{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Süddeutsche Zeitung (sueddeutsche.de)` — consolidation notebook\n",
    "\n",
    "This notebook is the second half of the pipeline behind\n",
    "[`SinclairSchneider/sueddeutsche_de`](https://huggingface.co/datasets/SinclairSchneider/sueddeutsche_de).\n",
    "\n",
    "`crawler.py` writes one JSON file per article into `pages/`. This notebook\n",
    "\n",
    "1. reads every `pages/*.json`,\n",
    "2. flattens the nested records into one `pandas` column per field,\n",
    "3. drops duplicate articles by headline (`drop_duplicates(subset=['title'])`),\n",
    "4. writes `sueddeutsche_de.json` and pushes the result to the Hugging Face Hub.\n",
    "\n",
    "Outputs have been cleared before publication.\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import json\n",
    "import pandas as pd\n",
    "from datasets import Dataset\n",
    "import huggingface_hub\n",
    "from dateutil.parser import parse\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"pages/https:__www.sueddeutsche.de_wissen_zyklon-mosambik-1.4375666.json\", \"r\", encoding=\"utf-8\") as f:\n",
    "    json_input = f.read()\n",
    "    dict_input_example = json.loads(json_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_input_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_files = glob.glob(\"pages/*.json\")\n",
    "len(json_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = []\n",
    "content = []\n",
    "author = []\n",
    "description = []\n",
    "keywords = []\n",
    "category = []\n",
    "about = []\n",
    "dateCreated = []\n",
    "datePublished = []\n",
    "dateModified = []\n",
    "wordCount = []\n",
    "alternativeHeadline = []\n",
    "url = []\n",
    "for file in tqdm(json_files):\n",
    "    with open(file, \"r\") as f:\n",
    "        d = json.loads(f.read())\n",
    "        title.append(d['title'])\n",
    "        content.append(d['content'])\n",
    "        author.append(d['additional_attributes']['author'])\n",
    "        description.append(d['additional_attributes']['description'])\n",
    "        keywords.append(d['additional_attributes']['keywords'])\n",
    "        category.append(d['additional_attributes']['category'])\n",
    "        about.append(d['additional_attributes']['about'])\n",
    "        dateCreated.append(d['additional_attributes']['dateCreated'])\n",
    "        datePublished.append(d['additional_attributes']['datePublished'])\n",
    "        dateModified.append(d['additional_attributes']['dateModified'])\n",
    "        wordCount.append(d['additional_attributes']['wordCount'])\n",
    "        alternativeHeadline.append(d['additional_attributes']['alternativeHeadline'])\n",
    "        url.append(d['additional_attributes']['url'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        'title': title,\n",
    "        'content': content,\n",
    "        'author': author,\n",
    "        'description': description,\n",
    "        'keywords': keywords,\n",
    "        'category': category,\n",
    "        'about':about,\n",
    "        'dateCreated': dateCreated,\n",
    "        'datePublished': datePublished,\n",
    "        'dateModified': dateModified,\n",
    "        'wordCount': wordCount,\n",
    "        'alternativeHeadline': alternativeHeadline,\n",
    "        'url': url,\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(subset=['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_json(\"sueddeutsche_de.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = Dataset.from_pandas(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.push_to_hub(\"SinclairSchneider/sueddeutsche_de\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.push_to_hub(\"NLP-UniBW/sueddeutsche_de\")"
   ]
  }
 ],
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