 \\n적절한 것은?\\n①(B) - (C) - (D)\\n②(C) - (B) - (D)\\n③(C) - (D) - (B)\\n④(D) - (B) - (C)\\n⑤(D) - (C) - (B)',\n",
       " '\\n44. 밑줄 친 (a)~(e) 중에서 가리키는 대상이 나머지 넷과 다른 것은?\\n① (a)\\n② (b)\\n③ (c)\\n④ (d)\\n⑤ (e)',\n",
       " '\\n45. 윗글에 관한 내용으로 적절하지 않은 것은?\\n①Robert와 James는 40년간 나란히 농사를 지었다.\\n②Robert는 떠나려는 목수에게 더 머무르라고 말했다.\\n③James는 불도저로 초원에 샛강을 만들었다.\\n④Robert는 목수가 필요로 하는 재료들을 준비해 주었다.\\n⑤목수는 샛강에 다리 대신 울타리를 설치했다.\\n※ ']"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 문제 분류(Rule base)\n",
    "\n",
    "start_list = [] \n",
    "\n",
    "# i=36\n",
    "for i in list(range(18, 46)): \n",
    "    if re.compile(f'\\[{i}').search(text_all):\n",
    "        globals()[f'm{i}'] = re.compile(f'\\[{i}').search(text_all).span()[0] \n",
    "    else:\n",
    "        globals()[f'm{i}'] = re.compile(f'\\n{i}.\\s').search(text_all).span()[0] \n",
    "    start_list.append(globals()[f'm{i}']) \n",
    "\n",
    "# start_list = [] \n",
    "# for i in list(range(18, 46)): \n",
    "#     globals()[f'm{i}'] = re.compile(f'\\n{i}.\\s').search(text_all).span()[0] \n",
    "#     start_list.append(globals()[f'm{i}']) \n",
    "\n",
    "q_list = []\n",
    "for j in range(len(start_list)-1):\n",
    "    q_list.append(text_all[start_list[j]:start_list[j+1]])\n",
    "\n",
    "# 시험지 마지막 부분에 \"확인 사항\" 제거\n",
    "try:\n",
    "    last_q = re.split(\"확인 사항\", text_all[start_list[-1]:])[0]\n",
    "    print(\"확인 사항:\", re.split(\"확인 사항\", text_all[start_list[-1]:])[1])\n",
    "except:\n",
    "    last_q = text_all[start_list[-1]:]\n",
    "\n",
    "# 마지막 문제 추가\n",
    "q_list.append(last_q)\n",
    "\n",
    "print(f\"{len(q_list)}문제\")\n",
    "q_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ded9ad-3e9f-4e1a-825b-1c04a0914a5f",
   "metadata": {},
   "source": [
    "## gpt 답변 자동화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "0a4d0ebe-9242-4382-b35f-c9807993cae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = \"ft:gpt-3.5-turbo-0125:personal::9VBgyCEI\"  # GPT 파인튜닝   # gpt-3.5-turbo, gpt-4-0125-preview, gpt-4o\n",
    "\n",
    "# 질문을 받으면 답변하는 함수\n",
    "def gpt_response(question):\n",
    "\n",
    "    # 메시지 히스토리 생성\n",
    "    messages=[{\"role\": \"system\", \n",
    "               \"content\": \"\"\" 한국어로 대답해. 답변 이유는 영어선생님처럼 대답해줘.\n",
    "               답변 양식 : {\"문제번호\":number, \"답변\": 1 or 2 or 3 or 4 or 5, \"reason\": 답변 설명}\n",
    "               \"\"\"}]\n",
    "\n",
    "    question_dict = {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": question\n",
    "            }\n",
    "\n",
    "    # 메시지 히스토리에 질문 추가\n",
    "    messages.append(question_dict)\n",
    "    \n",
    "    # 답변 받기\n",
    "    completion = client.chat.completions.create(\n",
    "        model = model, \n",
    "        messages = messages\n",
    "    )\n",
    "    \n",
    "    response = completion.choices[0].message.content\n",
    "\n",
    "    return response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7121c1b3-f5cf-4872-905c-d2ae44c66011",
   "metadata": {},
   "source": [
    "### 답변 자동화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "3f9936b7-24f0-4898-b697-ac8de11786b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"문제번호\":18, \"답변\": 2, \"reason\": \"이 편지의 주된 목적은 농구 코트의 운영 시간을 제한하려는 것입니다. 글쓴이는 밤 늦게 농구를 하는 소리로 잠을 설치고 있다는 문제를 설명하고, 농구 코트 운영 시간을 9시 이전으로 제한해 줄 것을 요청하고 있습니다.\"}'"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = gpt_response(q_list[0])\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "771f66c8-6e3f-4648-b09a-c586a0a8948f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "문제지:  2023-12-19 고2 3. 영어영역 문제지.pdf\n",
      "사용 AI 모델:  ft:gpt-3.5-turbo-0125:personal::9VBgyCEI\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1050259a497a4fe58c36271eb61daad0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"문제번호\":18, \"답변\": 2, \"reason\": \"편지의 내용을 살펴보면, 글쓴이가 아파트에서 밤 늦게까지 농구를 즐기는 사람들로 인해 수면을 방해받고 있다는 문제를 알리고, 농구 코트의 운영 시간을 제한해 달라는 요청을 하고 있습니다. 따라서 '농구 코트의 운영 시간 제한을 요청하려고'가 가장 적절한 목적입니다.\"}\n",
      "{\"문제번호\":19, \"답변\": 2, \"reason\": \"글의 내용을 살펴보면, Chaske가 무서운 상황에서 그의 아버지의 존재에 안심하고 안정감을 찾는 과정이 나타납니다. 따라서 'horrified → relieved' (공포를 안심으로)가 가장 적절합니다.\"}\n",
      "{\"문제번호\":20, \"답변\": 5, \"reason\": \"글의 흐름을 따라 필자가 제시한 중심 주장은 '농업이 직면한 문제 해결 및 식량과 농산물의 지속적 생산을 위한 방안이 필요하다'는 것입니다. 글에서는 농업의 중요성, 세계적인 역할, 그러나 기후 변화, 수자원 부족, 토양 퇴비, 생물 다양성 감소와 같은 다양한 도전에 대해 언급하며, 이를 해결하기 위한 지속 가능한 해결책을 찾아야 한다는 점을 강조합니다.\"}\n",
      "{\"문제번호\": 21, \"답변\": 4, \"reason\": \"‘be more than just sugar on the tongue’은 '잼이 언어 속 단지 설탕일 뿐만이 아니라는 것'을 의미합니다. 이는 미술과 미적 감각이 즐거움 이상의 다양한 경험을 포용한다는 것을 나타냅니다. 즉, 미술이 즐거움 뿐 아니라 불편하고 도전적인 경험을 통해 변화와 변혁의 가능성을 제공한다는 것을 설명하고 있습니다.\"}\n",
      "{\"문제번호\":22, \"답변\":1, \"reason\": \"글의 요지는 공공 시계가 서양 경제 발전에 어떻게 영향을 미쳤는지에 대한 내용입니다. 따라서 '①공공 시계는 서양 사회의 경제적 진보에 영향을 미쳤다.'가 가장 적절한 요지로 선택됩니다.\"}\n",
      "{\"문제번호\":23, \"답변\": 1, \"reason\": \"주어진 글은 실버 골드만이 쇼핑카트를 선보이고, 고객들이 이를 사용하지 않는 이유와 그 문제를 해결한 방법에 대해 설명하고 있습니다. 실버 골드만이 모델을 고용해 카트를 사용하도록 유도한 사례는 주변 행동의 설득력을 잘 보여주는 예시입니다. 따라서 '동료 행동의 설득력'이 주제로 가장 적절합니다.\"}\n",
      "{\"문제번호\": 24, \"답변\":3, \"reason\": \"글의 내용에서는 치매 환자나 자폐 스펙트럼 아동들을 상대로 하는 로봇 상호작용에 대한 윤리적 문제와 로봇이 실제로 로봇으로 보이도록 하는 중요성이 강조됩니다. 따라서 'Vulnerable People Should Look Like Robots'로 '취약한 사람들을 위한 로봇들이 로봇처럼 보여야 하는 이유'가 가장 적절한 제목입니다.\"}\n",
      "{\"문제번호\":25, \"답변\":3, \"reason\": \"도표의 내용과 일치하지 않는 문항은 3번입니다. 25세에서 34세의 응답자와 45세에서 54세의 응답자가 동일한 비율로 타겟 광고를 방해받는 것으로 생각했다는데, 이는 도표와 일치하지 않습니다. 정확한 내용은 한 그룹이 다른 그룹과 동일한 비율로 생각했다는 것이 아니라 달랐다는 점입니다.\"}\n",
      "{\"문제번호\": 26, \"답변\":3, \"reason\": \"주어진 글에서는 Maggie L. Walker가 백인 은행가들의 인종차별적 태도로 은행과 재정법을 공부하기 시작했음을 명확히 설명하고 있습니다. 따라서 '인종 차별로 인해 은행 금융법 공부를 시작할 수 없었다'는 설명이 일치하지 않습니다.\"}\n",
      "{\"문제번호\": 27, \"답변\": 2, \"reason\": \"안내문에는 금요일에 대한 내용이 없으며, 강습이 있는 요일은 '월요일부터 목요일'로 명시되어 있습니다.\"}\n",
      "{\"문제번호\":28, \"답변\":5, \"reason\": \"안내문에서는 'Max of 4 colors can be used.' 라고 언급하고 있으므로, 사용 가능한 색상 수에 제한이 있다는 점이 일치합니다.\"}\n",
      "{\"문제번호\":29, \"답변\": 5, \"reason\": \"‘showed’ 뒤에는 ‘that’이 빠져있어서 문법적으로 맞지 않습니다. 'with animal studies that showed'로 수정해야 합니다.\"}\n",
      "{\"문제번호\": 30, \"답변\":2, \"reason\": \" 'protect' 뒤에 올바른 전치사는 'against'이다. 'protect individuals from'의 패턴을 사용하여야 한다.\"}\n",
      "{\"문제번호\": 31, \"답변\": 1, \"reason\": \"Montaigne이 말한 '기적의 기원은 우리의 무지에 있다'는 맥락을 고려하면 계시를 뜻하는 'ignorance(무지)'가 적절하다.\"}\n",
      "{\"문제번호\":32, \"답변\": 1, \"reason\": \"주어진 텍스트에서는 사후정보가 기억에 통합되기 쉽다는 내용이 나옵니다. 특히, 기억이 잘 인코딩되지 않았거나 먼 과거의 사건일수록 외부 정보가 쉽게 기억에 통합될 수 있다고 설명합니다. 이는 원래 기억이 시간이 지나거나 망각에 의해 소멸된 경우, 기존의 정보로 사후정보를 확인하기 어려워지기 때문입니다. 따라서, 주어진 선택지 중 어떤 것이 사후정보가 통합될 것인지를 설명하는 문장은 '①this new information will be rejected'입니다. 즉, 새로운 정보가 거부되지 않고 오히려 원본 경험의 일부로 통합될 것이라는 것입니다.\"}\n",
      "{\"문제번호\": 33, \"답변\": 5, \"reason\": \"상관관계는 인과관계를 분석하는 것보다 더욱 명확한 통찰력을 제공한다고 설명되었습니다. 따라서 이러한 상관관계의 원인을 설명하려는 시도를 하지 않는 것이 바람직하다고 합니다.\"}\n",
      "{\"문제번호\":34, \"답변\": 2, \"reason\": \"인간을 포함한 많은 동물 종들의 평균 수명은 자연 환경에서의 생존율과 관련이 있습니다. 주어진 맥락에서는 '동물 종의 평균 수명 또는 노화 속도는...으로 결정됩니다'라는 문장이 이어지고, 사자와 박쥐 간의 비교가 나옵니다. 이에 따르면, 사자와 박쥐의 생존능력 차이는 '동물 종이 야생에서 얼마나 오래 살 수 있는지'와 밀접하게 관련되어 있습니다. 따라서 정답은 2번입니다.\"}\n",
      "{\"문제번호\": 35, \"답변\":2, \"reason\": \"글의 전체 흐름은 도덕적 우수성이 어떻게 형성되는지에 대한 설명입니다. 2번 문장은 프로이트의 성격 변화에 관한 주장을 소개하는데, 이는 주된 흐름과 관련이 없습니다.\"}\n",
      "{\"문제번호\":36, \"답변\": 3, \"reason\": \"글의 흐름을 살펴보면, 먼저 일반적인 경향을 설명한 후, 실제로 크기 변화가 나타나는 예시를 들고, 마지막으로 그 변화의 원인을 소개하는 것이 적절하다. 따라서 (B) - (C) - (A) 순서가 가장 적절하다.\"}\n",
      "{\"문제번호\":37, \"답변\": 5, \"reason\": \"텍스트의 흐름을 살펴보면, (C)가 먼저 나오며, 빛 필드 카메라의 설명을 하고 있습니다. 그 다음으로 (B)가 나와, 샘플링이 데이터 손실을 일으킨다는 비유를 듭니다. 마지막으로 (A)가 나와, 빛 필드 카메라와 일반 사진의 차이를 비유적으로 설명합니다. 따라서 올바른 순서는 (C) - (B) - (A)인 5번입니다.\"}\n",
      "{\"문제번호\":38, \"답변\":3, \"reason\": \"Introverted leaders가 강조하고 있는 '내향성'이 리더십에 어떤 영향을 미치는지 설명한 문장이 가장 적절하다. 따라서 '2006년에 실시된 연구에서 고위 경영진의 65%가 내향성을 리더십의 장벽으로 인식한다'는 문장이 들어가는 것이 자연스럽다.\"}\n",
      "{\"문제번호\":39, \"답변\": 4, \"reason\": \"정확도 대신에 데이터의 양을 늘리는 것이 유리한 이유를 설명하라는 문장입니다. 따라서 4번이 정답입니다.\"}\n",
      "{\"문제번호\":40, \"답변\":1, \"reason\":\"문장을 요약하면, '연구결과, 자신의 집단에 호의적으로 행동하는 사람들이 사회적 이점을 더 많이 받는다.' 따라서 (A)에는 '자신의 집단에 대한 관대한 행동'이 들어가고, (B)에는 '이익을 받을 자격이 있다'가 가장 적절하다. 이에 (A)는 ① 'generosity'(관대함), (B)는 ① 'benefit'(이익)이다.\"}\n",
      "{\"문제번호\":41, \"답변\": 1, \"reason\": \"글의 내용을 살펴보면, 서양 사회에서의 음악 공연 설정에 대해 설명하고, 관객과 공연자 사이의 극명한 구분을 다루고 있습니다. 또한, 특별한 '접근 권한'을 통해 공연자에 대한 이해를 높이는 가치를 준다는 점, 그리고 관객 참여가 참가자들의 참여 수준과 즐거움을 향상시킬 수 있다는 내용을 담고 있습니다. 이에, 가장 적절한 제목은 '분할을 교량으로 이끄러: 관객의 참여'입니다. \"}\n",
