SoftwareDesign総集編の各号見出しのpdfを切り出して、特集集記事をテキスト起こしして、notionに放り込むまで。notionへの投入はn8nを使った

SDの目録をNotionに入れる.ipynb

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   "source": [
    "!python -m pip install pillow pymupdf\n",
    "!python -m pip install openai pydantic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c07d7ff2",
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    "やりたいこと：\n",
    "\n",
    "- SoftwareDesginの目録PDFから、各号の特集記事を抽出してNotionのDBに保存したい\n",
    "\n",
    "全体の仕様：\n",
    "- 各号の特集記事は、./index.pdfに記載されている\n",
    "- 処理はjupyter labで実装する\n",
    "  - 関数は細かめに分ける\n",
    "  - 各関数には日本語で説明コメントをつける\n",
    "\n",
    "index.pdfの仕様\n",
    "- SoftwareDesignの各号の記事が紹介されている\n",
    "- 3ページ目から最後まで\n",
    "- 月刊誌なので、1年で12冊分ある\n",
    "- 5年分なので60冊分の情報がある\n"
   ]
  },
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   "id": "c2f4b72e",
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   "source": [
    "step1 PDFから画像を切り抜き\n",
    "やりたいこと\n",
    "\n",
    "- index.pdfから、画像の切り抜きを行いたい。\n",
    "- 3ページ目から最後まで処理を行う\n",
    "  - 各ページの上下左右の余白(20%)を除去する。\n",
    "  - 上下に切り分けてpng画像として保存する\n",
    "- 出力ディレクトリは\"sd_img\"とする"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f42d5a26",
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    "import os\n",
    "from PIL import Image\n",
    "\n",
    "import fitz  # PyMuPDF\n",
    "\n",
    "# PDFファイルのパス\n",
    "pdf_path = 'index.pdf'\n",
    "# 出力ディレクトリ\n",
    "output_dir = 'sd_img'\n",
    "\n",
    "# 出力ディレクトリを削除\n",
    "if os.path.exists(output_dir):\n",
    "  import shutil\n",
    "  shutil.rmtree(output_dir)\n",
    "\n",
    "# 出力ディレクトリが存在しない場合は作成\n",
    "if not os.path.exists(output_dir):\n",
    "  os.makedirs(output_dir)\n",
    "\n",
    "# PDFを開く\n",
    "pdf_document = fitz.open(pdf_path)\n",
    "\n",
    "# 3ページ目から最後まで処理\n",
    "for page_num in range(3, pdf_document.page_count):\n",
    "  page = pdf_document.load_page(page_num)\n",
    "  # 解像度を上げるためにスケーリングを設定\n",
    "  zoom_x = 4.0  # 横方向のスケーリング\n",
    "  zoom_y = 4.0  # 縦方向のスケーリング\n",
    "  matrix = fitz.Matrix(zoom_x, zoom_y)\n",
    "  \n",
    "  pix = page.get_pixmap(matrix=matrix)\n",
    "\n",
    "\n",
    "  # 画像をPILで開く\n",
    "  img = Image.frombytes(\"RGB\", [pix.width, pix.height], pix.samples)\n",
    "\n",
    "  # 余白を除去するためのパラメータ\n",
    "  left = int(pix.width * 0.05)\n",
    "  top = int(pix.height * 0.05)\n",
    "  right = pix.width - left\n",
    "  bottom = pix.height - top\n",
    "\n",
    "  # 画像を切り抜く\n",
    "  img_cropped = img.crop((left, top, right, bottom))\n",
    "\n",
    "  # 上下に切り分ける\n",
    "  upper_half = img_cropped.crop((0, 0, img_cropped.width, img_cropped.height // 2))\n",
    "  lower_half = img_cropped.crop((0, img_cropped.height // 2, img_cropped.width, img_cropped.height))\n",
    "\n",
    "  # 画像を保存\n",
    "  upper_half.save(os.path.join(output_dir, f'page_{page_num + 1}_1.png'))\n",
    "  lower_half.save(os.path.join(output_dir, f'page_{page_num + 1}_2.png'))\n",
    "\n",
