{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import spacy\n",
    "from spacy.tokens import Span, Doc\n",
    "import os\n",
    "from spacy.training import biluo_tags_to_offsets, biluo_tags_to_spans, iob_to_biluo\n",
    "import srsly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_spacy_training_data(file_path):\n",
    "    # Load data from the Excel file\n",
    "    data = pd.read_excel(file_path)\n",
    "    # if \"Line_ID\" in data.columns:\n",
    "    #     group_col = \"Line_ID\"\n",
    "    if \"ACT\" in data.columns:\n",
    "        group_col = \"ACT\"\n",
    "    elif \"Original_Act_ID\" in data.columns:\n",
    "        group_col = \"Original_Act_ID\"\n",
    "    else:\n",
    "        \"unknown\"\n",
    "    # data = data[~data['Word_x'].apply(lambda x: isinstance(x, int))]\n",
    "    # data = data[~data['Word_x'].apply(lambda x: isinstance(x, float))]\n",
    "    data['Word_x'] = data['Word_x'].astype(str).str.strip()\n",
    "    \n",
    "    # Combine words into sentences, assumed by unique 'Line_ID'\n",
    "    grouped_data = data.groupby(group_col)\n",
    "    \n",
    "    # Prepare training data in spaCy format\n",
    "    training_data = []\n",
    "    for _, item in grouped_data:\n",
    "        bilo_loc = item[\"LOC_x\"].tolist()\n",
    "        bilo_person = item[\"PERS_x\"].tolist()\n",
    "        tokens = item[\"Word_x\"].tolist()\n",
    "        doc = Doc(nlp.vocab, words=tokens, spaces=[True for i in range(len(tokens))])\n",
    "        # doc = nlp(\" \".join(tokens))\n",
    "\n",
    "        spans = iob_to_biluo(bilo_person)\n",
    "        spans = biluo_tags_to_spans(doc, spans)\n",
    "\n",
    "\n",
    "        loc_spans = iob_to_biluo(bilo_loc)\n",
    "        loc_spans = biluo_tags_to_spans(doc, loc_spans)\n",
    "\n",
    "        spans = loc_spans + spans\n",
    "        doc.spans[\"sc\"] = spans\n",
    "        span_ents = []\n",
    "        for span in doc.spans[\"sc\"]:\n",
    "            span_ents.append({\"text\": span.text, \"label\": span.label_, \"start\": span.start, \"end\": span.end})\n",
    "        training_data.append({\"text\": doc.text, \"spans\": span_ents})\n",
    "    return training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n"
     ]
    }
   ],
   "source": [
    "files = glob.glob(\"Database/*/CONLL/*.xlsx\")\n",
    "print(len(files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Notre_Dame_Roche_Paris_BnF_10996\n",
      "1 Pontoise_Paris_BnF_5657\n",
      "2 Saint_Denis_Paris_AN_LL_1157\n",
      "3 Pontigny_Paris_BnF_lat_9887_inner_unmerged.xlsx\n",
      "4 Navarre_Pau_AD_E513\n",
      "5 Clairmarais_Troyes_AD_3H3700_inner_unmerged.xlsx\n",
      "6 Port_Royal_2_Paris_BnF_10998\n",
      "7 Nesle_Chantilly_GB_Reg12_14F22\n",
      "8 Fervaques_Paris_BnF_lat_11071\n",
      "9 Molesme_2_Dijon_ADCO_Cart_143_7H7\n",
      "10 Saint_Nicaise_Reims_BM_1843_inner_unmerged.xlsx\n",
      "11 Sommereux_Paris_Bnf_nal_1934\n",
      "12 Chartres_2_Paris_BnF_lat_10095\n",
      "13 Chartres_1_Paris_BnF_lat_10094\n",
      "14 Vauluisant_Paris_BnF_lat_9901\n",
      "15 Port_Royal_1_Paris_BnF_10997\n",
      "16 Molesme_1_Dijon_ADCO_Cart_142_7H6\n"
     ]
    }
   ],
   "source": [
    "training_data = []\n",
    "for i, filename in enumerate(files):\n",
    "    manuscript = os.path.basename(filename).split('_final_version_inner')[0]\n",
    "    print(i, manuscript)\n",
    "    res = create_spacy_training_data(filename)\n",
    "    for r in res:\n",
    "        r[\"ms\"] = manuscript\n",
    "        training_data.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "srsly.write_jsonl(\"home-alcar-ner.jsonl\", training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bow",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}