      "{\"문제번호\":42, \"답변\":3, \"reason\": \"이유를 대체 해주시구요.\"}\n",
      "{\"문제번호\":43, \"답변\": 2, \"reason\": \"글의 흐름을 살펴보면, (A)에서 대화가 시작된 후, 두 형제와 목수의 상황이 시작됩니다. (B)에서는 목수의 이야기와 형제 둘의 상황이 이어지며, (C)에서는 목수와 로버트의 대화가 진행됩니다. 마지막으로, (D)에서는 목수의 작품이 완성되고, 결과가 나오게 됩니다. 따라서 가장 적절한 순서는 2번 (C) - (B) - (D)입니다.\"}\n",
      "{\"문제번호\":44, \"답변\":2, \"reason\":\"한글의 'ㄴ'이 가리키는 대상은 (b)이다. 다른 네 가지는 모두 숫자 '4'를 가리킨다.\"}\n",
      "{\"문제번호\":45, \"답변\":1, \"reason\": \"Robert와 James가 40년간 나란히 농사를 지은 것은 적절한 내용이지만, 떠나려는 목수와의 대화 내용에서 Robert의 행동이 적절하지 않다. 일반적으로 목수가 떠나려는 것을 막기 위해 머물라고 말하는 것이 아니라 '안녕'이나 '건강하게 지내라' 등의 인사를 하는 것이 더 자연스럽다.\"}\n"
     ]
    }
   ],
   "source": [
    "# 답변을 딕셔너리로 받아서 리스트에 저장\n",
    "response_list = []\n",
    "print(\"문제지: \", path)\n",
    "print(\"사용 AI 모델: \", model)\n",
    "\n",
    "for i in tqdm_notebook(range(len(q_list))):\n",
    "    response = gpt_response(q_list[i])\n",
    "    if response[0] == \"{\" and response[-1] != \"}\":\n",
    "        response = response + \"}\" \n",
    "    elif response[0] != \"{\" and response[-1] != \"}\":\n",
    "        response = str({\"문제번호\":18+i, \"답변\":0, \"이유\":\"-\"})\n",
    "    print(response)\n",
    "    response_list.append(response)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "479d374d-af68-4020-8873-10dce234af80",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['{\"문제번호\":18, \"답변\": 2, \"reason\": \"편지의 내용을 살펴보면, 글쓴이가 아파트에서 밤 늦게까지 농구를 즐기는 사람들로 인해 수면을 방해받고 있다는 문제를 알리고, 농구 코트의 운영 시간을 제한해 달라는 요청을 하고 있습니다. 따라서 \\'농구 코트의 운영 시간 제한을 요청하려고\\'가 가장 적절한 목적입니다.\"}',\n",
       " '{\"문제번호\":19, \"답변\": 2, \"reason\": \"글의 내용을 살펴보면, Chaske가 무서운 상황에서 그의 아버지의 존재에 안심하고 안정감을 찾는 과정이 나타납니다. 따라서 \\'horrified → relieved\\' (공포를 안심으로)가 가장 적절합니다.\"}',\n",
       " '{\"문제번호\":20, \"답변\": 5, \"reason\": \"글의 흐름을 따라 필자가 제시한 중심 주장은 \\'농업이 직면한 문제 해결 및 식량과 농산물의 지속적 생산을 위한 방안이 필요하다\\'는 것입니다. 글에서는 농업의 중요성, 세계적인 역할, 그러나 기후 변화, 수자원 부족, 토양 퇴비, 생물 다양성 감소와 같은 다양한 도전에 대해 언급하며, 이를 해결하기 위한 지속 가능한 해결책을 찾아야 한다는 점을 강조합니다.\"}',\n",
       " '{\"문제번호\": 21, \"답변\": 4, \"reason\": \"‘be more than just sugar on the tongue’은 \\'잼이 언어 속 단지 설탕일 뿐만이 아니라는 것\\'을 의미합니다. 이는 미술과 미적 감각이 즐거움 이상의 다양한 경험을 포용한다는 것을 나타냅니다. 즉, 미술이 즐거움 뿐 아니라 불편하고 도전적인 경험을 통해 변화와 변혁의 가능성을 제공한다는 것을 설명하고 있습니다.\"}',\n",
       " '{\"문제번호\":22, \"답변\":1, \"reason\": \"글의 요지는 공공 시계가 서양 경제 발전에 어떻게 영향을 미쳤는지에 대한 내용입니다. 따라서 \\'①공공 시계는 서양 사회의 경제적 진보에 영향을 미쳤다.\\'가 가장 적절한 요지로 선택됩니다.\"}',\n",
       " '{\"문제번호\":23, \"답변\": 1, \"reason\": \"주어진 글은 실버 골드만이 쇼핑카트를 선보이고, 고객들이 이를 사용하지 않는 이유와 그 문제를 해결한 방법에 대해 설명하고 있습니다. 실버 골드만이 모델을 고용해 카트를 사용하도록 유도한 사례는 주변 행동의 설득력을 잘 보여주는 예시입니다. 따라서 \\'동료 행동의 설득력\\'이 주제로 가장 적절합니다.\"}',\n",
       " '{\"문제번호\": 24, \"답변\":3, \"reason\": \"글의 내용에서는 치매 환자나 자폐 스펙트럼 아동들을 상대로 하는 로봇 상호작용에 대한 윤리적 문제와 로봇이 실제로 로봇으로 보이도록 하는 중요성이 강조됩니다. 따라서 \\'Vulnerable People Should Look Like Robots\\'로 \\'취약한 사람들을 위한 로봇들이 로봇처럼 보여야 하는 이유\\'가 가장 적절한 제목입니다.\"}',\n",
       " '{\"문제번호\":25, \"답변\":3, \"reason\": \"도표의 내용과 일치하지 않는 문항은 3번입니다. 25세에서 34세의 응답자와 45세에서 54세의 응답자가 동일한 비율로 타겟 광고를 방해받는 것으로 생각했다는데, 이는 도표와 일치하지 않습니다. 정확한 내용은 한 그룹이 다른 그룹과 동일한 비율로 생각했다는 것이 아니라 달랐다는 점입니다.\"}',\n",
       " '{\"문제번호\": 26, \"답변\":3, \"reason\": \"주어진 글에서는 Maggie L. Walker가 백인 은행가들의 인종차별적 태도로 은행과 재정법을 공부하기 시작했음을 명확히 설명하고 있습니다. 따라서 \\'인종 차별로 인해 은행 금융법 공부를 시작할 수 없었다\\'는 설명이 일치하지 않습니다.\"}',\n",
       " '{\"문제번호\": 27, \"답변\": 2, \"reason\": \"안내문에는 금요일에 대한 내용이 없으며, 강습이 있는 요일은 \\'월요일부터 목요일\\'로 명시되어 있습니다.\"}',\n",
       " '{\"문제번호\":28, \"답변\":5, \"reason\": \"안내문에서는 \\'Max of 4 colors can be used.\\' 라고 언급하고 있으므로, 사용 가능한 색상 수에 제한이 있다는 점이 일치합니다.\"}',\n",
       " '{\"문제번호\":29, \"답변\": 5, \"reason\": \"‘showed’ 뒤에는 ‘that’이 빠져있어서 문법적으로 맞지 않습니다. \\'with animal studies that showed\\'로 수정해야 합니다.\"}',\n",
       " '{\"문제번호\": 30, \"답변\":2, \"reason\": \" \\'protect\\' 뒤에 올바른 전치사는 \\'against\\'이다. \\'protect individuals from\\'의 패턴을 사용하여야 한다.\"}',\n",
       " '{\"문제번호\": 31, \"답변\": 1, \"reason\": \"Montaigne이 말한 \\'기적의 기원은 우리의 무지에 있다\\'는 맥락을 고려하면 계시를 뜻하는 \\'ignorance(무지)\\'가 적절하다.\"}',\n",
       " '{\"문제번호\":32, \"답변\": 1, \"reason\": \"주어진 텍스트에서는 사후정보가 기억에 통합되기 쉽다는 내용이 나옵니다. 특히, 기억이 잘 인코딩되지 않았거나 먼 과거의 사건일수록 외부 정보가 쉽게 기억에 통합될 수 있다고 설명합니다. 이는 원래 기억이 시간이 지나거나 망각에 의해 소멸된 경우, 기존의 정보로 사후정보를 확인하기 어려워지기 때문입니다. 따라서, 주어진 선택지 중 어떤 것이 사후정보가 통합될 것인지를 설명하는 문장은 \\'①this new information will be rejected\\'입니다. 즉, 새로운 정보가 거부되지 않고 오히려 원본 경험의 일부로 통합될 것이라는 것입니다.\"}',\n",
       " '{\"문제번호\": 33, \"답변\": 5, \"reason\": \"상관관계는 인과관계를 분석하는 것보다 더욱 명확한 통찰력을 제공한다고 설명되었습니다. 따라서 이러한 상관관계의 원인을 설명하려는 시도를 하지 않는 것이 바람직하다고 합니다.\"}',\n",
       " '{\"문제번호\":34, \"답변\": 2, \"reason\": \"인간을 포함한 많은 동물 종들의 평균 수명은 자연 환경에서의 생존율과 관련이 있습니다. 주어진 맥락에서는 \\'동물 종의 평균 수명 또는 노화 속도는...으로 결정됩니다\\'라는 문장이 이어지고, 사자와 박쥐 간의 비교가 나옵니다. 이에 따르면, 사자와 박쥐의 생존능력 차이는 \\'동물 종이 야생에서 얼마나 오래 살 수 있는지\\'와 밀접하게 관련되어 있습니다. 따라서 정답은 2번입니다.\"}',\n",
       " '{\"문제번호\": 35, \"답변\":2, \"reason\": \"글의 전체 흐름은 도덕적 우수성이 어떻게 형성되는지에 대한 설명입니다. 2번 문장은 프로이트의 성격 변화에 관한 주장을 소개하는데, 이는 주된 흐름과 관련이 없습니다.\"}',\n",
       " '{\"문제번호\":36, \"답변\": 3, \"reason\": \"글의 흐름을 살펴보면, 먼저 일반적인 경향을 설명한 후, 실제로 크기 변화가 나타나는 예시를 들고, 마지막으로 그 변화의 원인을 소개하는 것이 적절하다. 따라서 (B) - (C) - (A) 순서가 가장 적절하다.\"}',\n",
       " '{\"문제번호\":37, \"답변\": 5, \"reason\": \"텍스트의 흐름을 살펴보면, (C)가 먼저 나오며, 빛 필드 카메라의 설명을 하고 있습니다. 그 다음으로 (B)가 나와, 샘플링이 데이터 손실을 일으킨다는 비유를 듭니다. 마지막으로 (A)가 나와, 빛 필드 카메라와 일반 사진의 차이를 비유적으로 설명합니다. 따라서 올바른 순서는 (C) - (B) - (A)인 5번입니다.\"}',\n",
       " '{\"문제번호\":38, \"답변\":3, \"reason\": \"Introverted leaders가 강조하고 있는 \\'내향성\\'이 리더십에 어떤 영향을 미치는지 설명한 문장이 가장 적절하다. 따라서 \\'2006년에 실시된 연구에서 고위 경영진의 65%가 내향성을 리더십의 장벽으로 인식한다\\'는 문장이 들어가는 것이 자연스럽다.\"}',\n",
       " '{\"문제번호\":39, \"답변\": 4, \"reason\": \"정확도 대신에 데이터의 양을 늘리는 것이 유리한 이유를 설명하라는 문장입니다. 따라서 4번이 정답입니다.\"}',\n",
       " '{\"문제번호\":40, \"답변\":1, \"reason\":\"문장을 요약하면, \\'연구결과, 자신의 집단에 호의적으로 행동하는 사람들이 사회적 이점을 더 많이 받는다.\\' 따라서 (A)에는 \\'자신의 집단에 대한 관대한 행동\\'이 들어가고, (B)에는 \\'이익을 받을 자격이 있다\\'가 가장 적절하다. 이에 (A)는 ① \\'generosity\\'(관대함), (B)는 ① \\'benefit\\'(이익)이다.\"}',\n",
       " '{\"문제번호\":41, \"답변\": 1, \"reason\": \"글의 내용을 살펴보면, 서양 사회에서의 음악 공연 설정에 대해 설명하고, 관객과 공연자 사이의 극명한 구분을 다루고 있습니다. 또한, 특별한 \\'접근 권한\\'을 통해 공연자에 대한 이해를 높이는 가치를 준다는 점, 그리고 관객 참여가 참가자들의 참여 수준과 즐거움을 향상시킬 수 있다는 내용을 담고 있습니다. 이에, 가장 적절한 제목은 \\'분할을 교량으로 이끄러: 관객의 참여\\'입니다. \"}',\n",
       " '{\"문제번호\":42, \"답변\":3, \"reason\": \"이유를 대체 해주시구요.\"}',\n",
       " '{\"문제번호\":43, \"답변\": 2, \"reason\": \"글의 흐름을 살펴보면, (A)에서 대화가 시작된 후, 두 형제와 목수의 상황이 시작됩니다. (B)에서는 목수의 이야기와 형제 둘의 상황이 이어지며, (C)에서는 목수와 로버트의 대화가 진행됩니다. 마지막으로, (D)에서는 목수의 작품이 완성되고, 결과가 나오게 됩니다. 따라서 가장 적절한 순서는 2번 (C) - (B) - (D)입니다.\"}',\n",
       " '{\"문제번호\":44, \"답변\":2, \"reason\":\"한글의 \\'ㄴ\\'이 가리키는 대상은 (b)이다. 다른 네 가지는 모두 숫자 \\'4\\'를 가리킨다.\"}',\n",
       " '{\"문제번호\":45, \"답변\":1, \"reason\": \"Robert와 James가 40년간 나란히 농사를 지은 것은 적절한 내용이지만, 떠나려는 목수와의 대화 내용에서 Robert의 행동이 적절하지 않다. 일반적으로 목수가 떠나려는 것을 막기 위해 머물라고 말하는 것이 아니라 \\'안녕\\'이나 \\'건강하게 지내라\\' 등의 인사를 하는 것이 더 자연스럽다.\"}']"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(response_list))\n",
    "response_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a19cc6-f2ca-4e77-b963-3d669ca87fd8",
   "metadata": {},
   "source": [
    "# 그래프 문제 풀기 - GPT4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "030a138d-f39e-418e-8d78-5baa50d9720e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 입력 예시(image_path, prompt)\n",
    "image_path = \"yolo_inference/predict/image0.jpg\"\n",
    "prompt = \"What’s in this image?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "0f119bca-f9cb-4395-8631-560589ee8488",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 메시지 히스토리 생성\n",
    "messages=[{\"role\": \"system\", \n",
    "           \"content\": \"\"\" 한국어로 대답해.\n",
    "           답변 양식 : {\"문제번호\":25, \"답변\": 1 or 2 or 3 or 4 or 5, \"reason\": 답변 설명}\n",
    "           \"\"\"}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "ac744f0f-f180-49bf-b775-3c5e88e4a374",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 첫번째 질문 생성\n",
    "question = f\"\"\"\n",
    "다음 도표와 내용이 다를 확률이 가장 높은 번호는?\n",
    "그래프에서 인식한 수치 데이터를 줄게. {q_info_ls[0]['deplot']} \n",
    "\"\"\"\n",
    "\n",
    "# Function to encode the image\n",
    "def encode_image(image_path):\n",
    "    with open(image_path, \"rb\") as image_file:\n",
    "        return base64.b64encode(image_file.read()).decode('utf-8')\n",
    "        \n",
    "# Getting the base64 string\n",
    "base64_image = encode_image(image_path)\n",
    "\n",
    "question_dict = {\n",
    "          \"role\": \"user\",\n",
    "          \"content\": [\n",
