Upload terra_x_scraper.ipynb

terra_x_scraper.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install google_colab_selenium pandas -q"
+      ],
+      "metadata": {
+        "id": "v6eFzLRMAZY-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "603694b7-378c-48a8-8ec9-8d934ddf7db5"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.5/9.5 MB\u001b[0m \u001b[31m23.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m467.7/467.7 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import json\n",
+        "from selenium.webdriver.common.by import By\n",
+        "from selenium.webdriver.support.ui import WebDriverWait\n",
+        "from selenium.webdriver.support import expected_conditions as EC\n",
+        "import google_colab_selenium as gs\n",
+        "\n",
+        "driver = gs.ChromeDriver()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 38
+        },
+        "id": "HuGRNkypD-WR",
+        "outputId": "b6d8cca6-28d4-4e86-b7bb-d0e88b28a59f"
+      },
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "            <div class=\"spinner-container\">\n",
+              "                <div class=\"spinner\" id=\"f8a02f04-ae6b-45c4-91fc-344002d27820-circle\"></div>\n",
+              "                <div class=\"spinner-text\" id=\"f8a02f04-ae6b-45c4-91fc-344002d27820-text\">Initializing Chromedriver</div>\n",
+              "            </div>\n",
+              "            <style>\n",
+              "                @keyframes spin {\n",
+              "                    from { transform: rotate(0deg); }\n",
+              "                    to { transform: rotate(360deg); }\n",
+              "                }\n",
+              "\n",
+              "                .spinner-container {\n",
+              "                    display: flex;\n",
+              "                    align-items: center;\n",
+              "                    margin-bottom: 3px;\n",
+              "                }\n",
+              "\n",
+              "                .spinner {\n",
+              "                    border: 3px solid rgba(0, 0, 0, 0.1);\n",
+              "                    border-left-color: lightblue;\n",
+              "                    border-radius: 50%;\n",
+              "                    width: 12px;\n",
+              "                    height: 12px;\n",
+              "                    animation: spin 1s linear infinite;\n",
+              "                }\n",
+              "\n",
+              "                .spinner-text {\n",
+              "                    padding-left: 6px;\n",
+              "                }\n",
+              "            </style>\n",
+              "        "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "            const element = document.getElementById(\"f8a02f04-ae6b-45c4-91fc-344002d27820-circle\");\n",
+              "            element.style.border = \"3px solid limegreen\";\n",
+              "            element.style.animation = \"none\";\n",
+              "\n",
+              "            const text = document.getElementById(\"f8a02f04-ae6b-45c4-91fc-344002d27820-text\");\n",
+              "            text.innerText = \"Initialized Chromedriver\";\n",
+              "        "
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def scrape_categories(url):\n",
+        "    category = url.split('#')[0].split('/')[-1]\n",
+        "    items = []\n",
+        "    # Send a GET request to the website\n",
+        "    try:\n",
+        "        driver.get(url)\n",
+        "        WebDriverWait(driver, 50).until(EC.presence_of_all_elements_located((By.TAG_NAME, \"tx-video-card\")))\n",
+        "        # Find all <tx-video-card> elements\n",
+        "        card_elements = driver.find_elements(By.TAG_NAME, \"tx-video-card\")\n",
+        "        for card in card_elements:\n",
+        "          url = card.find_element(By.TAG_NAME, 'a').get_attribute('href')\n",
+        "          title = card.find_element(By.TAG_NAME, 'h3').text\n",
+        "          items.append({\"url\": url, \"title\": title, \"category\": category})\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        print(\"An error occurred:\", e)\n",
+        "    return items"
+      ],
+      "metadata": {
+        "id": "dXTj1qTYU1F_"
+      },
+      "execution_count": 92,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 93,
+      "metadata": {
+        "id": "YHuy1H4aAUp-"
+      },
+      "outputs": [],
+      "source": [
+        "def scrape_article(item):\n",
+        "    # Send a GET request to the website\n",
+        "    try:\n",
+        "        driver.get(item['url'])\n",
+        "        item['text'] = []\n",
+        "        WebDriverWait(driver, 50).until(EC.presence_of_all_elements_located((By.TAG_NAME, \"p\")))\n",
+        "\n",
+        "        # Find all <p> elements and filter empty elements\n",
+        "        p_elements = driver.find_elements(By.TAG_NAME, \"p\")\n",
