{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import spacy\n",
    "from spacy.tokens import Span\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def join_words(group):\n",
    "    return ' '.join(group.astype(str))\n",
    "\n",
    "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",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    # Combine words into sentences, assumed by unique 'Line_ID'\n",
    "    grouped_data = data.groupby(group_col)['Word_x'].apply(' '.join).reset_index()\n",
    "    \n",
    "    # Prepare training data in spaCy format\n",
    "    training_data = []\n",
    "    \n",
    "    for _, row in grouped_data.iterrows():\n",
    "        text = row['Word_x']\n",
    "        entities = []\n",
    "        current_position = 0\n",
    "\n",
    "        # Iterate over words in the current line to build entities\n",
    "        for _, word_data in data[data[group_col] == row[group_col]].iterrows():\n",
    "            start = current_position\n",
    "            end = start + len(word_data['Word_x'])\n",
    "            # Check if there's a named entity\n",
    "            if word_data['LOC_x'] != 'O':\n",
    "                entities.append((start, end, 'LOC'))\n",
    "            if word_data['PERS_x'] != 'O':\n",
    "                entities.append((start, end, 'PER'))\n",
    "            \n",
    "            current_position = end + 1  # Update position, accounting for space\n",
    "\n",
    "        # Append to training data\n",
    "        training_data.append({\"text\": text, \"entities\": entities})\n",
    "    training_data = convert_to_spacy_docs(training_data)\n",
    "    return training_data\n",
    "\n",
    "def convert_to_spacy_docs(training_data):\n",
    "    # Load spaCy model, you can change it to whatever model you'd like to use or just use a blank one\n",
    "    nlp = spacy.blank('en')  # Assuming the data is in English; change the language code if needed\n",
    "    \n",
    "    # List to hold the spaCy docs\n",
    "    spacy_docs = []\n",
    "    \n",
    "    for record in training_data:\n",
    "        # Create a doc from the text\n",
    "        doc = nlp(record['text'])\n",
    "        \n",
    "        # Create a list to collect entity spans\n",
    "        spans = []\n",
    "        \n",
    "        for start, end, label in record['entities']:\n",
    "            span = doc.char_span(start, end, label=label)\n",
    "            if span is not None:  # Only add the span if it's correctly aligned with token boundaries\n",
    "                spans.append(span)\n",
    "        \n",
    "        # Overwrite the doc's 'ents' with our list of spans\n",
    "        try:\n",
    "            doc.spans[\"sc\"] = spans\n",
    "        except:\n",
    "            ValueError\n",
    "            print(spans)\n",
    "            \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",
    "        # Append the modified doc to the list\n",
    "        spacy_docs.append({\"text\": doc.text, 'spans': span_ents})\n",
    "    \n",
    "    return spacy_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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": 141,
   "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": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "srsly.write_jsonl(\"home-alcar-ner.jsonl\", training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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