    "            {\"type\": \"text\", \"text\": question},\n",
    "            {\n",
    "              \"type\": \"image_url\",\n",
    "              \"image_url\": {\n",
    "                \"url\": f\"data:image/jpeg;base64,{base64_image}\",\n",
    "              },\n",
    "            },\n",
    "          ],\n",
    "        }\n",
    "\n",
    "# 메시지 히스토리에 질문 추가\n",
    "messages.append(question_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "964588ce-8eb9-40c4-b79c-47bae2c953a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "다음 도표와 내용이 다를 확률이 가장 높은 번호는?\n",
      "그래프에서 인식한 수치 데이터를 줄게. TITLE | View of Targeted Online Advertising Among Americans, by Age Group<0x0A>Conducted on May 1, 2019 (missing percentages to 100%: \"don't know\") | Interesting(Targeted ads are a convenient<0x0A>way to see products I'm interested in) | Intrusive(Targeted ads are an inappropriate<0x0A>use of personal data) | Total <0x0A> 18–24 | 34% | 34% <0x0A> 25–34 | 37% | 45% <0x0A> 35–44 | 27% | 48% <0x0A> 45–54 | 31% | 45% <0x0A> 55+ (Age Group) | 18% | 61% \n",
      " \n",
      "\n",
      "{\"문제번호\":11, \"답변\": 4, \"reason\": \"디지털 그래프에 따르면 45-54세 연령 그룹의 'Interesting'라고 답한 비율은 31%이고 'Intrusive'라고 답한 비율은 45%입니다. 텍스트의 (3) 번 내용에서는 25-34세 연령 그룹이 'Intrusive'라고 대답한 비율이 45-54세 연령 그룹의 'Intrusive'라고 대답한 비율과 같다고 하고 있습니다. 그러나 그래프에 따르면 25-34세 연령 그룹의 'Intrusive'라고 대답한 비율은 45%로, 45-54세 연령 그룹의 비율과 같습니다. 이는 텍스트의 진술과 일치합니다. 그러나 텍스트의 (4) 번에서는 모든 연령 그룹 중에서 'Interesting'과 'Intrusive'라고 응답한 사람들 사이의 격차가 35-44세 그룹에서 가장 크다고 언급하고 있으나, 실제로는 해당 그룹에서 'Interesting'이라고 답한 비율은 27%이고 'Intrusive'라고 답한 비율은 48%로, 격차는 21%입니다. 디지털 그래프에 의하면 55세 이상 연령 그룹에서 'Interesting'이라고 답한 비율이 18%이고 'Intrusive'라고 답한 비율이 61%로, 이 연령대에서 격차는 43%로 가장 큽니다. 따라서 (4)의 내용이 그래프와 일치하지 않으며 이로 인해 오류 확률이 가장 높습니다.\"}\n"
     ]
    }
   ],
   "source": [
    "# gpt 비젼 답변 받기\n",
    "completion = client.chat.completions.create(\n",
    "    model=\"gpt-4-vision-preview\",\n",
    "    messages = messages,\n",
    ")\n",
    "\n",
    "response = completion.choices[0].message.content\n",
    "\n",
    "print(question, '\\n')\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "9642ca45-2738-466e-be4d-8f595167ebcb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 번 문제\n",
      " 편지의 내용을 살펴보면, 글쓴이가 아파트에서 밤 늦게까지 농구를 즐기는 사람들로 인해 수면을 방해받고 있다는 문제를 알리고, 농구 코트의 운영 시간을 제한해 달라는 요청을 하고 있습니다. 따라서 농구 코트의 운영 시간 제한을 요청하려고가 가장 적절한 목적입니다. \n",
      "\n",
      "정답: 2 \n",
      "\n",
      "19 번 문제\n",
      " 글의 내용을 살펴보면, Chaske가 무서운 상황에서 그의 아버지의 존재에 안심하고 안정감을 찾는 과정이 나타납니다. 따라서 horrified → relieved (공포를 안심으로)가 가장 적절합니다. \n",
      "\n",
      "정답: 2 \n",
      "\n",
      "20 번 문제\n",
      " 글의 흐름을 따라 필자가 제시한 중심 주장은 농업이 직면한 문제 해결 및 식량과 농산물의 지속적 생산을 위한 방안이 필요하다는 것입니다. 글에서는 농업의 중요성, 세계적인 역할, 그러나 기후 변화, 수자원 부족, 토양 퇴비, 생물 다양성 감소와 같은 다양한 도전에 대해 언급하며, 이를 해결하기 위한 지속 가능한 해결책을 찾아야 한다는 점을 강조합니다. \n",
      "\n",
      "정답: 5 \n",
      "\n",
      "21 번 문제\n",
      " ‘be more than just sugar on the tongue’은 잼이 언어 속 단지 설탕일 뿐만이 아니라는 것을 의미합니다. 이는 미술과 미적 감각이 즐거움 이상의 다양한 경험을 포용한다는 것을 나타냅니다. 즉, 미술이 즐거움 뿐 아니라 불편하고 도전적인 경험을 통해 변화와 변혁의 가능성을 제공한다는 것을 설명하고 있습니다. \n",
      "\n",
      "정답: 4 \n",
      "\n",
      "22 번 문제\n",
      " 글의 요지는 공공 시계가 서양 경제 발전에 어떻게 영향을 미쳤는지에 대한 내용입니다. 따라서 ①공공 시계는 서양 사회의 경제적 진보에 영향을 미쳤다.가 가장 적절한 요지로 선택됩니다. \n",
      "\n",
      "정답: 1 \n",
      "\n",
      "23 번 문제\n",
      " 주어진 글은 실버 골드만이 쇼핑카트를 선보이고, 고객들이 이를 사용하지 않는 이유와 그 문제를 해결한 방법에 대해 설명하고 있습니다. 실버 골드만이 모델을 고용해 카트를 사용하도록 유도한 사례는 주변 행동의 설득력을 잘 보여주는 예시입니다. 따라서 동료 행동의 설득력이 주제로 가장 적절합니다. \n",
      "\n",
      "정답: 1 \n",
      "\n",
      "24 번 문제\n",
      " 글의 내용에서는 치매 환자나 자폐 스펙트럼 아동들을 상대로 하는 로봇 상호작용에 대한 윤리적 문제와 로봇이 실제로 로봇으로 보이도록 하는 중요성이 강조됩니다. 따라서 Vulnerable People Should Look Like Robots로 취약한 사람들을 위한 로봇들이 로봇처럼 보여야 하는 이유가 가장 적절한 제목입니다. \n",
      "\n",
      "정답: 3 \n",
      "\n",
      "25 번 문제\n",
      " Robert와 James가 40년간 나란히 농사를 지은 것은 적절한 내용이지만, 떠나려는 목수와의 대화 내용에서 Robert의 행동이 적절하지 않다. 일반적으로 목수가 떠나려는 것을 막기 위해 머물라고 말하는 것이 아니라 안녕이나 건강하게 지내라 등의 인사를 하는 것이 더 자연스럽다. \n",
      "\n",
      "정답: 1 \n",
      "\n",
      "26 번 문제\n",
      " 주어진 글에서는 Maggie L. Walker가 백인 은행가들의 인종차별적 태도로 은행과 재정법을 공부하기 시작했음을 명확히 설명하고 있습니다. 따라서 인종 차별로 인해 은행 금융법 공부를 시작할 수 없었다는 설명이 일치하지 않습니다. \n",
      "\n",
      "정답: 3 \n",
      "\n",
      "27 번 문제\n",
      " 안내문에는 금요일에 대한 내용이 없으며, 강습이 있는 요일은 월요일부터 목요일로 명시되어 있습니다. \n",
      "\n",
      "정답: 2 \n",
      "\n",
      "28 번 문제\n",
      " 안내문에서는 Max of 4 colors can be used. 라고 언급하고 있으므로, 사용 가능한 색상 수에 제한이 있다는 점이 일치합니다. \n",
      "\n",
      "정답: 5 \n",
      "\n",
      "29 번 문제\n",
      " ‘showed’ 뒤에는 ‘that’이 빠져있어서 문법적으로 맞지 않습니다. with animal studies that showed로 수정해야 합니다. \n",
      "\n",
      "정답: 5 \n",
      "\n",
      "30 번 문제\n",
      "  protect 뒤에 올바른 전치사는 against이다. protect individuals from의 패턴을 사용하여야 한다. \n",
      "\n",
      "정답: 2 \n",
      "\n",
      "31 번 문제\n",
      " Montaigne이 말한 기적의 기원은 우리의 무지에 있다는 맥락을 고려하면 계시를 뜻하는 ignorance(무지)가 적절하다. \n",
      "\n",
      "정답: 1 \n",
      "\n",
      "32 번 문제\n",
      " 주어진 텍스트에서는 사후정보가 기억에 통합되기 쉽다는 내용이 나옵니다. 특히, 기억이 잘 인코딩되지 않았거나 먼 과거의 사건일수록 외부 정보가 쉽게 기억에 통합될 수 있다고 설명합니다. 이는 원래 기억이 시간이 지나거나 망각에 의해 소멸된 경우, 기존의 정보로 사후정보를 확인하기 어려워지기 때문입니다. 따라서, 주어진 선택지 중 어떤 것이 사후정보가 통합될 것인지를 설명하는 문장은 ①this new information will be rejected입니다. 즉, 새로운 정보가 거부되지 않고 오히려 원본 경험의 일부로 통합될 것이라는 것입니다. \n",
      "\n",
      "정답: 1 \n",
      "\n",
      "33 번 문제\n",
      " 상관관계는 인과관계를 분석하는 것보다 더욱 명확한 통찰력을 제공한다고 설명되었습니다. 따라서 이러한 상관관계의 원인을 설명하려는 시도를 하지 않는 것이 바람직하다고 합니다. \n",
      "\n",
      "정답: 5 \n",
      "\n",
      "34 번 문제\n",
      " 인간을 포함한 많은 동물 종들의 평균 수명은 자연 환경에서의 생존율과 관련이 있습니다. 주어진 맥락에서는 동물 종의 평균 수명 또는 노화 속도는...으로 결정됩니다라는 문장이 이어지고, 사자와 박쥐 간의 비교가 나옵니다. 이에 따르면, 사자와 박쥐의 생존능력 차이는 동물 종이 야생에서 얼마나 오래 살 수 있는지와 밀접하게 관련되어 있습니다. 따라서 정답은 2번입니다. \n",
      "\n",
      "정답: 2 \n",
      "\n",
      "35 번 문제\n",
      " 글의 전체 흐름은 도덕적 우수성이 어떻게 형성되는지에 대한 설명입니다. 2번 문장은 프로이트의 성격 변화에 관한 주장을 소개하는데, 이는 주된 흐름과 관련이 없습니다. \n",
      "\n",
      "정답: 2 \n",
      "\n",
      "36 번 문제\n",
      " 글의 흐름을 살펴보면, 먼저 일반적인 경향을 설명한 후, 실제로 크기 변화가 나타나는 예시를 들고, 마지막으로 그 변화의 원인을 소개하는 것이 적절하다. 따라서 (B) - (C) - (A) 순서가 가장 적절하다. \n",
      "\n",
      "정답: 3 \n",
      "\n",
      "37 번 문제\n",
      " 텍스트의 흐름을 살펴보면, (C)가 먼저 나오며, 빛 필드 카메라의 설명을 하고 있습니다. 그 다음으로 (B)가 나와, 샘플링이 데이터 손실을 일으킨다는 비유를 듭니다. 마지막으로 (A)가 나와, 빛 필드 카메라와 일반 사진의 차이를 비유적으로 설명합니다. 따라서 올바른 순서는 (C) - (B) - (A)인 5번입니다. \n",
      "\n",
      "정답: 5 \n",
      "\n",
      "38 번 문제\n",
      " Introverted leaders가 강조하고 있는 내향성이 리더십에 어떤 영향을 미치는지 설명한 문장이 가장 적절하다. 따라서 2006년에 실시된 연구에서 고위 경영진의 65%가 내향성을 리더십의 장벽으로 인식한다는 문장이 들어가는 것이 자연스럽다. \n",
      "\n",
      "정답: 3 \n",
      "\n",
      "39 번 문제\n",
      " 정확도 대신에 데이터의 양을 늘리는 것이 유리한 이유를 설명하라는 문장입니다. 따라서 4번이 정답입니다. \n",
      "\n",
      "정답: 4 \n",
      "\n",
      "40 번 문제\n",
      "문장을 요약하면, 연구결과, 자신의 집단에 호의적으로 행동하는 사람들이 사회적 이점을 더 많이 받는다. 따라서 (A)에는 자신의 집단에 대한 관대한 행동이 들어가고, (B)에는 이익을 받을 자격이 있다가 가장 적절하다. 이에 (A)는 ① generosity(관대함), (B)는 ① benefit(이익)이다. \n",
      "\n",
      "정답: 1 \n",
      "\n",
      "41 번 문제\n",
      " 글의 내용을 살펴보면, 서양 사회에서의 음악 공연 설정에 대해 설명하고, 관객과 공연자 사이의 극명한 구분을 다루고 있습니다. 또한, 특별한 접근 권한을 통해 공연자에 대한 이해를 높이는 가치를 준다는 점, 그리고 관객 참여가 참가자들의 참여 수준과 즐거움을 향상시킬 수 있다는 내용을 담고 있습니다. 이에, 가장 적절한 제목은 분할을 교량으로 이끄러 관객의 참여입니다.  \n",
      "\n",
      "정답: 1 \n",
      "\n",
      "42 번 문제\n",
      " 이유를 대체 해주시구요. \n",
      "\n",
      "정답: 3 \n",
      "\n",
      "43 번 문제\n",
      " 글의 흐름을 살펴보면, (A)에서 대화가 시작된 후, 두 형제와 목수의 상황이 시작됩니다. (B)에서는 목수의 이야기와 형제 둘의 상황이 이어지며, (C)에서는 목수와 로버트의 대화가 진행됩니다. 마지막으로, (D)에서는 목수의 작품이 완성되고, 결과가 나오게 됩니다. 따라서 가장 적절한 순서는 2번 (C) - (B) - (D)입니다. \n",
      "\n",
      "정답: 2 \n",
      "\n",
      "44 번 문제\n",
      "한글의 ㄴ이 가리키는 대상은 (b)이다. 다른 네 가지는 모두 숫자 4를 가리킨다. \n",
      "\n",
      "정답: 2 \n",
      "\n",
      "45 번 문제\n",
      " Robert와 James가 40년간 나란히 농사를 지은 것은 적절한 내용이지만, 떠나려는 목수와의 대화 내용에서 Robert의 행동이 적절하지 않다. 일반적으로 목수가 떠나려는 것을 막기 위해 머물라고 말하는 것이 아니라 안녕이나 건강하게 지내라 등의 인사를 하는 것이 더 자연스럽다. \n",
      "\n",
      "정답: 1 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 답변 번호, 이유 추출하기\n",
    "answer_list = []   # 답변 번호\n",
    "reason_list = []   # 답변 이유\n",
    "\n",
    "for j in range(len(response_list)):\n",
    "    print(18+j, \"번 문제\")\n",
    "    if j == 7:  # 25번 그래프 문제일 때\n",
    "        reason = response.split('reason')[1].replace(\":\", \"\").replace(\"\\'\", \"\").replace(\"}\", \"\").replace(\"\\\"\",\"\")\n",
    "    else:      \n",
    "        # 답변 이유 추출하기\n",
