{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed60d28c",
   "metadata": {},
   "source": [
    "# Libreries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95cf533e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "from Docker.config import settings\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b7265a",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fd7de78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_text_from_file(file_path: str) -> str:\n",
    "    \"\"\"\n",
    "    Load text content from a specified file.\n",
    "\n",
    "    Args:\n",
    "        file_path: Path to the .txt file to load.\n",
    "\n",
    "    Returns:\n",
    "        The text content of the file.\n",
    "\n",
    "    Raises:\n",
    "        FileNotFoundError: If the file does not exist.\n",
    "        IOError: If the file cannot be read.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n",
    "            return file.read()\n",
    "    except FileNotFoundError:\n",
    "        raise FileNotFoundError(f\"El archivo '{file_path}' no existe.\")\n",
    "    except IOError as error:\n",
    "        raise IOError(f\"Error al leer '{file_path}': {error}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22bcc0fe",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "19c815e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = os.getenv(\"HF_EMB_MODEL_NAME\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ff2484d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de tokens: 1073\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "\n",
    "text = load_text_from_file(settings.raw_path / '1_Introduction.txt')\n",
    "\n",
    "tokens = tokenizer.encode(text)\n",
    "\n",
    "print(\"Número de tokens:\", len(tokens))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "assistance-engine",
   "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": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}