    "print(\"画像の切り抜きと保存が完了しました。\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "114854c9",
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   "source": [
    "## 画像からテキスト情報を取得する\n",
    "\n",
    "やりたいこと：\n",
    "\n",
    "- sd_imgに保存した画像から、発行年月と記事内容をテキスト情報として取得したい\n",
    "- 画像から記事内容を取得するには、OpenAIのAPIを使う\n",
    "  - OpenAIのAPIキーは、環境変数の OPENAI_API_KEY を使う\n",
    "  - "
   ]
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   "source": [
    "from openai import OpenAI\n",
    "from pydantic import BaseModel\n",
    "\n",
    "import os\n",
    "import base64\n",
    "\n",
    "# OpenAI APIキーを設定\n",
    "#openai.api_key = os.getenv('OPENAI_API_KEY')\n",
    "client = OpenAI()\n",
    "\n",
    "# 画像ディレクトリ\n",
    "image_dir = 'sd_img'\n",
    "\n",
    "# 画像ファイルのリストを取得\n",
    "image_files = [f for f in os.listdir(image_dir) if f.endswith('.png')]\n",
    "\n",
    "class OcrResult(BaseModel):\n",
    "  title: str\n",
    "  text: str\n",
    "\n",
    "# 画像からテキストを抽出する関数\n",
    "def extract_text_from_image(image_path) -> OcrResult:\n",
    "  with open(image_path, \"rb\") as image_file:\n",
    "    image_data = image_file.read()\n",
    "    \n",
    "    base64_image = base64.b64encode(image_data).decode('utf-8')\n",
    "\n",
    "    # text = response['data']['text']\n",
    "    # beta.chat.completions.parse(\n",
    "    systemMessage = \"\"\"\\\n",
    "    この画像は、SoftwareDesginの各号の記事紹介です。\n",
    "    ここから発行年月と記事内容を教えてください。\n",
    "    - 出力はJSON形式です。\n",
    "    - JSONのtitleには発行年月日をxx号という形で入れてください。\n",
    "    - JSONのtextには特集記事の記事内容を入れてください。 \n",
    "    - JSONのtextには記事を書いた人の名前は不要です。\n",
    "    - JSONのtextは500文字以内に収めてください。\n",
    "    - それぞれ日本語でお願いします。\n",
    "    \"\"\"\n",
    "    completion = client.beta.chat.completions.parse(\n",
    "      model=\"gpt-4o\",  # モデルの指定\n",
    "      response_format=OcrResult,\n",
    "      messages=[\n",
    "          {\"role\": \"system\", \"content\": systemMessage},\n",
    "          {\"role\": \"user\",\n",
    "            \"content\": [\n",
    "              {\n",
    "                  \"type\": \"image_url\",\n",
    "                  \"image_url\": {\n",
    "                      \"url\": f\"data:image/png;base64,{base64_image}\"\n",
    "                  },\n",
    "              },\n",
    "            ],\n",
    "          }\n",
    "      ]\n",
    "  )\n",
    "\n",
    "  return completion.choices[0].message.content\n",
    "\n",
    "# sd_jsonディレクトリを削除\n",
    "if os.path.exists('sd_json'):\n",
    "  import shutil\n",
    "  shutil.rmtree('sd_json')\n",
    "\n",
    "# sd_jsonディレクトリが存在しない場合は作成\n",
    "if not os.path.exists('sd_json'):\n",
    "  os.makedirs('sd_json')\n",
    "\n",
    "\n",
    "# 各画像からテキストを抽出してsd_jsonディレクトリに保存\n",
    "for image_file in image_files:\n",
    "  image_path = os.path.join(image_dir, image_file)\n",
    "  result = extract_text_from_image(image_path)\n",
    "  print(result)\n",
    "  with open(f'sd_json/{image_file}.json', 'w') as f:\n",
    "    f.write(result)"
   ]
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