    "        try:      \n",
    "            reason = response_list[j].split('reason')[1].replace(\":\", \"\").replace(\"\\'\", \"\").replace(\"}\", \"\").replace(\"\\\"\",\"\")\n",
    "        except:\n",
    "            reason = \"문제가 명확하지 않습니다.\"\n",
    "    reason_list.append(reason)\n",
    "    print(reason, '\\n')\n",
    "    \n",
    "    # 답변 번호 추출하다가 에러나면 다시 gpt에게 답변 번호 물어보기    \n",
    "    if j == 7:  # 25번 그래프 문제일 때\n",
    "        raw_number = response.split('reason')[0].split(\"답변\")[1]\n",
    "        number = int(re.sub(r'[^0-9]', '', raw_number))\n",
    "    else:\n",
    "        try:\n",
    "            raw_number = response_list[j].split('reason')[0].split(\"답변\")[1]\n",
    "        #     print(raw_number)\n",
    "    \n",
    "            if '①' in raw_number:\n",
    "                number = 1\n",
    "            elif '②' in raw_number:\n",
    "                number = 2\n",
    "            elif '③' in raw_number:\n",
    "                number = 3\n",
    "            elif '④' in raw_number:\n",
    "                number = 4\n",
    "            elif '⑤' in raw_number:\n",
    "                number = 5\n",
    "            else:\n",
    "                # print(raw_number)\n",
    "                number = int(re.sub(r'[^0-9]', '', raw_number))\n",
    "        except:\n",
    "        #     print(j, \"에러 발생\", '\\n')\n",
    "            re_response = gpt_response(reason + \" -> 정답 번호만 숫자로 알려줘. 답변 양식 : int(number)\")\n",
    "            print(re_response)\n",
    "            number = int(re.sub(r'[^0-9]', '', re_response.split('reason')[0]))\n",
    "        \n",
    "    print(\"정답:\", number, '\\n')    \n",
    "#     print(\"---------------------------------------------\", '\\n')\n",
    "    answer_list.append(number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "b601e619-b24e-43d8-aae5-0708713de26b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>{\"문제번호\":25, \"답변\":3, \"reason\": \"도표의 내용과 일치하지 않는...</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>그래프 해석</td>\n",
       "      <td>Robert와 James가 40년간 나란히 농사를 지은 것은 적절한 내용이지만, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>{\"문제번호\": 26, \"답변\":3, \"reason\": \"주어진 글에서는 Maggi...</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>글의 세부 내용 파악</td>\n",
       "      <td>주어진 글에서는 Maggie L. Walker가 백인 은행가들의 인종차별적 태도로...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>{\"문제번호\": 27, \"답변\": 2, \"reason\": \"안내문에는 금요일에 대한...</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>글의 세부 내용 파악</td>\n",
       "      <td>안내문에는 금요일에 대한 내용이 없으며, 강습이 있는 요일은 월요일부터 목요일로 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>{\"문제번호\":28, \"답변\":5, \"reason\": \"안내문에서는 'Max of ...</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>글의 세부 내용 파악</td>\n",
       "      <td>안내문에서는 Max of 4 colors can be used. 라고 언급하고 있...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>{\"문제번호\":29, \"답변\": 5, \"reason\": \"‘showed’ 뒤에는 ‘...</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>어법상 틀린 표현 고르기</td>\n",
       "      <td>‘showed’ 뒤에는 ‘that’이 빠져있어서 문법적으로 맞지 않습니다. wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>{\"문제번호\": 30, \"답변\":2, \"reason\": \" 'protect' 뒤에 ...</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>문맥상 적절하지 않은 어휘 고르기</td>\n",
       "      <td>protect 뒤에 올바른 전치사는 against이다. protect indiv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>{\"문제번호\": 31, \"답변\": 1, \"reason\": \"Montaigne이 말한...</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>빈칸 내용 추론</td>\n",
       "      <td>Montaigne이 말한 기적의 기원은 우리의 무지에 있다는 맥락을 고려하면 계시...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>{\"문제번호\":32, \"답변\": 1, \"reason\": \"주어진 텍스트에서는 사후정...</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>빈칸 내용 추론</td>\n",
       "      <td>주어진 텍스트에서는 사후정보가 기억에 통합되기 쉽다는 내용이 나옵니다. 특히, 기...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
       "      <td>{\"문제번호\": 33, \"답변\": 5, \"reason\": \"상관관계는 인과관계를 분...</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>빈칸 내용 추론</td>\n",
       "      <td>상관관계는 인과관계를 분석하는 것보다 더욱 명확한 통찰력을 제공한다고 설명되었습니...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>{\"문제번호\":34, \"답변\": 2, \"reason\": \"인간을 포함한 많은 동물 ...</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>빈칸 내용 추론</td>\n",
       "      <td>인간을 포함한 많은 동물 종들의 평균 수명은 자연 환경에서의 생존율과 관련이 있습...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>{\"문제번호\": 35, \"답변\":2, \"reason\": \"글의 전체 흐름은 도덕적 ...</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>글 흐름과 무관한 문장 찾기</td>\n",
       "      <td>글의 전체 흐름은 도덕적 우수성이 어떻게 형성되는지에 대한 설명입니다. 2번 문장...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>{\"문제번호\":36, \"답변\": 3, \"reason\": \"글의 흐름을 살펴보면, 먼...</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>주어진 글 뒤에 글 순서 파악</td>\n",
       "      <td>글의 흐름을 살펴보면, 먼저 일반적인 경향을 설명한 후, 실제로 크기 변화가 나타...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>{\"문제번호\":37, \"답변\": 5, \"reason\": \"텍스트의 흐름을 살펴보면,...</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>주어진 글 뒤에 글 순서 파악</td>\n",
       "      <td>텍스트의 흐름을 살펴보면, (C)가 먼저 나오며, 빛 필드 카메라의 설명을 하고 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>{\"문제번호\":38, \"답변\":3, \"reason\": \"Introverted lea...</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>문장 삽입</td>\n",
       "      <td>Introverted leaders가 강조하고 있는 내향성이 리더십에 어떤 영향을...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>{\"문제번호\":39, \"답변\": 4, \"reason\": \"정확도 대신에 데이터의 양...</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>문장 삽입</td>\n",
       "      <td>정확도 대신에 데이터의 양을 늘리는 것이 유리한 이유를 설명하라는 문장입니다. 따...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>{\"문제번호\":40, \"답변\":1, \"reason\":\"문장을 요약하면, '연구결과,...</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>요약 문장 빈칸 추론</td>\n",
       "      <td>문장을 요약하면, 연구결과, 자신의 집단에 호의적으로 행동하는 사람들이 사회적 이점...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>{\"문제번호\":41, \"답변\": 1, \"reason\": \"글의 내용을 살펴보면, 서...</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>글의 제목 파악</td>\n",
       "      <td>글의 내용을 살펴보면, 서양 사회에서의 음악 공연 설정에 대해 설명하고, 관객과 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>{\"문제번호\":42, \"답변\":3, \"reason\": \"이유를 대체 해주시구요.\"}</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>적절치 않은 낱말</td>\n",
       "      <td>이유를 대체 해주시구요.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>{\"문제번호\":43, \"답변\": 2, \"reason\": \"글의 흐름을 살펴보면, (...</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>(A)에 이어질 글 순서 파악</td>\n",
       "      <td>글의 흐름을 살펴보면, (A)에서 대화가 시작된 후, 두 형제와 목수의 상황이 시...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "      <td>{\"문제번호\":44, \"답변\":2, \"reason\":\"한글의 'ㄴ'이 가리키는 대상...</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>지칭 대상 다른 하나</td>\n",
       "      <td>한글의 ㄴ이 가리키는 대상은 (b)이다. 다른 네 가지는 모두 숫자 4를 가리킨다.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>{\"문제번호\":45, \"답변\":1, \"reason\": \"Robert와 James가 ...</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>틀린 세부내용</td>\n",
       "      <td>Robert와 James가 40년간 나란히 농사를 지은 것은 적절한 내용이지만, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    questionNumber  CorrectAnswer  \\\n",
       "0               18              2   \n",
       "1               19              2   \n",
       "2               20              5   \n",
       "3               21              4   \n",
       "4               22              1   \n",
       "5               23              1   \n",
       "6               24              3   \n",
       "7               25              4   \n",
       "8               26              3   \n",
       "9               27              4   \n",
       "10              28              4   \n",
       "11              29              5   \n",
       "12              30              5   \n",
       "13              31              1   \n",
       "14              32              1   \n",
       "15              33              5   \n",
       "16              34              2   \n",
       "17              35              3   \n",
       "18              36              2   \n",
       "19              37              3   \n",
       "20              38              3   \n",
       "21              39              4   \n",
       "22              40              1   \n",
       "23              41              1   \n",
       "24              42              5   \n",
       "25              43              3   \n",
