{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc3852ca",
   "metadata": {},
   "source": [
    "# Evaluate Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2d9fbf",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45140c6e",
   "metadata": {},
   "source": [
    "#### Load the API key and libaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7bf1b8e",
   "metadata": {
    "height": 115,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from src.Language_Evaluation_LC import llm_language_evaluation\n",
    "from src.data_analysis import run_analysis\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e95383",
   "metadata": {
    "height": 30
   },
   "source": [
    "#### Load the Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "464a2aaa",
   "metadata": {
    "height": 47,
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "MODEL = \"meta-llama/Llama-3-70b-chat-hf\"\n",
    "TEMPERATURE = 0.0\n",
    "N_REPETITIONS = 0\n",
    "REASONING = False\n",
    "LANGUAGES = ['spanish', 'tagalog', 'portuguese', 'english']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92663014",
   "metadata": {},
   "source": [
    "#### Run The Experiments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c7ccfa1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Run evaluation:\n",
    "llm_language_evaluation(path=PATH, model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, reasoning=REASONING, languages=LANGUAGES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079dcbc4",
   "metadata": {},
   "source": [
    "#### See the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a58184aa",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "MODEL = os.path.basename(MODEL)\n",
    "\n",
    "if N_REPETITIONS > 1:\n",
    "    df = pd.read_csv(f\"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}_{N_REPETITIONS}Repetitions.csv\")\n",
    "else:\n",
    "    df = pd.read_csv(f\"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}.csv\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041dc525",
   "metadata": {},
   "source": [
    "### Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "85f6bb97",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "TEMPERATURE = str(TEMPERATURE).replace('.', '_')\n",
    "\n",
    "run_analysis(model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, languages=LANGUAGES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dffeddc1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "N_REPETITIONS = 1 if N_REPETITIONS < 1 else N_REPETITIONS\n",
    "pd.read_csv(f'results/results_{MODEL}_Temperature{TEMPERATURE}_Repetitions{N_REPETITIONS}/matches_results_{MODEL}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c23866b3-c0c6-42de-968c-6994b2b8b7fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv(f'results/results_{MODEL}_Temperature{TEMPERATURE}_Repetitions{N_REPETITIONS}/matches_results_{MODEL}.csv').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dcad7d6-7647-49c5-9804-17db4dfbc5a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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