+        "        p_elements = list(filter(lambda p: p.text, p_elements))\n",
+        "\n",
+        "        # Print the text of each <p> element\n",
+        "        for p in p_elements:\n",
+        "          if p.text.strip().startswith('ZDF'):\n",
+        "            item['attribution'] = p.text.strip()\n",
+        "            continue\n",
+        "          if p.text.strip().startswith('http'):\n",
+        "            item['source'] = p.text.strip()\n",
+        "\n",
+        "          item['text'].append(p.text)\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        print(\"An error occurred:\", e)\n",
+        "    return item"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "urls = [\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/geowissenschaften#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/der-menschliche-koerper#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/Biologie#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/Geschichte#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/religion#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/physik#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/technik#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/chemie#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/mathematik#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/sozialkunde#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/klima-und-klimawandel#videos',\n",
+        "    'https://terraxplaincommons.zdf.de/kategorie/unesco-welterbestaetten#videos'\n",
+        "]\n",
+        "\n",
+        "for url in urls:\n",
+        "  items = scrape_categories(url)\n",
+        "  items = map(scrape_article, items)\n",
+        "  with open('data.jsonl', 'a') as f:\n",
+        "    for item in items:\n",
+        "      f.write(json.dumps(item) + '\\n')\n",
+        "\n",
+        "driver.quit()"
+      ],
+      "metadata": {
+        "id": "quuELC-bdVPG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_json('data.jsonl', lines=True)"
+      ],
+      "metadata": {
+        "id": "IMzB4Qb9in71"
+      },
+      "execution_count": 233,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(len(df))\n",
+        "# Filter rows where 'url' is null\n",
+        "df = df[df['url'].notnull()]\n",
+        "# Filter rows where 'attribution' is null\n",
+        "df = df[df['attribution'].notnull()]\n",
+        "print(len(df))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R6QNBS6jkrf9",
+        "outputId": "045c92bb-73eb-4ae5-d80b-d45f88b6ce53"
+      },
+      "execution_count": 234,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "400\n",
+            "331\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['text'] = df['text'].apply(lambda arr: [s for s in arr if 'Mehr von Terra X' not in s])\n",
+        "df['text'] = df['text'].apply(lambda arr: [s for s in arr if 'https:' not in s])\n",
+        "df['text'] = df['text'].apply(lambda arr: [s for s in arr if len(s) >= 20])"
+      ],
+      "metadata": {
+        "id": "qlACB04Vps8Z"
+      },
+      "execution_count": 235,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Filter rows with text array length 0\n",
+        "df = df[df['text'].apply(len) > 0]\n",
+        "print(len(df))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nQrdXChExgrc",
+        "outputId": "41e65e15-c992-4db1-cd64-ba8924d5db14"
+      },
+      "execution_count": 236,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "331\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Filter rows with text array length < 3\n",
+        "df = df[df['text'].apply(len) < 3]\n",
+        "print(len(df))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SOKohp8PwQfK",
+        "outputId": "6d9ee70a-84e6-406f-bb4d-620a0f7e59f6"
+      },
+      "execution_count": 237,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "283\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create new column 'short_text' containing the shorter string from each array\n",
+        "df['short_text'] = df['text'].apply(lambda arr: min(arr, key=len))\n",
+        "\n",
+        "# Delete the 'short_text' from the 'text' array\n",
+        "df['text'] = df['text'].apply(lambda arr: max(arr, key=len))\n",
+        "\n",
+        "# Convert the 'text' array to a string\n",
+        "df['text'] = df['text'].str.join('')"
+      ],
+      "metadata": {
+        "id": "9rxgo5uwzXdS"
+      },
+      "execution_count": 238,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['source'] = df['source'].fillna('')"
+      ],
+      "metadata": {
+        "id": "lVc_gl8Nz5jO"
+      },
+      "execution_count": 239,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df.head())"
+      ],
+      "metadata": {
+        "id": "gxuik5azzdKl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.to_json('clean_data.jsonl', orient='records', lines=True)"
+      ],
+      "metadata": {
+        "id": "bKzASrQii7su"
+      },
+      "execution_count": 241,
+      "outputs": []
+    }
+  ]
+}