       "26              44              5   \n",
       "27              45              5   \n",
       "\n",
       "                                             response  answer  right  \\\n",
       "0   {\"문제번호\":18, \"답변\": 2, \"reason\": \"편지의 내용을 살펴보면, ...       2   True   \n",
       "1   {\"문제번호\":19, \"답변\": 2, \"reason\": \"글의 내용을 살펴보면, C...       2   True   \n",
       "2   {\"문제번호\":20, \"답변\": 5, \"reason\": \"글의 흐름을 따라 필자가 ...       5   True   \n",
       "3   {\"문제번호\": 21, \"답변\": 4, \"reason\": \"‘be more than...       4   True   \n",
       "4   {\"문제번호\":22, \"답변\":1, \"reason\": \"글의 요지는 공공 시계가 서...       1   True   \n",
       "5   {\"문제번호\":23, \"답변\": 1, \"reason\": \"주어진 글은 실버 골드만이...       1   True   \n",
       "6   {\"문제번호\": 24, \"답변\":3, \"reason\": \"글의 내용에서는 치매 환자...       3   True   \n",
       "7   {\"문제번호\":25, \"답변\":3, \"reason\": \"도표의 내용과 일치하지 않는...       1  False   \n",
       "8   {\"문제번호\": 26, \"답변\":3, \"reason\": \"주어진 글에서는 Maggi...       3   True   \n",
       "9   {\"문제번호\": 27, \"답변\": 2, \"reason\": \"안내문에는 금요일에 대한...       2  False   \n",
       "10  {\"문제번호\":28, \"답변\":5, \"reason\": \"안내문에서는 'Max of ...       5  False   \n",
       "11  {\"문제번호\":29, \"답변\": 5, \"reason\": \"‘showed’ 뒤에는 ‘...       5   True   \n",
       "12  {\"문제번호\": 30, \"답변\":2, \"reason\": \" 'protect' 뒤에 ...       2  False   \n",
       "13  {\"문제번호\": 31, \"답변\": 1, \"reason\": \"Montaigne이 말한...       1   True   \n",
       "14  {\"문제번호\":32, \"답변\": 1, \"reason\": \"주어진 텍스트에서는 사후정...       1   True   \n",
       "15  {\"문제번호\": 33, \"답변\": 5, \"reason\": \"상관관계는 인과관계를 분...       5   True   \n",
       "16  {\"문제번호\":34, \"답변\": 2, \"reason\": \"인간을 포함한 많은 동물 ...       2   True   \n",
       "17  {\"문제번호\": 35, \"답변\":2, \"reason\": \"글의 전체 흐름은 도덕적 ...       2  False   \n",
       "18  {\"문제번호\":36, \"답변\": 3, \"reason\": \"글의 흐름을 살펴보면, 먼...       3  False   \n",
       "19  {\"문제번호\":37, \"답변\": 5, \"reason\": \"텍스트의 흐름을 살펴보면,...       5  False   \n",
       "20  {\"문제번호\":38, \"답변\":3, \"reason\": \"Introverted lea...       3   True   \n",
       "21  {\"문제번호\":39, \"답변\": 4, \"reason\": \"정확도 대신에 데이터의 양...       4   True   \n",
       "22  {\"문제번호\":40, \"답변\":1, \"reason\":\"문장을 요약하면, '연구결과,...       1   True   \n",
       "23  {\"문제번호\":41, \"답변\": 1, \"reason\": \"글의 내용을 살펴보면, 서...       1   True   \n",
       "24     {\"문제번호\":42, \"답변\":3, \"reason\": \"이유를 대체 해주시구요.\"}       3  False   \n",
       "25  {\"문제번호\":43, \"답변\": 2, \"reason\": \"글의 흐름을 살펴보면, (...       2  False   \n",
       "26  {\"문제번호\":44, \"답변\":2, \"reason\":\"한글의 'ㄴ'이 가리키는 대상...       2  False   \n",
       "27  {\"문제번호\":45, \"답변\":1, \"reason\": \"Robert와 James가 ...       1  False   \n",
       "\n",
       "            TypeString                                             reason  \n",
       "0             글의 목적 추론   편지의 내용을 살펴보면, 글쓴이가 아파트에서 밤 늦게까지 농구를 즐기는 사람들로 ...  \n",
       "1        주인공의 심경 변화 추론   글의 내용을 살펴보면, Chaske가 무서운 상황에서 그의 아버지의 존재에 안심하...  \n",
       "2            필자의 주장 파악   글의 흐름을 따라 필자가 제시한 중심 주장은 농업이 직면한 문제 해결 및 식량과 ...  \n",
       "3             함축 의미 추론   ‘be more than just sugar on the tongue’은 잼이 언...  \n",
       "4             글의 요지 추론   글의 요지는 공공 시계가 서양 경제 발전에 어떻게 영향을 미쳤는지에 대한 내용입니...  \n",
       "5             글의 주제 추론   주어진 글은 실버 골드만이 쇼핑카트를 선보이고, 고객들이 이를 사용하지 않는 이유...  \n",
       "6             글의 제목 추론   글의 내용에서는 치매 환자나 자폐 스펙트럼 아동들을 상대로 하는 로봇 상호작용에 ...  \n",
       "7               그래프 해석   Robert와 James가 40년간 나란히 농사를 지은 것은 적절한 내용이지만, ...  \n",
       "8          글의 세부 내용 파악   주어진 글에서는 Maggie L. Walker가 백인 은행가들의 인종차별적 태도로...  \n",
       "9          글의 세부 내용 파악   안내문에는 금요일에 대한 내용이 없으며, 강습이 있는 요일은 월요일부터 목요일로 ...  \n",
       "10         글의 세부 내용 파악   안내문에서는 Max of 4 colors can be used. 라고 언급하고 있...  \n",
       "11       어법상 틀린 표현 고르기   ‘showed’ 뒤에는 ‘that’이 빠져있어서 문법적으로 맞지 않습니다. wit...  \n",
       "12  문맥상 적절하지 않은 어휘 고르기    protect 뒤에 올바른 전치사는 against이다. protect indiv...  \n",
       "13            빈칸 내용 추론   Montaigne이 말한 기적의 기원은 우리의 무지에 있다는 맥락을 고려하면 계시...  \n",
       "14            빈칸 내용 추론   주어진 텍스트에서는 사후정보가 기억에 통합되기 쉽다는 내용이 나옵니다. 특히, 기...  \n",
       "15            빈칸 내용 추론   상관관계는 인과관계를 분석하는 것보다 더욱 명확한 통찰력을 제공한다고 설명되었습니...  \n",
       "16            빈칸 내용 추론   인간을 포함한 많은 동물 종들의 평균 수명은 자연 환경에서의 생존율과 관련이 있습...  \n",
       "17     글 흐름과 무관한 문장 찾기   글의 전체 흐름은 도덕적 우수성이 어떻게 형성되는지에 대한 설명입니다. 2번 문장...  \n",
       "18    주어진 글 뒤에 글 순서 파악   글의 흐름을 살펴보면, 먼저 일반적인 경향을 설명한 후, 실제로 크기 변화가 나타...  \n",
       "19    주어진 글 뒤에 글 순서 파악   텍스트의 흐름을 살펴보면, (C)가 먼저 나오며, 빛 필드 카메라의 설명을 하고 ...  \n",
       "20               문장 삽입   Introverted leaders가 강조하고 있는 내향성이 리더십에 어떤 영향을...  \n",
       "21               문장 삽입   정확도 대신에 데이터의 양을 늘리는 것이 유리한 이유를 설명하라는 문장입니다. 따...  \n",
       "22         요약 문장 빈칸 추론  문장을 요약하면, 연구결과, 자신의 집단에 호의적으로 행동하는 사람들이 사회적 이점...  \n",
       "23            글의 제목 파악   글의 내용을 살펴보면, 서양 사회에서의 음악 공연 설정에 대해 설명하고, 관객과 ...  \n",
       "24           적절치 않은 낱말                                      이유를 대체 해주시구요.  \n",
       "25    (A)에 이어질 글 순서 파악   글의 흐름을 살펴보면, (A)에서 대화가 시작된 후, 두 형제와 목수의 상황이 시...  \n",
       "26         지칭 대상 다른 하나     한글의 ㄴ이 가리키는 대상은 (b)이다. 다른 네 가지는 모두 숫자 4를 가리킨다.  \n",
       "27             틀린 세부내용   Robert와 James가 40년간 나란히 농사를 지은 것은 적절한 내용이지만, ...  "
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 답변과 정답 맞춰보기\n",
    "right = df['정답'] == answer_list\n",
    "\n",
    "# print(len(reason_list))\n",
    "# reason_list\n",
    "\n",
    "df_final = pd.DataFrame({\"questionNumber\":range(18, 46), \"CorrectAnswer\":df['정답'], \"response\":response_list, \"answer\": answer_list, \"right\":right, \"TypeString\":df['TypeString'], \"reason\":reason_list})\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "917462db-fb44-4a86-a60a-4efbf9a00335",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2_2312\n",
      "맞춘 개수: 17\n",
      "틀린 개수: 11\n",
      "틀린 문제 유형: \n"
     ]
    },
    {
     "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>questionNumber</th>\n",
       "      <th>TypeString</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25</td>\n",
       "      <td>그래프 해석</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>글의 세부 내용 파악</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28</td>\n",
       "      <td>글의 세부 내용 파악</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30</td>\n",
       "      <td>문맥상 적절하지 않은 어휘 고르기</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35</td>\n",
       "      <td>글 흐름과 무관한 문장 찾기</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36</td>\n",
       "      <td>주어진 글 뒤에 글 순서 파악</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>37</td>\n",
       "      <td>주어진 글 뒤에 글 순서 파악</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>42</td>\n",
       "      <td>적절치 않은 낱말</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>43</td>\n",
       "      <td>(A)에 이어질 글 순서 파악</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>44</td>\n",
       "      <td>지칭 대상 다른 하나</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>45</td>\n",
       "      <td>틀린 세부내용</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    questionNumber          TypeString\n",
       "7               25              그래프 해석\n",
       "9               27         글의 세부 내용 파악\n",
       "10              28         글의 세부 내용 파악\n",
       "12              30  문맥상 적절하지 않은 어휘 고르기\n",
       "17              35     글 흐름과 무관한 문장 찾기\n",
       "18              36    주어진 글 뒤에 글 순서 파악\n",
       "19              37    주어진 글 뒤에 글 순서 파악\n",
       "24              42           적절치 않은 낱말\n",
       "25              43    (A)에 이어질 글 순서 파악\n",
       "26              44         지칭 대상 다른 하나\n",
       "27              45             틀린 세부내용"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(year_month)\n",
    "# 맞춘 갯수\n",
    "print(\"맞춘 개수:\", sum(df_final['right'].values))\n",
    "\n",
    "# 틀린 갯수\n",
    "print(\"틀린 개수:\", 28 - sum(df_final['right'].values))\n",
    "\n",
    "# 틀린 문제 유형\n",
    "print(\"틀린 문제 유형: \")\n",
    "globals()[f'df_final_{year_month}'] = df_final[df_final['right'] == False][['questionNumber', 'TypeString']]\n",
    "globals()[f'df_final_{year_month}']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e83fb6e-a093-4747-a66e-b9106d72bc6b",
   "metadata": {},
   "source": [
    "# HTTP 통신으로 서버에 문제별 해설 보내기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "634b39c0-f07e-46a0-a3ca-96432370c68a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# post로 요청할 데이터 설정\n",
    "data_new = []\n",
    "\n",
    "f = 2\n",
    "for f in range(len(df_final)):\n",
    "    dict_temp = {\"testNumber\": year_month, \n",
    "    \"testType\": int(6),\n",
    "    \"question\": \"\",\n",
    "    \"questionNumber\": int(df_final['questionNumber'][f]),\n",
    "    \"article\": \"\",\n",
    "    \"options\": \"\",\n",
    "    \"answer\": int(df_final['answer'][f]),\n",
    "    \"questionType\": \"5\",\n",
    "    \"solution\": df_final['reason'][f],\n",
    "    \"keyWords\": \"\"}\n",
    "    \n",
    "    data_new.append(dict_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "1089f0fe-576d-415e-aa9a-b65448ee56c2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 18,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 2,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 글쓴이는 농구 코트에서 발생하는 소음 문제를 해결하기 위해 농구 코트의 운영 시간을 밤 9시 이전으로 제한해 줄 것을 요청하고 있습니다. 글의 목적은 명확하게 운영 시간의 제한을 요청하는 것입니다. 다른 선택지들은 글의 내용과 직접적인 관련이 없습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 19,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 2,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 글에서 Chaske는 처음에 두려움(dread)과 공포(horrified)를 느꼈지만, 그의 아버지의 목소리를 듣고 안도(relief)를 느꼈습니다. 이로 인해 그의 공포가 사라졌다는 내용이 나와 있습니다. 따라서 적절한 심경 변화는 horrified → relieved입니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 20,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 5,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 글에서는 농업이 여러 중요성에도 불구하고 많은 도전 과제에 직면해 있음을 강조하고 있습니다. 필자는 증가하는 세계 인구와 같은 상황을 감안할 때, 지속 가능한 방식으로 이러한 문제를 해결하여 식량과 농산물의 지속적인 생산을 보장해야 한다고 주장합니다. 따라서 ⑤번이 필자가 주장하는 바와 가장 적절하게 부합합니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 21,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 4,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 글에서 예술이 단순히 즐거움을 주는 것 이상의 역할을 한다고 설명하고 있습니다. 예술은 어려운 주제나 불편한 진실을 다루면서, 그것들을 통해 변화를 유도하거나 강력한 미적 경험을 제공할 수 있다는 점을 강조하고 있습니다. 이는 다양한 경험을 포용하는 것과 밀접한 관련이 있습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 22,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 1,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 이 글의 주요 요지는 공공 시계가 서양 사회의 경제적 진보에 큰 영향을 미쳤다는 것입니다. 글에서 다양한 예시를 들어 기계적인 공공 시계가 어떻게 상거래와 비즈니스 활동을 정리하고 활성화시켰는지를 설명하고 있습니다. 따라서 가장 적절한 답변은 ①번입니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 23,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 1,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 이 글의 주제는 골드만이 초기에는 쇼핑 카트를 사용하도록 설득하는 데 어려움을 겪었으나, 모델을 고용하여 다른 사람들이 카트를 사용하는 모습을 보여줌으로써 소비자들에게 영향을 미쳤다는 내용입니다. 따라서 동료 행동의 설득력(persuasive power of peer behavior)을 주제로 가장 적절하게 설명한 것이 1번입니다.)',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 24,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 3,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 이 글은 인간-로봇 상호작용이 치매 환자와 같은 취약한 사용자들에게 미칠 수 있는 윤리적 문제를 논의하고 있습니다. 특히, 로봇이 진짜 친구처럼 보일 때 발생할 수 있는 문제를 다루고 있으며, 로봇이 인간처럼 생겨서 생길 수 있는 문제를 해결하기 위해 로봇이 명백히 로봇처럼 보이도록 해야 한다는 주장을 참고로 하고 있습니다. 따라서 제목으로 가장 적절한 것은 왜 취약한 사람들을 위한 로봇은 로봇처럼 보여야 하는가라는 의미를 담은 ③번입니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 25,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 5,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 5번 진술에서는 55세 이상의 연령대가 타겟 광고를 침입적이라고 생각한 비율이 50% 이상이라고 말하고 있지만, 그래프에서는 다른 연령대에서도 50% 이상이 침입적이라고 대답한 비율을 보일 수 있습니다. 따라서 이 진술은 도표의 내용과 일치하지 않을 가능성이 큽니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 26,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 3,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 본문에서 Maggie L. Walker는 인종 차별로 인해 은행 금융법 공부를 시작할 수 없었다고 하지 않았습니다. 오히려 인종 차별이 그녀를 은행 금융법을 공부하도록 동기를 부여했다고 했습니다. 따라서 3번이 글의 내용과 일치하지 않습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 27,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 4,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 안내문에 따르면 강습 당일 및 이후에는 환불이 불가능합니다. 따라서 강습 당일 취소 시 환불받을 수 있다는 내용과 일치하지 않습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 28,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 4,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 안내문에 따르면 출품작에는 자신의 학번과 이름을 써야 한다고 명시되어 있습니다. 다른 선택지들은 정확하지 않습니다. 교사들이 아닌 학생회가 디자인을 선정하며, 수상자 발표일은 제출 마감일의 다음 날이 아니고, 출품작은 학생회실이 아닌 미술 선생님의 사무실에 제출해야 합니다. 또한, 색상 수는 최대 4가지로 제한되어 있습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 29,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 5,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 어법상 틀린 부분은 5번입니다. 5번의 동사 showed는 과거형으로 사용되었습니다. 그러나, 문장의 구조상 적절한 시제는 현재완료형 have shown 입니다. 연구 결과를 현재 시점에서도 유효하게 언급하기 때문에 현재완료형을 사용해야 합니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 30,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 5,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 문맥상 감시카메라의 확산에 의해 관찰당하는 것을 포기하는 것이 공동체에 더 해를 끼친다는 것이 아니라, 감시카메라가 없어서 생기는 피해가 더 크다고 봐서 5번 more harm의 사용이 적절하지 않습니다. 적절한 표현은 less harm입니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 31,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 1,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 문맥을 살펴보면, 기적은 우리의 지식과 이해 부족에서 비롯된다고 설명하고 있습니다. 몽테뉴의 인용구에서도 기적의 기원은 자연 자체가 아니라 우리의 무지함에 있다고 말하고 있습니다. 따라서 빈칸에 들어갈 가장 적절한 단어는 ignorance이며, 이는 무지를 의미하는 단어로 문맥과 잘 어울립니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 32,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 1,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 문제의 빈칸 부분은 새로운 정보가 기억에 통합되지 않는 상황을 묘사해야 합니다. 본문에 따르면, 이벤트가 잘 인코딩되지 않았을 때 외부 정보가 증인의 기억에 쉽게 통합될 수 있다고 이야기합니다. 따라서 빈칸에 들어갈 가장 적절한 답변은 이 새로운 정보가 거부될 것이다입니다. 왜냐하면 본문 앞 문장에서 정보가 부족한 상황에서는 새로운 정보의 유효성을 확인할 수 있는 가능성이 적기 때문입니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 33,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 5,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 문제 본문에서 상관관계의 중요성을 강조하고 있는 반면, 인과관계를 설명하려는 시도가 혼란을 일으킬 수 있다는 점을 지적하고 있습니다. 상관관계는 수학적으로 명확하게 나타낼 수 있지만, 인과관계는 그렇지 않기 때문에 상관관계의 이유를 설명하려는 시도를 자제할 필요가 있다는 결론을 내리고 있습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 34,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 2,\n",
       "  'questionType': '5',\n",
       "  'solution': '지문에서는 동물 종의 평균 수명 또는 노화 속도가 무엇에 의해 결정되는지에 대해 논의하고 있습니다. 쥐와 박쥐를 비교하면서 설명하고 있으며, 박쥐가 쥐보다 오래 사는 이유는 박쥐가 위험으로부터 더 잘 도망치고 먹이를 찾을 수 있기 때문이라고 합니다. 이는 특정 종이 자연 상태에서 얼마나 오래 살아남을 수 있는지와 관련이 있습니다. 따라서, 가장 적절한 답변은 ② 그 동물 종이 야생에서 생존할 수 있는 평균 시간입니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 35,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 3,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 이 문장은 도덕적 탁월함을 설명하는 다른 문장들과 연결되지 않습니다. 문맥상으로도 도덕적 탁월함에 대한 구체적인 예시나 정의를 제공하지 않아 전체 흐름과 관계가 적습니다. 나머지 문장들은 도덕적 탁월함의 형성 시기와 관련된 이론들을 소개하고 있습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 36,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 2,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 글의 흐름을 고려했을 때, 주어진 문장은 생물 종의 크기가 환경과 상호작용의 결과임을 설명하며, (B)에서는 수백만 년 동안 생물의 크기가 커져온 경향을 언급합니다. (A)는 이 경향이 변화하고 있다는 내용으로 이어지고 있으며, 마지막으로 (C)는 구체적인 예시로 이탈리아 알프스의 동물들이 온도 상승으로 인해 작아진다는 사례를 제시하여 논지를 마무리합니다. 따라서 B-A-C의 순서가 글의 흐름에 가장 적절합니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 37,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 3,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 첫 번째 문장에서 무작위 샘플링이 디지털 이전 시대에 큰 데이터 문제를 분석하는 데 유용했다고 언급하고 있습니다. (B)는 샘플링을 사용하는 것의 단점과 전체 데이터셋을 갖는 것의 이점을 설명하며, 이는 첫 번째 문장의 생각을 확장합니다. 그 후, (C)는 전체 데이터셋을 사용하는 예로서 라이트 필드 카메라에 대해 언급하고, (A)는 전체 데이터를 수집하는 것이 사후 분석을 가능하게 해주는 이유를 설명하며 자연스럽게 이어집니다. ',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 38,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 3,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 주어진 문장은 기존의 고정관념을 재검토해야 한다는 내용을 담고 있습니다. 따라서 그 전에 소개된 내향적인 지도자가 겪는 어려움과 관련된 내용 후에, 그리고 새로운 정보가 제시되기 전에 위치시키는 것이 자연스럽습니다. 1, 2는 현 상황과 문제에 대한 설명이고, 3 이후부터는 내향적인 지도자의 장점에 대해 언급하고 있기 때문에, 3번째 위치에 주어진 문장이 들어가는 것이 가장 적절합니다.\\n',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 39,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 4,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 문장의 흐름을 살펴보면 앞 문장들은 기존의 정확한 측정의 중요성을 강조하고 이에 따른 문제점을 언급하고 있습니다. 그러나 (④)번 문장은 정확도를 낮추는 대신 더 많은 데이터를 얻을 수 있다는 내용으로, 맥락상 새로운 관점을 도입하는 문장입니다. 이는 기존 내용과의 흐름을 잇기보다는 이후 내용과 연결되는 새로운 관점의 전환이라 볼 수 있습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 40,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 1,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 해당 글에서 협력적인 행동을 하는 사람들이 공동체 내에서 더 많은 사회적 이득을 받는다고 명시하고 있습니다. 이것을 요약하면 (A)에는 generosity(관대함, 즉 협력적인 행동)가, (B)에는 benefit(이익)이 들어가는 것이 가장 적절합니다. 따라서 답변은 1번입니다.)',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 41,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 1,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 글에서 공연자와 관객 사이의 분명한 구분에 대해 설명하면서, 관객의 참여를 통해서 어떻게 더 큰 참여와 즐거움을 얻을 수 있는지에 대해 이야기하고 있습니다. 이런 이유로 Bridge the Divide and Get the Audience Involved 즉, 차이를 메우고 관객을 참여시키다가 가장 적절한 제목입니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 42,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 3,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 문맥상 낱말의 쓰임을 검토했을 때 (c)가 적절하지 않습니다. 문제가 주어진 문장에서 해당 단어가 문맥에 맞지 않거나 문법적으로 어울리지 않기 때문에 이 답변을 선택했습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 43,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 3,\n",
       "  'questionType': '5',\n",
       "  'solution': ' (A)에서 이어지는 이야기를 고려해 보면, 먼저 갈등이 시작되고 (C)에서 로버트가 목수에게 담을 쌓아달라고 부탁하는 부분이 나옵니다. 그 다음 (D)에서 로버트가 돌아와 목수가 만든 다리를 보고 놀라는 장면이 이어지고, 마지막으로 (B)에서 두 형제가 화해하고 목수가 떠나가는 장면이 나와 순서가 자연스럽게 연결됩니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 44,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 44,\n",
       "  'questionType': '5',\n",
       "  'solution': '문제가 명확하지 않습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 45,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 5,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 지문에서 목수는 샛강에 다리를 설치했지만, 울타리를 설치했다는 내용은 없다. 따라서 ⑤번이 적절하지 않은 내용이다.',\n",
       "  'keyWords': ''}]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "a16b37ca-d12c-4033-9913-849215bb21ec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"18\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"2\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 글쓴이는 농구 코트에서 발생하는 소음 문제를 해결하기 위해 농구 코트의 운영 시간을 밤 9시 이전으로 제한해 줄 것을 요청하고 있습니다. 글의 목적은 명확하게 운영 시간의 제한을 요청하는 것입니다. 다른 선택지들은 글의 내용과 직접적인 관련이 없습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"19\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"2\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 글에서 Chaske는 처음에 두려움(dread)과 공포(horrified)를 느꼈지만, 그의 아버지의 목소리를 듣고 안도(relief)를 느꼈습니다. 이로 인해 그의 공포가 사라졌다는 내용이 나와 있습니다. 따라서 적절한 심경 변화는 horrified → relieved입니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"20\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"5\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 글에서는 농업이 여러 중요성에도 불구하고 많은 도전 과제에 직면해 있음을 강조하고 있습니다. 필자는 증가하는 세계 인구와 같은 상황을 감안할 때, 지속 가능한 방식으로 이러한 문제를 해결하여 식량과 농산물의 지속적인 생산을 보장해야 한다고 주장합니다. 따라서 ⑤번이 필자가 주장하는 바와 가장 적절하게 부합합니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"21\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"4\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 글에서 예술이 단순히 즐거움을 주는 것 이상의 역할을 한다고 설명하고 있습니다. 예술은 어려운 주제나 불편한 진실을 다루면서, 그것들을 통해 변화를 유도하거나 강력한 미적 경험을 제공할 수 있다는 점을 강조하고 있습니다. 이는 다양한 경험을 포용하는 것과 밀접한 관련이 있습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"22\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"1\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 이 글의 주요 요지는 공공 시계가 서양 사회의 경제적 진보에 큰 영향을 미쳤다는 것입니다. 글에서 다양한 예시를 들어 기계적인 공공 시계가 어떻게 상거래와 비즈니스 활동을 정리하고 활성화시켰는지를 설명하고 있습니다. 따라서 가장 적절한 답변은 ①번입니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"23\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"1\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 이 글의 주제는 골드만이 초기에는 쇼핑 카트를 사용하도록 설득하는 데 어려움을 겪었으나, 모델을 고용하여 다른 사람들이 카트를 사용하는 모습을 보여줌으로써 소비자들에게 영향을 미쳤다는 내용입니다. 따라서 동료 행동의 설득력(persuasive power of peer behavior)을 주제로 가장 적절하게 설명한 것이 1번입니다.)\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"24\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"3\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 이 글은 인간-로봇 상호작용이 치매 환자와 같은 취약한 사용자들에게 미칠 수 있는 윤리적 문제를 논의하고 있습니다. 특히, 로봇이 진짜 친구처럼 보일 때 발생할 수 있는 문제를 다루고 있으며, 로봇이 인간처럼 생겨서 생길 수 있는 문제를 해결하기 위해 로봇이 명백히 로봇처럼 보이도록 해야 한다는 주장을 참고로 하고 있습니다. 따라서 제목으로 가장 적절한 것은 왜 취약한 사람들을 위한 로봇은 로봇처럼 보여야 하는가라는 의미를 담은 ③번입니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"25\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"5\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 5번 진술에서는 55세 이상의 연령대가 타겟 광고를 침입적이라고 생각한 비율이 50% 이상이라고 말하고 있지만, 그래프에서는 다른 연령대에서도 50% 이상이 침입적이라고 대답한 비율을 보일 수 있습니다. 따라서 이 진술은 도표의 내용과 일치하지 않을 가능성이 큽니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"26\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"3\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 본문에서 Maggie L. Walker는 인종 차별로 인해 은행 금융법 공부를 시작할 수 없었다고 하지 않았습니다. 오히려 인종 차별이 그녀를 은행 금융법을 공부하도록 동기를 부여했다고 했습니다. 따라서 3번이 글의 내용과 일치하지 않습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"27\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"4\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 안내문에 따르면 강습 당일 및 이후에는 환불이 불가능합니다. 따라서 강습 당일 취소 시 환불받을 수 있다는 내용과 일치하지 않습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"28\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"4\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 안내문에 따르면 출품작에는 자신의 학번과 이름을 써야 한다고 명시되어 있습니다. 다른 선택지들은 정확하지 않습니다. 교사들이 아닌 학생회가 디자인을 선정하며, 수상자 발표일은 제출 마감일의 다음 날이 아니고, 출품작은 학생회실이 아닌 미술 선생님의 사무실에 제출해야 합니다. 또한, 색상 수는 최대 4가지로 제한되어 있습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"29\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"5\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 어법상 틀린 부분은 5번입니다. 5번의 동사 showed는 과거형으로 사용되었습니다. 그러나, 문장의 구조상 적절한 시제는 현재완료형 have shown 입니다. 연구 결과를 현재 시점에서도 유효하게 언급하기 때문에 현재완료형을 사용해야 합니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"30\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"5\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 문맥상 감시카메라의 확산에 의해 관찰당하는 것을 포기하는 것이 공동체에 더 해를 끼친다는 것이 아니라, 감시카메라가 없어서 생기는 피해가 더 크다고 봐서 5번 more harm의 사용이 적절하지 않습니다. 적절한 표현은 less harm입니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"31\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"1\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 문맥을 살펴보면, 기적은 우리의 지식과 이해 부족에서 비롯된다고 설명하고 있습니다. 몽테뉴의 인용구에서도 기적의 기원은 자연 자체가 아니라 우리의 무지함에 있다고 말하고 있습니다. 따라서 빈칸에 들어갈 가장 적절한 단어는 ignorance이며, 이는 무지를 의미하는 단어로 문맥과 잘 어울립니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"32\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"1\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 문제의 빈칸 부분은 새로운 정보가 기억에 통합되지 않는 상황을 묘사해야 합니다. 본문에 따르면, 이벤트가 잘 인코딩되지 않았을 때 외부 정보가 증인의 기억에 쉽게 통합될 수 있다고 이야기합니다. 따라서 빈칸에 들어갈 가장 적절한 답변은 이 새로운 정보가 거부될 것이다입니다. 왜냐하면 본문 앞 문장에서 정보가 부족한 상황에서는 새로운 정보의 유효성을 확인할 수 있는 가능성이 적기 때문입니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"33\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"5\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 문제 본문에서 상관관계의 중요성을 강조하고 있는 반면, 인과관계를 설명하려는 시도가 혼란을 일으킬 수 있다는 점을 지적하고 있습니다. 상관관계는 수학적으로 명확하게 나타낼 수 있지만, 인과관계는 그렇지 않기 때문에 상관관계의 이유를 설명하려는 시도를 자제할 필요가 있다는 결론을 내리고 있습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"34\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"2\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \"지문에서는 동물 종의 평균 수명 또는 노화 속도가 무엇에 의해 결정되는지에 대해 논의하고 있습니다. 쥐와 박쥐를 비교하면서 설명하고 있으며, 박쥐가 쥐보다 오래 사는 이유는 박쥐가 위험으로부터 더 잘 도망치고 먹이를 찾을 수 있기 때문이라고 합니다. 이는 특정 종이 자연 상태에서 얼마나 오래 살아남을 수 있는지와 관련이 있습니다. 따라서, 가장 적절한 답변은 ② 그 동물 종이 야생에서 생존할 수 있는 평균 시간입니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"35\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"3\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 이 문장은 도덕적 탁월함을 설명하는 다른 문장들과 연결되지 않습니다. 문맥상으로도 도덕적 탁월함에 대한 구체적인 예시나 정의를 제공하지 않아 전체 흐름과 관계가 적습니다. 나머지 문장들은 도덕적 탁월함의 형성 시기와 관련된 이론들을 소개하고 있습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"36\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"2\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 글의 흐름을 고려했을 때, 주어진 문장은 생물 종의 크기가 환경과 상호작용의 결과임을 설명하며, (B)에서는 수백만 년 동안 생물의 크기가 커져온 경향을 언급합니다. (A)는 이 경향이 변화하고 있다는 내용으로 이어지고 있으며, 마지막으로 (C)는 구체적인 예시로 이탈리아 알프스의 동물들이 온도 상승으로 인해 작아진다는 사례를 제시하여 논지를 마무리합니다. 따라서 B-A-C의 순서가 글의 흐름에 가장 적절합니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"37\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"3\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 첫 번째 문장에서 무작위 샘플링이 디지털 이전 시대에 큰 데이터 문제를 분석하는 데 유용했다고 언급하고 있습니다. (B)는 샘플링을 사용하는 것의 단점과 전체 데이터셋을 갖는 것의 이점을 설명하며, 이는 첫 번째 문장의 생각을 확장합니다. 그 후, (C)는 전체 데이터셋을 사용하는 예로서 라이트 필드 카메라에 대해 언급하고, (A)는 전체 데이터를 수집하는 것이 사후 분석을 가능하게 해주는 이유를 설명하며 자연스럽게 이어집니다. \",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"38\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"3\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 주어진 문장은 기존의 고정관념을 재검토해야 한다는 내용을 담고 있습니다. 따라서 그 전에 소개된 내향적인 지도자가 겪는 어려움과 관련된 내용 후에, 그리고 새로운 정보가 제시되기 전에 위치시키는 것이 자연스럽습니다. 1, 2는 현 상황과 문제에 대한 설명이고, 3 이후부터는 내향적인 지도자의 장점에 대해 언급하고 있기 때문에, 3번째 위치에 주어진 문장이 들어가는 것이 가장 적절합니다.\\n\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"39\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"4\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 문장의 흐름을 살펴보면 앞 문장들은 기존의 정확한 측정의 중요성을 강조하고 이에 따른 문제점을 언급하고 있습니다. 그러나 (④)번 문장은 정확도를 낮추는 대신 더 많은 데이터를 얻을 수 있다는 내용으로, 맥락상 새로운 관점을 도입하는 문장입니다. 이는 기존 내용과의 흐름을 잇기보다는 이후 내용과 연결되는 새로운 관점의 전환이라 볼 수 있습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"40\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"1\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 해당 글에서 협력적인 행동을 하는 사람들이 공동체 내에서 더 많은 사회적 이득을 받는다고 명시하고 있습니다. 이것을 요약하면 (A)에는 generosity(관대함, 즉 협력적인 행동)가, (B)에는 benefit(이익)이 들어가는 것이 가장 적절합니다. 따라서 답변은 1번입니다.)\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"41\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"1\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 글에서 공연자와 관객 사이의 분명한 구분에 대해 설명하면서, 관객의 참여를 통해서 어떻게 더 큰 참여와 즐거움을 얻을 수 있는지에 대해 이야기하고 있습니다. 이런 이유로 Bridge the Divide and Get the Audience Involved 즉, 차이를 메우고 관객을 참여시키다가 가장 적절한 제목입니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"42\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"3\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 문맥상 낱말의 쓰임을 검토했을 때 (c)가 적절하지 않습니다. 문제가 주어진 문장에서 해당 단어가 문맥에 맞지 않거나 문법적으로 어울리지 않기 때문에 이 답변을 선택했습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"43\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"3\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" (A)에서 이어지는 이야기를 고려해 보면, 먼저 갈등이 시작되고 (C)에서 로버트가 목수에게 담을 쌓아달라고 부탁하는 부분이 나옵니다. 그 다음 (D)에서 로버트가 돌아와 목수가 만든 다리를 보고 놀라는 장면이 이어지고, 마지막으로 (B)에서 두 형제가 화해하고 목수가 떠나가는 장면이 나와 순서가 자연스럽게 연결됩니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"44\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"44\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \"문제가 명확하지 않습니다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    },\n",
      "    {\n",
      "        \"testNumber\": \"2_2312\",\n",
      "        \"testType\": \"1\",\n",
      "        \"question\": \"\",\n",
      "        \"questionNumber\": \"45\",\n",
      "        \"article\": \"\",\n",
      "        \"options\": \"\",\n",
      "        \"answer\": \"5\",\n",
      "        \"questionType\": \"5\",\n",
      "        \"solution\": \" 지문에서 목수는 샛강에 다리를 설치했지만, 울타리를 설치했다는 내용은 없다. 따라서 ⑤번이 적절하지 않은 내용이다.\",\n",
      "        \"keyWords\": \"\"\n",
      "    }\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "json_data = json.dumps(data_new, ensure_ascii=False, indent=4)\n",
    "print(json_data)\n",
    "# with open('persons.json', 'w') as f : \n",
    "# \tjson.dump(data_new, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "407294d4-73d4-40b3-a09f-29107f9bc275",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 서버의 URL 및 엔드포인트 설정\n",
    "url = 'https://ai-eng-tutor.xyz/question/many'  # 실제 엔드포인트 URL로 대체\n",
    "\n",
    "# Bearer Token 설정\n",
    "token = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJlbWFpbCI6InRlc3RAbmF2ZXIuY29tIiwic3ViIjoxLCJpYXQiOjE3MTcyMDc0NjAsImV4cCI6MTc0ODc2NTA2MH0.Po_MarkhXEVMLeoU4WlBgf_2nOlXlcF421TmvRdDbO4'  # 실제 Bearer Token으로 대체\n",
    "\n",
    "# 요청 헤더 설정\n",
    "headers = {\n",
    "    'Authorization': f'Bearer {token}',\n",
    "    'Content-Type': 'application/json'\n",
    "}\n",
    "\n",
    "# 요청 데이터 설정\n",
    "data = [\n",
    "   {\n",
    "    \"testNumber\": \"2_2309\",\n",
    "    \"testType\": 1,\n",
    "    \"question\": \"다음 글의 요지로 가장 적절한 것은?\",\n",
    "    \"questionNumber\": 22,\n",
    "    \"article\": \" It’s remarkable that positive fantasies help us relax to \\nsuch an extent that it shows up in physiological tests. If \\nyou want to unwind, you can take some deep breaths, get a \\nmassage, or go for a walk―but you can also try simply \\nclosing your eyes and fantasizing about some future outcome \\nthat you might enjoy. But what about when your objective is \\nto make your wish a reality? The last thing you want to be \\nis relaxed. You want to be energized enough to get off the \\ncouch and lose those pounds or find that job or study for \\nthat test, and you want to be motivated enough to stay \\nengaged even when the inevitable obstacles or challenges \\narise. The principle of “Dream it. Wish it. Do it.” does not \\nhold true, and now we know why: in dreaming it, you \\nundercut the energy you need to do it. You put yourself in \\na temporary state of complete happiness, calmness―and \\ninactivity.\\n *physiological: 생리학적인  \",\n",
    "    \"options\": \"① 과도한 목표 지향적 태도는 삶의 만족감을 떨어뜨린다.\\n② 긍정적 자세로 역경을 극복할 때 잠재 능력이 발휘된다.\\n③ 편안함을 느끼는 상황에서 자기 개선에 대한 동기가 생긴다.\\n④ 낙관적인 상상은 소망을 실현하는 데 필요한 동력을 약화시킨다.\\n⑤ 막연한 목표보다는 명확하고 구체적인 목표가 실현 가능성이 크다.\",\n",
    "    \"answer\": 4,\n",
    "    \"questionType\": \"5\",\n",
    "    \"solution\": \"해당 글의 요지로 가장 적절한 것은 옵션 ④입니다. 글에서는 긍정적인 상상이 우리를 이완시키지만, 이러한 이완 상태가 소망을 현실로 만들기 위해 필요한 에너지를 약화시킬 수 있다는 점을 설명합니다. 소망을 실현하려는 목적이 있을 때, 이완된 상태보다는 에너지가 충전되어 활동적이고 동기부여된 상태가 되어야 함을 강조합니다. 이는 \\\"낙관적인 상상은 소망을 실현하는 데 필요한 동력을 약화시킨다\\\"는 옵션 ④와 일치합니다.\",\n",
    "    \"keyWords\": \"'positive fantasies':'긍정적인 상상' | 'physiological tests':'생리학적 검사' | 'energy':'에너지'\"\n",
    "  }]\n",
    "\n",
    "data = [\n",
    "  {\n",
    "  'testNumber': '2_2312',\n",
    "  'testType': 6,\n",
    "  'question': '',\n",
    "  'questionNumber': 18,\n",
    "  'article': '',\n",
    "  'options': '',\n",
    "  'answer': 2,\n",
    "  'questionType': '5',\n",
    "  'solution': ' 글쓴이는 농구 코트에서 발생하는 소음 문제를 해결하기 위해 농구 코트의 운영 시간을 밤 9시 이전으로 제한해 줄 것을 요청하고 있습니다. 글의 목적은 명확하게 운영 시간의 제한을 요청하는 것입니다. 다른 선택지들은 글의 내용과 직접적인 관련이 없습니다.',\n",
    "  'keyWords': ''},\n",
    " {'testNumber': '2_2312',\n",
    "  'testType': 6,\n",
    "  'question': '',\n",
    "  'questionNumber': 19,\n",
    "  'article': '',\n",
    "  'options': '',\n",
    "  'answer': 2,\n",
    "  'questionType': '5',\n",
    "  'solution': ' 글에서 Chaske는 처음에 두려움(dread)과 공포(horrified)를 느꼈지만, 그의 아버지의 목소리를 듣고 안도(relief)를 느꼈습니다. 이로 인해 그의 공포가 사라졌다는 내용이 나와 있습니다. 따라서 적절한 심경 변화는 horrified → relieved입니다.',\n",
    "  'keyWords': ''}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "a2097b49-00dc-4c0f-b010-368c630636b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 18,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 2,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 글쓴이는 농구 코트에서 발생하는 소음 문제를 해결하기 위해 농구 코트의 운영 시간을 밤 9시 이전으로 제한해 줄 것을 요청하고 있습니다. 글의 목적은 명확하게 운영 시간의 제한을 요청하는 것입니다. 다른 선택지들은 글의 내용과 직접적인 관련이 없습니다.',\n",
       "  'keyWords': ''},\n",
       " {'testNumber': '2_2312',\n",
       "  'testType': 6,\n",
       "  'question': '',\n",
       "  'questionNumber': 19,\n",
       "  'article': '',\n",
       "  'options': '',\n",
       "  'answer': 2,\n",
       "  'questionType': '5',\n",
       "  'solution': ' 글에서 Chaske는 처음에 두려움(dread)과 공포(horrified)를 느꼈지만, 그의 아버지의 목소리를 듣고 안도(relief)를 느꼈습니다. 이로 인해 그의 공포가 사라졌다는 내용이 나와 있습니다. 따라서 적절한 심경 변화는 horrified → relieved입니다.',\n",
       "  'keyWords': ''}]"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "b2543969-8ee3-4438-9061-e7d6e5dd4455",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "History created successfully!\n",
      "{'success': True, 'data': {'count': 28}}\n"
     ]
    }
   ],
   "source": [
    "# HTTP POST 요청 보내기\n",
    "response = requests.post(url, headers=headers, json=data_new)\n",
    "\n",
    "# 응답 확인\n",
    "if response.status_code == 201:  # 성공적인 요청의 상태 코드는 보통 201\n",
    "    print(\"History created successfully!\")\n",
    "    print(response.json())\n",
    "else:\n",
    "    print(f\"Failed to create history. Status code: {response.status_code}\")\n",
    "    print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cada311-6874-44b7-a588-23c2d9a977a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77dbcd62-4a6a-485a-aa5f-dbf4e1ab7c91",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a6a7dbe-7cd5-4e55-bf77-18048cadaaa8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "297b5f31-b57c-45f1-b3de-f08c1f39317c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ff1cac9-9b23-4f28-9384-ff2793e662ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3508a432-96f0-46aa-8da0-e7f612e9d556",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb9f92ea-47a8-4d74-91b7-7615d0d4d7bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2753ab3-c0cb-4a6b-9184-7938e7f5bfe6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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